DeepAI AI Chat
Log In Sign Up

Regularized Compression of MRI Data: Modular Optimization of Joint Reconstruction and Coding

by   Veronica Corona, et al.

The Magnetic Resonance Imaging (MRI) processing chain starts with a critical acquisition stage that provides raw data for reconstruction of images for medical diagnosis. This flow usually includes a near-lossless data compression stage that enables digital storage and/or transmission in binary formats. In this work we propose a framework for joint optimization of the MRI reconstruction and lossy compression, producing compressed representations of medical images that achieve improved trade-offs between quality and bit-rate. Moreover, we demonstrate that lossy compression can even improve the reconstruction quality compared to settings based on lossless compression. Our method has a modular optimization structure, implemented using the alternating direction method of multipliers (ADMM) technique and the state-of-the-art image compression technique (BPG) as a black-box module iteratively applied. This establishes a medical data compression approach compatible with a lossy compression standard of choice. A main novelty of the proposed algorithm is in the total-variation regularization added to the modular compression process, leading to decompressed images of higher quality without any additional processing at/after the decompression stage. Our experiments show that our regularization-based approach for joint MRI reconstruction and compression often achieves significant PSNR gains between 4 to 9 dB at high bit-rates compared to non-regularized solutions of the joint task. Compared to regularization-based solutions, our optimization method provides PSNR gains between 0.5 to 1 dB at high bit-rates, which is the range of interest for medical image compression.


page 1

page 6

page 8


Benefiting from Duplicates of Compressed Data: Shift-Based Holographic Compression of Images

Storage systems often rely on multiple copies of the same compressed dat...

Compression for Multiple Reconstructions

In this work we propose a method for optimizing the lossy compression fo...

Medical Image Compression using Wavelet Decomposition for Prediction Method

In this paper offers a simple and lossless compression method for compre...

Comparison of Algorithms for Compressed Sensing of Magnetic Resonance Images

Magnetic resonance imaging (MRI) is an essential medical tool with inher...

Lossy Medical Image Compression using Residual Learning-based Dual Autoencoder Model

In this work, we propose a two-stage autoencoder based compressor-decomp...

Optimized Pre-Compensating Compression

In imaging systems, following acquisition, an image/video is transmitted...

Joint Reconstruction of Multi-view Compressed Images

The distributed representation of correlated multi-view images is an imp...