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Erasing artifacts from arterial phase MRI: Motion Artifact Reduction using a Convolutional network (MARC)

FITs-CNN: A Very Deep Cascaded Convolutional Neural Networks Using Folded Image Training Strategy for Abdominal MRI Reconstruction

Perfusion analyses of breast benign and malignant lesions using super high frame rate reconstruction with stack-of-stars acquisition

PROPELLER Diffusion-Weighted Imaging of the Prostate with Deep-Learning Reconstruction

Retrospective Motion Artifact Reduction with CNN (MARC) combined with model-based artifact simulation for T2WI of the liver

Author:Motohide Kawamura  Daiki Tamada  Tetsuya Wakayama  Hiroshi Onishi  Utaroh Motosugi  

Institution:GE Healthcare  University of Yamanashi  

Session Type:Digital Poster  

Session Live Q&A Date:Digital Poster (All Week)  

Topic:Abdominopelvic MRI - Benign  

Session Name:Machine Learning & Radiomics in Body MRI  

Program Number:2602  

Room Live Q&A Session:

Unsupervised radial streak artifact reduction in time resolved MRI

Utility of Stack-of-stars Acquisition for Arterial Phase Imaging without Breath-holding on Dynamic MRI of the Liver

Author:Shintaro Ichikawa  Daiki Tamada  Tetsuya Wakayama  Sagar Mandava  Ty Cashen  Hiroshi Onishi  Utaroh Motosugi  

Institution:GE Healthcare  University of Yamanashi  

Session Type:Oral  

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

Topic:Hepatobiliary & Pancreas  

Session Name:Diffuse Liver & Metabolism  

Program Number:0322  

Room Live Q&A Session:Monday Parallel 3