Magnetic resonance shoulder imaging using deep learning–based algorithm

Liu, Jing; Li, Wei; Li, Ziyuan; Yang, Junzhe; Wang, Ke; Cao, Xinming; Qin, Naishan; Xue, Ke; Dai, Yongming; Wu, Peng; Qiu, Jianxing

doi:10.1007/s00330-023-09470-x

Cited by 7 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In patients presenting with hip pain, the susceptibility to motion artifacts stemming from the patients' movements introduces varying degrees of diagnostic complexity. Signi cantly, reducing scan time holds the potential to mitigate motion artifacts [24]. Our results thus substantiate the pivotal role of DL-MRI in catalyzing the adoption of accelerated sequences as a viable alternative to conventional sequences, with the overarching aim of minimizing motion-induced artifacts in hip imaging.…”

Section: Discussionsupporting

confidence: 73%

Optimizing Hip MRI: Enhancing Image Quality and Elevating Inter- observer Consistency using Deep Learning-Powered Reconstruction

Kang,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality. Methods We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements. Results Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI. Conclusion Compared with conventional MRI, DL-MRI could greatly reduce scan time while improving the imaging quality. And inter-observer consistency showed an overall improvement in DL-MRI.

show abstract

Section: Discussionsupporting

confidence: 73%

Optimizing Hip MRI: Enhancing Image Quality and Elevating Inter- observer Consistency using Deep Learning-Powered Reconstruction

Kang,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The results indicated that the accelerated sequences with DLR provided similar subjective image quality, artifacts, and diagnostic performance compared to standard sequences [41]. Liu et al [42] showed a significantly reduced scan time (6 min 1 s vs. 11 min 25 s) and higher image quality in DLR MRI compared to conventional method. The image quality satisfaction survey among 400 patients received high scores in DL-MRI from all radiologists.…”

Section: Diagnosismentioning

confidence: 88%

Application of Artificial Intelligence in Shoulder Pathology

Cheng,

Liang,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

The artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopaedic-specific problems. One particular area of investigation focuses on the shoulder pathology, which is a range of disorders or abnormalities of shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate the current AI applications in the shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI are also discussed.

show abstract

“…Previous research has indicated that DL-MRI has the potential to generate images of higher quality compared to Conventional-MRI in the FS PDWI and FS T2WI sequences ( 23 ). Besides, Dratsch et al ( 24 ) have assessed the image quality of shoulder MRI utilizing a combination of compressed sensing (CS) acquisition and DL reconstruction.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research has explored the application of DL reconstruction technology in shoulder MRI, demonstrating its efficacy in reducing acquisition times. Similarly, studies focusing on knee MRI have shown that DL-MRI has the potential to shorten scan times, suggesting that DL-MRI can serve as a viable alternative to Conventional-MRI ( 23 , 26 ). Moreover, DL-MRI has exhibited remarkable performance in various anatomical regions, including the pituitary, temporomandibular joint, prostate, and brain ( 7 , 27 - 29 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint

Liu,

Wen,

Wang

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Accelerated magnetic resonance imaging sequences reconstructed using the vendor-provided Recon deep learning algorithm (DL-MRI) were found to be more likely than conventional magnetic resonance imaging (MRI) sequences to detect subacromial (SbA) bursal thickening. However, the extent of this thickening was not extensively explored. This study aimed to compare the image quality of DL-MRI with conventional MRI sequences reconstructed via conventional pipelines (Conventional-MRI) for shoulder examinations and evaluate the effectiveness of DL-MRI in accurately demonstrating the degree of SbA bursal and subcoracoid (SC) bursal thickening. Methods This prospective cross-sectional study enrolled 41 patients with chronic shoulder pain who underwent 3-T MRI (including both Conventional-MRI and accelerated MRI sequences) of the shoulder between December 2022 and April 2023. Each protocol consisted of oblique axial, coronal, and sagittal images, including proton density-weighted imaging (PDWI) with fat suppression (FS) and oblique coronal T1-weighted imaging (T1WI) with FS. The image quality and degree of artifacts were assessed using a 5-point Likert scale for both Conventional-MRI and DL-MRI. Additionally, the degree of SbA and SC bursal thickening, as well as the relative signal-to-noise ratio (rSNR) and relative contrast-to-noise ratio (rCNR) were analyzed using the paired Wilcoxon test. Statistical significance was defined as P<0.05. Results The utilization of accelerated sequences resulted in a remarkable 54.7% reduction in total scan time. Overall, DL-MRI exhibited superior image quality scores and fewer artifacts compared to Conventional-MRI. Specifically, DL-MRI demonstrated significantly higher measurements of SC bursal thickenings in the oblique sagittal PDWI sequence compared to Conventional-MRI [3.92 (2.83, 5.82) vs. 3.74 (2.92, 5.96) mm, P=0.028]. Moreover, DL-MRI exhibited higher detection of SbA bursal thickenings in the oblique coronal PDWI sequence [2.61 (1.85, 3.46) vs. 2.48 (1.84, 3.25) mm], with a notable trend towards significant differences (P=0.071). Furthermore, the rSNRs of the muscle in DL-MRI images were significantly higher than those in Conventional-MRI images across most sequences (P<0.001). However, the rSNRs of bone on Conventional-MRI of oblique axial and oblique coronal PDWI sequences showed adverse results [oblique axial: 1.000 (1.000, 1.000) vs. 0.444 (0.367, 0.523); and oblique coronal: 1.000 (1.000, 1.000) vs. 0.460 (0.387, 0.631); all P<0.001]. Additionally, all DL-MRI images exhibited significantly greater rSNRs and rCNRs compared to accelerated MRI sequences reconstructed using traditional pipelines (P<0.001). Conclusions In conclusion, the utilization of DL-MRI enhances image quality and improves diagnostic capabilities, making it a promising alternative to Conv...

show abstract

Magnetic resonance shoulder imaging using deep learning–based algorithm

Cited by 7 publications

References 31 publications

Optimizing Hip MRI: Enhancing Image Quality and Elevating Inter- observer Consistency using Deep Learning-Powered Reconstruction

Optimizing Hip MRI: Enhancing Image Quality and Elevating Inter- observer Consistency using Deep Learning-Powered Reconstruction

Application of Artificial Intelligence in Shoulder Pathology

Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint

Contact Info

Product

Resources

About