Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning–Based Reconstruction

Hahn, Seong Tai; Yi, Jisook; Lee, Ho‐Joon; Lee, Yedaun; Lim, Yun‐Jung; Bang, Jin-Young; Kim, Hyun-Woong; Lee, Joonsung

doi:10.2214/ajr.21.26577

Cited by 53 publications

(36 citation statements)

References 23 publications

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“…These findings are in accordance with the study of Hahn et al who investigated the retrospective application of DL reconstructions for fast spin-echo sequences for accelerated shoulder MRI [19]. The mean scan time for accelerated MRI sequences in the study of Hahn et al was 3 min 5 s with the image quality lower than that in conventional MRI sequences, whereas application of deep-learning reconstruction resulted in image quality comparable with that of conventional sequences.…”

Section: Discussionsupporting

confidence: 89%

“…a Conventional PROPELLER sagittal oblique T1weighted (T1w) image, (b) sagittal oblique T1w image after postprocessing using DL images shows degenerative changes of the acromioclavicular joint (broad white arrow), subchondral cysts in the humeral head (thin white arrow), and a joint effusion (triangle). All pathologies can be delineated in both sequences; however, the postprocessed sequence is less noisy so the pathologies can be identified easily DL as previously described, there is lack of literature on application of the PROPELLER technique for deep-learningbased reconstructions [19][20][21].…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning-based convolutional neural networks (DL) have been recently introduced to accelerate image reconstruction of conventional sequences, as they allow the reduction of image noise and scan time while maintaining optimal image contrast [17,18]. Most MRI protocols with deep learningbased reconstructions routinely use FSE sequences, and their successful implementation for assessment of different musculoskeletal structures has been shown in various studies [19][20][21]; however, the application of the deep-learning reconstruction to the PROPELLER sequences has not been examined yet.…”

Section: Introductionmentioning

confidence: 99%

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Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

et al. 2022

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Objectives To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. Methods In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen’s kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Results Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. Conclusions The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. Key Points • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning–based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

et al. 2022

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“…DLR techniques can mitigate image noise induced by acceleration techniques and improve SNR/ spatial resolution, enabling a previously unattainable level of fast imaging. 12,13 We hypothesized that the application of DLR to an accelerated synthetic MR imaging protocol can reduce the scan time while maintaining image quality, facilitating the use of synthetic MR imaging in pediatric neuroimaging. 12,13 For this study, we created an accelerated synthetic MR imaging protocol by increasing both the bandwidth and parallel imaging acceleration factor and then applied a vendor-supplied DLR (AIR Recon DL; GE Healthcare) to the accelerated synthetic protocol.…”

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confidence: 99%

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Kim

Cho

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learningbased reconstruction in pediatric neuroimaging and to investigate the impact of deep learning-based reconstruction on image quality and quantitative values in synthetic MR imaging. MATERIALS AND METHODS:This study included 47 children 2.3-14.7 years of age who underwent both standard and accelerated synthetic MR imaging at 3T. The accelerated synthetic MR imaging was reconstructed using a deep learning pipeline. The image quality, lesion detectability, tissue values, and brain volumetry were compared among accelerated deep learning and accelerated and standard synthetic data sets. RESULTS:The use of deep learning-based reconstruction in the accelerated synthetic scans significantly improved image quality for all contrast weightings (P , .001), resulting in image quality comparable with or superior to that of standard scans. There was no significant difference in lesion detectability between the accelerated deep learning and standard scans (P . .05). The tissue values and brain tissue volumes obtained with accelerated deep learning and the other 2 scans showed excellent agreement and a strong linear relationship (all, R 2 . 0.9). The difference in quantitative values of accelerated scans versus accelerated deep learning scans was very small (tissue values, ,0.5%; volumetry, À1.46%-0.83%). CONCLUSIONS:The use of deep learning-based reconstruction in synthetic MR imaging can reduce scan time by 42% while maintaining image quality and lesion detectability and providing consistent quantitative values. The accelerated deep learning synthetic MR imaging can replace standard synthetic MR imaging in both contrast-weighted and quantitative imaging.

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“…More recently deep learning-based methods such as convolutional neural networks (CNNs) and generative adversarial networks (GAN) have shown promising results to accelerate the MR imaging data acquisition process [15]. These methods apply deep learning-based reconstruction schemes to create highquality images from undersampled MR data [9,[16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based acceleration of Compressed Sense MR imaging of the ankle

et al. 2022

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Objectives To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. Methods Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences. Results The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). Conclusions Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. Key Points • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.

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Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning–Based Reconstruction

Cited by 53 publications

References 23 publications

Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

Accelerated Synthetic MRI with Deep Learning–Based Reconstruction for Pediatric Neuroimaging

Deep learning–based acceleration of Compressed Sense MR imaging of the ankle

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