| Liu Jinghong,Liu Ailian,Tao Fengming,Liu Yijun,Fang Xin,Pan Judong.Application of a deep machine learning technique for low dose renal CT perfusion[J].Chinese Journal of Radiological Medicine and Protection,2018,38(5):386-389 |
| Application of a deep machine learning technique for low dose renal CT perfusion |
| Received:September 03, 2017 |
| DOI：10.3760/cma.j.issn.0254-5098.2018.05.012 |
| KeyWords:Low dose Deep learning technique CT perfusion |
| FundProject: |
| Author Name | Affiliation | E-mail | | Liu Jinghong | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Liu Ailian | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | cjr.liuailian@vip.163.com | | Tao Fengming | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Liu Yijun | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Fang Xin | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Pan Judong | Department of Radiology & Biomedical Imaging, University of California, San Francisco 94143, USA | |
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| Abstract:: |
| Objective To assess the ability of a deep machine learning technique for improving the quality of one-stop renal low dose CTP images. Methods Twenty-one cases who underwent renal noncontrast CT, triple-phase contrast enhanced CT, and CT perfusion(CTP) were collected prospectively. Revolution CT scanner was used with the scan protocol as followed:120 kVp, 20 mA for CTP and 100 mA for triple-phase conctrast enhancement, axial scan, ASIR-V80%, rotation 0.5 s, coverage area for z-axial 160 mm, thickness 5 mm. A total of 15 phases were obtained for the first 28 s and then scanned once at 39, 43, 47, 51, 63, 83, 113, 213, 353, 593 s for CTP, which the phases at the 22, 51 and 153 s were the cortical phase, medullary phase and excretory phase, respectively. All CTP data was reconstructed with a deep machine learning technique pixel shine A7 model. The data before and after reconstruction was in group A and in group B, respectively. Compared the all data of cortex in the cortical phase and CTP parameters between the two groups. Results There were significant differences of CT values of SD of cortex (9.04±1.77 and 5.75±1.00, respectively), CT values of SD of elector spinae (8.52±2.28 and 5.67±0.98, respectively), CNR(16.28±6.61 and 28.90±1.50, respectively) and SNR (21.41±6.67 and 30.65±7.67, respectively) between the two groups(t=1.562, 6.286, 5.925, -5.892, -17.274, P<0.05). The SD of images after PS-B was lower than that before PS-B significantly and SNR was improved obviously. There were no differences of cortical blood flow (BF), blood volume (BV), time to peak (TP) and medullary permeability of surface (PS) between the two groups(P>0.05). Conclusions The reconstruction of deep machine learning PixelShine technique PS-A7 can reduce the noise of images obtained with low tube current, improve the SNR and can not effect the CTP parameters. |
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