| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2021: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2019: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2018: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
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| Outline of Final Research Achievements |
Radiation dose to patients who undergo computed tomography (CT) exams was a serious issue. To solve this problem, we developed a radiation dose reduction technology based on our original massive-training artificial neural network (MTANN) deep learning model. We trained our MTANN model with input ultra-low-dose CT images and corresponding teaching high-dose CT images to produce high-dose-CT-like images. Quantitative evaluation demonstrated that our virtual deep-learning imaging based on MTANNs was able to reduce radiation dose by more than 90% in CT, which was higher than dose reduction rates of 17-44% by the state-of-the-art iterative reconstruction.
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