Three contrasts in 3 min: Rapid, high‐resolution, and bone‐selective UTE MRI for craniofacial imaging with automated deep‐learning skull segmentation

Abstract

AbstractPurposeUltrashort echo time (UTE) MRI can be a radiation‐free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone‐specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time‐consuming bone segmentation workflow.MethodsA rapid, high‐resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual‐RF, dual‐echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm3). A weighted least‐squares conjugate gradient method for computing the bone‐selective image improves bone specificity while retaining bone sensitivity. Additionally, a deep‐learning U‐Net model was trained to automatically segment the skull from the bone‐selective images. Ten healthy adult volunteers (six male, age 31.5 ± 10 years) and three pediatric patients (two male, ages 12 to 15 years) were scanned at 3 T. Clinical CT for the three patients were obtained for validation. Similarities in 3D skull reconstructions relative to clinical standard CT were evaluated based on the Dice similarity coefficient and Hausdorff distance. Craniometric measurements were used to assess geometric accuracy of the 3D skull renderings.ResultsThe weighted least‐squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full‐skull binary masks of the optimized UTE and CT in the testing dataset.ConclusionAn optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed.