ArticleQuantitative imaging in medicine and surgery2024
Deep learning-based reconstruction for 3-dimensional heavily T2-weighted fat-saturated magnetic resonance (MR) myelography in epidural fluid detection: image quality and diagnostic performance.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
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6 authors.
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Abstract
Background: Heavily T2-weighted fat-saturated (HT2W-FS) magnetic resonance myelography (MRM) is useful for diagnosing the cause of intracranial hypotension. Recently, deep learning-based reconstruction (DLR) has been utilized to improve image signal-to-noise ratios and sharpness while reducing artifacts, all without lengthening acquisition times. This study aimed to compare the diagnostic performance and image quality of conventional reconstruction (CR) and DLR of 3-dimensional (3D) HT2W-FS MRM applied to detecting epidural fluid in patients with clinically suspected intracranial hypotension. Methods: This retrospective study included 21 magnetic resonance myelography examinations using both CR and DLR in 21 patients who experienced orthostatic headache between April 2021 and September 2022. Quantitative image quality evaluation was performed by comparing signal-to-noise ratios at the lower thoracic levels. The image quality and artifacts were graded by three readers. The presence of epidural fluid was reported with a confidence score by two readers, and the area under the receiver operating curve, interobserver agreement, and inter-image-set agreement were evaluated. The conspicuity of the dura mater where the epidural fluid was detected was also investigated. Results: Quantitative and subjective image quality, and artifacts significantly improved with DLR (all P<0.001). Diagnostic performance of DLR was higher for both readers [reader 1: area under the curve (AUC) of CR =0.929; 95% confidence interval (CI): 0.902-0.950, AUC of DLR =0.965 (95% CI: 0.944-0.979), P=0.007; reader 2: AUC of CR =0.834 (95% CI: 0.798-0.866), AUC of DLR =0.877 (0.844-0.905), P=0.040]. Correlation with standard care of MRM in CR and DLR were both strong in reader 1 (rho =0.868-0.919, P<0.001), but was respectively strong and moderate in reader 2 (rho =0.734-0.805, P<0.001). Interobserver agreement was substantial (κ=0.708-0.762). The inter-image-set agreement was almost perfect for reader 1 (κ=0.907) and was substantial for reader 2 (κ=0.750). Dura mater conspicuity significantly improved with DLR (P<0.014, reader 1; P<0.001, readers 2 and 3). Conclusions: HT2W-FS magnetic resonance myelography with DLR demonstrates substantial improvements in image quality and may improve confidence in detecting epidural fluid.
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