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GitHub - eigenvivek/xvr: Training patient-specific 2D/3D registration models in 5 min
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d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Feigenvivek%2Fxvr" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/eigenvivek/xvr","user_id":null}}" data-hydro-click-hmac="bcb2e6555c4626331558f724fdfb874e9e9bd6b40a40e77ae1eabe3b0b097a92" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 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class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 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width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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id="validation-3025cad5-77c0-47d5-befe-8965562f47ae" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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X-ray to Volume Registration\u003c/h1\u003e\u003ca id=\"user-content-xvr-x-ray-to-volume-registration\" class=\"anchor\" aria-label=\"Permalink: xvr: X-ray to Volume Registration\" href=\"#xvr-x-ray-to-volume-registration\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://arxiv.org/abs/2503.16309\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8d4fdf56a675879964e16a753ce2048d6e0037a8814911ec995fb960346b0b63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323530332e31363330392d7265642e737667\" alt=\"Paper shield\" data-canonical-src=\"https://img.shields.io/badge/arXiv-2503.16309-red.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"/eigenvivek/xvr/blob/main/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cce5a2a14b0faab422e0bfcdc074afb46089831a0bf5930a7d8af3f31b98f847/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d626c75652e737667\" alt=\"License: MIT\" data-canonical-src=\"https://img.shields.io/badge/License-MIT-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://colab.research.google.com/drive/1K9lBPxcLh55mr8o50Y7aHkjzjEWKPCrM?usp=sharing\" 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src=\"https://camo.githubusercontent.com/4ab8b0cb96c66d58f1763826bbaa0002c7e4aea0c91721bdda3395b986fe30f2/68747470733a2f2f696d672e736869656c64732e696f2f656e64706f696e743f75726c3d68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f61737472616c2d73682f75762f6d61696e2f6173736574732f62616467652f76302e6a736f6e\" alt=\"uv\" data-canonical-src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA PyTorch package for training patient-specific 2D/3D registration models \u003cstrong\u003ein 5 minutes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/29757116/425203515-8a01c184-f6f1-420e-82b9-1cbe733adf7f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.flmEQlM7p0hXCAjGjguZLVloIvmsR1sCnB9AL0cegHM\"\u003e\u003cimg width=\"410\" alt=\"image\" src=\"https://private-user-images.githubusercontent.com/29757116/425203515-8a01c184-f6f1-420e-82b9-1cbe733adf7f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.flmEQlM7p0hXCAjGjguZLVloIvmsR1sCnB9AL0cegHM\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHighlights\u003c/h2\u003e\u003ca id=\"user-content-highlights\" class=\"anchor\" aria-label=\"Permalink: Highlights\" href=\"#highlights\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e🚀 A single CLI/API for training models and registering clinical data\u003c/li\u003e\n\u003cli\u003e⚡️ \u003cstrong\u003e100x faster\u003c/strong\u003e patient-specific model training than \u003ca href=\"https://github.com/eigenvivek/DiffPose\"\u003e\u003ccode\u003eDiffPose\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e📐 Submillimeter registration accuracy with new image-similarity metrics\u003c/li\u003e\n\u003cli\u003e🩺 Human-interpretable pose parameters for \u003cstrong\u003etraining your own models\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e🐍 Pure Python/PyTorch implementation\u003c/li\u003e\n\u003cli\u003e🖥️ Supports macOS, Linux, and Windows\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003exvr\u003c/code\u003e is built upon \u003ca href=\"https://github.com/eigenvivek/DiffDRR\"\u003e\u003ccode\u003eDiffDRR\u003c/code\u003e\u003c/a\u003e, the differentiable X-ray renderer.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInstall the Python API and CLI:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install git+https://github.com/eigenvivek/xvr.git\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install git+https://github.com/eigenvivek/xvr.git\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eVerify the installation version (should match the latest release on GitHub):\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"xvr --version\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003exvr --version\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003exvr\u003c/code\u003e provides a command-line interface for training, finetuning, and performing registration (i.e., test-time optimization) with pose regression models. The API is designed to be modular and extensible, allowing users to easily train models on new datasets and anatomical structures without any manual annotations.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ xvr --help\n\nUsage: xvr [OPTIONS] COMMAND [ARGS]...\n\n xvr is a PyTorch package for training, fine-tuning, and performing 2D/3D\n X-ray to CT/MR registration with pose regression models.\n\nOptions:\n --version Show the version and exit.\n --help Show this message and exit.\n\nCommands:\n train Train a pose regression model from scratch.\n restart Restart model training from a checkpoint.\n finetune Optimize a pose regression model for a specific patient.\n register Use gradient-based optimization to register XRAY to a CT/MR.\n animate Animate the trajectory of iterative optimization.\n dcm2nii Convert a DICOMDIR to a NIfTI file.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ xvr --help\n\nUsage: xvr [OPTIONS] COMMAND [ARGS]...\n\n xvr is a PyTorch package for training, fine-tuning, and performing 2D/3D\n X-ray to CT/MR registration with pose regression models.\n\nOptions:\n --version Show the version and exit.\n --help Show this message and exit.\n\nCommands:\n train Train a pose regression model from scratch.\n restart Restart model training from a checkpoint.\n finetune Optimize a pose regression model for a specific patient.\n register Use gradient-based optimization to register XRAY to a CT/MR.\n animate Animate the trajectory of iterative optimization.\n dcm2nii Convert a DICOMDIR to a NIfTI file.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h3\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo train a pose regression model from scratch on a single patient or a set of preregistered subjects, use \u003ccode\u003exvr train\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ xvr train --help\n\nUsage: xvr train [OPTIONS]\n\n Train a pose regression model from scratch.\n\nOptions:\n -i, --inpath PATH A single CT or a directory of CTs for pretraining [required]\n -o, --outpath PATH Directory in which to save model weights [required]\n --r1 \u0026lt;FLOAT FLOAT\u0026gt;... Range for primary angle (in degrees) [required]\n --r2 \u0026lt;FLOAT FLOAT\u0026gt;... Range for secondary angle (in degrees) [required]\n --r3 \u0026lt;FLOAT FLOAT\u0026gt;... Range for tertiary angle (in degrees) [required]\n --tx \u0026lt;FLOAT FLOAT\u0026gt;... Range for x-offset (in millimeters) [required]\n --ty \u0026lt;FLOAT FLOAT\u0026gt;... Range for y-offset (in millimeters) [required]\n --tz \u0026lt;FLOAT FLOAT\u0026gt;... Range for z-offset (in millimeters) [required]\n --sdd FLOAT Source-to-detector distance (in millimeters) [required]\n --height INTEGER DRR height (in pixels) [required]\n --delx FLOAT DRR pixel size (in millimeters / pixel) [required]\n --renderer [siddon|trilinear] Rendering equation [default: trilinear]\n --orientation [AP|PA] Orientation of CT volumes [default: PA]\n --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right)\n --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles]\n --convention TEXT If parameterization is Euler angles, specify order [default: ZXY]\n --model_name TEXT Name of model to instantiate [default: resnet18]\n --pretrained Load pretrained ImageNet-1k weights\n --norm_layer TEXT Normalization layer [default: groupnorm]\n --lr FLOAT Maximum learning rate [default: 0.005]\n --weight-geo FLOAT Weight on geodesic loss term [default: 0.01]\n --batch_size INTEGER Number of DRRs per batch [default: 116]\n --n_epochs INTEGER Number of epochs [default: 1000]\n --n_batches_per_epoch INTEGER Number of batches per epoch [default: 100]\n --name TEXT WandB run name\n --project TEXT WandB project name [default: xvr]\n --help Show this message and exit.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ xvr train --help\n\nUsage: xvr train [OPTIONS]\n\n Train a pose regression model from scratch.\n\nOptions:\n -i, --inpath PATH A single CT or a directory of CTs for pretraining [required]\n -o, --outpath PATH Directory in which to save model weights [required]\n --r1 \u0026lt;FLOAT FLOAT\u0026gt;... Range for primary angle (in degrees) [required]\n --r2 \u0026lt;FLOAT FLOAT\u0026gt;... Range for secondary angle (in degrees) [required]\n --r3 \u0026lt;FLOAT FLOAT\u0026gt;... Range for tertiary angle (in degrees) [required]\n --tx \u0026lt;FLOAT FLOAT\u0026gt;... Range for x-offset (in millimeters) [required]\n --ty \u0026lt;FLOAT FLOAT\u0026gt;... Range for y-offset (in millimeters) [required]\n --tz \u0026lt;FLOAT FLOAT\u0026gt;... Range for z-offset (in millimeters) [required]\n --sdd FLOAT Source-to-detector distance (in millimeters) [required]\n --height INTEGER DRR height (in pixels) [required]\n --delx FLOAT DRR pixel size (in millimeters / pixel) [required]\n --renderer [siddon|trilinear] Rendering equation [default: trilinear]\n --orientation [AP|PA] Orientation of CT volumes [default: PA]\n --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right)\n --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles]\n --convention TEXT If parameterization is Euler angles, specify order [default: ZXY]\n --model_name TEXT Name of model to instantiate [default: resnet18]\n --pretrained Load pretrained ImageNet-1k weights\n --norm_layer TEXT Normalization layer [default: groupnorm]\n --lr FLOAT Maximum learning rate [default: 0.005]\n --weight-geo FLOAT Weight on geodesic loss term [default: 0.01]\n --batch_size INTEGER Number of DRRs per batch [default: 116]\n --n_epochs INTEGER Number of epochs [default: 1000]\n --n_batches_per_epoch INTEGER Number of batches per epoch [default: 100]\n --name TEXT WandB run name\n --project TEXT WandB project name [default: xvr]\n --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotes\u003c/h4\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe \u003ccode\u003e--inpath\u003c/code\u003e argument should point to a directory containing CT volumes for training.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf the directory contains a single CT scan, the resulting model be patient-specific.\u003c/li\u003e\n\u003cli\u003eIf the directory contains multiple CTs, it's beneficial to preregister them to a common reference frame (e.g., using \u003ca href=\"https://github.com/ANTsX/ANTs\"\u003eANTs\u003c/a\u003e). This will improve the accuracy of the model, but this isn't strictly necessary.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetuning\u003c/h3\u003e\u003ca id=\"user-content-finetuning\" class=\"anchor\" aria-label=\"Permalink: Finetuning\" href=\"#finetuning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo finetune a pretrained pose regression model on a new patient, use \u003ccode\u003exvr finetune\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ xvr finetune --help\n\nUsage: xvr finetune [OPTIONS]\n\n Optimize a pose regression model for a specific patient.\n\nOptions:\n -i, --inpath PATH Input CT volume for patient-specific pretraining [required]\n -o, --outpath PATH Output directory for finetuned model weights [required]\n -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required]\n --lr FLOAT Maximum learning rate [default: 0.005]\n --batch_size INTEGER Number of DRRs per batch [default: 116]\n --n_epochs INTEGER Number of epochs [default: 10]\n --n_batches_per_epoch INTEGER Number of batches per epoch [default: 25]\n --rescale FLOAT Rescale the virtual detector plane [default: 1.0]\n --name TEXT WandB run name\n --project TEXT WandB project name [default: xvr]\n --help Show this message and exit.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ xvr finetune --help\n\nUsage: xvr finetune [OPTIONS]\n\n Optimize a pose regression model for a specific patient.\n\nOptions:\n -i, --inpath PATH Input CT volume for patient-specific pretraining [required]\n -o, --outpath PATH Output directory for finetuned model weights [required]\n -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required]\n --lr FLOAT Maximum learning rate [default: 0.005]\n --batch_size INTEGER Number of DRRs per batch [default: 116]\n --n_epochs INTEGER Number of epochs [default: 10]\n --n_batches_per_epoch INTEGER Number of batches per epoch [default: 25]\n --rescale FLOAT Rescale the virtual detector plane [default: 1.0]\n --name TEXT WandB run name\n --project TEXT WandB project name [default: xvr]\n --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotes\u003c/h4\u003e\u003ca id=\"user-content-notes-1\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe \u003ccode\u003e--inpath\u003c/code\u003e argument should point to a single CT volume for which the pose regression model will be finetuned.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003e--ckpt\u003c/code\u003e argument specifies the path to a checkpoint of a pretrained pose regression model produced by \u003ccode\u003exvr train\u003c/code\u003e.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIn addition to model weights, this checkpoint also contains the configurations used for training (e.g., pose parameters and intrinsic parameters), which are reused for finetuning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRegistration (test-time optimization)\u003c/h3\u003e\u003ca id=\"user-content-registration-test-time-optimization\" class=\"anchor\" aria-label=\"Permalink: Registration (test-time optimization)\" href=\"#registration-test-time-optimization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo register \u003cstrong\u003ereal\u003c/strong\u003e X-ray images using a pretrained model followed by iterative pose refinement with differentiable rendering, use \u003ccode\u003exvr register model\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"$ xvr register model --help\n\nUsage: xvr register model [OPTIONS] XRAY...\n\n Initialize from a pose regression model.\n\nOptions:\n -v, --volume PATH Input CT volume (3D image) [required]\n -m, --mask PATH Labelmap for the CT volume (optional)\n -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required]\n -o, --outpath PATH Directory for saving registration results [required]\n --crop INTEGER Preprocessing: center crop the X-ray image [default: 0]\n --subtract_background Preprocessing: subtract mode X-ray image intensity\n --linearize Preprocessing: convert X-ray from exponential to linear form\n --reducefn TEXT If DICOM is multiframe, how to extract a single 2D image for registration [default: max]\n --warp PATH SimpleITK transform to warp input CT to template reference frame\n --invert Invert the warp\n --labels TEXT Labels in mask to exclusively render (comma separated)\n --scales TEXT Scales of downsampling for multiscale registration (comma separated) [default: 8]\n --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right)\n --renderer [siddon|trilinear] Rendering equation [default: trilinear]\n --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles]\n --convention TEXT If parameterization is Euler angles, specify order [default: ZXY]\n --lr_rot FLOAT Initial step size for rotational parameters [default: 0.01]\n --lr_xyz FLOAT Initial step size for translational parameters [default: 1.0]\n --patience INTEGER Number of allowed epochs with no improvement after which the learning rate will be reduced [default: 10]\n --threshold FLOAT Threshold for measuring the new optimum [default: 0.0001]\n --max_n_itrs INTEGER Maximum number of iterations to run at each scale [default: 500]\n --max_n_plateaus INTEGER Number of times loss can plateau before moving to next scale [default: 3]\n --init_only Directly return the initial pose estimate (no iterative pose refinement)\n --saveimg Save ground truth X-ray and predicted DRRs\n --pattern TEXT Pattern rule for glob is XRAY is directory [default: *.dcm]\n --verbose INTEGER RANGE Verbosity level for logging [default: 1; 0\u0026lt;=x\u0026lt;=3]\n --help Show this message and exit.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e$ xvr register model --help\n\nUsage: xvr register model [OPTIONS] XRAY...\n\n Initialize from a pose regression model.\n\nOptions:\n -v, --volume PATH Input CT volume (3D image) [required]\n -m, --mask PATH Labelmap for the CT volume (optional)\n -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required]\n -o, --outpath PATH Directory for saving registration results [required]\n --crop INTEGER Preprocessing: center crop the X-ray image [default: 0]\n --subtract_background Preprocessing: subtract mode X-ray image intensity\n --linearize Preprocessing: convert X-ray from exponential to linear form\n --reducefn TEXT If DICOM is multiframe, how to extract a single 2D image for registration [default: max]\n --warp PATH SimpleITK transform to warp input CT to template reference frame\n --invert Invert the warp\n --labels TEXT Labels in mask to exclusively render (comma separated)\n --scales TEXT Scales of downsampling for multiscale registration (comma separated) [default: 8]\n --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right)\n --renderer [siddon|trilinear] Rendering equation [default: trilinear]\n --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles]\n --convention TEXT If parameterization is Euler angles, specify order [default: ZXY]\n --lr_rot FLOAT Initial step size for rotational parameters [default: 0.01]\n --lr_xyz FLOAT Initial step size for translational parameters [default: 1.0]\n --patience INTEGER Number of allowed epochs with no improvement after which the learning rate will be reduced [default: 10]\n --threshold FLOAT Threshold for measuring the new optimum [default: 0.0001]\n --max_n_itrs INTEGER Maximum number of iterations to run at each scale [default: 500]\n --max_n_plateaus INTEGER Number of times loss can plateau before moving to next scale [default: 3]\n --init_only Directly return the initial pose estimate (no iterative pose refinement)\n --saveimg Save ground truth X-ray and predicted DRRs\n --pattern TEXT Pattern rule for glob is XRAY is directory [default: *.dcm]\n --verbose INTEGER RANGE Verbosity level for logging [default: 1; 0\u0026lt;=x\u0026lt;=3]\n --help Show this message and exit.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotes\u003c/h4\u003e\u003ca id=\"user-content-notes-2\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes-2\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBy passing a \u003ccode\u003e--mask\u003c/code\u003e and a comma-separated set of \u003ccode\u003e--labels\u003c/code\u003e, registration will be performed with respect to specific structures.\u003c/li\u003e\n\u003cli\u003eIf the model was trained with a coordinate frame different to that of the \u003ccode\u003e--volume\u003c/code\u003e, you can pass a \u003ccode\u003e--warp\u003c/code\u003e to rigidly realign the model's predictions to the new patient.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExperiments\u003c/h2\u003e\u003ca id=\"user-content-experiments\" class=\"anchor\" aria-label=\"Permalink: Experiments\" href=\"#experiments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModels\u003c/h4\u003e\u003ca id=\"user-content-models\" class=\"anchor\" aria-label=\"Permalink: Models\" href=\"#models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePretrained models are available \u003ca href=\"https://huggingface.co/eigenvivek/xvr/tree/main\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData\u003c/h4\u003e\u003ca id=\"user-content-data\" class=\"anchor\" aria-label=\"Permalink: Data\" href=\"#data\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBenchmarks datasets, reformatted into DICOM/NIfTI files, are available \u003ca href=\"https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you use the \u003ca href=\"https://github.com/rg2/DeepFluoroLabeling-IPCAI2020\"\u003e\u003ccode\u003eDeepFluoro\u003c/code\u003e\u003c/a\u003e dataset, please cite:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{grupp2020automatic,\n title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration},\n author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H},\n journal={International journal of computer assisted radiology and surgery},\n volume={15},\n pages={759--769},\n year={2020},\n publisher={Springer}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{grupp2020automatic,\n title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration},\n author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H},\n journal={International journal of computer assisted radiology and surgery},\n volume={15},\n pages={759--769},\n year={2020},\n publisher={Springer}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use the \u003ca href=\"https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/\" rel=\"nofollow\"\u003e\u003ccode\u003eLjubljana\u003c/code\u003e\u003c/a\u003e dataset, please cite:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{pernus20133d,\n title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images},\n author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo},\n journal={IEEE transactions on medical imaging},\n volume={32},\n number={8},\n pages={1550--1563},\n year={2013},\n publisher={IEEE}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{pernus20133d,\n title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images},\n author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo},\n journal={IEEE transactions on medical imaging},\n volume={32},\n number={8},\n pages={1550--1563},\n year={2013},\n publisher={IEEE}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLogging\u003c/h4\u003e\u003ca id=\"user-content-logging\" class=\"anchor\" aria-label=\"Permalink: Logging\" href=\"#logging\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe use \u003ccode\u003ewandb\u003c/code\u003e to log experiments. To use this feature, set the \u003ccode\u003eWANDB_API_KEY\u003c/code\u003e environment variable by adding the following line to your \u003ccode\u003e.zshrc\u003c/code\u003e or \u003ccode\u003e.bashrc\u003c/code\u003e file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export WANDB_API_KEY=your_api_key\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e WANDB_API_KEY=your_api_key\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"xvr: X-ray to Volume Registration","anchor":"xvr-x-ray-to-volume-registration","htmlText":"xvr: X-ray to Volume Registration"},{"level":2,"text":"Highlights","anchor":"highlights","htmlText":"Highlights"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Usage","anchor":"usage","htmlText":"Usage"},{"level":3,"text":"Training","anchor":"training","htmlText":"Training"},{"level":4,"text":"Notes","anchor":"notes","htmlText":"Notes"},{"level":3,"text":"Finetuning","anchor":"finetuning","htmlText":"Finetuning"},{"level":4,"text":"Notes","anchor":"notes-1","htmlText":"Notes"},{"level":3,"text":"Registration (test-time optimization)","anchor":"registration-test-time-optimization","htmlText":"Registration (test-time 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fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div 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js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><code>xvr</code>: X-ray to Volume Registration</h1><a id="user-content-xvr-x-ray-to-volume-registration" class="anchor" aria-label="Permalink: xvr: X-ray to Volume Registration" href="#xvr-x-ray-to-volume-registration"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://arxiv.org/abs/2503.16309" rel="nofollow"><img src="https://camo.githubusercontent.com/8d4fdf56a675879964e16a753ce2048d6e0037a8814911ec995fb960346b0b63/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323530332e31363330392d7265642e737667" alt="Paper shield" data-canonical-src="https://img.shields.io/badge/arXiv-2503.16309-red.svg" style="max-width: 100%;"></a> <a href="/eigenvivek/xvr/blob/main/LICENSE"><img src="https://camo.githubusercontent.com/cce5a2a14b0faab422e0bfcdc074afb46089831a0bf5930a7d8af3f31b98f847/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d626c75652e737667" alt="License: MIT" data-canonical-src="https://img.shields.io/badge/License-MIT-blue.svg" style="max-width: 100%;"></a> <a href="https://colab.research.google.com/drive/1K9lBPxcLh55mr8o50Y7aHkjzjEWKPCrM?usp=sharing" rel="nofollow"><img alt="Colab" src="https://camo.githubusercontent.com/96889048f8a9014fdeba2a891f97150c6aac6e723f5190236b10215a97ed41f3/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" style="max-width: 100%;"></a> <a href="https://huggingface.co/eigenvivek/xvr/tree/main" rel="nofollow"><img alt="Hugging Face" src="https://camo.githubusercontent.com/6619bca0f19e2cc604e0252e169f0154815aa1c29b2adc4f6674a4442f39d120/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d4d6f64656c732d6666633130373f636f6c6f723d666663313037266c6f676f436f6c6f723d7768697465" data-canonical-src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-ffc107?color=ffc107&logoColor=white" style="max-width: 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data-canonical-src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json" style="max-width: 100%;"></a></p> <p dir="auto">A PyTorch package for training patient-specific 2D/3D registration models <strong>in 5 minutes.</strong></p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/29757116/425203515-8a01c184-f6f1-420e-82b9-1cbe733adf7f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.flmEQlM7p0hXCAjGjguZLVloIvmsR1sCnB9AL0cegHM"><img width="410" alt="image" src="https://private-user-images.githubusercontent.com/29757116/425203515-8a01c184-f6f1-420e-82b9-1cbe733adf7f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.flmEQlM7p0hXCAjGjguZLVloIvmsR1sCnB9AL0cegHM" style="max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Highlights</h2><a id="user-content-highlights" class="anchor" aria-label="Permalink: Highlights" href="#highlights"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>🚀 A single CLI/API for training models and registering clinical data</li> <li>⚡️ <strong>100x faster</strong> patient-specific model training than <a href="https://github.com/eigenvivek/DiffPose"><code>DiffPose</code></a></li> <li>📐 Submillimeter registration accuracy with new image-similarity metrics</li> <li>🩺 Human-interpretable pose parameters for <strong>training your own models</strong></li> <li>🐍 Pure Python/PyTorch implementation</li> <li>🖥️ Supports macOS, Linux, and Windows</li> </ul> <p dir="auto"><code>xvr</code> is built upon <a href="https://github.com/eigenvivek/DiffDRR"><code>DiffDRR</code></a>, the differentiable X-ray renderer.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Install the Python API and CLI:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install git+https://github.com/eigenvivek/xvr.git"><pre class="notranslate"><code>pip install git+https://github.com/eigenvivek/xvr.git </code></pre></div> <p dir="auto">Verify the installation version (should match the latest release on GitHub):</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="xvr --version"><pre class="notranslate"><code>xvr --version </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage</h2><a id="user-content-usage" class="anchor" aria-label="Permalink: Usage" href="#usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>xvr</code> provides a command-line interface for training, finetuning, and performing registration (i.e., test-time optimization) with pose regression models. The API is designed to be modular and extensible, allowing users to easily train models on new datasets and anatomical structures without any manual annotations.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ xvr --help Usage: xvr [OPTIONS] COMMAND [ARGS]... xvr is a PyTorch package for training, fine-tuning, and performing 2D/3D X-ray to CT/MR registration with pose regression models. Options: --version Show the version and exit. --help Show this message and exit. Commands: train Train a pose regression model from scratch. restart Restart model training from a checkpoint. finetune Optimize a pose regression model for a specific patient. register Use gradient-based optimization to register XRAY to a CT/MR. animate Animate the trajectory of iterative optimization. dcm2nii Convert a DICOMDIR to a NIfTI file."><pre class="notranslate"><code>$ xvr --help Usage: xvr [OPTIONS] COMMAND [ARGS]... xvr is a PyTorch package for training, fine-tuning, and performing 2D/3D X-ray to CT/MR registration with pose regression models. Options: --version Show the version and exit. --help Show this message and exit. Commands: train Train a pose regression model from scratch. restart Restart model training from a checkpoint. finetune Optimize a pose regression model for a specific patient. register Use gradient-based optimization to register XRAY to a CT/MR. animate Animate the trajectory of iterative optimization. dcm2nii Convert a DICOMDIR to a NIfTI file. </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Training</h3><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To train a pose regression model from scratch on a single patient or a set of preregistered subjects, use <code>xvr train</code>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ xvr train --help Usage: xvr train [OPTIONS] Train a pose regression model from scratch. Options: -i, --inpath PATH A single CT or a directory of CTs for pretraining [required] -o, --outpath PATH Directory in which to save model weights [required] --r1 <FLOAT FLOAT>... Range for primary angle (in degrees) [required] --r2 <FLOAT FLOAT>... Range for secondary angle (in degrees) [required] --r3 <FLOAT FLOAT>... Range for tertiary angle (in degrees) [required] --tx <FLOAT FLOAT>... Range for x-offset (in millimeters) [required] --ty <FLOAT FLOAT>... Range for y-offset (in millimeters) [required] --tz <FLOAT FLOAT>... Range for z-offset (in millimeters) [required] --sdd FLOAT Source-to-detector distance (in millimeters) [required] --height INTEGER DRR height (in pixels) [required] --delx FLOAT DRR pixel size (in millimeters / pixel) [required] --renderer [siddon|trilinear] Rendering equation [default: trilinear] --orientation [AP|PA] Orientation of CT volumes [default: PA] --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right) --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles] --convention TEXT If parameterization is Euler angles, specify order [default: ZXY] --model_name TEXT Name of model to instantiate [default: resnet18] --pretrained Load pretrained ImageNet-1k weights --norm_layer TEXT Normalization layer [default: groupnorm] --lr FLOAT Maximum learning rate [default: 0.005] --weight-geo FLOAT Weight on geodesic loss term [default: 0.01] --batch_size INTEGER Number of DRRs per batch [default: 116] --n_epochs INTEGER Number of epochs [default: 1000] --n_batches_per_epoch INTEGER Number of batches per epoch [default: 100] --name TEXT WandB run name --project TEXT WandB project name [default: xvr] --help Show this message and exit."><pre class="notranslate"><code>$ xvr train --help Usage: xvr train [OPTIONS] Train a pose regression model from scratch. Options: -i, --inpath PATH A single CT or a directory of CTs for pretraining [required] -o, --outpath PATH Directory in which to save model weights [required] --r1 <FLOAT FLOAT>... Range for primary angle (in degrees) [required] --r2 <FLOAT FLOAT>... Range for secondary angle (in degrees) [required] --r3 <FLOAT FLOAT>... Range for tertiary angle (in degrees) [required] --tx <FLOAT FLOAT>... Range for x-offset (in millimeters) [required] --ty <FLOAT FLOAT>... Range for y-offset (in millimeters) [required] --tz <FLOAT FLOAT>... Range for z-offset (in millimeters) [required] --sdd FLOAT Source-to-detector distance (in millimeters) [required] --height INTEGER DRR height (in pixels) [required] --delx FLOAT DRR pixel size (in millimeters / pixel) [required] --renderer [siddon|trilinear] Rendering equation [default: trilinear] --orientation [AP|PA] Orientation of CT volumes [default: PA] --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right) --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles] --convention TEXT If parameterization is Euler angles, specify order [default: ZXY] --model_name TEXT Name of model to instantiate [default: resnet18] --pretrained Load pretrained ImageNet-1k weights --norm_layer TEXT Normalization layer [default: groupnorm] --lr FLOAT Maximum learning rate [default: 0.005] --weight-geo FLOAT Weight on geodesic loss term [default: 0.01] --batch_size INTEGER Number of DRRs per batch [default: 116] --n_epochs INTEGER Number of epochs [default: 1000] --n_batches_per_epoch INTEGER Number of batches per epoch [default: 100] --name TEXT WandB run name --project TEXT WandB project name [default: xvr] --help Show this message and exit. </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Notes</h4><a id="user-content-notes" class="anchor" aria-label="Permalink: Notes" href="#notes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>The <code>--inpath</code> argument should point to a directory containing CT volumes for training. <ul dir="auto"> <li>If the directory contains a single CT scan, the resulting model be patient-specific.</li> <li>If the directory contains multiple CTs, it's beneficial to preregister them to a common reference frame (e.g., using <a href="https://github.com/ANTsX/ANTs">ANTs</a>). This will improve the accuracy of the model, but this isn't strictly necessary.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Finetuning</h3><a id="user-content-finetuning" class="anchor" aria-label="Permalink: Finetuning" href="#finetuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To finetune a pretrained pose regression model on a new patient, use <code>xvr finetune</code>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ xvr finetune --help Usage: xvr finetune [OPTIONS] Optimize a pose regression model for a specific patient. Options: -i, --inpath PATH Input CT volume for patient-specific pretraining [required] -o, --outpath PATH Output directory for finetuned model weights [required] -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required] --lr FLOAT Maximum learning rate [default: 0.005] --batch_size INTEGER Number of DRRs per batch [default: 116] --n_epochs INTEGER Number of epochs [default: 10] --n_batches_per_epoch INTEGER Number of batches per epoch [default: 25] --rescale FLOAT Rescale the virtual detector plane [default: 1.0] --name TEXT WandB run name --project TEXT WandB project name [default: xvr] --help Show this message and exit."><pre class="notranslate"><code>$ xvr finetune --help Usage: xvr finetune [OPTIONS] Optimize a pose regression model for a specific patient. Options: -i, --inpath PATH Input CT volume for patient-specific pretraining [required] -o, --outpath PATH Output directory for finetuned model weights [required] -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required] --lr FLOAT Maximum learning rate [default: 0.005] --batch_size INTEGER Number of DRRs per batch [default: 116] --n_epochs INTEGER Number of epochs [default: 10] --n_batches_per_epoch INTEGER Number of batches per epoch [default: 25] --rescale FLOAT Rescale the virtual detector plane [default: 1.0] --name TEXT WandB run name --project TEXT WandB project name [default: xvr] --help Show this message and exit. </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Notes</h4><a id="user-content-notes-1" class="anchor" aria-label="Permalink: Notes" href="#notes-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>The <code>--inpath</code> argument should point to a single CT volume for which the pose regression model will be finetuned.</li> <li>The <code>--ckpt</code> argument specifies the path to a checkpoint of a pretrained pose regression model produced by <code>xvr train</code>. <ul dir="auto"> <li>In addition to model weights, this checkpoint also contains the configurations used for training (e.g., pose parameters and intrinsic parameters), which are reused for finetuning.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Registration (test-time optimization)</h3><a id="user-content-registration-test-time-optimization" class="anchor" aria-label="Permalink: Registration (test-time optimization)" href="#registration-test-time-optimization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To register <strong>real</strong> X-ray images using a pretrained model followed by iterative pose refinement with differentiable rendering, use <code>xvr register model</code>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="$ xvr register model --help Usage: xvr register model [OPTIONS] XRAY... Initialize from a pose regression model. Options: -v, --volume PATH Input CT volume (3D image) [required] -m, --mask PATH Labelmap for the CT volume (optional) -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required] -o, --outpath PATH Directory for saving registration results [required] --crop INTEGER Preprocessing: center crop the X-ray image [default: 0] --subtract_background Preprocessing: subtract mode X-ray image intensity --linearize Preprocessing: convert X-ray from exponential to linear form --reducefn TEXT If DICOM is multiframe, how to extract a single 2D image for registration [default: max] --warp PATH SimpleITK transform to warp input CT to template reference frame --invert Invert the warp --labels TEXT Labels in mask to exclusively render (comma separated) --scales TEXT Scales of downsampling for multiscale registration (comma separated) [default: 8] --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right) --renderer [siddon|trilinear] Rendering equation [default: trilinear] --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles] --convention TEXT If parameterization is Euler angles, specify order [default: ZXY] --lr_rot FLOAT Initial step size for rotational parameters [default: 0.01] --lr_xyz FLOAT Initial step size for translational parameters [default: 1.0] --patience INTEGER Number of allowed epochs with no improvement after which the learning rate will be reduced [default: 10] --threshold FLOAT Threshold for measuring the new optimum [default: 0.0001] --max_n_itrs INTEGER Maximum number of iterations to run at each scale [default: 500] --max_n_plateaus INTEGER Number of times loss can plateau before moving to next scale [default: 3] --init_only Directly return the initial pose estimate (no iterative pose refinement) --saveimg Save ground truth X-ray and predicted DRRs --pattern TEXT Pattern rule for glob is XRAY is directory [default: *.dcm] --verbose INTEGER RANGE Verbosity level for logging [default: 1; 0<=x<=3] --help Show this message and exit."><pre class="notranslate"><code>$ xvr register model --help Usage: xvr register model [OPTIONS] XRAY... Initialize from a pose regression model. Options: -v, --volume PATH Input CT volume (3D image) [required] -m, --mask PATH Labelmap for the CT volume (optional) -c, --ckptpath PATH Checkpoint of a pretrained pose regressor [required] -o, --outpath PATH Directory for saving registration results [required] --crop INTEGER Preprocessing: center crop the X-ray image [default: 0] --subtract_background Preprocessing: subtract mode X-ray image intensity --linearize Preprocessing: convert X-ray from exponential to linear form --reducefn TEXT If DICOM is multiframe, how to extract a single 2D image for registration [default: max] --warp PATH SimpleITK transform to warp input CT to template reference frame --invert Invert the warp --labels TEXT Labels in mask to exclusively render (comma separated) --scales TEXT Scales of downsampling for multiscale registration (comma separated) [default: 8] --reverse_x_axis Enable to obey radiologic convention (e.g., heart on right) --renderer [siddon|trilinear] Rendering equation [default: trilinear] --parameterization TEXT Parameterization of SO(3) for regression [default: euler_angles] --convention TEXT If parameterization is Euler angles, specify order [default: ZXY] --lr_rot FLOAT Initial step size for rotational parameters [default: 0.01] --lr_xyz FLOAT Initial step size for translational parameters [default: 1.0] --patience INTEGER Number of allowed epochs with no improvement after which the learning rate will be reduced [default: 10] --threshold FLOAT Threshold for measuring the new optimum [default: 0.0001] --max_n_itrs INTEGER Maximum number of iterations to run at each scale [default: 500] --max_n_plateaus INTEGER Number of times loss can plateau before moving to next scale [default: 3] --init_only Directly return the initial pose estimate (no iterative pose refinement) --saveimg Save ground truth X-ray and predicted DRRs --pattern TEXT Pattern rule for glob is XRAY is directory [default: *.dcm] --verbose INTEGER RANGE Verbosity level for logging [default: 1; 0<=x<=3] --help Show this message and exit. </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Notes</h4><a id="user-content-notes-2" class="anchor" aria-label="Permalink: Notes" href="#notes-2"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>By passing a <code>--mask</code> and a comma-separated set of <code>--labels</code>, registration will be performed with respect to specific structures.</li> <li>If the model was trained with a coordinate frame different to that of the <code>--volume</code>, you can pass a <code>--warp</code> to rigidly realign the model's predictions to the new patient.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Experiments</h2><a id="user-content-experiments" class="anchor" aria-label="Permalink: Experiments" href="#experiments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Models</h4><a id="user-content-models" class="anchor" aria-label="Permalink: Models" href="#models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pretrained models are available <a href="https://huggingface.co/eigenvivek/xvr/tree/main" rel="nofollow">here</a>.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Data</h4><a id="user-content-data" class="anchor" aria-label="Permalink: Data" href="#data"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Benchmarks datasets, reformatted into DICOM/NIfTI files, are available <a href="https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main" rel="nofollow">here</a>.</p> <p dir="auto">If you use the <a href="https://github.com/rg2/DeepFluoroLabeling-IPCAI2020"><code>DeepFluoro</code></a> dataset, please cite:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{grupp2020automatic, title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration}, author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H}, journal={International journal of computer assisted radiology and surgery}, volume={15}, pages={759--769}, year={2020}, publisher={Springer} }"><pre class="notranslate"><code>@article{grupp2020automatic, title={Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration}, author={Grupp, Robert B and Unberath, Mathias and Gao, Cong and Hegeman, Rachel A and Murphy, Ryan J and Alexander, Clayton P and Otake, Yoshito and McArthur, Benjamin A and Armand, Mehran and Taylor, Russell H}, journal={International journal of computer assisted radiology and surgery}, volume={15}, pages={759--769}, year={2020}, publisher={Springer} } </code></pre></div> <p dir="auto">If you use the <a href="https://lit.fe.uni-lj.si/en/research/resources/3D-2D-GS-CA/" rel="nofollow"><code>Ljubljana</code></a> dataset, please cite:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{pernus20133d, title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images}, author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo}, journal={IEEE transactions on medical imaging}, volume={32}, number={8}, pages={1550--1563}, year={2013}, publisher={IEEE} }"><pre class="notranslate"><code>@article{pernus20133d, title={3D-2D registration of cerebral angiograms: A method and evaluation on clinical images}, author={Mitrović, Uros˘ and S˘piclin, Z˘iga and Likar, Bos˘tjan and Pernus˘, Franjo}, journal={IEEE transactions on medical imaging}, volume={32}, number={8}, pages={1550--1563}, year={2013}, publisher={IEEE} } </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Logging</h4><a id="user-content-logging" class="anchor" aria-label="Permalink: Logging" href="#logging"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We use <code>wandb</code> to log experiments. To use this feature, set the <code>WANDB_API_KEY</code> environment variable by adding the following line to your <code>.zshrc</code> or <code>.bashrc</code> file:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export WANDB_API_KEY=your_api_key"><pre><span class="pl-k">export</span> WANDB_API_KEY=your_api_key</pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="Nv/kpIFDWoV4t/cBE24WYlfYQX++5vWY7/X778R84ZHhd444L/T43gQ4jS7ZFv5zitUWQF/ITfLFUZOHs0KGZA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Training 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</svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>