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Open Access 29-06-2023 | Magnetic Resonance Imaging | Musculoskeletal

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study

Authors: Qizheng Wang, Weili Zhao, Xiaoying Xing, Ying Wang, Peijin Xin, Yongye Chen, Yupeng Zhu, Jiajia Xu, Qiang Zhao, Huishu Yuan, Ning Lang

Published in: European Radiology | Issue 12/2023

Abstract

Objectives

To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans.

Methods

This prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong’s test. The threshold for statistical significance was set at p < 0.05.

Results

A total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75–0.98) and between protocols (κ = 0.73–0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05).

Conclusions

Compared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half.

Clinical relevance statement

Artificial intelligence–assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients.

Key Points

• The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found.

• Reduced scan time, sharper delineation, and less noise with ACS reconstruction.

• Improved efficiency of the clinical knee MRI examination by the ACS acceleration.

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Title: Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study
Authors: Qizheng Wang
Weili Zhao
Xiaoying Xing
Ying Wang
Peijin Xin
Yongye Chen
Yupeng Zhu
Jiajia Xu
Qiang Zhao
Huishu Yuan
Ning Lang
Publication date: 29-06-2023
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Artificial Intelligence
Published in: European Radiology / Issue 12/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-09823-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study

Abstract

Objectives

Methods

Results

Conclusions

Clinical relevance statement

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Clinical relevance statement

Key Points

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