  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.

Image and Video Processing;Computer Vision and Pattern Recognition