J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction

Abstract

Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce scan time. The image quality of these approaches is heavily dependent on the sampling pattern. We introduce a continuous strategy to jointly optimize the sampling pattern and network parameters. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code of the proposed joint learning framework is available at https://github.com/hkaggarwal/J-MoDL.

References in corpus (15)

• Sparsity and Incoherence in Compressive Sampling
• Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
• Image reconstruction by domain transform manifold learning
• MoDL: Model Based Deep Learning Architecture for Inverse Problems
• Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
• fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
• Plug and play methods for magnetic resonance imaging (long version)
• Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy
• Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning
• MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
• OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI
• Self-Supervised Deep Active Accelerated MRI
• Convolutional neural networks that teach microscopes how to image
• $\Sigma$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
• Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI

Cited by in corpus (15)

• PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
• SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
• CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
• Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
• Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
• Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
• HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination
• AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI
• Learning Task-Specific Strategies for Accelerated MRI
• FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
• Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems
• TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
• Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI
• Bayesian Optimization of Sampling Densities in MRI
• Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering