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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10580">arXiv:2502.10580</a> <span> [<a href="https://arxiv.org/pdf/2502.10580">pdf</a>, <a href="https://arxiv.org/format/2502.10580">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yan Chen</a>, <a href="/search/eess?searchtype=author&query=Chand%2C+J+R">Jyothi Rikhab Chand</a>, <a href="/search/eess?searchtype=author&query=Kecskemeti%2C+S+R">Steven R. Kecskemeti</a>, <a href="/search/eess?searchtype=author&query=Holmes%2C+J+H">James H. Holmes</a>, <a href="/search/eess?searchtype=author&query=Jacob%2C+M">Mathews Jacob</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10580v1-abstract-short" style="display: inline;"> Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10580v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10580v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10580v1-abstract-full" style="display: none;"> Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10580v1-abstract-full').style.display = 'none'; document.getElementById('2502.10580v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.06595">arXiv:2501.06595</a> <span> [<a href="https://arxiv.org/pdf/2501.06595">pdf</a>, <a href="https://arxiv.org/format/2501.06595">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Fast multi-contrast MRI using joint multiscale energy model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yaghoobi%2C+N">Nima Yaghoobi</a>, <a href="/search/eess?searchtype=author&query=Chand%2C+J+R">Jyothi Rikhab Chand</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yan Chen</a>, <a href="/search/eess?searchtype=author&query=Kecskemeti%2C+S+R">Steve R. Kecskemeti</a>, <a href="/search/eess?searchtype=author&query=Holmes%2C+J+H">James H. Holmes</a>, <a href="/search/eess?searchtype=author&query=Jacob%2C+M">Mathews Jacob</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.06595v1-abstract-short" style="display: inline;"> The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.06595v1-abstract-full').style.display = 'inline'; document.getElementById('2501.06595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.06595v1-abstract-full" style="display: none;"> The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serves as the prior. We use a majorize-minimize algorithm to solve the optimization scheme. The proposed model leverages the redundancies across different contrasts to improve image fidelity. The proposed scheme is observed to preserve fine details and contrast, offering sharper reconstructions compared to reconstruction methods that independently recover the contrasts. While we focus on 3D MPNRAGE acquisitions in this work, the proposed approach is generalizable to arbitrary multi-contrast settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.06595v1-abstract-full').style.display = 'none'; document.getElementById('2501.06595v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.00102">arXiv:2304.00102</a> <span> [<a href="https://arxiv.org/pdf/2304.00102">pdf</a>, <a href="https://arxiv.org/format/2304.00102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yan Chen</a>, <a href="/search/eess?searchtype=author&query=Holmes%2C+J+H">James H. Holmes</a>, <a href="/search/eess?searchtype=author&query=Corum%2C+C">Curtis Corum</a>, <a href="/search/eess?searchtype=author&query=Magnotta%2C+V">Vincent Magnotta</a>, <a href="/search/eess?searchtype=author&query=Jacob%2C+M">Mathews Jacob</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.00102v1-abstract-short" style="display: inline;"> Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor Model(DFM), which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the representation enables the acquisition of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.00102v1-abstract-full').style.display = 'inline'; document.getElementById('2304.00102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.00102v1-abstract-full" style="display: none;"> Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor Model(DFM), which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the representation enables the acquisition of the images in a highly undersampled fashion, which translates to reduced scan time in 3D high-resolution multi-contrast applications. The approach integrates motion estimation and compensation, making the approach robust to subject motion during the scan. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.00102v1-abstract-full').style.display = 'none'; document.getElementById('2304.00102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 4 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div 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