Abstract
Background: In addition to fiber tracking, stereoscopic display of the peripheral nerves can be obtained based on magnetic resonance (MR) diffusion tensor imaging (DTI) data using post-processing methods, including volume rendering (VR) and maximum intensity projection (MIP). However, sufficient suppression of the image noise remains a challenge. Objectives: To achieve three-dimensional (3D) display of the peripheral nerves in the wrist region using two post-processing methods for DTI, i.e. VR reconstruction for single-direction images and the subtraction of unidirectionally encoded images for suppression of heavily isotropic objects (SUSHI); to compare the quality of images obtained via the two approaches; and to explore their clinical applications. Materials and Methods: We performed DTI scans using 6 (DTI6) and 25 (DTI25) encoding diffusion directions for 20 wrists of 10 healthy adult volunteers. We used VR to reconstruct 2 types of images: 1, single-direction (anterior-posterior [AP] direction) and 2, SUSHI (AP direction with the subtraction of the superior-inferior [SI] direction). The 3D nerve image quality, noise level, and degree of noise-removal difficulty were evaluated according to custom evaluation scales. The preliminary clinical applications of these methods were explored through follow-ups with patients with nerve laceration in the wrist region. Results: Single-direction VR reconstruction clearly showed the nerves for both DTI6 and DTI25 but with obvious noise. In DTI25, VR reconstruction for SUSHI showed the nerves clearly with excellent nerve signal intensity. In DTI6, SUSHI post-processing lost some ulnar nerve signal intensity, resulting in a significant difference in image quality scores between single-direction images and SUSHI. Most of the noise was removed after SUSHI post-processing. Conclusion: VR reconstruction for both single-direction images and SUSHI using DTI25 raw data provides excellent 3D displays of the peripheral nerves in the wrist region. SUSHI post-processing is a useful denoising tool because it automatically reduces the majority of isotropic object noise.
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