Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI

Abstract

There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements. Often sophisticated reconstruction algorithms are deployed to maintain high image quality in such settings. In this work, we propose a data-driven sampler using a convolutional neural network, MNet, to provide object-specific sampling patterns adaptive to each scanned object. The network observes very limited low-frequency k-space data for each object and rapidly predicts the desired undersampling pattern in one go that achieves high image reconstruction quality. We propose an accompanying alternating-type training framework with a mask-backward procedure that efficiently generates training labels for the sampler network and jointly trains an image reconstruction network. Experimental results on the fastMRI knee dataset demonstrate the ability of the proposed learned undersampling network to generate object-specific masks at fourfold and eightfold acceleration that achieve superior image reconstruction performance than several existing schemes. The source code for the proposed joint sampling and reconstruction learning framework is available at https://github.com/zhishenhuang/mri.

References in corpus (12)

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
• Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
• J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
• Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
• B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
• Solving Mixed Integer Programs Using Neural Networks
• Active MR k-space Sampling with Reinforcement Learning
• Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
• Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
• Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
• Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI

Cited by in corpus (2)

• Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
• Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering