Feasibility of low-dose CT brain perfusion scanning based on deep learning reconstruction algorithm: a preliminary study
10.3760/cma.j.cn112271-20231020-00128
- VernacularTitle:基于深度学习重建算法的低剂量CT脑灌注扫描可行性研究
- Author:
Limin LEI
1
;
Yuhan ZHOU
;
Xiaoxu GUO
;
Hui WANG
;
Jinping MA
;
Zhihao WANG
;
Weimeng CAO
;
Yuan GAO
;
Yuming XU
;
Songwei YUE
Author Information
1. 郑州大学第一附属医院放射科，郑州 450052
- Keywords:
Deep learning image reconstruction algorithm;
Acute ischemic stroke;
Brain perfusion imaging;
Low dose;
Image quality
- From:
Chinese Journal of Radiological Medicine and Protection
2024;44(7):613-621
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To compare image quality and diagnostic parameters of whole-brain CT perfusion scans under different scanning conditions and assess the utility of deep learning image reconstruction algorithm (DLIR) in reducing tube current during low-dose scans.Methods:Method A total of 105 patients with suspected acute ischemic stroke (AIS) were prospectively enrolled in the First Affiliated Hospital of Zhengzhou University from March, 2022 to March, 203 and their baseline information was recorded. All patients underwent head non-contrast CT and CT perfusion (CTP) examinations. CTP scanning was performed at 80 kV in two groups with the tube current of 150 mA (regular dose) and 100 mA (low dose), respectively. The CTP images of 150 mA group were reconstructed using filtered back-projection algorithm as well as adaptive statistical iterative reconstruction-V (ASIR-V) at 40% and 80% strength levels, which were denoted as groups A-C. The CTP images of 100 mA group were reconstructed using ASIR-V80%, DLIR-M, and DLIR-H, which were denoted as groups D-F. Clinical baseline characteristics and radiation doses were compared between the two groups under different scanning conditions. Furthermore, we assessed the subjective and objective image quality, conventional perfusion parameters, and abnormal perfusion parameters of AIS patients across the six groups of reconstructed CTP images.Results:Under the scanning conditions of 150 mA and 100 mA, 47 and 48 patients were diagnosed with AIS, respectively. There were no significant differences in the baseline characteristics between the two groups. However, there was a significant difference in the mean effective radiation dose (5.71 mSv vs. 3.80 mSv, t = 2 768.30, P < 0.001). The standard deviation (SD) of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of gray matter (GM) and white matter (WM) were significantly different among the six groups of reconstructed images ( F = 40.58-212.13, P < 0.001). In GM, the SD values in groups C, D, and F were lower than those in other groups ( P < 0.05), and the SNR values in groups C and F were higher than those in other groups ( P < 0.05). In WM, the SD and SNR values in groups C and F were significantly different from those in other groups ( P < 0.05). Additionally, CNR values in groups C and F were higher than those in other groups ( P < 0.05). There was no significant difference in subjective scores among groups B, C, and F ( P > 0.05). Regarding perfusion parameters in the brain GM, groups D and E had lower cerebral blood volume (CBV) values compared to groups A to C ( P < 0.05), and group F had lower CBV values than group B ( P < 0.05). In the brain WM, group D had consistently lower mean transit time (MTT) values compared to the other groups ( P < 0.05). Notably, there were no significant differences in AIS lesion detection rates and relevant diagnostic parameters across the six image groups. Conclusions:Low-tube current CTP scan combined with the DLIR-H algorithm can enhance image quality without affecting perfusion parameters such as CBV and MTT, while reducing radiation dose by 30%. This algorithm can be routinely applied in brain CTP examinations.
