| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-04-24 |
| タイトル |
|
|
タイトル |
Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
Tatsuya Nishii
Tomoro Morikawa
Hiroki Nakajima
Yasutoshi Ohta
Takuma Kobayashi
Umehara Kensuke
Ota Junko
Takashi Kakuta
Satsuki Fukushima
Tetsuya Fukuda
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV prolapse and assess their diagnostic performance and efficiency. We retrospectively reviewed the cardiac CTs of consecutive patients who underwent MV repair in 2023. The original images were iteratively reconstructed and denoised with a residual dense network. 3DVR images of the “surgeon’s view” were created with blood chamber transparency to display the MV leaflets. We compared the 3DVR image quality between the original and denoised images with a 100-point scoring system. Diagnostic confidence for prolapse was evaluated across eight MV segments: A1-3, P1-3, and the anterior and posterior commissures. Surgical findings were used as the reference to assess diagnostic ability with the area under curve (AUC). The interpretation time for the denoised 3DVR images was compared with that for multiplanar reformat images. For fifty patients (median age 64 years, 30 males), denoising the 3DVR images significantly improved their image quality scores from 50 to 76 (P?<.001). The AUC in identifying MV prolapse improved from 0.91 (95% CI 0.87?0.95) to 0.94 (95% CI 0.91?0.98) (P?=.009). The denoised 3DVR images were interpreted five-times faster than the multiplanar reformat images (P?<.001). Deep learning-based denoising enhanced the quality of 3DVR imaging of the MV, improving the performance and efficiency in detecting MV prolapse on cardiac CT. |
| 書誌情報 |
The International Journal of Cardiovascular Imaging
発行日 2025-04
|
| PubMed番号 |
|
|
|
識別子タイプ |
PMID |
|
|
関連識別子 |
40266552 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1007/s10554-025-03403-z |
