Accelerated T2 Mapping of the Lumbar Intervertebral Discs: Robust Quantification in Clinically Feasible Acquisition Times

Automated Grading of Lumbar Disc Degeneration Using T-test Regularized Network

Compressed Sensing with and without Deep Learning Reconstruction: Comparison of Capability for Improving Lumber Spine MRI with Parallel Imaging

Computer Aided Detection AI Reduces Inter-Reader Variability in Grading Hip Abnormalities from MRI

Deep Learning Assisted Full Knee 3D MRI-Based Lesion Severity Staging

Deep learning for the detection and differentiation of vertebral fracture

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI

Author:Jie Ding  Varut Vardhanabhuti  Eric Lai  Yuan Gao  Sophelia Chan  Peng Cao  

Institution:The University of Hong Kong  

Session Type:Oral  

Session Live Q&A Date:Monday, 10 August 2020  

Topic:Emerging Methods and Machine Learning in Musculoskeletal MRI  

Session Name:Machine Learning in Musculoskeletal  

Program Number:0249  

Room Live Q&A Session:Monday Parallel 4  

Deep Shoulder CT Image Synthesis from MR via Context-aware 2.5D Generative Adversarial Networks

Deep-learning Diagnosis of Supraspinatus Tendon Tears: Comparison of Multi-sequence Versus Single Sequence Input

Evaluating the Relationship Between Dynamic [18F]-Sodium Fluoride Uptake Parameters and MRI Knee Osteoarthritic Findings

Intermuscular Variability of Phosphocreatine Recovery Constants in Exercised Muscle Measured using  31PMRS and CrCEST at 7.0T

Lymphatic insufficiency observed by noninvasive MR lymphangiography and multi-nuclear 23Na-MRI in patients with lymphedema and lipedema

Machine Learning for MSK Image Acquisition & Reconstruction

Author:Fang Liu  

Institution:Harvard University  

Session Type:Combined Educational & Scientific Session  

Session Live Q&A Date:Monday, 10 August 2020  

Topic:Emerging Methods and Machine Learning in Musculoskeletal MRI  

Session Name:Machine Learning in MSK  

Program Number:

Room Live Q&A Session:Monday Parallel 4  

    Machine Learning for MSK Image Processing & Interpretation

    Author:Cem Deniz  

    Institution:New York University Langone Health  

    Session Type:Combined Educational & Scientific Session  

    Session Live Q&A Date:Monday, 10 August 2020  

    Topic:Emerging Methods and Machine Learning in Musculoskeletal MRI  

    Session Name:Machine Learning in MSK  

    Program Number:

    Room Live Q&A Session:Monday Parallel 4  

    Noise and Bias Reduction in Two-Point Dixon Peripheral Nerve Imaging and Muscle Denervation Assessment

    Author:Ek Tan  Julia Sternberg  Bin Lin  Hollis Potter  Darryl Sneag  

    Institution:Hospital for Special Surgery  

    Session Type:Oral  

    Session Live Q&A Date:Monday, 10 August 2020  

    Topic:Emerging Methods and Machine Learning in Musculoskeletal MRI  

    Session Name:Musculoskeletal Emerging Methods  

    Program Number:0255  

    Room Live Q&A Session:Monday Parallel 4  

    Principal Component Analysis of Simultaneous PET/MRI Reveals Patterns of Cartilage-Bone Interactions in Osteoarthritis

    Quantitative Assessment of Articular Cartilage Degeneration Using 3D Ultrashort Echo Time Cones Adiabatic T1? (3D UTE Cones AdiabT1?) Imaging

    Quantitative Measurements of Bone Water and 31P in Postmenopausal Women: A Preliminary Study

    Quantitative T1 Mapping from Incoherently Undersampled MR Images Using Self-Attention Convolutional Neural Networks

    Author:Yan Wu  Yajun Ma  Jiang Du  Lei Xing  

    Institution:Stanford University  University of California San Diego  

    Session Type:Oral  

    Session Live Q&A Date:Monday, 10 August 2020  

    Topic:Emerging Methods and Machine Learning in Musculoskeletal MRI  

    Session Name:Machine Learning in Musculoskeletal  

    Program Number:0248  

    Room Live Q&A Session:Monday Parallel 4  

    Semi-Quantitative Grading of the Anterior Cruciate Ligament using Deep Learning

    Task-Based UltraFast MRI: Simultaneous Image Reconstruction and Tissue Segmentation