Reduction of respiratory motion artifacts in free-breathing abdominal MRI using strategic averaging of reassembled k-space data with Self-Modeled respiratory state sorting

Abstract

Objective

Motion during MRI acquisition leads to varying phase errors in k-space, resulting in motion artifacts that degrade image quality. This study aimed to develop a novel reconstruction method called Strategic Averaging of Reassembled k-space Data (STREAK), which utilizes self-modeled respiratory signals to reduce motion artifacts in free-breathing abdominal MRI.

Approach

We compared the proposed STREAK method with conventional signal averaging (NSA) and free-breathing image acquisition. Three image groups were evaluated: free-breathing, NSA with three signal averages (NSA 3), and STREAK. Image quality was assessed using structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and artifact power (AP), along with subjective grading performed by experienced radiologists. Statistical analysis was conducted using the Mann–Whitney U and Dunn’s tests, with p-values less than 0.05 considered statistically significant.

Results

The STREAK group showed significantly improved SSIM, PSNR, and AP metrics in the liver (p < 0.05). Compared with free-breathing and NSA 3 images, STREAK significantly enhanced image quality in all objective and subjective assessments (p < 0.001). STREAK showed superior motion artifact reduction and image clarity, demonstrating its potential for enhanced MRI imaging quality compared to the NSA method. Inter-reader agreement among radiologists was above moderate (≥ 0.55).

Conclusions

STREAK, combining Cartesian sampling, sensitivity encoding, respiratory signal modeling, and strategic k-space reconstruction, significantly reduced motion artifacts and surpassed the NSA method, showing clinical potential for improved imaging quality.