Calendar

Thursday MIPS Roundtable: Faculty Lab Showcase

1:30-2:00 PM | Dr. Gozde Durmus, Ph.D.

Levitating Cells for Applications in Biology and Medicine

Assistant Professor of Radiology

Stanford University

2:00-2:30 PM | Dr. Ramasamy Paulmurugan, Ph.D.

Cellular Pathway Imaging Laboratory (CPIL)

Associate Professor of Radiology

Stanford University

MIPS Roundtables will be every Thursday through May from 1:30-2:30pm showcasing various topics and are open to all interested.

Please note Zoom information does change week to week.

5/14 Meeting URL: https://stanford.zoom.us/j/97661102825
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 976 6110 2825

Thursday MIPS Roundtable: Faculty Lab Showcase

1:30-2:00 PM | Dr. Sandip Biswal, M.D.

Biswal Lab for Pain Imaging

Associate Professor of Radiology

Stanford University

2:00-2:30 PM | Dr. Lei Xing, Ph.D.

Xing Lab: Where Sciences & Engineering Meet Medicine

Jacob Haimson Professor and Professor, by courtesy, of Electrical Engineering

Stanford University

MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

Please note Zoom information does change week to week.

5/21 Meeting URL: https://stanford.zoom.us/j/98018934679
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 980 1893 4679

Thursday MIPS Roundtable: Faculty Lab Showcase

1:30-2:00 PM | Dr. Jianghong Rao, Ph.D.

Cellular and Molecular Imaging Laboratory (CMIL)

Professor of Radiology and, by courtesy, of Chemistry

Stanford University

2:00-2:30 PM | Dr. Zhen Cheng, Ph.D.

Cancer Molecular Imaging Chemistry Laboratory (CMICL)

Associate Professor of Radiology

Stanford University

MIPS Roundtables will be every Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

Please note Zoom information does change week to week.

5/28 Meeting URL: https://stanford.zoom.us/j/92834097988
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 928 3409 7988

Thursday MIPS Roundtable: Meet our MIPS Instructors 

1:30-2:00 PM | Dr. Katie Wilson, Ph.D.
“Optical and Acoustic Molecular Imaging to Identify Lymph Node Metastasis in Head and Neck Cancer.”
Instructor, Radiology
Stanford University

2:00-2:30 PM | Dr. Corinne Beinat, Ph.D.
“Molecular Imaging of Tumor Metabolism”
Instructor, Radiology
Stanford University

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

Please note Zoom information does change week to week.

6/11 Meeting URL: https://stanford.zoom.us/j/95475611159
Dial: +1 650 724 9799 or +1 833 302 1536
Meeting ID: 954 7561 1159

Thursday MIPS Roundtable: Faculty Lab Showcase

MIPS Roundtables are every other Thursday from 1:30-2:30pm showcasing various topics and are open to all interested.

1:30-2:00 PM | Dr. Brian Rutt, Ph.D.
Cellular & Molecular MRI Laboratory (CMMRIL)
Professor of Radiology
Stanford University

2:00-2:30 PM | Dr. Kathy Ferrara, Ph.D.
Ferrara Laboratory: Image-guided Drug Delivery
Professor of Radiology
Stanford University

Please note Zoom information does change week to week.

6/25 Webinar URL: https://stanford.zoom.us/j/91635637393?pwd=c09vUXYyeU5VeHJBaUJVRHQrT3FJdz09
Dial: +1 650 724 9799 or +1 833 302 1536
Webinar ID: 916 3563 7393
Webinar Password: 271364

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

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

Judy Gichoya, MD
Assistant Professor
Emory University School of Medicine

Measuring Learning Gains in Man-Machine Assemblage When Augmenting Radiology Work with Artificial Intelligence

Abstract
The work setting of the future presents an opportunity for human-technology partnerships, where a harmonious connection between human-technology produces unprecedented productivity gains. A conundrum at this human-technology frontier remains – will humans be augmented by technology or will technology be augmented by humans? We present our work on overcoming the conundrum of human and machine as separate entities and instead, treats them as an assemblage. As groundwork for the harmonious human-technology connection, this assemblage needs to learn to fit synergistically. This learning is called assemblage learning and it will be important for Artificial Intelligence (AI) applications in health care, where diagnostic and treatment decisions augmented by AI will have a direct and significant impact on patient care and outcomes. We describe how learning can be shared between assemblages, such that collective swarms of connected assemblages can be created. Our work is to demonstrate a symbiotic learning assemblage, such that envisioned productivity gains from AI can be achieved without loss of human jobs.

Specifically, we are evaluating the following research questions: Q1: How to develop assemblages, such that human-technology partnerships produce a “good fit” for visually based cognition-oriented tasks in radiology? Q2: What level of training should pre-exist in the individual human (radiologist) and independent machine learning model for human-technology partnerships to thrive? Q3: Which aspects and to what extent does an assemblage learning approach lead to reduced errors, improved accuracy, faster turn-around times, reduced fatigue, improved self-efficacy, and resilience?

Zoom: https://stanford.zoom.us/j/93580829522?pwd=ZVAxTCtEdkEzMWxjSEQwdlp0eThlUT09

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.