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

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