Feasibility of knee magnetic resonance imaging protocol using artificial intelligence-assisted iterative algorithm protocols: comparison with standard MRI protocols.

OBJECTIVE

To evaluate the image quality and diagnostic performance of AI-assisted iterative algorithm protocols (AIIA) in accelerated fast spin-echo magnetic resonance imaging (MRI) versus standard (SD) fast spin-echo MRI for clinical 3.0 T rapid knee scans.

MATERIALS AND METHODS

The accelerated sequence, which includes fat-suppression proton density-weighted imaging (FS-PDWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI), was used in conjunction with the SD sequence in 61 patients who underwent MRI scans. SD images were processed using standard reconstruction techniques, while accelerated images utilized AIIA reconstruction. Quantitative assessments of image quality were conducted, measuring noise levels, signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR). Additionally, subjective evaluations were performed using a Likert five-point scale to assess image quality.

RESULTS

The SD group completed the entire knee scan in 466 s, while the AIIA group completed the scan in 312 s. Compared to the SD group, the AIIA group had a noticeably higher SNR of T1WI in the femur and subpatellar fat pad ( = 0.04, 0.001). On the other hand, T2WI femur SNR was noticeably higher in the SD group ( = 0.004). Measurements of SNR, CNR and other noise levels showed no statistically significant changes. Compared to the SD group, the AIIA group had significantly higher subjective image quality scores for every sequence ( < 0.05). There was a modest to large intraclass correlation value (ICC = 0.65-0.90) for the anomalies that were examined among readers. Both the AIIA and SD procedures were shown to have comparable diagnostic performance for meniscal and cruciate ligament rupture ( > 0.05).

CONCLUSION

Images processed using AIIA reconstruction were acquired faster while maintaining comparable image quality and diagnostic capability, meeting the requirements for clinical diagnosis.