Optimization strategy for fat-suppressed T2-weighted images in liver imaging: The combined application of AI-assisted compressed sensing and respiratory triggering.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, PR China.
- Central Research Institute, United Imaging Healthcare, Shanghai 201807, PR China.
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, PR China. Electronic address: [email protected].
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, PR China. Electronic address: [email protected].
Abstract
This study aimed to optimize the imaging time and image quality of T2WI-FS through the integration of Artificial Intelligence-Assisted Compressed Sensing (ACS) and respiratory triggering (RT). A prospective cohort study was conducted on one hundred thirty-four patients (99 males, 35 females; average age: 57.93 ± 9.40 years) undergoing liver MRI between March and July 2024. All patients were scanned using both breath-hold ACS-assisted T2WI (BH-ACS-T2WI) and respiratory-triggered ACS-assisted T2WI (RT-ACS-T2WI) sequences. Two experienced radiologists retrospectively analyzed regions of interest (ROIs), recorded primary lesions, and assessed key metrics including signal intensity (SI), standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), motion artifacts, hepatic vessel clarity, liver edge sharpness, lesion conspicuity, and overall image quality. Statistical comparisons were conducted using Mann-Whitney U test, Wilcoxon signed-rank test and intraclass correlation coefficient (ICC). Compared to BH-ACS-T2WI, RT-ACS-T2WI significantly reduced average imaging time from 38 s to 22.91 ± 3.36 s, achieving a 40 % reduction in scan duration. Additionally, RT-ACS-T2WI demonstrated superior performance across multiple parameters, including SI, SD, SNR, CNR, motion artifact reduction, hepatic vessel clarity, liver edge sharpness, lesion conspicuity (≤5 mm), and overall image quality (P < 0.05). Notably, the lesion detection rate was slightly higher with RT-ACS-T2WI (94 %) compared to BH-ACS-T2WI (90 %). The RT-ACS-T2WI sequence not only enhanced image quality but also reduced imaging time to approximately 23 s, making it particularly beneficial for patients unable to perform prolonged breath-holding maneuvers. This approach represents a promising advancement in optimizing liver MRI protocols.