AI-Assisted Compressed Sensing for Optimized Inner Ear Imaging: a Multi-Rater MRI Evaluation of CSAI T2-DRIVE

Vollständiger Abstract

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Abstract Purpose High-resolution T2-weighted imaging is essential for preoperative assessment before cochlear implantation. Compressed sensing (CS) with AI-based reconstruction (CSAI) reduces acquisition times whilst preserving image quality. Although CSAI has been established in various clinical applications, its performance in inner ear imaging remains unclear. This study assesses CSAI-optimized T2-DRIVE sequences at different resolutions and acquisition times for visualizing inner ear structures. Methods In 30 healthy participants, CS T2-DRIVE was acquired at isotropic resolutions of 0.65, 0.5, and 0.4 mm. 0.5 and 0.4 mm datasets were also reconstructed using a commercially available AI-based reconstruction algorithm. Three raters independently assessed the imaging quality of anatomical landmarks (cochlea, semicircular canals, vestibulocochlear nerve), artifacts, and signal-to-noise ratio (SNR) using a 5-point Likert scale. Each rater re-rated a subset of images after ≥ 4 weeks. Inter- and intra-rater reliability were calculated using quadratically weighted Cohen’s kappa, and differences between sequences were analyzed using cumulative link mixed models (CLMM). Results 0.4 mm isotropic imaging exhibited lower SNR compared to CSAI 0.5 mm, regardless of reconstruction algorithm ( p < 0.001). Across all raters, CSAI T2 at 0.5 mm resolution significantly improved delineation of the cochlea and vestibulocochlear nerve compared to 0.65 mm imaging ( p < 0.001), while assessability of semicircular canals was reduced ( p = 0.082). Acquisition times increased with higher resolutions (0.65/0.5/0.4 mm: 4:02/4:25/4:34 min). Conclusion AI-driven reconstruction algorithms enable statistically significant improvements in imaging of key inner-ear structures with minimal increases in scan time at 0.5 mm resolution.