An Optimized Binning and Probabilistic Slice Sharing Algorithm for Motion Correction in Abdominal DW-MRI

Abdominal diffusion-weighted magnetic resonance imaging (DW-MRI) is a powerful, non-invasive technique for characterizing lesions and facilitating early diagnosis. However, respiratory motion during a scan can degrade image quality. Binning image slices into respiratory phases may reduce motion artifacts, but when the standard binning algorithm is applied to DW-MRI, reconstructed volumes are often incomplete because they lack slices along the superior-inferior axis. Missing slices create black stripes within images, and prolonged scan times are required to generate complete volumes. In this study, we propose a new binning algorithm to minimize missing slices. We acquired free-breathing and shallow-breathing abdominal DW-MRI scans on seven volunteers and used our algorithm to correct for motion in free-breathing scans. First, we drew the optimal rigid bin partitions in the respiratory signal using a dynamic programming approach, assigning each slice to one bin. We then designed a probabilistic approach for selecting some slices to belong in two bins. Our proposed binning algorithm resulted in significantly fewer missing slices than standard binning (p<1.0e-16), yielding an average reduction of 82.98+/-6.07%. Our algorithm also improved lesion conspicuity and reduced motion artifacts in DW-MR images and Apparent Diffusion Coefficient (ADC) maps. ADC maps created from free-breathing images corrected for motion with our algorithm showed lower intra-subject variability compared to uncorrected free-breathing and shallow-breathing maps (p<0.001). Additionally, shallow-breathing ADC maps showed more consistency with corrected free-breathing maps rather than uncorrected free-breathing maps (p<0.01). Our proposed binning algorithm's efficacy in reducing missing slices increases anatomical accuracy and allows for shorter acquisition times compared to standard binning.