Calendar

Jul
9
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Thursday MIPS Roundtable
Jul 9 @ 1:30 pm – 2:30 pm Zoom - See Description for Zoom Link
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Meet our MIPS Instructors 

 

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested. Note we will take a break through late July and August. 

 

1:30-2:00 PM | Dr. Ahmed El Kaffas, Ph.D.
Translational Ultrasound for Tissue Characterization and Stimulation
Instructor, Radiology
Stanford University

 

2:00-2:30 PM | Dr. Brett Fite, Ph.D.
Combining Focal and Immunotherapies
Instructor, Radiology
Stanford University

 

Please note Zoom information does change week to week.

7/9 Webinar URL: https://stanford.zoom.us/j/91909413178
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 919 0941 3178
Password: 572746

Jul
16
Thu
2020
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link
Thursday MIPS Roundtable
Jul 16 @ 1:30 pm – 2:30 pm Zoom - See Description for Zoom Link
Thursday MIPS Roundtable @ Zoom - See Description for Zoom Link

Thursday MIPS Roundtable: Meet our MIPS Instructors 

 

MIPS Roundtables are Thursdays from 1:30-2:30pm showcasing various topics and are open to all interested. Note this will be our last summer Roundtable and we will take a break through late July and August. 

 

1:30-2:00 PM | Dr. Josquin Foiret, Ph.D.
High throughput ultrasound imaging for improved diagnosis
Instructor, Radiology
Stanford University

 

2:00-2:30 PM | Dr. Jinghang Xie, Ph.D.
TESLA probes for imaging T cell-mediated cytotoxic response to immunotherapy
Instructor, Radiology
Stanford University

 

Please note Zoom information does change week to week.

7/16 Webinar URL: https://stanford.zoom.us/j/94952044130
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 949 5204 4130
Password: 963699

Aug
4
Tue
2020
SMIS Quarterly Seminar @ Zoom:
SMIS Quarterly Seminar
Aug 4 @ 12:00 pm – 1:00 pm Zoom:
SMIS Quarterly Seminar @ Zoom:

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”

 

Oct
21
Wed
2020
SCIT Quarterly Seminar @ See description for ZOOM link
SCIT Quarterly Seminar
Oct 21 @ 10:00 am – 11:00 am See description for ZOOM link

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.

Nov
18
Wed
2020
IBIIS & AIMI Seminar: Deep Tomographic Imaging @ Zoom: https://stanford.zoom.us/j/96731559276?pwd=WG5zcEFwSGlPcDRsOUFkVlRhcEs2Zz09
IBIIS & AIMI Seminar: Deep Tomographic Imaging
Nov 18 @ 12:00 pm – 1:00 pm Zoom: https://stanford.zoom.us/j/96731559276?pwd=WG5zcEFwSGlPcDRsOUFkVlRhcEs2Zz09

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.

Apr
30
Fri
2021
Racial Equity Challenge: Race in society @ Zoom
Racial Equity Challenge: Race in society
Apr 30 @ 12:00 pm – 1:00 pm Zoom
Racial Equity Challenge: Race in society @ Zoom

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.

Jun
21
Mon
2021
International Conference on Functional Imaging and Modeling of the Heart @ Virtual Event
International Conference on Functional Imaging and Modeling of the Heart
Jun 21 – Jun 25 all-day Virtual Event
International Conference on Functional Imaging and Modeling of the Heart @ Virtual Event

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

Jul
16
Fri
2021
Radiology-Wide Research Conference @ Zoom – Details can be found here: https://radresearch.stanford.edu
Radiology-Wide Research Conference
Jul 16 @ 12:00 pm – 1:00 pm Zoom – Details can be found here: https://radresearch.stanford.edu
Radiology-Wide Research Conference @ Zoom – Details can be found here: https://radresearch.stanford.edu

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

Aug
3
Tue
2021
2021 AIMI Symposium + BOLD-AIR Summit @ Virtual Livestream
2021 AIMI Symposium + BOLD-AIR Summit
Aug 3 @ 8:00 am – Aug 4 @ 3:00 pm Virtual Livestream
2021 AIMI Symposium + BOLD-AIR Summit @ Virtual Livestream

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.

 

REGISTER HERE

Apr
17
Wed
2024
IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare @ Clark Center S360 - Zoom Details on IBIIS website
IBIIS & AIMI Seminar: Building Fair and Trustworthy AI for Healthcare
Apr 17 @ 12:00 pm – 1:00 pm Clark Center S360 - Zoom Details on IBIIS website

Roxana Daneshjou, MD, PhD
Assistant Professor, Biomedical Data Science & Dermatology
Assistant Director, Center of Excellence for Precision Heath & Pharmacogenomics
Director of Informatics, Stanford Skin Innovation and Interventional Research Group
Stanford University

Title: Building Fair and Trustworthy AI for Healthcare

Abstract: AI for healthcare has the potential to revolutionize how we practice medicine. However, to do this in a fair and trustworthy manner requires special attention to how AI models work and their potential biases. In this talk, I will cover the considerations for building AI systems that improve healthcare.