Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine

Seo, Geojeong; Lee, Sun Joo; Park, Dae-Hyun; Paeng, Sung Hwa; Koerzdoerfer, Gregor; Nickel, Dominik; Sung, JaeKon

doi:10.1007/s00256-023-04364-x

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Similar ndings were reported by Seok et al [15], who observed comparable sensitivity and speci city of shoulder imaging in terms of subjective image quality, artifacts, and diagnostic performance when compared to standard sequences. Additionally, Geojeong et al [20] demonstrated that DLR sequences substantially reduced acquisition time for cervical spine imaging using the Dixon sequence, with subjective image quality and lesion detectability at least on par with conventional sequences. Importantly, sequences reconstructed using DL yielded higher rSNR and rCNR in our research.…”

Section: Discussionmentioning

confidence: 99%

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

Kang,

Li,

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Kang,

Li,

et al. 2023

Preprint

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Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging

Yoo,

Choi

2024

magn reson med sci

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Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

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Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine

Cited by 2 publications

References 16 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

Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging

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