Optimizing knee joint imaging: A comparative study of 7T MRI sequences.

Journal: Magnetic Resonance Imaging
Published:
Abstract

Objective: To reduce long scan durations and improve patient comfort while maintaining image quality by assessing varying 7 T MRI sequences to optimize knee joint imaging.

Methods: In this prospective study, healthy participants underwent knee joint scans using 7 T proton density fat-saturated (PD-FS), 3-dimensional double-echo steady-state (3D-DESS), and susceptibility-weighted imaging (SWI) sequences. We evaluated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of cartilage, meniscus, ligaments, synovial fluid, and adipose tissue and the imaging quality of cartilage, meniscus, and ligaments. Also, we assessed the concurrence reached by two independent evaluators using the intraclass correlation coefficient (ICC).

Results: Twenty participants [mean age, 25.6 years ±8.4 (SD); 13 women] were evaluated. The 3D-DESS sequence demonstrated the highest SNR for cartilage, ligament, joint fluid, and meniscus structures (P < .001). It performed similarly to the PD sequence for fat but outperformed the SWI sequence. The CNR analysis revealed that 3D-DESS produced the highest contrast between joint fluid and other structures (P < .001), followed by PD-FS, whereas SWI exhibited the lowest contrast. The SWI sequence demonstrated superior CNR between ligament and fat (P < .001). The PD-FS sequence exhibited the highest CNR between cartilage and meniscus (P < .001). Both observers reported substantial concordance in their evaluations (ICC > 0.7). The cartilage visualization was excellent in all sequences, with the SWI sequence displaying slight superiority (P < .05). The ligament and meniscus images were of the highest quality when using PD-FS (P < .001).

Conclusions: A combination of PD-FS and 3D DESS sequences is recommended for comprehensive and comfortable 7 T MRI assessments of knee joints, ensuring detailed visualization of various vital knee structures.

Authors
Xiaoqi Yi, Zhiming Zhen, Xiaoli Gou, Wei Chen