Stanford Molecular Imaging Scholars (SMIS) Program Quarterly Seminar
Zoom meeting: https://stanford.zoom.us/j/99117388314?pwd=R29OSjlTdUt0a3pLaG5Zc1BFNTJIUT09
Password: 922183
Guolan Lu, PhD
Mentor: Eben Rosenthal, MD; Garry Nolan, PhD
“Co-administered Antibody Improves the Penetration of Antibody-Dye Conjugates into Human Cancers: Implications for AntibodyDrug Conjugates”
Dianna Jeong, PhD
Mentors: Craig Levin, PhD; Shan Wang, PhD
“Novel Detection Approaches for Achieving Ultra-fast time resolution for PET”
Dear WMIS trainees, colleagues and friends,
We welcome you to join our upcoming virtual WMIS – Stanford Diversity conference on September 9-11, 2020. We are coming together to reinforce our commitment to diversity and to provide a forum for our team members to engage in meaningful discussions. The conference will provide keynote lectures, scientific presentations and educational lectures from leaders and pioneers in the field, who will discuss important topics related to racial justice, women in STEM and Global Health. We are also offering breakout sessions whereby carefully selected individuals will facilitate a discussion about how to implement more supportive and inclusive practices into our daily professional and personal life. The breakout sessions are designed to enable active involvement of smaller groups where people feel safe to discuss current challenges in the STEM field and actionable solutions.
This conference is free of charge and will provide 9.5 CME credits. Abstracts of all conference presentations and a summary of discussion points and insights provided by all conference participants will be published in Molecular Imaging & Biology. The organizing committee will provide 10 trainee prizes in the form of free WMIS memberships to conference attendants for the 2021 WMIC in Miami.
Website: https://www.wmislive.org
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.
Radiology Department-Wide Research Meeting
• Curt Langlotz, MD, PhD: Overview of the AIMI Center
• Brian Hargreaves, PhD: Research Details from Town Hall, Q&A, and COVID19 Updates
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Radiology Department-Wide Research Meeting
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
February 19 Speakers:
Bruce Daniel, MD – Center Overview: IMMERS
Jennifer McNab, PhD – Encoding and Decoding Diffusion MRI
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
Radiology Department-Wide Research Meeting
• Dominik Fleischmann, MD: 3DQ Lab Overview
• Tom Soh, PhD: Research Updates
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
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
Radiology Department-Wide Research Meeting
• Research Announcements
• Michelle James, PhD – Detecting and Tracking Immune Responses in the Brain and Beyond using PET
• Ryan Spitler, PhD – Precision Health and Integrated Diagnostics (PHIND) Center
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Brian Hargreaves, PhD
Sponsored by: the the Department of Radiology
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.
Radiology Department-Wide Research Meeting
• Research Announcements
• Koen Nieman, M.D., PhD – Coronary Artery Disease by Computed Tomography
• Martin Willemink, M.D., PhD – Translating Emerging Cardiovascular CT Techniques into the Clinical Setting
Location: Zoom – Details can be found here: https://radresearch.stanford.edu
Meetings will be the 3rd Friday of each month.
Hosted by: Daniel Ennis, PhD
Sponsored by: the the Department of Radiology