REINFORCEMENT LEARNING

COMPUTER VISION

Active MR k-space Sampling with Reinforcement Learning

November 04, 2020

Abstract

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors.

Download the Paper

AUTHORS

Written by

Luis Pineda

Adriana Romero Soriano

Michal Drozdzal

Roberto Calandra

Sumana Basu

Publisher

MICCAI

Related Publications

December 15, 2021

ROBOTICS

REINFORCEMENT LEARNING

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Roberto Calandra, Nathan Owen Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister

December 15, 2021

December 06, 2021

COMPUTER VISION

CORE MACHINE LEARNING

Debugging the Internals of Convolutional Networks

Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Shubham Muttepawar, Edward Wang (AI Infra), Sara Zhang, David Adkins, Orion Reblitz-Richardson

December 06, 2021

December 06, 2021

COMPUTER VISION

Early Convolutions Help Transformers See Better

Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollar, Ross Girshick

December 06, 2021

December 05, 2021

REINFORCEMENT LEARNING

Local Differential Privacy for Regret Minimization in Reinforcement Learning

Evrard Garcelon, Vianney Perchet, Ciara Pike-Burke, Matteo Pirotta

December 05, 2021