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MC-PDNET: Deep unrolled neural network for multi-contrast mr image reconstruction from undersampled k-space data

Abstract : Multi-contrast (MC) MR images are similar in structure and can leverage anatomical structure to perform joint reconstruction especially from a limited number of k-space data in the Compressed Sensing (CS) setting. However CS-based multi-contrast image reconstruction has shown limited performance in these highly accelerated regimes due to the use of hand-crafted group sparsity priors. Deep learning can improve outcomes by learning the joint prior across multiple weighting contrasts. In this work, we extend the primal-dual neural network (PDNet) in the multi-contrast sense. We propose a MC-PDNet architecture which takes full advantage of multi-contrast information. Using an in-house database consisting of images from T2TSE, T2*GRE and FLAIR contrasts acquired in 65 healthy volunteers, we performed a retrospective study from 4fold under-sampled data. It was shown that MC-PDNet improves image quality by at least 1dB in PSNR for each contrast individually in comparison with PD-Net and U-Net architectures.
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https://hal.inria.fr/hal-03389390
Contributor : Philippe Ciuciu Connect in order to contact the contributor
Submitted on : Thursday, October 21, 2021 - 9:48:08 AM
Last modification on : Saturday, April 2, 2022 - 3:10:16 AM
Long-term archiving on: : Saturday, January 22, 2022 - 6:16:51 PM

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  • HAL Id : hal-03389390, version 1

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Kumari Pooja, Zaccharie Ramzi, Chaithya G R, Philippe Ciuciu. MC-PDNET: Deep unrolled neural network for multi-contrast mr image reconstruction from undersampled k-space data. 2021. ⟨hal-03389390⟩

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