1. Department of Radiology, Qingdao Municipal Hospital Affiliated to Qingdao University, Qingdao Shandong 266011, China丨
2. Department of Radiology, Qingdao No.8 People’s Hospital, Qingdao Shandong 266121, China丨
3. CT Research Centre, GE China, Shanghai 200240, China
Abstract:Objective To compare the conventional filter back projection (FBP) and adaptive statistical iterative reconstructionveo
(ASIR-V), and to evaluate the clinical possibility of deep learning image reconstruction (DLIR) algorithm for improving CT
image quality and diagnostic confidence in patients with liver metastases. Methods A total of 32 patients with liver metastasis were
continuously collected and underwent dynamic enhanced CT scanning of the upper abdomen. Raw data of portal vein phase were
reconstructed using 0% ASir-V (FBP), 30% ASir-V and DLIR (L, M, H) of three reconstruction intensities. The CT values of the
lesion and liver tissue as well as the image noise standard deviations (SD) of right erector spinae were measured in the five groups
of reconstructed images. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. At the same time, two
radiologists scored the five groups of images subjectively in terms of image quality, lesion display and diagnostic confidence.
Results In terms of image quality, the,objective image quality indexes of DLIR group were better than FBP and 30% ASiR-V
(P<0.001). DLIR-H had the lowest noise (6.35±1.27), which was 70% and 53% lower than FBP and 30% ASIR-V, respectively.
Compared with FBP and 30% ASir-V, SNR in liver (16.32±5.1) increased by 64% and 53%, respectively. The SNR of lesion
(10.09±4.16) was increased by 66% and 54% compared with FBP and 30% ASiR-V. CNR of lesion (6.21±2.61) was increased
by 65% and 53% compared with FBP and 30% ASiR-V (P<0.001). In terms of diagnosis information of lesions, DLIR scores were
higher than FBP and 30% ASIR-V for lesion display and diagnostic confidence. DLIR-M had the highest score because DLIR-H
caused fine structure blur and excessive smoothness. Conclusion Compared with ASiR-V and FBP, DLIR can significantly reduce
image noise, improve image quality, and improve the details and diagnostic confidence of lesions.
许艺馨1,李辉坚2,王国华1,王铭君3,张振1. 深度学习重建算法对肝脏图像质量及肝转移瘤诊断的研究[J]. 中国医疗设备, 2021, 36(10): 28-31.
XU Yixin1,LI Huijian2,WANG Guohua1,WANG Mingjun3,ZHANG Zhen1. Study on Deep Learning Image Reconstruction in Image Quality and Clinical Diagnosis for
Patients with Liver Metastases. China Medical Devices, 2021, 36(10): 28-31.