An edge guided cascaded U‐net approach for accelerated magnetic resonance imaging reconstruction

Dhengre, Nikhil; Sinha, Saugata

doi:10.1002/ima.22567

Cited by 5 publications

(4 citation statements)

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“…An AWGN noise level map-guided image denoising network based on Unet was proposed in [16], which has the plug-and-play capability that makes the network more portable. Similar U-net inspired approaches with different residual and dense connections were proposed in [17], [18], [19], [20], [21], [22], and [23]. Another U-net based architecture in [20] used a cascading U-net with multi-scale input feature supervision and residual dense blocks.…”

Section: Introductionmentioning

confidence: 99%

“…Only a handful of edge-focused image-reconstruction CNNs are available in the literature. An edge-guided U-net architecture for denoising magnetic resonance images (MRI) in [19] uses four directional edge maps (vertical, horizontal, and diagonal) are calculated for each block and fed into the next block of this network. Here, the non-trainable edge weights in each block help the network learn the fine details in an image.…”

Section: Introductionmentioning

confidence: 99%

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EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

Park

Lee

2023

IEEE Access

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In this paper, we propose an edge-focused image denoising convolutional neural network for the restoration of noisy images corrupted with additive white Gaussian noise (AWGN). First, the edge information for an input image is obtained for each RGB channel using the simple Laplacian edge operator on a smoothened image using the smoothen mask. Second, residual convolutional blocks, where each block comprises two parallel depth layers (one for an image channel and the other for respective edge channels), perform image and edge processing. Finally, the processed image and edge features are mixed and mapped using a single convolutional layer into the restored image. Our proposed parallel image and edge processing blocks can recover edges and fine structures while smoothing out the noise. In addition, our proposed network is efficiently designed such that the total number of weight parameters can be considerably reduced compared with conventional methods. Experimental results show that the proposed model is more effective in preserving textures and edges while removing noise and achieves up to 0.8 dB higher PSNR and 0.05 higher SSIM on a real image dataset than conventional methods.INDEX TERMS Convolutional neural networks, deep learning, denoising, edge guidance, Gaussian noise.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

Park

Lee

2023

IEEE Access

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“…Deep learning MRI reconstruction frameworks span a wide variety of different architectures, from U-Net-based models [8] with [9] and without [10] data consistency in k-space; general adversarial networks [11]; k-space reconstruction networks [12]; cascaded networks that consisting of sub-networks for temporal dynamic acquisitions [13][14][15] and non-temporal static acquisitions [6,[16][17][18][19][20], in which the latter performs excellently [21,22]. The sub-networks perform reconstruction in the image domain, frequency domain or both, and reconstruct the complete image or frequency information from the subsampled scan.…”

Section: Introductionmentioning

confidence: 99%

“…The sub-networks perform reconstruction in the image domain, frequency domain or both, and reconstruct the complete image or frequency information from the subsampled scan. CNNs are commonly used as sub-networks, and the architectures range from shallow sub-networks tallying a few convolutional layers per cascade [18,23], to deeper architectures [6,16,24].…”

Section: Introductionmentioning

confidence: 99%

A densely interconnected network for deep learning accelerated MRI

Ottesen

Caan

Groote

et al. 2022

Magn Reson Mater Phy

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Objective To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and methods A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). Results The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2–4%, 4–9%, and 0.5–1%, respectively. Conclusion The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.

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Transformer‐ and joint learning‐based dual‐domain networks for undersampled MRI segmentation

Duan,

Huang,

Xie

et al. 2024

Medical Physics

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BackgroundRecently, magnetic resonance imaging (MRI) has become a crucial medical imaging technology widely used in clinical practice. However, MRI faces challenges such as the lengthy acquisition time for k‐space data and the need for time‐consuming manual annotation by radiologists. Traditionally, these challenges have been addressed individually through undersampled MRI reconstruction and automatic segmentation algorithms. Whether undersampled MRI segmentation can be enhanced by treating undersampled MRI reconstruction and segmentation as an end‐to‐end task, trained simultaneously, rather than as serial tasks should be explored.PurposeWe introduce a novel Transformer‐ and Joint Learning‐based Dual‐domain Network (TJLD‐Net) for undersampled MRI segmentation.MethodsThis method significantly enhances feature recognition in the segmentation process by fully utilizing the rich detail obtained during the image reconstruction phase. Consequently, the method can achieve precise and reliable image segmentation even with undersampled k‐space data. Additionally, it incorporates an attention mechanism for feature enhancement, which improves the representation of shared features by learning the contextual information in MR images.ResultsSimulation experiments demonstrate that the segmentation performance of TJLD‐Net on three datasets is significantly higher than that of the joint model (RecSeg) and six baseline models (where reconstruction and segmentation are regarded as serial tasks). On the CHAOS dataset, the Dice scores of TJLD‐Net are, on average, 9.87%, 2.17%, 1.90%, 1.80%, 9.60%, 0.80%, and 6.50% higher than those of the seven compared models. On the ATLAS challenge dataset, the average Dice scores of TJLD‐Net improve by 4.23%, 5.63%, 2.30%, 1.53%, 3.57%, 0.93%, and 6.60%. Similarly, on the SKM‐TEA dataset, the average Dice scores of TJLD‐Net improve by 4.73%, 12.80%, 14.83%, 8.67%, 4.53%, 11.60%, and 12.10%. The novel TJLD‐Net model provides a promising solution for undersampled MRI segmentation, overcoming the poor performance issues encountered by automated segmentation algorithms in low‐quality accelerated imaging.

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An edge guided cascaded U‐net approach for accelerated magnetic resonance imaging reconstruction

Cited by 5 publications

References 35 publications

EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

A densely interconnected network for deep learning accelerated MRI

Transformer‐ and joint learning‐based dual‐domain networks for undersampled MRI segmentation

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