RESEARCH

COMPUTER VISION

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition

May 10, 2019

Abstract

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we present a novel method for MRI reconstruction that, at inference time, dynamically selects the measurements to take and iteratively refines the prediction in order to best reduce the reconstruction error and, thus, its uncertainty. We validate our method on a large scale knee MRI dataset, as well as on ImageNet. Results show that (1) our system successfully outperforms active acquisition baselines; (2) our uncertainty estimates correlate with error maps; and (3) our ResNet-based architecture surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The proposed method not only shows high-quality reconstructions but also paves the road towards more applicable solutions for accelerating MRI.

Download the Paper

AUTHORS

Written by

Michal Drozdzal

Adriana Romero Soriano

Pascal Vincent

Lin Yang

Matthiew Muckley

Zizhao Zhang

Publisher

CVPR

Research Topics

Computer Vision

Related Publications

March 09, 2023

COMPUTER VISION

The Casual Conversations v2 Dataset

Bilal Porgali, Vítor Albiero, Jordan Ryda, Cristian Canton Ferrer, Caner Hazirbas

March 09, 2023

February 21, 2023

COMPUTER VISION

CORE MACHINE LEARNING

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

Felix Xu, Fuyuan Zhang, Hua Qi, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Zhijie Wang

February 21, 2023

January 10, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Online Backfilling with No Regret for Large-Scale Image Retrieval

Gokhan Uzunbas, Joena Zhang, Sara Cao, Ser-Nam Lim, Taipeng Tian, Bohyung Han, Seonguk Seo

January 10, 2023

January 04, 2023

COMPUTER VISION

CORE MACHINE LEARNING

Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

Xi Liu, Panganamala Kumar, Ruida Zhou, Tao Liu

January 04, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.