Accelerating multi‐echo chemical shift encoded water–fat <scp>MRI</scp> using model‐guided deep learning

Li, Shuo; Shen, Chenfei; Ding, Zekang; She, Huajun; Du, Yiping P.

doi:10.1002/mrm.29307

Cited by 3 publications

(5 citation statements)

References 50 publications

(127 reference statements)

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“…Recently, several algorithms have been proposed for rapid fat and

quantification using stack-of-stars MRI, such as model-guided deep learning for water-fat separation (MGDL-WF) ( 36 ), an uncertainty-aware physics-driven DL network (UP-Net) ( 47 ) and multitasking multi-echo MRI (MT-ME) ( 15 ). The MGDL-WF utilizes a U-net in combination with MG reconstruction to accelerate 3D static water-fat imaging.…”

Section: Discussionmentioning

confidence: 99%

“…The 3D blipped golden-angle stack-of-stars multi-gradient-echo pulse sequence was designed for the prospective acquisition of the multi-channel data ( 36 ). The diagram of the sequence was shown in Figure S2 .…”

Section: Methodsmentioning

confidence: 99%

“…The number of epochs was 150. The other parameters were

, and

, where

, and

are the number of filters, the number of outer iterations, and the number of inner iterations, respectively ( 36 ). In the PNCRNN algorithm, the network parameters were set as described in Zhang’s work ( 30 ).…”

Section: Methodsmentioning

confidence: 99%

“…Free-breathing water-fat imaging is of specific value in certain applications ( 17 , 18 ). The MG reconstruction ( 36 ) is extended to solve an additional respiratory motion dimension for 4D water-fat imaging by exploring water and fat image features along spatial and respiratory motion dimensions. The proposed HMDDL was compared with the CS-WF ( 13 , 14 ) and the PNCRNN on the prospectively undersampled data at acceleration rates (R) of 6, 8, and 10.…”

Section: Introductionmentioning

confidence: 99%

“…where

is the Jacobian of

is a trainable regularization weight. The iteratively regularized Gauss-Newton (IRGN) algorithm ( 36 ) was used to solve the problem in Eq. [8] :…”

Section: Introductionmentioning

confidence: 99%

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Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling

Li,

Wang,

Ding

et al. 2024

Quant Imaging Med Surg

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Background Multi-echo chemical-shift-encoded magnetic resonance imaging (MRI) has been widely used for fat quantification and fat suppression in clinical liver examinations. Clinical liver water-fat imaging typically requires breath-hold acquisitions, with the free-breathing acquisition method being more desirable for patient comfort. However, the acquisition for free-breathing imaging could take up to several minutes. The purpose of this study is to accelerate four-dimensional free-breathing whole-liver water-fat MRI by jointly using high-dimensional deep dictionary learning and model-guided (MG) reconstruction. Methods A high-dimensional model-guided deep dictionary learning (HMDDL) algorithm is proposed for the acceleration. The HMDDL combines the powers of the high-dimensional dictionary learning neural network (hdDLNN) and the chemical shift model. The neural network utilizes the prior information of the dynamic multi-echo data in spatial respiratory motion, and echo dimensions to exploit the features of images. The chemical shift model is used to guide the reconstruction of field maps, maps, water images, and fat images. Data acquired from ten healthy subjects and ten subjects with clinically diagnosed nonalcoholic fatty liver disease (NAFLD) were selected for training. Data acquired from one healthy subject and two NAFLD subjects were selected for validation. Data acquired from five healthy subjects and five NAFLD subjects were selected for testing. A three-dimensional (3D) blipped golden-angle stack-of-stars multi-gradient-echo pulse sequence was designed to accelerate the data acquisition. The retrospectively undersampled data were used for training, and the prospectively undersampled data were used for testing. The performance of the HMDDL was evaluated in comparison with the compressed sensing-based water-fat separation (CS-WF) algorithm and a parallel non-Cartesian recurrent neural network (PNCRNN) algorithm. Results Four-dimensional water-fat images with ten motion states for whole-liver are demonstrated at several R values. In comparison with the CS-WF and PNCRNN, the HMDDL improved the mean peak signal-to-noise ratio (PSNR) of images by 9.93 and 2.20 dB, respectively, and improved the mean structure similarity (SSIM) of images by 0.058 and 0.009, respectively, at R=10. The paired t -test shows that there was no significant difference between HMDDL and ground truth for proton-density fat fraction (PDFF) and values at R up to 10. Conclusions The proposed HMDDL enables features of water images and fat images from the highly undersampled multi-echo data along spatial, respiratory motion, and echo dimensions, to improve the performance of accelerated four-dimensional (4D) free-breathing water-fat imaging.

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“…Recently, several algorithms have been proposed for rapid fat and

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The number of epochs was 150. The other parameters were

, and

, where

, and

Section: Methodsmentioning

confidence: 99%