ZOOM LINK HERE
“High Resolution Breast Diffusion Weighted Imaging”
Jessica McKay, PhD
ABSTRACT: Diffusion-weighted imaging (DWI) is a quantitative MRI method that measures the apparent diffusion coefficient (ADC) of water molecules, which reflects cell density and serves as an indication of malignancy. Unfortunately, however, the clinical value of DWI is severely limited by the undesirable features in images that common clinical methods produce, including large geometric distortions, ghosting and chemical shift artifacts, and insufficient spatial resolution. Thus, in order to exploit information encoded in diffusion characteristics and fully assess the clinical value of ADC measurements, it is first imperative to achieve technical advancements of DWI.
In this talk, I will largely focus on the background of breast DWI, providing the clinical motivation for this work and explaining the current standard in breast DWI and alternatives proposed throughout the literature. I will also present my PhD dissertation work in which a novel strategy for high resolution breast DWI was developed. The purpose of this work is to improve DWI methods for breast imaging at 3 Tesla to robustly provide diffusion-weighted images and ADC maps with anatomical quality and resolution. This project has two major parts: Nyquist ghost correction and the use of simultaneous multislice imaging (SMS) to achieve high resolution. Exploratory work was completed to characterize the Nyquist ghost in breast DWI, showing that, although the ghost is mostly linear, the three-line navigator is unreliable, especially in the presence of fat. A novel referenceless ghost correction, Ghost/Object minimization was developed that reduced the ghost in standard SE-EPI and advanced SMS. An advanced SMS method with axial reformatting (AR) is presented for high resolution breast DWI. In a reader study, AR-SMS was preferred by three breast radiologists compared to the standard SE-EPI and readout-segmented-EPI.
“Machine-learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study”
Michael Zhang, MD
ABSTRACT: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) is a diagnostic challenge with important management implications. We sought to develop a radiomics classifier based on 900 features extracted from gadolinium-enhanced, T1-weighted MRI, using the Quantitative Imaging Feature Pipeline and the PyRadiomics package. Additional patient-specific clinical variables were recorded. A radiomic signature was derived from least absolute shrinkage and selection operator, followed by gradient boost machine learning. A training and test set were selected randomly in a 70:30 ratio. We further evaluated the performance of radiomics-based classifier models against human readers of varying medical-training backgrounds. Following image pre-processing, 95 malignant and 171 benign PNSTs were available. The final classifier included 21 features and achieved a sensitivity 0.676, specificity 0.882, and area under the curve (AUC) 0.845. Collectively, human readers achieved sensitivity 0.684, specificity 0.742, and AUC 0.704. We concluded that radiomics using routine gadolinium enhanced, T1-weighted MRI sequences and clinical features can aid in the evaluation of PNSTs, particularly by increasing specificity for diagnosing malignancy. Further improvement may be achieved with incorporation of additional imaging sequences.
Ge Wang, PhD
Clark & Crossan Endowed Chair Professor
Director of the Biomedical Imaging Center
Rensselaer Polytechnic Institute
Troy, New York
Abstract:
AI-based tomography is an important application and a new frontier of machine learning. AI, especially deep learning, has been widely used in computer vision and image analysis, which deal with existing images, improve them, and produce features. Since 2016, deep learning techniques are actively researched for tomography in the context of medicine. Tomographic reconstruction produces images of multi-dimensional structures from externally measured “encoded” data in the form of various transforms (integrals, harmonics, and so on). In this presentation, we provide a general background, highlight representative results, and discuss key issues that need to be addressed in this emerging field.
About:
AI-based X-ray Imaging System (AXIS) lab is led by Dr. Ge Wang, affiliated with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute and the Center for Biotechnology and Interdisciplinary Studies in the Biomedical Imaging Center. AXIS lab focuses on innovation and translation of x-ray computed tomography, optical molecular tomography, multi-scale and multi-modality imaging, and AI/machine learning for image reconstruction and analysis, and has been continuously well funded by federal agencies and leading companies. AXIS group collaborates with Stanford, Harvard, Cornell, MSK, UTSW, Yale, GE, Hologic, and others, to develop theories, methods, software, systems, applications, and workflows.
![SMMR Panel Discussion: Mixed Reality for Surgical Guidance @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/03/immers-300x198.png)
Mixed Reality for Surgical Guidance will take place on Thursday, April 1st from 9:00 – 10:30 am PDT.
The event will start with a one-hour panel discussion featuring Dr. Bruce Daniel of Stanford Radiology and the Stanford IMMERS Lab; Christoffer Hamilton of Brainlab, a surgical software and hardware leader in Germany; and Dr. Thomas Grégory of Orthopedic Surgery at the Université Sorbonne Paris Nord.
This panel will be moderated by Dr. Christoph Leuze of Stanford University and the Stanford Medical Mixed Reality (SMMR) program.
Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.
Register here: https://stanford.zoom.us/meeting/register/tJcqf-GrqToiHNKL4D-5haRLowQylIwMEAve
![Stanford School of Medicine's 2nd Annual Conference on Disability in Healthcare and Medicine](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2020/10/Screen-Shot-2021-03-04-at-10.39.22-AM-e1614883544333-300x300.jpeg)
Date: April 10, 2021 (8 AM-6PM)
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- 8 AM-8:20 AM opening remarks Zainub and Pete
- 8:20 AM-9:20 AM Talk 1 “I fought the law and no one won”
- 10 minute Break
- 9:30 AM-10:30 AM talk 2 students and doctors with disabilities panel
- 20 minute break
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- 10:50 AM-11:50 AM Breakout
- One hour lunch (TBD)
- 12:50 PM-1:50 PM Talk 3 the frontiers of disability research
- Lisa Meeks is moderating
- Bonnie Swenor invited
- 10 minute break
- 2:00 PM-3:00 PM breakout 2
- 10 minute break
- 3:10 PM-4:10 PM talk 4 do-it-yourself disability advocacy (Poullos/Tolchin with students)
- 4:10 PM-4:30 PM closing remarks
- 4:30 PM-6 PM virtual happy hour
![Racial Equity Challenge: Race in society @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/04/shield.png)
Targeted violence continues against Black Americans, Asian Americans, and all people of color. The department of radiology diversity committee is running a racial equity challenge to raise awareness of systemic racism, implicit bias and related issues. Participants will be provided a list of resources on these topics such as articles, podcasts, videos, etc., from which they can choose, with the “challenge” of engaging with one to three media sources prior to our session (some videos are as short as a few minutes). Participants will meet in small-group breakout sessions to discuss what they’ve learned and share ideas.
Please reach out to Marta Flory, flory@stanford.edu with questions. For details about the session, including recommended resources and the Zoom link, please reach out to Meke Faaoso at mfaaoso@stanford.edu.
![IMMERS - Stanford Medical Mixed Reality Panel Discussion Series @ Zoom](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/logo_friendly_small.png)
Join us for a panel on Behavioral XR on Thursday, June 3rd from 9:00 – 10:30 am PDT. The event will start with a one-hour panel discussion featuring Dr. Elizabeth McMahon, a psychologist with a private practice in California; Sarah Hill of Healium, a company developing XR apps for mental fitness based in Missouri; Christian Angern of Sympatient, a company developing VR for anxiety therapy based in Germany; and Marguerite Manteau-Rao of Penumbra, a medical device company based in California. This panel will be moderated by Dr. Walter Greenleaf of Stanford’s Virtual Human Interaction Lab (VHIL) and Dr. Christoph Leuze of the Stanford Medical Mixed Reality (SMMR) program. Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.
Register here to save your place now! After registering, you will receive a confirmation email containing information about joining the meeting.
Please visit this page to subscribe to our events mailing list.
Sponsored by Stanford Medical Mixed Reality (SMMR)
![International Conference on Functional Imaging and Modeling of the Heart @ Virtual Event](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/FIMH_sidebar_logo.jpg)
Join us for the 11th biennial International Conference on Functional Imaging and Modeling of the Heart (FIMH). FIMH-2021 will celebrate 20 years of bringing together friends, colleagues, and collaborators to share and discuss the latest in cardiac and cardiovascular imaging, electrophysiology, computational modeling, and translational applications. The event will take place June 21-25, 2021 virtually, via Livestream, Zoom meeting workshops, and Spatial Chat networking.
Sponsored by: Functional Imaging and Modeling of the Heart Conference
![Radiology-Wide Research Conference @ Zoom – Details can be found here: https://radresearch.stanford.edu](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/RWRC-July-300x195.jpeg)
Radiology Department-Wide Research Meeting
• Research Announcements
• Mirabela Rusu, PhD – Learning MRI Signatures of Aggressive Prostate Cancer: Bridging the Gap between Digital Pathologists and Digital Radiologists
• Akshay Chaudhari, PhD – Data-Efficient Machine Learning for Medical Imaging
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Kawin Setsompop, PhD
Sponsored by: the the Department of Radiology
![2021 AIMI Symposium + BOLD-AIR Summit @ Virtual Livestream](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/2021-Symposium-and-BOLD-Banner_06182121-300x112.png)
Stanford AIMI Director Curt Langlotz and Co-Directors Matt Lungren and Nigam Shah invite you to join us on August 3 for the 2021 Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) Symposium. The virtual symposium will focus on the latest, best research on the role of AI in diagnostic excellence across medicine, current areas of impact, fairness and societal impact, and translation and clinical implementation. The program includes talks, interactive panel discussions, and breakout sessions. Registration is free and open to all.
Also, the 2nd Annual BiOethics, the Law, and Data-sharing: AI in Radiology (BOLD-AIR) Summit will be held on August 4, in conjunction with the AIMI Symposium. The summit will convene a broad range of speakers in bioethics, law, regulation, industry groups, and patient safety and data privacy, to address the latest ethical, regulatory, and legal challenges regarding AI in radiology.
![CME Grand Rounds Sanjiv Sam Gambhir Lectureship - Simon Cherry, PhD @ LKSC 101/102 & Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/simon_cherry_website-269x300.jpg)
CME Grand Rounds Sanjiv Sam Gambhir Lectureship – “Imaging at the Speed of Light: Innovations in Positron Emission Tomography”
Simon R. Cherry, PhD
Professor
Biomedical Engineering & Radiology
UC Davis
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT
ABSTRACT
Positron emission tomography (PET) allows for sensitive and quantitative measurement of physiology, metabolism and molecular targets noninvasively in the human body. However, typical clinical PET scanners capture less than 1% of the available signal produced in the body. PET scanners also are not currently capable of precisely determining the location at which a particular decay occurs. These limitations present opportunities for further innovation that ultimately will impact molecular imaging research and diagnostic imaging with PET. This presentation focuses on 1) total-body PET imaging which greatly improves signal collection, allowing radiotracer kinetics to be assessed across the entire human body for the first time, and 2) the development of detector technologies that have a timing precision of ~ 30 picoseconds, enabling direct localization of radiotracer decays without tomographic reconstruction.
BIO
Simon R. Cherry, Ph.D. received his B.Sc.(Hons) in Physics with Astronomy from University College London in 1986 and a Ph.D. in Medical Physics from the Institute of Cancer Research, University of London in 1989. After a postdoctoral fellowship at UCLA, he joined the faculty in the Department of Molecular and Medical Pharmacology, also at UCLA, in 1993. In 2001, Dr. Cherry joined UC Davis and established the Center for Molecular and Genomic Imaging, which he directed from 2004-2016. Currently Dr. Cherry is Distinguished Professor in the Departments of Biomedical Engineering and Radiology at UC Davis.
Dr. Cherry’s research interests center around biomedical imaging and in particular the development and application of in vivo molecular imaging systems. His major accomplishments have been in developing systems for positron emission tomography (PET), in particular the invention of the microPET technology that was subsequently widely adopted in academia and industry and as co-leader of the EXPLORER consortium which has developed the world’s first total-body PET scanner. He also has contributed to detector technology innovations for PET, conducted early biomedical studies using Cerenkov luminescence, and developed the first proof-of-concept hybrid PET/MRI (magnetic resonance imaging) systems.
Dr. Cherry is a founding member of the Society of Molecular Imaging and an elected fellow of six professional societies, including the Institute for Electronic and Electrical Engineers (IEEE) and the Biomedical Engineering Society (BMES). He served as Editor-in-Chief of the journal Physics in Medicine and Biology from 2011-2020. Dr. Cherry received the Academy of Molecular Imaging Distinguished Basic Scientist Award (2007), the Society for Molecular Imaging Achievement Award (2011) and the IEEE Marie Sklodowska-Curie Award (2016). In 2016, he was elected as a member of the National Academy of Engineering and in 2017 he was elected to the National Academy of Inventors. Dr. Cherry is the author of more than 240 peer-reviewed journal articles, review articles and book chapters in the field of biomedical imaging. He is also lead author of the widely-used textbook “Physics in Nuclear Medicine”.