| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-05-23 |
| タイトル |
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タイトル |
Deep Learning-based Post Hoc CT Denoising for the Coronary Perivascular Fat Attenuation Index |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Tatsuya Nishii
Takuma Kobayashi
Tatsuya Saito
Akiyuki Kotoku
Yasutoshi Ohta
Satoshi Kitahara
Umehara Kensuke
Ota Junko
Hiroki Horinouchi
Yoshiaki Morita
Teruo Noguchi
Takayuki Ishida
Tetsuya Fukuda
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Rationale and Objectives
Coronary inflammation related to high-risk hemorrhagic plaques can be captured by the perivascular fat attenuation index (FAI) using coronary computed tomography angiography (CCTA). Since the FAI is susceptible to image noise, we believe deep learning (DL)-based post hoc noise reduction can improve diagnostic capability. We aimed to assess the diagnostic performance of the FAI in DL-based denoised high-fidelity CCTA images compared with coronary plaque magnetic resonance imaging (MRI) delivered high-intensity hemorrhagic plaques (HIPs).
Materials and methods
We retrospectively reviewed 43 patients who underwent CCTA and coronary plaque MRI. We generated high-fidelity CCTA images by denoising the standard CCTA images using a residual dense network that supervised the denoising task by averaging three cardiac phases with nonrigid registration. We measured the FAIs as the mean CT value of all voxels (range of ?190 to ?30 HU) located within a radial distance from the outer proximal right coronary artery wall. The diagnostic reference standard was defined as HIPs (high-risk hemorrhagic plaques) using MRI. The diagnostic performance of the FAI in the original and denoised images was assessed using receiver operating characteristic curves.
Results
Of 43 patients, 13 had HIPs. The denoised CCTA improved the area under the curve (0.89 [95% confidence interval (CI) 0.78?0.99]) of the FAI compared with that in the original image (0.77 [95% CI, 0.62?0.91], p = 0.008). The optimal cutoff value for predicting HIPs in denoised CCTA was ?69 HU with 0.85 (11/13) sensitivity, 0.79 (25/30) specificity, and 0.80 (36/43) accuracy.
Conclusion
DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs. |
| 書誌情報 |
Academic Radiology
巻 30,
号 11,
p. 2505-2513,
発行日 2023-03
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| 出版者 |
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出版者 |
Elsevier |
| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
1878-4046 |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1016/j.acra.2023.01.023 |
