Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI

Schlemper, Jo; Yang, Guang; Ferreira, Pedro; Scott, Andrew D.; McGill, Laura-Ann; Khalique, Zohya; Gorodezky, Margarita; Roehl, Malte; Keegan, Jennifer; Pennell, Dudley J.; Firmin, David; Rueckert, Daniel

doi:10.1007/978-3-030-00928-1_34

Cited by 51 publications

(37 citation statements)

References 25 publications

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“…Guo et al [11] proposed a WGAN with recurrent context-awareness to reconstruct MRI images from highly undersampled k-space data. Schlemper et al propose a cascaded CNN-based compressive sensing (CS) technique for the reconstruction of diffusion tensor cardiac MRI [12]. Yang et al proposed a conditional GANbased architecture for de-aliasing and fast CS-MRI [13], [14].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

et al. 2020

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Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil tracks. This demonstrates the superior performance and wide applicability of the method.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

et al. 2020

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“…Although deep learning has been quite useful in other medical imaging domains, it is still in its nascent stages for DW-MRI. Recent work has been seen in microstructure estimation, harmonization and k-space reconstruction [12][13][14]. For this specific problem, we explore two different network architectures for recovery of MT-CSD microstructure.…”

Section: Introductionmentioning

confidence: 99%

Deep learning estimation of multi-tissue constrained spherical deconvolution with limited single shell DW-MRI

Nath

Pathak

Schilling

et al. 2020

Medical Imaging 2020: Image Processing

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Diffusion-weighted magnetic resonance imaging (DW-MRI) is the only non-invasive approach for estimation of intravoxel tissue microarchitecture and reconstruction of in vivo neural pathways for the human brain. With improvement in accelerated MRI acquisition technologies, DW-MRI protocols that make use of multiple levels of diffusion sensitization have gained popularity. A well-known advanced method for reconstruction of white matter microstructure that uses multishell data is multi-tissue constrained spherical deconvolution (MT-CSD). MT-CSD substantially improves the resolution of intra-voxel structure over the traditional single shell version, constrained spherical deconvolution (CSD). Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm 2 from the Human Connectome Project (HCP)) to estimate the information content captured by 8 th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm 2 from HCP). Briefly, we examine two network architectures: 1.) Sequential network of fully connected dense layers with a residual block in the middle (ResDNN), 2.) Patch based convolutional neural network with a residual block (ResCNN). For both networks an additional output block for estimation of voxel fraction was used with a modified loss function. Each approach was compared against the baseline of using MT-CSD on all data on 15 subjects from the HCP divided into 5 training, 2 validation, and 8 testing subjects with a total of 6.7 million voxels. The fiber orientation distribution function (fODF) can be recovered with high correlation (0.77 vs 0.74 and 0.65) and low root mean squared error ResCNN:0.0124, ResDNN:0.0168 and sCSD:0.0323 as compared to the ground truth of MT-CST, which was derived from the multi-shell DW-MRI acquisitions. The mean squared error between the MT-CSD estimates for white matter tissue fraction and for the predictions are ResCNN:0.0249 vs ResDNN:0.0264. We illustrate the applicability of high definition fiber tractography on a single testing subject with arcuate and corpus callosum Tractography. In summary, the proposed approach provides a promising framework to estimate MT-CSD with limited single shell data. Source code and models have been made publicly available.

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“…Mardani et al used a ResNet-based generator and discriminator with a least square GAN loss to solve the CS-MRI de-aliasing problem [35]. Despite these researches on CS-MRI using CNNs, there are very few studies on the use of deep learning for CS-DTI [36]. The interest of CS-DTI resides in the fact that k-space can be highly undersampled to reduce substantially the amount of k-space data to acquire.…”

Section: Introductionmentioning

confidence: 99%

CS-GAN for High-Quality Diffusion Tensor Imaging

Cao

Wang

Huang

et al. 2020

Preprint

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Background: Compressed sensing magnetic resonance imaging (CS-MRI) is a promising technique for accelerating MRI speed. However, image quality in CS-MRI is still a pertinent problem. In particular, there is little work on reducing aliasing artefacts in compressed sensing diffusion tensor imaging (CS-DTI), which constitute a serious obstacle to obtaining high-quality images. Method: We propose a CS-DTI de-aliasing method based on conditional generative adversarial (cGAN), called CS-GAN, to tackle de-aliasing problems in CS-DTI with highly undersampled k-space data. The method uses a nested-UNet based generator, a ResNet-based discriminator, and a content loss function defined in both image domain and frequency domain. Result and Concludions: Compared to existing state-of-the-art de-aliasing methods based on deep learning, our method achieves superior imaging quality in terms of both diffusion weighted (DW) image quality and DTI diffusion metrics. Moreover, even at extremely low sampling ratio and low SNR, our method can still reconstruct texture details and spatial information.

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Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI

Cited by 51 publications

References 25 publications

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

Deep learning estimation of multi-tissue constrained spherical deconvolution with limited single shell DW-MRI

CS-GAN for High-Quality Diffusion Tensor Imaging

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