Medical Imaging Conferences 2016 USA |Conferenceseries

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content-box-dotted clearfix"> <div class="col-md-8 col-sm-6"> <p>Conference Series Ltd invites all the participants across the globe to attend International Conference on Medical Imaging & Diagnosis Chicago, Illinois, USA.</p> </div> <div class="col-md-4 col-sm-6 help-desk"><a class="btn btn-danger" href="https://clinical-medicalimaging.conferenceseries.com/abstract-submission.php" title="Click for more information">Submit your Abstract</a> <h5>or e-mail to <i class="fa fa-arrow-down"></i></h5> <p> <i class="fa fa-envelope-o"></i> <a href="mailto:events@conferenceseries.com">events@conferenceseries.com</a><br> <i class="fa fa-envelope-o"></i> <a href="mailto:medicalimaging@conferenceseries.net">medicalimaging@conferenceseries.net</a><br> <i class="fa fa-envelope-o"></i> <a href="mailto:medicalimaging@conferenceseries.net">medicalimaging@conferenceseries.net</a><br> </div> </div> <div class="show-special day-schedule text-center clearfix"> <div class="col-md-4 col-sm-4"> <a class="btn btn-success" href="https://clinical-medicalimaging.conferenceseries.com/2016/scientific-program.php?day=1&sid=1969&date=2016-10-20" title="Click for more information">Scientific Program <span class="badge">Day 1</span></a> </div> <div class="col-md-4 col-sm-4"> <a class="btn btn-success" href="https://clinical-medicalimaging.conferenceseries.com/2016/scientific-program.php?day=2&sid=1970&date=2016-10-21" title="Click for more information">Scientific Program <span class="badge">Day 2</span></a> </div> </div> <article class="scientific-prog"> <h3 class="heading heading-out">Day 1 : <time datetime="2015-12-07">October 20, 2016</time><span class="heading-shadow"></span></h3> <section class="col-md-12 content-box-dotted"> <div class="affiliation bs-callout"> <h4>Keynote Forum</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/zang-hee-cho-seoul-national-university-south-korea" >Zang-Hee Cho</a></h4> <p>Seoul National University, South Korea</p> <h6>Keynote: <a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/super-resolution-mr-tractography-with-7t-mri-and-its-applications-from-the-study-of-language-circuitry-to-microstructural-analysis-of-the-affective-neural-network" >Super-resolution MR tractography with 7T MRI and its applications - From the study of language circuitry to microstructural analysis of the affective neural network</a></h6> <p>Time : <b>10:00-10:45</b></p> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Zang-Hee-Cho-3625.jpg" alt="Conference Series Medical Imaging 2016 International Conference Keynote Speaker Zang-Hee Cho photo" title="Zang-Hee Cho" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><div style="text-align: justify;"> Zang-Hee Cho received PhD in Physics, from University of Uppsala, Sweden. Since then, he has been faculty of UCLA, Columbia University, and University of California, Irvine. Last ten years, he served as a Director of the Neuroscience Research Institute, Gachon University and established one of the leading PET-MRI brain imaging centers in the world. He is an early pioneer of CT and PET, developing world’s first circular ring PET (at UCLA, 1975) and BGO (PET-detector, 1976) and more recently 7.0T MRI+PET Fusion Brain Imaging System at NRI, in Korea. Currently, he is serving as a Distinguished Research Fellow at Seoul National University, Seoul, Korea, and also as a member of National Academy of Medicine, Washington DC, USA.</div> </p> <h5>Abstract:</h5> <p><div style="text-align: justify;"> New imaging system, the brain dedicated <a href="https://clinical-medicalimaging.conferenceseries.com/">PET-MRI</a>, using high resolution PET and Ultra High Field 7.0T Magnetic Resonance Imaging (MRI) and their applications to brain research, especially to the areas of neuropsychiatry, neurosurgery and neuroscience will be discussed. Among the interesting topical areas, applications of the high resolution brain PET (HRRT) and the ultrahigh field MRI (7.0T) will be highlighted, especially for the in vivo human brain imaging with ultra-high field MRI, such as the 7.0T MRI, one can now visualize the substructures of the thalamus and brainstem in</div> <div style="text-align: justify;"> vivo as well as tractography hitherto unable to do with existing MRI systems. Together with <a href="https://clinical-medicalimaging.conferenceseries.com/">molecular imaging</a> using <a href="https://clinical-medicalimaging.conferenceseries.com/">Positron Emission Tomography </a>(PET), that is the brain dedicated PET-MRI fusion system developed recently, now, it is possible to visualize molecular mechanisms quantitatively in our human brain in vivo as well as tractography. Lastly, ultra-high field MRI also began to provide excellent tractographic images delineating fine fibers such as medial forebrain bundles and internal medullary laminars in the thalamo-limbic areas suggesting future potential applications of these fibers to, among others, such as the DBS (Deep Brain Stimulation). Some recent results of brain PET-MRI fusion system as well as the new tractographic images obtained with 7.0T will be discussed and highlighted.</div> </p> </div> </div> </div> </section> <section class="col-md-12 content-box-dotted"> <div class="affiliation bs-callout"> <h4>Keynote Forum</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/sanjay-gandhi-north-bristol-nhs-trust-uk" >Sanjay Gandhi</a></h4> <p>North Bristol NHS Trust, UK</p> <h6>Keynote: <a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/latest-developments-in-radiology-healthcare-it-digital-imaging-and-telehealth" >Latest developments in radiology healthcare IT, digital imaging and telehealth</a></h6> <p>Time : <b>11:05-11:50</b></p> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Sanjay-Gandhi-3626.jpg" alt="Conference Series Medical Imaging 2016 International Conference Keynote Speaker Sanjay Gandhi photo" title="Sanjay Gandhi" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><div style="text-align: justify;"> Sanjay Gandhi is a senior attending Radiologist at one of the largest teaching hospitals and regional trauma units in the UK. For the past 17 years, he has been teaching the University of Bristol and University of West of England trainees. As Honorary Professor, he also teaches at Sri Devaraj Urs University, India. He has won multiple academic awards and has been involved in numerous research projects and collaborative trials. He has published widely on use of cutting-edge technology and co-authored and edited 8 medical textbooks. He is an internationally recognized leader in HealthcareIT and development of Smart Apps.</div> </p> <h5>Abstract:</h5> <p><div style="text-align: justify;"> Exciting new innovations in healthcare technology such advanced image analysis, Tele-Health, Micro Robotics, etc., are changing the landscape of healthcare. These advances are enabling us to deliver high-quality care to a wider population. New technology often brings improved productivity and efficiency savings. New developments in Healthcare-IT are leading the way in developing new care pathways such as image and model guided therapies. Teleconferencing and specialist opinion for complex cases, <a href="https://clinical-medicalimaging.conferenceseries.com/">Computer Aided Detection/Diagnosis </a>(CAD), automated image analysis are helping professionals to deal with increasing workload. As a result, we are benefiting from both the technical efficacy and improved diagnostic accuracy. These innovations have additional benefits in medical education, training, assessment and quality assurance. We will discuss the benefits of the latest developments to patients, radiologists, clinicians along with potential savings for hospital management. At the same time, there is a fear that digital health tools may lead to burning out of physicians and impede care. Studies have shown that the majority of self-proclaimed health apps in the market aren’t tested on patients and more worryingly, rouge</div> <div style="text-align: justify;"> organizations make false or exaggerated claims. Some of the melanoma detection apps were fined recently by the FTC for</div> <div style="text-align: justify;"> claiming to diagnose skin cancer. In addition to these subjects, this presentation will highlight several practical examples of medical innovations through the stages of concept, design, pilot and successful implementation. Furthermore, there will be practical information for healthcare sector’s entrepreneurs, who wish to develop pioneering products for the future.</div> </p> </div> </div> </div> </section> <div class="well well-sm col-md-12 content-box-dotted"> <div class="content-box tracks" style="clear:both"> <ul class="list-group show"> <li class="list-group-item">Special Session </li> </ul> <h5>Location: Vienna</h5> </div> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4>Session Introduction</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/bin-zheng-university-of-oklahoma-usa" >Bin Zheng</a></h4> <p>University of Oklahoma, USA</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/identifying-and-testing-new-quantitative-image-an-analysis-based-clinical-markers-to-predict-breast-cancer-risk-and-prognosis" >Identifying and testing new quantitative image, an analysis based clinical markers to predict breast cancer risk and prognosis</a> </h6> <p>Time : <b>11:50-12:30</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Bin-Zheng-47646.jpg" alt="Speaker" title="Bin Zheng" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Bin Zheng has received his PhD from University of Delaware in 1993. After working in Department of Radiology, University of Pittsburgh for 20 years, he joined the Faculty in University of Oklahoma in 2013 as a Professor at School of Electrical and Computer Engineering and Oklahoma TSET cancer research scholar at Stephenson Cancer Center. Since 1998, he has served as Principal Investigator in five R01 and one R21 grant awards from NCI, NIH, one DOD breast cancer project, and subcontract PI of two other NIH R01 grant awards. Currently, he is AIMBE Fellow and an Editor-in-Chief of <em>Journal of X-ray Science and Technology</em>. </p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> Quantitative image feature analysis plays an important role in <a href="https://clinical-medicalimaging.conferenceseries.com/">cancer screening</a>, diagnosis, and prognosis assessment. In our laboratory, we focus on identifying and testing new quantitative image analysis based clinical markers to help more accurately predict cancer risk and prognosis. In this presentation, I will discuss our work and recent progress in assessing near-term breast cancer risk and breast tumor response to neoadjuvant chemotherapy. First, we applied image feature analysis method based on detection of bilateral mammographic density and tissue asymmetry to build a new machine learning model to predict risk of the individual women developing early breast cancer in the near-term (i.e., within the 2 years after a negative mammography screening). Second, we extracted kinetic image features from breast MR images to build another new model to predict the likelihood of complete response of breast tumors to neoadjuvant chemotherapy. To increase confidence of clinians to consider and/or accept the prediction results of our new models, we also developed and implemented the graphic user interface (GUI) platforms for interactively applying our new image processing scheme and prediction models to process and analyze images. Our preliminary testing results using several image datasets demonstrated that applying the new quantitative image feature analysis based models could yield significantly higher discriminatory power in predicting near-term breast cancer risk and tumor response to the chemotherapy. If it is successfully applied, the new image marker based prediction models has potential to help establish a new and more effective personalized breast cancer screening and/or treatment paradigms in the future.</p> <p>  </p> </p> </div> </div> </div> </section> <div class="content-box tracks" style="clear:both"> <ul class="list-group show"> <li class="list-group-item">Medical Image Processing | Radiography | Radiology & Nuclear Medicine</li> </ul> <h5>Location: Vienna</h5> </div> <section> <div class="col-md-12" > <div class="affiliation bs-callout speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/7334-Session-Photo-T.jpg" alt="Speaker" title="Sanjay Gandhi" class="img-responsive thumbnail pull-left"> <div class="bio"> <h4>Chair</h4> <h4>Sanjay Gandhi</h4> <p>North Bristol NHS Trust, UK</p> </div> </div> </div> </div> </section> <section> <div class="col-md-12" > <div class="affiliation bs-callout speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/7334-CO-Session-Photo-T.jpg" alt="Speaker" title="Michael L Goris" class="img-responsive thumbnail pull-left"> <div class="bio"> <h4>Co-Chair</h4> <h4>Michael L Goris</h4> <p>Stanford University School of Medicine, USA</p> </div> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4>Session Introduction</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/michael-l-goris-stanford-university-school-of-medicine-usa" >Michael L Goris</a></h4> <p>Stanford University School of Medicine, USA</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/analytical-fusion-of-different-modality-images-based-on-prior-knowledge" >Analytical fusion of different modality images based on prior knowledge</a> </h6> <p>Time : <b>12:30-12:55</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Michael-L-Goris-47628.jpg" alt="Speaker" title="Michael L Goris" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Michael L Goris has a Medical degree from the University of Leuven in Belgium and a PhD degree in Medical Physics from UC Berkeley. He has been a Professor in the Stanford Medical School and is Emeritus since 2012 and served as a Chairman for University panel on Radiation safety during 2003-2010. He has more than 120 publications in peer reviewed journals. His research interests are radio-immunotherapy, medical imaging processing and quantification for diagnosis of clinical validations.</p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> Fusion is the simultaneous and combined analysis of two images mapping identically in the same object space, but recording a different attribute of the object. Most fusion has been performed as visual representation in which the attributes are represented independently into overlapping but independent color scales. In this work we explore fusion, in which the attributes are combined in a mathematical or logical manner, to address a specific goal.</p> <p style="text-align: justify;"> <strong>The first approach</strong> is mathematical, and concerns a particular combination of <a href="https://clinical-medicalimaging.conferenceseries.com/">brain imaging</a>: In patients treated for brain tumors, the usually clear delineation of pathology by MRI is compromised because the treatment itself may produce an ambiguous signal. Specifically, a FLAIR sequence will show post-treatment edema and recurrent tumor as a high signal intensity region. <a href="https://clinical-medicalimaging.conferenceseries.com/">FDG PET</a> on the other hand will show little or no density in the former, and (near normal) in the latter. A viable tumor would also show increased density in PET and post contrast T1 sequence, but not all post T1 high densities represent regions with high metabolic activity. The combination of these a-priori judgments (or prior knowledge) can be done in different manners: After normalization, the product MxP would favor viable tumor and the artangent of (P/M) would likely represent non-viable or non-malignant lesions.</p> <p style="text-align: justify;"> <strong>In the second approach</strong>, in preparation for a treatment of liver metastases with radioactive <sup>90</sup>Y-labeled microspheres, the liver is infused intra-arterially with 99mTc macro-aggregates, imaged, and reinjected with 99mTc colloid and imaged. The result is two in-line registered image volumes, defining MAA perfused tumor and liver, and functional liver (colloid). The analysis of the fusion allows the computation of relative and absolute volumes, and relative doses to liver and tumor. We found that the relative dose to normal liver perfused by MAA is the best predictor of post therapy toxicity (as measured by the liver enzyme elevation). In cases of toxicity, the average relative volume was 66%, in the absence of toxicity, the relative volume was 33% (p<0.01), with only one case overlapping. Response to therapy is related to the tumor absolute dose.</p> <p style="text-align: justify;"> In conclusion fusion is more than a combined visual display, but is a potentially powerful analytical tool, combining the data from more than one image or object attributes.</p> </p> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/bital-savir-baruch-loyola-university-medical-center-usa" >Bital Savir-Baruch</a></h4> <p>Loyola University Medical Center, USA</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/the-role-of-nuclear-medicine-in-early-breast-cancer-diagnosis-how-do-we-do" >The role of nuclear medicine in early breast cancer diagnosis - â€œHow do we do?â€</a> </h6> <p>Time : <b>12:55-13:20</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Bital-Savir-Baruch-47629.jpg" alt="Speaker" title="Bital Savir-Baruch" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Bital Savir-Baruch is a board certified Nuclear Medicine Physician. She received her Medical degree from Semmelweis University, Budapest, Hungary. She completed the Nuclear Medicine residency program at Emory university Hospital, Atlanta, GA, In 2014, she joined Loyola University Medical Center, Maywood, Illinois as an Assistant Professor of Radiology. </p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> Breast cancer is the most common diagnosed cancer worldwide. Since the introduction of mammography as gold standard for screening purposes in the diagnosis of breast cancer, mortality decreased significantly. However, its performance in a population with dense breast is very low. Among all screening women, approximately 48% women will present with heterogeneously or extremely dense breast. Low performance of mammography is likely due to overlying glandular tissue masking tumor lesions. Hence, other modalities such as molecular breast imaging are been evaluated. Tc99m Sestamibi <a href="https://clinical-medicalimaging.conferenceseries.com/">Molecular Breast Imaging</a> (MBI) has demonstrated significantly higher sensitivity and equivalent specificity in the detection of breast cancer among high risk women when compared to mammography and is thus being used increasingly as an adjunct to mammography and ultrasound in selected women. Large trial by Rhodes <em>et. al.</em> evaluated the performance of MBI using as low as 8 mCi of Tc99m Sestamibi in the screening of high risk women with mammographically dense breasts. MBI sensitivity was significantly higher than that of mammography, 81% versus 24%, with same specificity of 93% vs. 89%. However, when compared to mammography, <a href="https://clinical-medicalimaging.conferenceseries.com/">nuclear medicine</a> breast modality generates all body radiation dose. When targeting a younger population with increased probability of having dense breast, appropriate risk to benefit ratio has to be established. In this presentation, we will review the role of nuclear medicine in the diagnosis of early breast cancer.</p> </p> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/ilona-kowalik-urbaniak-client-outlook-inc-canada" >Ilona Kowalik-Urbaniak</a></h4> <p>Client Outlook Inc., Canada</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/does-interpolation-affect-diagnosis-quantitative-assessment-of-the-effects-of-interpolation-on-uncompressed-and-compressed-medical-images" >Does interpolation affect diagnosis? â€“ Quantitative assessment of the effects of interpolation on uncompressed and compressed medical images</a> </h6> <p>Time : <b>14:00-14:25</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Ilona-Kowalik-Urbaniak-47630.png" alt="Speaker" title="Ilona Kowalik-Urbaniak" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Ilona Kowalik-Urbaniak has completed her PhD in 2015 at the University of Waterloo in Applied Mathematics. Currently, she holds a Postdoctoral research position (TalentEdge Postdoctoral Fellowship) at Client Outlook Inc., Waterloo, Ontario. Her work is mainly focused in the area of quality assessment of compressed medical images and the effects of image compression on diagnosis. Her PhD dissertation dealt with mathematical modeling of subjective radiologists’ responses using objective image quality assessment methods. She has been awarded for her work on medical image quality at conferences on several occasions. </p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> The resolution at which <a href="https://clinical-medicalimaging.conferenceseries.com/">medical images</a> are displayed on output devices often differs from the original. Image rescaling is accomplished by estimating pixels at unknown locations through interpolation. The most common artifacts resulting from this estimation include blurring, edge distortion, ringing and aliasing. Even for the same image, different interpolation techniques may produce images that differ significantly. As a result, interpolation may have an impact on diagnostic image quality. Objective quality assessment of interpolated images is a challenging task since there is no one-to-one mapping between the original and the interpolated image. No objective model has been yet established for medical images. A quantitative evaluation of the impact of interpolation on medical image quality with the use of the most common interpolation techniques is presented. The quality of 60 interpolated compressed and uncompressed neuro- and abdominal <a href="https://clinical-medicalimaging.conferenceseries.com/">CT</a> images was evaluated objectively using the mean squared error (MSE), signal-to-noise ratio (SNR), structural similarity index (SSIM) and a proposed technique based on the deterministic and statistical information of the signal. This work is an attempt to capture the loss of diagnostic information of interpolated compressed and uncompressed medical images. We propose a full-reference objective measure of quality for interpolated images, which considers deterministic and statistical knowledge about the image. The statistical properties are acquired from the frequency domain (high-frequency content) of the signal and are combined with the elements of SSIM. Future work will involve validation of the proposed image fidelity measure based on subjective radiological assessments using a modified Receiver Operating Characteristic (ROC) analysis. We aim to present a model that could serve as a predictor of quality of interpolated images at different rescaling factors for a given image modality and anatomical region.</p> </p> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/sanjay-gandhi-north-bristol-nhs-trust-uk" >Sanjay Gandhi</a></h4> <p>North Bristol NHS Trust, UK</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/role-of-radiology-in-changing-the-landscape-of-managing-multiple-myeloma" >Role of radiology in changing the landscape of managing multiple myeloma</a> </h6> <p>Time : <b>14:25-14:50</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Sanjay-Gandhi-47631.jpg" alt="Speaker" title="Sanjay Gandhi" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Sanjay Gandhi is a Senior Attending Radiologist at one of the largest teaching hospitals and Regional Trauma Units in the UK. For the past 17 years, he has been teaching at the University of Bristol and University of West of England. As a Professor, he also teaches at Sri Devaraj Urs University, India. He has won multiple academic awards and has been involved in numerous research projects and collaborative trials. He has published widely on use of cutting-edge technology and co-authored and edited eight medical textbooks. He is an internationally recognized leader in Healthcare IT and development of Smart Apps.</p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> Imaging of multiple myeloma has been transformed in the past few years due to new research and developments. It is important to keep abreast of the new recommendations, for example smoldering multiple myeloma, the risk of progression to malignant disease in the first 5 years is about 10% per year and therefore a robust strategy for diagnosis, follow up and staging is essential. In particular, low dose whole-body CT (LDWBCT), whole-body diffusion weighted <a href="https://clinical-medicalimaging.conferenceseries.com/">MRI</a> and <a href="https://clinical-medicalimaging.conferenceseries.com/">PET/CT</a> are increasingly being used and likely to replace plain films skeletal surveys. This lecture will focus on morphologic and functional imaging techniques and the latest recommendations of the international myeloma working group (IMWG). Strategies for managing monoclonal gammopathy of unclear significance (MGUS) and smoldering multiple myeloma along with the role of interventional and palliative radiology techniques such as vertebroplasty or kyphoplasty, radio-frequency thermal ablation and targeted cryoablation therapy will be discussed. In addition, this presentation will also discuss Durie-Salmon PLUS staging system, the relative merits and limitations of different <a href="https://clinical-medicalimaging.conferenceseries.com/">imaging modalities</a> and practical challenges faced in the management of multiple myeloma.</p> </p> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/monika-beresova-university-of-debrecen-hungary" >Monika Beresova</a></h4> <p>University of Debrecen, Hungary</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/differentiation-between-osteoblastic-osteolytic-and-healthy-bone-tissue-in-ct-images-by-texture-analysis" >Differentiation between osteoblastic, osteolytic and healthy bone tissue in CT images by texture analysis</a> </h6> <p>Time : <b>14:50-15:15</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Monika-Beresova-47633.jpg" alt="Speaker" title="Monika Beresova" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Monika Béresová is a PhD student at University of Debrecen. She is working on Texture Analysis in Medical Images. She works in the Department of Biomedical Laboratory and Imaging Science Faculty of Medicine (University of Debrecen) as Biomedical Engineer. She is a Lecturer for basics of MRI, Anatomy and she also works in the following research areas: NMR measurement on Earth magnetic field, image post-processing and in fMRI study.</p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> <strong>Objectives:</strong> The aim of this study was to perform texture analysis of the bone metastasis, to find and describe the difference of heterogeneity parameters, in osteolytic osteoblastic, mixed (osteolytic and osteoblastic) and healthy bone lesions.</p> <p style="text-align: justify;"> <strong>Methods: </strong>In this study, 5 patients with vertebral metastatic lesions were examined using <a href="https://clinical-medicalimaging.conferenceseries.com/">CT</a> images. Pathological lesions were manually segmented and binary masks were created for the all osteoblastic, osteolytic, mixed and healthy spine areas for each patient. Histogram (min, max, mean, SD, variance and SD/mean) and co-occurrence matrix (contrast, correlation, energy, entropy and homogeneity) features were extracted. For statistical comparisons, the segmented lesions were split into four groups according to their sizes: (1) 0-0.25cm<sup>3</sup>, (2) 0.25-0.5cm<sup>3</sup>, (3) 0.5-1 cm<sup>3</sup> and (4) >1 cm<sup>3</sup>. ROC analysis and Kolmogorov-Smirnov test were done and we used non-parametric Kruskal-Wallis tests with Bonferroni correction to compare the different bone lesions.</p> <p style="text-align: justify;"> <strong>Results: </strong>Based on reliability and ROC analysis, smallest ROIs (group 1) were discarded, and osteolytic lesions were also excluded from this study, because the sizes were falling into 0-0.25 cm<sup>3</sup> range. The maximum, mean, SD, variance, SD/mean and contrast were significantly different between mixed and healthy lesions at ROI size larger than 0.25 cm<sup>3</sup>. At the same size range, the maximum, mean, SD, variance, contrast and correlation were significantly different between healthy and osteoblastic lesions. At last, comparing the mixed and the osteoblastic lesions, the maximum, mean, SD, variance and contrast parameters were found significantly different.</p> <p style="text-align: justify;"> <strong>Conclusions:</strong> The heterogeneity parameters allowed us to describe the differences between pathological bone lesions. Texture analysis was not reliable in small lesions, but we could differentiate the healthy, the mixed and the osteoblastic areas utilizing the following textural parameters: maximum, mean, SD, variance and contrast.</p> </p> </div> </div> </div> </section> <div class="content-box tracks" style="clear:both"> <ul class="list-group show"> <li class="list-group-item">Computer assisted-tomography | Elastography</li> </ul> <h5>Location: Vienna</h5> </div> <section> <div class="col-md-12" > <div class="affiliation bs-callout speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/7335-Session-Photo-T.jpg" alt="Speaker" title="Hang Joon Jo" class="img-responsive thumbnail pull-left"> <div class="bio"> <h4>Chair</h4> <h4>Hang Joon Jo</h4> <p>Mayo Clinic, USA</p> </div> </div> </div> </div> </section> <section> <div class="col-md-12" > <div class="affiliation bs-callout speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/7335-CO-Session-Photo-T.jpg" alt="Speaker" title="Elaine Iuanow" class="img-responsive thumbnail pull-left"> <div class="bio"> <h4>Co-Chair</h4> <h4>Elaine Iuanow</h4> <p>QT Ultrasound Labs, USA</p> </div> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4>Session Introduction</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/elaine-iuanow-qt-ultrasound-labs-usa" >Elaine Iuanow</a></h4> <p>QT Ultrasound Labs, USA</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/accuracy-of-cyst-vs-solid-diagnosis-in-the-breast-using-quantitative-transmission-qt-ultrasound" >Accuracy of cyst vs. solid diagnosis in the breast using quantitative transmission (QT) ultrasound</a> </h6> <p>Time : <b>15:40-16:05</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Elaine-Iuanow-47638.jpg" alt="Speaker" title="Elaine Iuanow" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Elaine Iuanow has graduated with her Medical degree from Tuft’s University School of Medicine and has completed her Fellowship in Breast Imaging at Brigham and Women’s/Faulkner-Sagoff Breast Center in Boston, MA. She is the Chief Medical Officer working with the research and development team at QT Ultrasound Labs, a novel breast ultrasound development company based in the San Francisco Bay Area. She brings her significant expertise in Breast Imaging as a Board Certified Radiologist with experience in administering world class care at premier medical institutions in the United States. Her research interests include breast ultrasound, entrepreneurship in health care delivery models, providing care to underserved women, breast disease in female and male patients, and advocacy regarding preventative breast health on the local, national and global arenas.</p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> We present the results of a receiver operator characteristic (ROC) study using an emerging ultrasound technology, quantitative transmission (QT) ultrasound. We present the readers the accuracy in determining whether a breast lesion is a cyst versus a solid using QT <a href="https://clinical-medicalimaging.conferenceseries.com/">ultrasound</a>. <a href="https://clinical-medicalimaging.conferenceseries.com/">Digital mammograms</a> (XRM) and QT ultrasound imaging were selected from the QT ultrasound library of images. All solid cases had ground truth pathology. Hand held ultrasound images were used as ground truth for cysts. Thirteen readers performed blinded reading of 32 cases (15 solids and 17 cysts) using XRM+QT, assigning both a confidence score (0-100) and a binary classification (solid/cyst) to classify lesions.  A 95% percentile bootstrap confidence interval (CI) was computed for the mean readers’ area under the ROC curve, sensitivity (proportion of solids correctly classified as solid) and specificity (proportion of cysts correctly classified as cysts). Results show that when a speed of sound measurement >1571 m/s was used to indicate a solid, mean sensitivity and specificity of QT ultrasound were 0.75 (95% CI: 0.56, 0.92) and 0.85 (CI: 0.67, 1.00), respectively. Using the readers’ binary classifications with XRM+QT, mean sensitivity and specificity were 0.95 (CI: 0.87, 1.00) and 0.84 (CI: 0.66, 0.98), respectively. When the readers’ confidence scores with XRM+QT were used to distinguish solids versus cysts, mean ROC area was 0.923 (CI: 0.830, 0.988). QT ultrasound is an emerging ultrasound technology that demonstrates high accuracy in distinguishing cyst versus solid lesions in the breast.</p> </p> </div> </div> </div> </section> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/barath-narayanan-narayanan-university-of-dayton-usa" >Barath Narayanan Narayanan</a></h4> <p>University of Dayton, USA</p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/feature-selection-using-linear-classifiers-for-computer-aided-detection-of-pulmonary-nodules-in-ct" >Feature selection using linear classifiers for computer aided detection of pulmonary nodules in CT</a> </h6> <p>Time : <b>16:05-16:20</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Barath-Narayanan-Narayanan-47639.jpg" alt="Speaker" title="Barath Narayanan Narayanan" class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><p style="text-align: justify;"> Barath Narayanan is currently a Graduate Teaching Assistant for the ECE department at University of Dayton. He has done his graduation from SRM University, Chennai, India in 2012 with a Bachelor’s degree in Electrical and Electronics Engineering. He obtained his Master’s degree in Electrical Engineering from University of Dayton in 2013. He is currently pursuing his PhD research in the field of Medical Image Analysis. His research focuses on "Computer Aided Detection for identifying lung nodules on Computer Tomography and Chest Radiography". Early detection of such potential cancerous nodules could potentially save people’s lives from lung cancer. His specific areas of interests include Pattern Recognition and Image processing. His Master's publication revolved around the application of Super-Resolution for JPEG2000 compressed images which are utilized for Airborne Imaging.</p> </p> <h5>Abstract:</h5> <p><p style="text-align: justify;"> Lung cancer is the leading cause of cancer death in the United States. It usually exhibits its presence with the formation of pulmonary nodules. Nodules are round or oval-shaped growth present in the lung. <a href="https://clinical-medicalimaging.conferenceseries.com/">Computed tomography</a> (CT) scans are used by <a href="https://clinical-medicalimaging.conferenceseries.com/">radiologists</a> to detect such nodules. Computer aided detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening. In this research, we study various feature selection methods using linear classifier for the CAD system framework proposed in FlyerScan. Algorithmic steps of FlyerScan include: (i) Local contrast enhancement, (ii) automated anatomical segmentation, (iii) detection of potential nodule candidates, (iv) feature computation and selection, and (v) candidate classification. In this paper, we focus on backend processor which comprises of (iv) and (v). We study the performance of the FlyerScan by implementing various feature selection methods such as sequential forward selection, sequential backward removal or implementation of these after holding onto certain robust features. We also evaluate the tuning of Fischer linear discriminant classifier and study the impact of feature selection on it. This algorithm is implemented using a publicly available lung image database consortium-image database resource initiative (LIDC-IDRI) dataset. 107 cases from LIDC-IDRI are handpicked in particular for this paper and performance of the CAD system would be studied based on k-fold validation. This research will aid in improving the nodule detection rate in CT scans, thereby enhancing a patient’s chance of survival.</p> </p> </div> </div> </div> </section> <div class="content-box tracks" style="clear:both"> <ul class="list-group show"> <li class="list-group-item">Special Session </li> </ul> <h5>Location: Vienna</h5> </div> <section> <div class="col-md-12"> <div class="affiliation bs-callout col-md-12"> <h4>Session Introduction</h4> <h4><a style="color:#63a7f4;" href="https://clinical-medicalimaging.conferenceseries.com/speaker/2016/zang-hee-cho-seoul-national-university-south-korea" >Zang Hee Cho </a></h4> <p>Seoul National University, South Korea </p> <h6>Title: <a style="color:#63a7f4" href="https://clinical-medicalimaging.conferenceseries.com/abstract/2016/ultra-high-field-mri-and-pet-fusion-imaging-for-neuroscience-research-from-parkinsons-disease-to-cognitive-sciences" >Ultra high field MRI and PET fusion imaging for neuroscience research â€“ From Parkinsons Disease to cognitive sciences</a> </h6> <p>Time : <b>16:40-17:20</b></p> </div> </div> <div class="speaker-bio-abs"> <div class="bio-main"> <img src="https://d2cax41o7ahm5l.cloudfront.net/cs/speaker-photo/Imaging-2016-Zang-Hee-Cho--47663.jpg" alt="Speaker" title="Zang Hee Cho " class="img-responsive thumbnail pull-left"> <div class="bio"> <h5>Biography:</h5> <p><h3 style="text-align: justify;">  </h3> <p style="text-align: justify;"> Zang Hee Cho was the Professor of Radiological Science at University of California at Irvine and the University Professor and Director of the Neuroscience Research Institute, Incheon, Korea since 1985, until he moved to Advanced Institutes of Convergence Technology, Seoul National University. He has been a pioneer in positron emission tomography (PET) and magnetic resonance imaging since the inception of the computerized tomography (CT) in 1972. He was the first one who pioneered world’s first "Ring PET", the first molecular imaging device, in 1975. More recently, he pioneered the first PET-MRI (Proteomics 2008) demonstrating that <em>in vivo</em> human sub-millimeter high resolution molecular imaging is possible and published over one hundred neuroscience and related scientific publications. He has more than 300 peer reviewed scientific publications covering from nuclear physics to neuroscience and published 3 graduate level text books. Among the numerous honors and awards, he was elected as a Member of the US National Academy of Sciences, Institute of Medicine in 1997.</p> </p> <h5>Abstract:</h5> <p><h3 style="text-align:justify">  </h3> <p> Recent progresses on new imaging and system developments, especially on the brain dedicated PET-MRI, using high resolution HRRT-PET and ultra-high field 7.0 T magnetic resonance imaging (MRI) and their applications to basic and clinical neuroradiology will be discussed. With high field MRI, such as the 7.0 T MRI, one can now visualize the subfields of the hippocampus and brainstem <em>in vivo</em> as well as tractography hitherto unable to do with existing MRI systems. Together with molecular imaging using positron emission tomography (PET), now, it is possible to visualize metabolic functional changes quantitatively in human brain.</p> </p> </div> </div> </div> </section> </div> </article> </div> </div> </section> </div>  <hr /> <link href="https://d2cax41o7ahm5l.cloudfront.net/cs/css/sprite.css" rel="stylesheet" /> <link href='https://fonts.googleapis.com/css?family=Alegreya+Sans:400,700' rel='stylesheet' type='text/css'> <link href="https://www.conferenceseries.com/css/conf_custom.css" rel="stylesheet" /> <div class="col-md-12 text-center bannerObjects"> <h2>Conference Series Destinations</h2> </div> <div class="conference-category-contact-main"> <div class="clearfix conference-category"> <div class="col-md-12"> <div class="row conference-category-sub"> <div class="col-md-4 clearfix" style="padding-right:0px;"> <div class="conference-country conf_border"> <h4 class="text-center">Webinars & Conferences By <strong>Continents</strong></h4> <div 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