Academic Journal
Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis
| العنوان: | Simulation Study of Low-Dose Sparse-Sampling CT with Deep Learning-Based Reconstruction: Usefulness for Evaluation of Ovarian Cancer Metastasis |
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| المؤلفون: | Urase, Yasuyo, Nishio, Mizuho, Ueno, Yoshiko, Kono, Atsushi K., Sofue, Keitaro, Kanda, Tomonori, Maeda, Takaki, Nogami, Munenobu, Hori, Masatoshi, Murakami, Takamichi |
| بيانات النشر: | MDPI |
| سنة النشر: | 2020 |
| المجموعة: | Kobe University Repository (Kernel) / 神戸大学学術成果リポジトリ |
| مصطلحات موضوعية: | deep learning, neoplasm metastasis, ovarian neoplasms, radiation exposure, tomography, x-ray computed |
| الوصف: | The usefulness of sparse-sampling CT with deep learning-based reconstruction for detection of metastasis of malignant ovarian tumors was evaluated. We obtained contrast-enhanced CT images (n= 141) of ovarian cancers from a public database, whose images were randomly divided into 71 training, 20 validation, and 50 test cases. Sparse-sampling CT images were calculated slice-by-slice by software simulation. Two deep-learning models for deep learning-based reconstruction were evaluated: Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) and deeper U-net. For 50 test cases, we evaluated the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as quantitative measures. Two radiologists independently performed a qualitative evaluation for the following points: entire CT image quality; visibility of the iliac artery; and visibility of peritoneal dissemination, liver metastasis, and lymph node metastasis. Wilcoxon signed-rank test and McNemar test were used to compare image quality and metastasis detectability between the two models, respectively. The mean PSNR and SSIM performed better with deeper U-net over RED-CNN. For all items of the visual evaluation, deeper U-net scored significantly better than RED-CNN. The metastasis detectability with deeper U-net was more than 95%. Sparse-sampling CT with deep learning-based reconstruction proved useful in detecting metastasis of malignant ovarian tumors and might contribute to reducing overall CT-radiation exposure. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | info:doi/10.3390/app10134446 |
| الاتاحة: | http://www.lib.kobe-u.ac.jp/handle_kernel/90007337 http://www.lib.kobe-u.ac.jp/repository/90007337.pdf |
| Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| رقم الانضمام: | edsbas.3316C611 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |