ISMRM Workshop Series • 25-28 October 2018
Back

ISMRM Workshop on
Machine Learning
Part II

Capital Hilton, Washington, D.C., USA

Program
Registration & Setup – Thursday, 25 October 2018 – No CME Available
15:00   Registration & Speaker Upload Available until 18:00
 
Day 1 – Friday, 26 October 2018 – No CME Available
07:30   Breakfast on Own
Registration & Speaker Upload Available
 
Session 1: Overview of Machine Learning Methods
  Moderators: Matthew Rosen, Ph.D. & Jong Chul Ye, Ph.D.
08:25   Welcome Greg Zaharchuk, M.D., Ph.D.
Stanford University
Stanford, CA, USA
08:30   Machine Learning Basics Jeffrey A. Fessler, Ph.D.
University of Michigan
Ann Arbor, MI, USA
09:10   Machine Learning: Historical Perspectives Kyunghyun Cho, Ph.D.
New York University
New York, NY, USA
09:30   Deep Learning in 20 Minutes Krzysztof J. Geras, Ph.D.
New York University School of Medicine
New York, NY, USA
09:50   Break, Poster Viewing & Speaker Upload Available
 
Session 2: Hands-On Demo: Deep Learning Institute
10:30 Generative Adversarial Networks for MR Infarct Segmentation Based on CT Perfusion Hoo Chang Shin, Ph.D.
NVIDIA
Santa Clara, CA, USA
12:15   Lunch, Poster Viewing & Speaker Upload Available
 
Session 3: Data Acquisition & Image Reconstruction
  Moderators: Florian Knoll, Ph.D. & Martin Uecker, Dr. rer. nat.
13:30   Overview of Machine Learning Methods for Reconstruction of Imaging Data Jong Chul Ye, Ph.D.
KAIST
Daejeon, Republic of Korea
  Proffered Papers - Oral Session
13:50 Reinforcement Learning for Online Sampling Trajectory Optimization David Zeng, M.S.
Stanford University
Stanford, CA, USA
14:00 AUTOmated Pulse SEQuence Generation (AUTOSEQ) for MR Spatial Encoding in Unknown Inhomogeneous B0 Fields Bo Zhu, Ph.D.
A. A. Martinos Center for Biomedical Imaging
Charlestown, MA, USA
14:10 Deep U-Net Reconstruction for Undersampled Spiral Diffusion Tensor Cardiovascular Magnetic Resonance Augustine Yui Hei Luk
The University of Hong Kong
Hong Kong, China
14:20 Multi-Channel MRI Reconstruction with Unsupervised Learning of Latent Coils Joseph Y. Cheng, Ph.D.
Stanford University
Stanford, CA, USA
14:30 NEATR-SMS for Highly Accelerated Multi-Shot EPI Berkin Bilgic, Ph.D.
A. A. Martinos Center for Biomedical Imaging
Charlestown, MA, USA
14:40 Exploring Complex-Valued Neural Networks with Trainable Activation Functions for Magnetic Resonance Imaging Guillaume Daval-Frerot, M.Sc.
Siemens Healthineers
Princeton, NJ, USA
14:50 Machine Learning Image Reconstruction Challenge: Announcement & Community Feedback Discussion Florian Knoll, Ph.D.
New York University School of Medicine
New York, NY, USA
15:10   Break, Poster Viewing & Speaker Upload Available
 
Session 4: Artifact Correction & Post-Processing
  Moderator: Moderators: Mehmet Akcakaya, Ph.D. & Bo Zhu, Ph.D.
15:40 Machine Learning for PET/MRI Kevin T. Chen, Ph.D.
Stanford University
Stanford, CA, USA
16:00 Machine Learning Medical Imaging: Lessons from CT Ge Wang, Ph.D.
Rensselaer Polytechnic Institute
Troy, NY, USA
16:20   Machine Learning in Medical Image Registration Xiaohuan Cao, Ph.D.
Shanghai United Imaging Intelligence Co., Ltd.
Shanghai, China
  Proffered Papers - Oral Session
16:40 OneforAll: Generalizing Single Model for Multi-Contrast Synthesis Using Multi-Domain Conditional GAN Guanhua Wang
Tsinghua University
Beijing, China
16:50 Uncertainty Estimates for Synthetic-CTs Obtained Using Generative Adversarial Networks Matt Hemsley, B.Sc.
University of Toronto
Toronto, ON, Canada
17:00 Learning Systematic Imperfections with Deep Neural Networks for Wave-Encoded Single-Shot Fast Spin Echo Feiyu Chen, M.S.
Stanford University
Stanford, CA, USA
17:10 Improving Mean Kurtosis Measurements in Diffusion MRI via Learned Gibbs Removal Matthew Muckley, Ph.D.
New York University School of Medicine
New York, NY, USA
17:20 MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for Efficient Estimation of MR Parameters Fang Liu, Ph.D.
University of Wisconsin-Madison
Madison, WI, USA
17:30 Inline AI: A Gadgetron Solution for AI Inference on MR Scanner Hui Xue, Ph.D.
National Heart, Lung & Blood Institute
Bethesda, MD, USA
17:40   Dinner on own
 
Day 2 – Saturday, 27 October 2018 – No CME Available
07:30   Breakfast on Own
Registration & Speaker Upload Available
 
Session 5: Practical Aspects of Machine Learning
  Moderators: Alan B. McMillan, Ph.D. & Linda Moy, M.D.
08:30   Keynote: Bringing Artificial Intelligence into Radiology Charles E. Kahn, M.D., M.S.
University of Pennsylvania
Philadelphia, PA, USA
09:10     Data Preparation, Labeling & Deployment of Imaging Datasets for Deep Learning
- Video Permission Withheld		 Michael Muelly, M.D.
Google
Mountain View, CA, USA
09:30   Integrating Machine Learning into the Clinical Imaging Workflow & Other Practical Considerations Martin Uecker, Dr. rer. nat.
University Medical Center Göttingen
Göttingen, Germany
09:50   Platforms & Hardware Considerations for Machine Learning/Digital Learning Holger Roth, Ph.D.
NVIDIA
Bethesda, MD, USA
10:10   Panel Discussion
10:30   Break, Poster Viewing & Speaker Upload Available
 
Session 6: Clinical Applications: Neuro
  Moderators: Berkin Bilgic, Ph.D. & Greg Zaharchuk, M.D., Ph.D.
11:00   Brain Tumor Segmentation with Multimodal MRI & Deep Learning Jayashree Kalpathy-Cramer, Ph.D.
Harvard Medical School
Charlestown, MA, USA
  Proffered Papers - Oral Session
11:20 Deep Learning for MR-Based Attenuation Correction for Pediatric Brain Tumor Patients Claes Ladefoged, Ph.D.
Rigshospitalet
Copenhagen, Denmark
11:30 Deep Learning Diffusion Tensor Imaging with Accelerated q-Space Acquisition Hongyu Li, M.S.
State University of New York at Buffalo
Buffalo, NY, USA
11:40 Adversarial Variational Autoencoder for Visualizing & Interpreting Deep Features of Brain Aging Luis A. Souto Maior Neto, M.Sc. Candidate
University of Calgary
Calgary, AB, Canada
11:50 Predicting Infarct Development in Acute Ischemic Stroke with Baseline Multimodal MRI & Convolutional Neural Networks Yuan Xie, M.S.
Stanford University
Stanford, CA, USA
12:00 Improved TWIST Imaging Using k-Space Deep Learning Eunju Cha, M.S.
Korea Advanced Institute of Science & Technology (KAIST)
Daejeon, South Korea
12:10   Deep Learning for Ischemic Stroke Lesion Segmentation Kim Mourdisen, Ph.D.
Århus University Hospital
Århus, Denmark
12:30   Lunch & Speaker Upload Available
 
Session 7: Clinical Applications: Musculoskeletal & Breast
  Moderators: Fang Liu, Ph.D. & Kim Mouridsen, Ph.D.
14:00   Musculoskeletal Applications of Deep Learning Akshay S. Chaudhari, Ph.D.
Stanford University
Palo Alto, CA, USA
14:20   Utilizing Machine Learning Tools to Improve Breast Cancer Detection
- Video Permission Withheld		 Linda Moy, M.D.
New York University
New York, NY, USA
  Proffered Papers - Oral Session
14:40 Comparing Learned Variational Networks & Compressed Sensing for T1ρ Mapping of Knee Cartilage Marcelo Zibetti, Ph.D.
New York University School of Medicine
New York, NY, USA
14:50 Automated Cartilage Segmentation of Knee MR Imaging Data with Conditional Generative Adversarial Nets Sibaji Gaj, Ph.D.
Cleveland Clinic
Cleveland, OH, USA
15:00 3D Convolutional Networks for Prediction of Total Knee Replacement Using Structural MRI Tianyu Wang, M.S.
New York University
New York, NY, USA
15:10 Ultra-Low-Dose Amyloid PET-MR Reconstruction Using 2.5D Generative Adversarial Network with Feature Matching Jiahong Ouyang, M.Sc.
Carnegie Mellon University
Pittsburgh, PA, USA
15:20 Automatic Labeling of Resting-State fMRI Networks Using 3D Convolutional Neural Networks Gowtham Krishnan Murugesan, M.S.
University Of Texas Southwestern Medical Center
Dallas, TX, USA
15:30 Improving Cortical Surface Reconstruction Using Super-Resolution Sub-Millimeter Structural MRI Data Synthesized with a Convolutional Neural Network Qiyuan Tian, Ph.D.
A. A. Martinos Center for Biomedical Imaging
Charlestown, MA, USA
15:40   Break, Poster Viewing & Speaker Upload Available
 
Session 8: Power Pitch & Poster Session
  Moderators: Joseph Yitan Cheng, Ph.D. & Tolga Cukur, Ph.D.
16:10   Power Pitch Presentations
17:00   Poster Viewing
18:00   Reception offsite with Light Appetizers
 
Day 3 – Sunday, 28 October 2018 – No CME Available
07:30   Breakfast on Own
Registration & Speaker Upload Available
 
Session 9: Clinical Applications: Prostate & Cardiac
  Moderators: Li Feng, Ph.D. & Peter Kellman, Ph.D.
08:30   Cardiac Machine Learning Bob S. Hu, M.D.
Stanford University
Stanford, CA, USA
08:50 Machine Learning in Prostate MRI Diagnosis & Intervention Henkjan Huisman, Ph.D.
Radboud University Medical Centre
Nijmegen, The Netherlands
  Proffered Papers - Oral Session
09:10 Deep Convolutional Neural Network for Segmentation of Myocardial ASL Short-Axis Data: Accuracy, Uncertainty & Adaptability Hung Do, Ph.D.
Canon Medical Systems USA, Inc.
Tustin, CA, USA
09:20 Pearls & Pitfalls of Cardiovascular MR Water-Fat Separation & Parametric Mapping with End-to-End Deep Learning James Goldfarb, Ph.D.
St. Francis Hospital
Roslyn, NY, USA
09:30 Automated Classification of Ischemic Heart Disease Using Machine Learning with Multi-Parametric Perfusion Mapping Peter Kellman, Ph.D.
National Institutes of Health
Bethesda, MD, USA
09:40 Machine Learning Prediction of Liver Stiffness Using Clinical Data & T2-Weighted MRI Radiomic Data Lili He, Ph.D.
Cincinnati Children’s Hospital Medical Center
Cincinnati, OH, USA
09:50 Low-Dose PET/MR Imaging in Crohn’s Disease Wei-Jie Chen, Master
University of Wisconsin-Madison
Madison, WI, USA
10:00 Deep Learning for Contouring the Prostate & Prostate Zones Using MRI from Different MRI Vendors Olmo Zavala-Romero, Ph.D.
University of Miami Miller School of Medicine
Miami, FL, USA
10:10   Break, Poster Viewing & Speaker Upload Available
 
Session 10: Broader Issues in Medical Machine Learning
  Moderators: Jeffrey A. Fessler, Ph.D. & Greg Zaharchuk, M.D., Ph.D.
10:40   Machine Learning & Digital Learning in Medical Imaging: Progress So Far & Perspectives Shravya Shetty, Ph.D.
Google
Mountain View, CA, USA
11:00 Regulatory Strategies for Machine Learning & Artificial Intelligence-Based Medical Device Software Christian G. Graff, Ph.D.
Food & Drug Administration
Silver Spring, MD, USA
11:20   Running an Interdisciplinary Artificial Intelligence Center Mark H. Michalski, M.D.
MGH & BWH Center for Clinical Data Science
Boston, MA, USA
11:40   Exploring Machine Learning Approaches & Techniques for MRI Reconstruction in an Interdisciplinary Cooperation Between Academia & Industry Michal Drozdzal, Ph.D.
Facebook Artificial Intelligence Research
Montreal, QC, Canada
12:00     The Role of Academia & Industry in Developing Artificial Intelligence for Healthcare
- Did Not Attend		 Christopher Austin, M.D., M.Sc.
Kheiron Medical Technologies
London, England, UK
12:20   Panel Discussion
12:40   Best Poster/Paper Awards
12:50   Wrap-Up Florian Knoll, Ph.D.
New York University School of Medicine
New York, NY, USA
13:05   Adjournment & Boxed Lunches
 
The International Society for Magnetic Resonance in Medicine is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
This workshop does not offer CME credits.