Rapid 3D breath-hold MR cholangiopancreatography using deep learning–constrained compressed sensing reconstruction

Peng, Wanlin; Xiao, Yi; Ming, Yue; Ma, Kehang; Hu, Si-Xian; Zeng, Wen; Zeng, Ling-Ming; Liang, Zejun; Zhang, Xiaoyong; Xia, Chunchao; Li, Zhenlin

doi:10.1007/s00330-022-09227-y

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…In addition, qualitative reader assessment indicated that the DL cine reconstructions improved image quality, particularly in terms of endocardial edge definition and contrast ( P < 0.001). These results are in concordance with previously reported measurements in prostatic lesions and intrahepatic bile ducts ( 23 , 36 ). Furthermore, the level of diagnostic confidence was increased for six different cardiovascular structures, including the cardiac chambers, foramen ovale, valves, great thoracic vessels, aortic arch, and pulmonary veins.…”

Section: Discussionsupporting

confidence: 94%

“…The DL model used in this study has been proven to alleviate the limitations of CS techniques in knee and prostate MRI and in MR cholangiopancreaticography and has received 510(k) clearance from the U.S. Food and Drug Administration ( 22 , 23 , 36 , 37 ). Therefore, the purpose of this study was to evaluate the performance of the technique for feasible and easy-to-implement image denoising of fetal cardiac cine images in a clinical setting of fetuses with various forms of CHD.…”

Section: Discussionmentioning

confidence: 99%

“…Initially described by Pezzotti et al, this network merges a CNN-based sparsification approach with the CS reconstruction approach ( 22 ). In doing so, the multi-scale sparsification from the wavelet transform of the conventional CS algorithm is replaced by a learned implementation, ensuring data consistency and incorporating domain-specific prior knowledge such as coil sensitivity distribution and location of the image background, as previously described ( 23 ). Trained for subsampling and reduction of related noise, the CNN is applicable to various MRI applications irrespective of the underlying anatomy and can therefore be used for fetal cardiac MRI, even though these images were not part of the training dataset.…”

Section: Methodsmentioning

confidence: 99%

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Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Vollbrecht,

Hart,

Zhang

et al. 2024

Front. Cardiovasc. Med.

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PurposeThis study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD).MethodsTwenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins.ResultsFetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2–4) vs. 5 (4–5), P < 0.001] and endocardial edge definition [3 (2–4) vs. 4 (4–5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3–4.75) vs. 4 (3–4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003).ConclusionDL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Vollbrecht,

Hart,

Zhang

et al. 2024

Front. Cardiovasc. Med.

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Advanced MRI techniques in abdominal imaging

Lee,

Yoon,

Paek

et al. 2024

Abdom Radiol

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Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography

Kondo,

Nakamura,

Higaki

et al. 2024

Jpn J Radiol

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Purpose Under-sampled scans with iterative reconstruction and high-frequency preserving transform (Us-IRHF) can increase the acquisition speed without degrading the image quality by recovering image information from under-sampled data. We investigate the clinical applicability of high spatial resolution magnetic resonance cholangiopancreatography (MRCP) images without extending the scanning time using Us-IRHF. Methods A slit phantom was scanned with conventional- (without Us-IRHF), Us-IR- (without HF), and Us-IRHF scanning. The matrix size was 320 × 320 for Us-IR- and Us-IRHF- and 288 × 208 for conventional scanning. Modulation transfer function (MTF) focused on the 1.0 lp/cm gauge for each scanning was calculated. For clinical study we acquired respiratory-triggered 3D MRCP scans with and without Us-IRHF (U+-, U−MRCP) in 41 patients. The matrix size was 320 × 320 for U+- and 288 × 208 for U−MRCP. The acquisition time and the relative duct-to-periductal contrast ratios (RCs) for the right- and left intrahepatic bile-, the common bile-, and the main pancreatic duct were recorded. Visualization of each duct and overall image quality was scored on 5-point confidence scales. For visualization of each duct the score ranged from 1 (not visible) to 5 (visible with excellent details), for the image quality, it ranged from 1 (undiagnostic) to 5 (excellent). Superiority for the qualitative visualization score and non-inferiority for the RC values with prespecified margins were assessed. Results Phantom study showed that compared to the conventional- and Us-IR (without HF) images, the MTF for the Us-IRHF image revealed the highest response. For clinical study, the mean acquisition time was 161 s for U+- and 165 s for U−MRCP. For all ducts, the RC value of U+MRCP was non-inferior to U−MRCP and the qualitative visualization score assigned to U+MRCP was superior to U−MRCP. Conclusion Us-IRHF improved the image quality of high spatial resolution MRCP without extending the scanning time.

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Rapid 3D breath-hold MR cholangiopancreatography using deep learning–constrained compressed sensing reconstruction

Cited by 12 publications

References 32 publications

Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI

Advanced MRI techniques in abdominal imaging

Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography

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