Neonatal White Matter Damage Analysis Using DTI Super-Resolution and Multi-Modality Image Registration

Punctate White Matter Damage (PWMD) is a common neonatal brain disease, which can easily cause neurological disorder and strongly affect life quality in terms of neuromotor and cognitive performance. Especially, at the neonatal stage, the best cure time can be easily missed because PWMD is not conducive to the diagnosis based on current existing methods. The lesion of PWMD is relatively straightforward on T1-weighted Magnetic Resonance Imaging (T1 MRI), showing semi-oval, cluster or linear high signals. Diffusion Tensor Magnetic Resonance Image (DT-MRI, referred to as DTI) is a noninvasive technique that can be used to study brain microstructures in vivo, and provide information on movement and cognition-related nerve fiber tracts. Therefore, a new method was proposed to use T1 MRI combined with DTI for better neonatal PWMD analysis based on DTI super-resolution and multi-modality image registration. First, after preprocessing, neonatal DTI super-resolution was performed with the three times B-spline interpolation algorithm based on the Log-Euclidean space to improve DTIs' resolution to fit the T1 MRIs and facilitate nerve fiber tractography. Second, the symmetric diffeomorphic registration algorithm and inverse b0 image were selected for multi-modality image registration of DTI and T1 MRI. Finally, the 3D lesion models were combined with fiber tractography results to analyze and predict the degree of PWMD lesions affecting fiber tracts. Extensive experiments demonstrated the effectiveness and super performance of our proposed method. This streamlined technique can play an essential auxiliary role in diagnosing and treating neonatal PWMD.

Automated summary

Punctate White Matter Damage (PWMD) is a common neonatal brain disease that affects neurological function. The difficulty in diagnosis leads to missed treatment opportunities. This study proposes a new method using T1 MRI and DTI to analyze PWMD, which effectively predicts and analyzes the impact of lesions on fiber tracts through super-resolution and image registration.