Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

Eun, Da-in; Jang, Ryoungwoo; Ha, Woosung; Lee, Hyunna; Jung, Sung Eun; Kim, Namkug

doi:10.1038/s41598-020-69932-w

Cited by 46 publications

(31 citation statements)

References 54 publications

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“…Despite all the progress in unsupervised denoising in other areas, there is not that much work done in unsupervised MRI denoising. One example is by Eun et al (2020), where they introduce a cycle generative adversarial network, CycleGAN to denoise compressed sensing MRI. Thus, we wanted to further explore this path, given the potential that unsupervised learning showed in other fields and the lack of clean ground truth data when working with MRI.…”

Section: Unsupervised Learningmentioning

confidence: 99%

Evaluation of MRI Denoising Methods Using Unsupervised Learning

Lopez

Frederick

Ventura

2021

Front. Artif. Intell.

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In this paper we evaluate two unsupervised approaches to denoise Magnetic Resonance Images (MRI) in the complex image space using the raw information that k-space holds. The first method is based on Stein’s Unbiased Risk Estimator, while the second approach is based on a blindspot network, which limits the network’s receptive field. Both methods are tested on two different datasets, one containing real knee MRI and the other consists of synthetic brain MRI. These datasets contain information about the complex image space which will be used for denoising purposes. Both networks are compared against a state-of-the-art algorithm, Non-Local Means (NLM) using quantitative and qualitative measures. For most given metrics and qualitative measures, both networks outperformed NLM, and they prove to be reliable denoising methods.

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Section: Unsupervised Learningmentioning

confidence: 99%

Evaluation of MRI Denoising Methods Using Unsupervised Learning

Lopez

Frederick

Ventura

2021

Front. Artif. Intell.

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“…The latter is based on factors such as luminance, contrast, and structure that are appropriate for the human visual system. For the performance comparison, we evaluated the proposed SS-CNN model, bicubic interpolation, and two other state-of-the-art methods: ESPCN [13], and FMISR [23]. For the up-sampling procedure after feature extraction, the ESPCN SR model also utilizes an SPC technique.…”

Section: A Visual Assessmentmentioning

confidence: 99%

Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

Tamang¹

2021

Preprint

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In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves an exquisite reconstruction performance of ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), while providing compelling SR reconstruction time.

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“…Moreover, compressed sensing entails the use of specialized imaging sequences (for customized undersampling of k‐space) and associated iterative image reconstruction routines, which despite growing commercial offerings in recent years, remain unavailable on many installed MR systems. Moreover, even with judiciously tuned image acquisitions and reconstructions, compressed sensing inevitably degrades fine image detail due to deliberate undersampling of high frequency k‐space 13‐19 . Additionally, compressed sensing reconstructions enforce data consistency and image sparsity and, thus, are not designed to recover unacquired image details, which are largely defined by high frequency k‐space.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, even with judiciously tuned image acquisitions and reconstructions, compressed sensing inevitably degrades fine image detail due to deliberate undersampling of high frequency k-space. [13][14][15][16][17][18][19] Additionally, compressed sensing reconstructions enforce data consistency and image sparsity and, thus, are not designed to recover unacquired image details, which are largely defined by high frequency k-space.…”

Section: Introductionmentioning

confidence: 99%

Super‐resolution head and neck MRA using deep machine learning

Koktzoglou

Ankenbrandt

Walker

et al. 2021

Magnetic Resonance in Med

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To probe the feasibility of deep learning-based super-resolution (SR) reconstruction applied to nonenhanced MR angiography (MRA) of the head and neck. Methods: High-resolution 3D thin-slab stack-of-stars quiescent interval sliceselective (QISS) MRA of the head and neck was obtained in eight subjects (seven healthy volunteers, one patient) at 3T. The spatial resolution of high-resolution ground-truth MRA data in the slice-encoding direction was reduced by factors of 2 to 6. Four deep neural network (DNN) SR reconstructions were applied, with two based on U-Net architectures (2D and 3D) and two (2D and 3D) consisting of serial convolutions with a residual connection. SR images were compared to ground-truth high-resolution data using Dice similarity coefficient (DSC), structural similarity index measure (SSIM), arterial diameter, and arterial sharpness measurements. Image review of the optimal DNN SR reconstruction was done by two experienced neuroradiologists. Results: DNN SR of up to twofold and fourfold lower-resolution (LR) input volumes provided images that resembled those of the original high-resolution groundtruth volumes for intracranial and extracranial arterial segments, and improved DSC, SSIM, arterial diameters, and arterial sharpness relative to LR volumes (P < .001). The 3D DNN SR outperformed 2D DNN SR reconstruction. According to two neuroradiologists, 3D DNN SR reconstruction consistently improved image quality with respect to LR input volumes (P < .001). Conclusion: DNN-based SR reconstruction of 3D head and neck QISS MRA offers the potential for up to fourfold reduction in acquisition time for neck vessels without the need to commensurately sacrifice spatial resolution.

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Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

Cited by 46 publications

References 54 publications

Evaluation of MRI Denoising Methods Using Unsupervised Learning

Evaluation of MRI Denoising Methods Using Unsupervised Learning

Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

Super‐resolution head and neck MRA using deep machine learning

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