Session Item

The significance of diffusion magnetic resonance imaging (dMRI) in planning of intracranial pathology radiotherapy (RT) is growing. dMRI are generally acquired with echo planar imaging (EPI) that is prone to spatial distortions related to off-resonance effects. It is important to correct these distortions to provide a representation of the anatomy as accurate as possible. dMRI requires post-processing, in which the description of diffusion in a voxel is a non-trivial task.
The objective of this study is development of algorithms for pre- and post-processing, analysis and visualization of dMRI for planning of intracranial targets RT.

15 patients (12 with glioblastoma and 3 with Parkinson’s disease) were investigated. The protocol included: T1-, T2-weighted images, FLAIR, T1 with contrast enhancement. Diffusion tensor imaging (DTI) was performed on a 3T GE Signa HDxt scanner using an SE EPI pulse sequence: TR/TE, ms - 15350/88.9, 𝑏 = 0, 1000 mm2/s, matrix - 256×256; slice thickness/gap, mm — 2.5/2.5; field of view, cm/voxel , mm2 — 24/0.9375×0.9375; directions of diffusion gradient /number of excitations – 33/2. Proposed dMRI processing consists of: distortion correction by deformable image registration with anatomical series based on autocorrelation of local structure; mathematical correction of the echo intensity variation due to the spatial RF-field inhomogeneity based on the Markov Random Field modeling and optimization by iterated conditional modes (ICM); brain segmentation; multimodal tumor clustering using the k-means algorithm; probabilistic fiber tracking by the Hough transform.

The software allows to split the image into separate b-value and extrapolate to any user-specified value. "Brain masking" of dMRI was developed using a deformable surface model. Within the framework of proposed distortion correction algorithm maximum voxel dimension was 1,03 ± 0,12 mm. The tools make it possible to build quantitative maps of diffusion parameters, analyze brain regions, and perform multimodal target clustering (median dice score = 0.82 ± 0,16). Nonparametric distribution of the observed MR echo intensities and neighborhood tissue correlations is used to correct dMRI signal inhomogeneities and segment MR-volume on clusters: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Brain white matter tracts were visualized by probabilistic algorithm using the Hough transform. The tractogram was filtered to discard streamlines that terminate inside the WM or that enter the CSF. In the context of functional radiosurgery, it is feasible to reduce the volume of the internal capsule receiving 12 Gy by 40%.

The proposed algorithms for dMRI processing can improve the efficiency and accuracy of diagnosis and RT of brain pathology. Segmentation data make it possible to reduce the clinical target volume, minimizing the dose on the normal tissues. White matter fiber tractography by Hough transform allows to effectively optimize the dose to organs at risk.