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Deep Learning Reconstruction for High-resolution Computed Tomography Images of the Temporal Bone: Comparison with Hybrid Iterative Reconstruction by Nana Fujita; Koichiro Yasaka; Sosuke Hatano; Naoya Sakamoto; Ryo Kurokawa; Osamu Abe is a Medicine article available to read on EtoBox.
What is Deep Learning Reconstruction for High-resolution Computed Tomography Images of the Temporal Bone: Comparison with Hybrid Iterative Reconstruction about?
## Abstract ## Purpose We investigated whether the quality of high-resolution computed tomography (CT) images of the temporal bone improves with deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (HIR). ## Methods This retrospective study enrolled 36 patients (15 men, 21 women; age, 53.9 ± 19.5 years) who had undergone high-resolution CT of the temporal bone. Axial and coronal images were reconstructed using DLR, HIR, and filtered back projection (FBP). In qualitative image analyses, two radiologists independently compared the DLR and HIR images with FBP in terms of depiction of structures, image noise, and overall quality, using a 5-point scale (5 = better than FBP, 1 = poorer than FBP) to evaluate image quality. The other two radiologists placed regions of interest on the tympanic cavity and measured the standard deviation of CT attenuation (i.e., quantitative image noise). Scores from the qualitative and quantitative analyses of the DLR and HIR images were compared using, respectively, the Wilcoxon signed-rank test and the paired __t-__test. ## Results Qualitative and quantitative image noise was significantly reduced in DLR images compared with HIR
Who reads Deep Learning Reconstruction for High-resolution Computed Tomography Images of the Temporal Bone: Comparison with Hybrid Iterative Reconstruction?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Nana Fujita; Koichiro Yasaka; Sosuke Hatano; Naoya Sakamoto; Ryo Kurokawa; Osamu Abe
- Publisher
- Springer Science and Business Media LLC
- Published
- 2024
- Language
- EN
- Field
- Medicine (Health Sciences)