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Ablation Studies in 3D Encoder-Decoder Networks for Brain MRI-to-PET Cerebral Blood Flow Transformation

Anomaly-aware multi-contrast deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI - a feasibility study

Author:Pasumarthi, Srivathsa   Gong, Enhao   Zaharchuk, Greg   Zhang, Tao   

Session Type:Oral  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Image Reconstruction  

Session Name:Machine Learning for Image Reconstruction  

Program Number:0278  

Room Session:Concurrent 1  

Institution:Subtle Medical Inc.  

Automated quantitative evaluation of deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI

Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance – A Prospective, Multicenter Trial

Does Simultaneous Morphological Inputs Matter for Deep Learning Enhancement of Ultra-low Amyloid PET/MRI?

Generalizing Ultra-low-dose PET/MRI Networks Across Radiotracers: From Amyloid to Tau

High resolution PET image denoising using anatomical priors by K-nearest neighborhood method in the feature space

High Resolution PET/MR Imaging Using Anatomical Priors & Motion Correction

Multi-parametric R2' Measurement of Brain Oxygen Extraction Fraction: Reproducibility and Application in Moyamoya Disease

Reliability of Arterial Spin Labeling derived Cerebral Blood Flow measurements in Periventricular White Matter

Robust and Generalizable Quality Control of Structural MRI images

Zero-dose FDG PET Brain Imaging