Reduction of systematic errors in diffusion tensor imaging of the human brain as a prospect for increasing the precision of planning neurosurgical operations with particular emphasis on fiber tracking

Diffusion Tensor Imaging (DTI) is integral to presurgical planning and early detection of neurodegenerative diseases. It reconstructs white matter pathways and enhances brain connectivity insights. However, systematic errors and noise hinder DTI's utility, disrupting the visualization of critical anatomical details necessary for understanding brain function and disorders. This study evaluated the combined impact of denoising and B-matrix Spatial Distribution (BSD) correction on DTI accuracy and tractography quality using two datasets: a single-subject scan and a 40-subject cohort - each acquired with corresponding phantoms. Each was processed using six configurations-three preprocessing levels (RAW, DENOISED, PREPROC), with and without BSD correction. In vivo, significant changes in FA and MD were observed across major white matter tracts, with the combined use of denoising and BSD. DTI metrics were assessed in specific brain structures, including the corpus callosum, internal capsule, putamen, and thalamus, using both manually defined and atlas-based ROIs. Visual and quantitative evaluations showed that denoising and BSD are complementary steps and should be used together to reduce both random and systematic errors. In phantom experiments, BSD correction had a substantially greater effect on improving DTI metric accuracy than the full preprocessing pipeline alone, highlighting its critical role in correcting errors associated with nonuniformity of magnetic field gradients. This study underscores the importance of correcting spatial systematic errors and noise to ensure precise neuroimaging data. Such advancements are critical for deepening our understanding of neural connectivity and improving its clinical applications in diagnosing and treating neurological conditions.