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Effect of deep learning-based reconstruction on high-resolution three-dimensional T2-weighted fast asymmetric spin-echo imaging in the preoperative evaluation of cerebellopontine angle tumors.
- Source :
- Neuroradiology; Jul2024, Vol. 66 Issue 7, p1123-1130, 8p
- Publication Year :
- 2024
-
Abstract
- Purpose: We aimed to evaluate the effect of deep learning-based reconstruction (DLR) on high-spatial-resolution three-dimensional T2-weighted fast asymmetric spin-echo (HR-3D T2-FASE) imaging in the preoperative evaluation of cerebellopontine angle (CPA) tumors. Methods: This study included 13 consecutive patients who underwent preoperative HR-3D T2-FASE imaging using a 3 T MRI scanner. The reconstruction voxel size of HR-3D T2-FASE imaging was 0.23 × 0.23 × 0.5 mm. The contrast-to-noise ratios (CNRs) of the structures were compared between HR-3D T2-FASE images with and without DLR. The observers' preferences based on four categories on the tumor side on HR-3D T2-FASE images were evaluated. The facial nerve in relation to the tumor on HR-3D T2-FASE images was assessed with reference to intraoperative findings. Results: The mean CNR between the tumor and trigeminal nerve and between the cerebrospinal fluid and trigeminal nerve was significantly higher for DLR images than non-DLR-based images (14.3 ± 8.9 vs. 12.0 ± 7.6, and 66.4 ± 12.0 vs. 53.9 ± 8.5, P < 0.001, respectively). The observer's preference for the depiction and delineation of the tumor, cranial nerves, vessels, and location relation on DLR HR-3D T2FASE images was superior to that on non-DLR HR-3D T2FASE images in 7 (54%), 6 (46%), 6 (46%), and 6 (46%) of 13 cases, respectively. The facial nerves around the tumor on HR-3D T2-FASE images were visualized accurately in five (38%) cases with DLR and in four (31%) without DLR. Conclusion: DLR HR-3D T2-FASE imaging is useful for the preoperative assessment of CPA tumors. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00283940
- Volume :
- 66
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Neuroradiology
- Publication Type :
- Academic Journal
- Accession number :
- 177648383
- Full Text :
- https://doi.org/10.1007/s00234-024-03328-9