i-RIM applied to the fastMRI challenge

Putzky, Patrick; Karkalousos, Dimitrios; Teuwen, Jonas; Miriakov, Nikita; Bakker, Boudewijn; Caan, Matthan W.A.; Welling, Max

doi:10.48550/arxiv.1910.08952

Cited by 8 publications

(14 citation statements)

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“…The proposed KV-Net is compared with TV model, 17 KIKI-Net, 51 MD-Recon-Net, 48 U-Net Baseline Model, 17 XPDNet, 55 and i-RIM 56 on the test data of fastMRI dataset. In addition, we compare KV-Net with GRAPPA, PANO, MD-Recon-Net, U-Net baseline, and KIKI-Net on the validation set because the source codes of these methods are available and we can produce the curves of reconstruction quality versus epoch number.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI

Liu

Pang

Jin

et al. 2022

Magnetic Resonance in Med

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To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data.Methods: Most state-of-the-art reconstruction methods apply U-Net or cascaded U-Nets in the image domain and/or k-space domain. Nevertheless, these methods have the following problems: (1) directly applying U-Net in the k-space domain is not optimal for extracting features; (2) classical image-domain-oriented U-Net is heavyweighted and hence inefficient when cascaded many times to yield good reconstruction accuracy; (3) classical image-domain-oriented U-Net does not make full use of information of the encoder network for extracting features in the decoder network; and (4) existing methods are ineffective in simultaneously extracting and fusing features in the image domain and its dual k-space domain. To tackle these problems, we present 3 different methods: (1) V-Net, an image-domain encoder-decoder subnetwork that is more lightweight for cascading and effective in fully utilizing features in the encoder for decoding; (2) K-Net, a k-space domain subnetwork that is more suitable for extracting hierarchical features in the k-space domain, and (3) KV-Net, a dual-domain reconstruction network in which V-Nets and K-Nets are effectively combined and cascaded. Results: Extensive experimental results on the fastMRI dataset demonstrate that the proposed KV-Net can reconstruct high-quality images and outperform state-of-the-art approaches with fewer parameters. Conclusions: To reconstruct images effectively and efficiently from incomplete k-space data, we have presented a dual-domain KV-Net to combine K-Nets and V-Nets. The KV-Net achieves better results with 9% and 5% parameters than comparable methods (XPD-Net and i-RIM).

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI

Liu

Pang

Jin

et al. 2022

Magnetic Resonance in Med

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“…In the past few years, many parallel MRI reconstruction methods using deep learning have been proposed [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Model-based deep learning architectures have recently gained popularity and achieved state-of-the-art performance [ 30 , 31 , 32 , 33 ]. This model-based method uses a regularizer R as a deep neural network in Equation ( 5 ) and reconstructs an MR image using an unrolled optimization.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, deep learning-based methods show great promise in parallel MRI reconstruction when a high acceleration factor is used [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In addition, model-based deep learning, which sets parallel MRI reconstruction as an inverse problem and implements a forward model using prior knowledge, including coil sensitivity maps, shows excellent performance [ 30 , 31 , 32 , 33 ]. Coil sensitivity maps used in model-based deep learning for parallel MRI reconstruction are obtained in advance or calculated from auto-calibration signal (ACS) lines of the MR data, using an estimation method such as the ESPIRiT method [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction

Lee

Kang

et al. 2022

Sensors

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MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the long scan time of MRI, the parallel MRI method for reconstructing a high-fidelity MR image from under-sampled multi-coil k-space data is widely used. In this study, we propose a method to reconstruct a high-fidelity MR image from under-sampled multi-coil k-space data using deep-learning. The proposed multi-domain Neumann network with sensitivity maps (MDNNSM) is based on the Neumann network and uses a forward model including coil sensitivity maps for parallel MRI reconstruction. The MDNNSM consists of three main structures: the CNN-based sensitivity reconstruction block estimates coil sensitivity maps from multi-coil under-sampled k-space data; the recursive MR image reconstruction block reconstructs the MR image; and the skip connection accumulates each output and produces the final result. Experiments using the fastMRI T1-weighted brain image dataset were conducted at acceleration factors of 2, 4, and 8. Qualitative and quantitative experimental results show that the proposed MDNNSM method reconstructs MR images more accurately than other methods, including the generalized autocalibrating partially parallel acquisitions (GRAPPA) method and the original Neumann network.

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“…In the context of MRI reconstruction, one can view network unrolling as solving a sequence of smaller denoising problems, instead of the complete recovery problem in one step. Various convolutional neural networks have been employed in the unrolling framework achieving excellent performance in accelerated MRI reconstruction Putzky et al [2019], Hammernik et al [2018Hammernik et al [ , 2019. E2E-VarNet [Sriram et al, 2020] is the current state-of-the-art convolutional model on the fastMRI dataset.…”

Section: Deep Learning-based Accelerated Mri Reconstructionmentioning

confidence: 99%

“…fastMRI knee multi-coil ×8 test data Method SSIM(↑) PSNR(↑) NMSE(↓) E2E-VarNet [Sriram et al, 2020] 0.8900 36.9 0.0089 E2E-VarNet † [Sriram et al, 2020] 0.8920 37.1 0.0085 XPDNet [Ramzi et al, 2020] 0.8893 37.2 0.0083 Σ-Net [Hammernik et al, 2019] 0.8877 36.7 0.0091 i-RIM [Putzky et al, 2019] 0.8875 36.7 0.0091 U-Net [Zbontar et al, 2019] 0 Stanford 3D -Finally, we evaluate our model on the Stanford Fullysampled 3D FSE Knees dataset [Sawyer et al, 2013], a public MRI dataset including 20 volumes of knee MRI scans. We generate train-validation splits using the method described for Stanford 2D and perform 3 runs.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction

Fabian¹,

Soltanolkotabi²

2022

Preprint

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In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of undersampled and noisy measurements. Deep learning approaches have been proven to be successful in solving this ill-posed inverse problem and are capable of producing very high quality reconstructions. However, current architectures heavily rely on convolutions, that are content-independent and have difficulties modeling long-range dependencies in images. Recently, Transformers, the workhorse of contemporary natural language processing, have emerged as powerful building blocks for a multitude of vision tasks. These models split input images into non-overlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of convolutional architectures. However, Transformers incur extremely high compute and memory cost when 1) the input image resolution is high and 2) when the image needs to be split into a large number of patches to preserve fine detail information, both of which are typical in low-level vision problems such as MRI reconstruction, having a compounding effect. To tackle these challenges, we propose HUMUS-Net, a hybrid architecture that combines the beneficial implicit bias and efficiency of convolutions with the power of Transformer blocks in an unrolled and multi-scale network. HUMUS-Net extracts high-resolution features via convolutional blocks and refines low-resolution features via a novel Transformer-based multi-scale feature extractor. Features from both levels are then synthesized into a high-resolution output reconstruction. Our network establishes new state of the art on the largest publicly available MRI dataset, the fastMRI dataset. We further demonstrate the performance of HUMUS-Net on two other popular MRI datasets and perform fine-grained ablation studies to validate our design. We note that the current experimental results are preliminary, we will update this paper soon with our final results involving larger models.

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i-RIM applied to the fastMRI challenge

Cited by 8 publications

References 0 publications

Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI

Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI

Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction

HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction

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