Single-breath-hold T2WI MRI with artificial intelligence-assisted technique in liver imaging: As compared with conventional respiratory-triggered T2WI

Hualing Li; Chenglin Hu; Yang Yang; Yanjie Zhao; Chen Lin; Zhen Li; Qiufeng Liu

doi:10.1016/j.mri.2022.08.012

Single-breath-hold T2WI MRI with artificial intelligence-assisted technique in liver imaging: As compared with conventional respiratory-triggered T2WI

Magn Reson Imaging. 2022 Nov:93:175-180. doi: 10.1016/j.mri.2022.08.012. Epub 2022 Aug 18.

Authors

Hualing Li¹, Chenglin Hu¹, Yang Yang¹, Yanjie Zhao¹, Chen Lin², Zhen Li³, Qiufeng Liu⁴

Affiliations

¹ Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, China.
² United Imaging Healthcare, Shanghai, China, 2258 Chengbei Rd., Jiading District, Shanghai 201807, China. Electronic address: chen.lin@united-imaging.com.
³ Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, China. Electronic address: zhenli@hust.edu.cn.
⁴ Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, China. Electronic address: nuodaxir1@126.com.

PMID: 35987419
DOI: 10.1016/j.mri.2022.08.012

Abstract

Objective: To investigate the clinical feasibility of single-breath-hold T2-weighted (SBH-T2WI) liver MRI using Artificial Intelligence-assisted Compressed Sensing (ACS) technique in liver imaging as compared with conventional respiratory-triggered T2WI (RT-T2WI).

Methods: From January 2021 to October 2021, 81 patients suspected of liver lesions were enrolled in this prospective study. The liver MRI was performed, including both RT-T2WI and ACS SBH-T2WI. Two experienced radiologists reviewed all images of each studied sequence, and recorded the lesion location and the largest diameter of the lesions. The image quality was quantitatively and qualitatively analyzed regarding signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), motion artifact, lesion conspicuity, liver boundary sharpness, and overall image quality. The lesion detection and image quality were compared between two sequences using the Chi-square test or Wilcoxon signed-rank test.

Results: For lesion detection, 64 lesions were identified in 53 enrolled patients as the reference standard. The average size was 12.09 ± 7.4 mm for the benign lesions and 45.89 ± 22.01 mm for the malignant lesions. Of 64 liver lesions, ACS SBH-T2WI detected 60 lesions (93.8%), and RT-T2WI detected 58 lesions (90.6%). For image quality analysis, the motion artifact of ACS SBH-T2WI sequence was significantly reduced compared with the conventional RT-T2WI sequence (p < 0.05). The SNR, liver boundary sharpness, and overall image quality showed no statistical differences between the two sequences. While the CNR, CR, and lesion conspicuity of ACS SBH-T2WI were significantly better than RT-T2WI (all p < 0.05).

Conclusions: The SBH-T2WI with ACS technique showed promising performance as it provided significantly better image quality and lesion detectability with a considerable decrease in scanning time as compared with the conventional RT-T2WI.

Keywords: Artificial intelligence; Compressed sensing; Liver; Magnetic resonance imaging; Respiratory-triggered.

MeSH terms

Artifacts
Artificial Intelligence
Humans
Liver Neoplasms* / diagnostic imaging
Liver Neoplasms* / pathology
Magnetic Resonance Imaging / methods
Prospective Studies