Combining 3D iterative image reconstruction and deep learning to improve image quality of knee joint MRI fast sequences: a focus on meniscal injury evaluation.

BACKGROUND

Knee joint diseases such as meniscal injuries are highly prevalent, affecting over 100 million people worldwide and impairing mobility and quality of life. Magnetic resonance imaging (MRI) is the gold standard for diagnosing meniscal injuries due to its non-invasiveness, multi-parametric imaging, and excellent tissue contrast. However, conventional knee MRI sequences [T1-weighted imaging (T1WI) and proton density-weighted imaging (PDWI)] on 1.5 T scanners require 7-9 minutes, reducing equipment efficiency and causing patient discomfort or motion artifacts. Accelerated techniques such as GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) shorten scan time but compromise image quality [e.g., reduced signal-to-noise ratio (SNR), increased artifacts]. This study aimed to investigate whether a hybrid pipeline combining three-dimensional (3D) iterative reconstruction and deep learning (DL) improves image quality of fast knee MRI while maintaining diagnostic performance for meniscal injuries via Stoller grading.

METHODS

This retrospective study included 116 patients with suspected knee lesions (53 males, 63 females; mean age 53.7±16.9 years). All underwent conventional T1WI and PDWI, and accelerated sequences (GRAPPA factor: T1WI =3, PDWI =2). Accelerated data were processed with standard GRAPPA reconstruction ('Fast' group) and with a commercial software (iQMR™) integrating iterative reconstruction and a DL enhancement module ('After Processing' group). Two radiologists qualitatively evaluated overall image quality using a 5-point Likert scale. SNR and contrast-to-noise ratio (CNR) were quantitatively compared. Meniscal injuries were graded using the Stoller classification. Inter-reader agreement was assessed using weighted Kappa and intraclass correlation coefficient (ICC).

RESULTS

Scan times for fast T1WI and PDWI were reduced by 66.3% (from 92 to 31 s, P<0.001) and 66.5% (from 158 to 53 s, P<0.001), respectively. The 'After Processing' group showed significantly higher qualitative image quality scores compared to the 'Fast' group (P<0.05), and was comparable to conventional sequences. Quantitatively, SNR in the 'After Processing' group was significantly improved over the 'Fast' group (e.g., Patellar cartilage T1WI-SNR: 84.9 vs. 70.3, P<0.05) and reached levels comparable to conventional sequences. No significant differences in CNR were found among the three groups (P>0.05). For the 85 patients with meniscal tears, Stoller grading showed almost perfect inter-reader agreement between conventional and 'After Processing' images (Kappa: 0.767-0.914). No statistically significant differences in Stoller classification distributions were observed among the three groups (P>0.05).

CONCLUSIONS

The hybrid 3D iterative reconstruction and DL pipeline significantly improves image quality of accelerated knee MRI, achieving a ~66% scan time reduction while maintaining SNR/CNR and diagnostic consistency with conventional sequences. This approach enhances workflow efficiency and optimizes healthcare resource utilization, supporting its clinical application.