Ruo-Fan Sheng1, Li-Yun Zheng2, Kai-Pu Jin1, Wei Sun3, Shu Liao4, Meng-Su Zeng5, Yong-Ming Dai6. 1. Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai, 200032, China.; Shanghai Institute of Medical Imaging, Shanghai, China. 2. Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai, 200032, China.; Shanghai Institute of Medical Imaging, Shanghai, China.; Central Research Institute, United Imaging Healthcare, Shanghai, China. 3. Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai, 200032, China. 4. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. 5. Department of Radiology, Zhongshan Hospital, Fudan University; Shanghai, 200032, China.; Shanghai Institute of Medical Imaging, Shanghai, China.. Electronic address: mengsuzeng@163.com. 6. Central Research Institute, United Imaging Healthcare, Shanghai, China.
Abstract
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hold (MBH) T2WI. METHODS: One hundred and fifty-two adult patients with suspected liver disease were prospectively enrolled. Two independent readers reviewed images acquired with conventional MBH-T2WI and SBH-T2WI at 3.0 T MR scanner. For image quality analyses, motion artifacts scores and boundary sharpness scores were compared using nonparametric Wilcoxon matched pairs tests between MBH-T2WI and SBH-T2WI. With the reference standard, 89 patients with 376 index lesions were included for lesion analyses. The lesion detection rates were compared by chi-square test, the lesion conspicuity scores and lesion-liver contrast ratio (CR) were compared using nonparametric Wilcoxon matched pairs tests between the two sequences. RESULTS: For both readers, motion artifacts scores of SBH-T2WI were significantly lower than MBH-T2WI (P < 0.001). Boundary sharpness scores of SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001). The lesion detection rates for SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001); the differences of lesion detection rates between the two sequences were statistically significant for small (≤ 10 mm) liver lesions (P < 0.001), while not significant for larger (> 10 mm) lesions (P > 0.05). Lesion conspicuity scores were significantly higher on SBH-T2WI than MBH-T2WI in the entire cohort as well as in both stratified subgroups of lesions ≤10 mm and > 10 mm (P < 0.001 for all). CRs for focal liver lesions were also significantly higher with SBH-T2WI (P < 0.001). CONCLUSION: The SBH-T2WI sequence with deep-learning based reconstruction showed promising performance as it provided significantly better image quality, lesion detectability, lesion conspicuity and contrast within a single breath-hold, compared with the conventional MBH-T2WI.
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hold (MBH) T2WI. METHODS: One hundred and fifty-two adult patients with suspected liver disease were prospectively enrolled. Two independent readers reviewed images acquired with conventional MBH-T2WI and SBH-T2WI at 3.0 T MR scanner. For image quality analyses, motion artifacts scores and boundary sharpness scores were compared using nonparametric Wilcoxon matched pairs tests between MBH-T2WI and SBH-T2WI. With the reference standard, 89 patients with 376 index lesions were included for lesion analyses. The lesion detection rates were compared by chi-square test, the lesion conspicuity scores and lesion-liver contrast ratio (CR) were compared using nonparametric Wilcoxon matched pairs tests between the two sequences. RESULTS: For both readers, motion artifacts scores of SBH-T2WI were significantly lower than MBH-T2WI (P < 0.001). Boundary sharpness scores of SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001). The lesion detection rates for SBH-T2WI were significantly higher than MBH-T2WI (P < 0.001); the differences of lesion detection rates between the two sequences were statistically significant for small (≤ 10 mm) liver lesions (P < 0.001), while not significant for larger (> 10 mm) lesions (P > 0.05). Lesion conspicuity scores were significantly higher on SBH-T2WI than MBH-T2WI in the entire cohort as well as in both stratified subgroups of lesions ≤10 mm and > 10 mm (P < 0.001 for all). CRs for focal liver lesions were also significantly higher with SBH-T2WI (P < 0.001). CONCLUSION: The SBH-T2WI sequence with deep-learning based reconstruction showed promising performance as it provided significantly better image quality, lesion detectability, lesion conspicuity and contrast within a single breath-hold, compared with the conventional MBH-T2WI.