Enhancing Pediatric Peri-Lesional Edema Tractography with Constrained Spherical Deconvolution
Poster No:
1302
Submission Type:
Abstract Submission
Authors:
OSCAR ALATERAS1, J-Donald Tournier2, Joseph Yang1, Sila Genc1
Institutions:
1Royal Children's Hospital, Melbourne, Australia, 2King's College London, London, United Kingdom
First Author:
Co-Author(s):
Introduction:
Tractography is critical for presurgical planning, enabling visualisation of white matter tracts to balance lesion resection with functional preservation (Kamagata, 2023). Peri-lesional oedema presents a challenge for tractography due to the increased isotropic diffusion and obscure fibre orientations (Parker, 2020). Traditional single-fibre models often fail in these regions (Mormina, 2015), whereas multi-fibre models show improved performance but remain sensitive to isotropic diffusion and partial volume effects (Jeurissen, 2014; Gong, 2018). Free water correction (FWC) has shown promise but is underexplored in higher-order constrained spherical deconvolution (CSD) approaches. This study evaluates tractography performance in peri-lesional oedema, based on two classes of CSD models: single-tissue CSD (1-tissue CSD and 1-tissue CSD with FWC) and multi-tissue CSD (3-tissue MSMT-CSD and 2-tissue MSMT-CSD) using clinical diffusion MRI from a cohort of paediatric neurosurgical patients.
Methods:
MRI data from 14 brain tumour and epilepsy patients (ages 2.1–19 years) with peri-lesional oedema were acquired at 3T and retrospectively analysed. Multi-shell diffusion-weighted imaging data (bvalues = 0, 1000, 3000 s/mm2) were preprocessed with denoising, gibbs ringing, motion and eddy current correction and bias field correction. Whole-brain tractography was performed using the iFOD2 probabilistic algorithm for each model within a combined mask of the white matter and perilesional oedematous region. Single-tissue CSD was used to compute white matter (WM) fibre orientation distributions (FODs), using a WM response function derived iteratively (Jeurissen, 2014). Multi-tissue CSD extends this approach by accounting for 3 distinct tissue compartments: WM, grey matter (GM), and CSF (Jeurissen, 2014). To address challenges with tracking in peri-lesional oedema where WM was often misclassified as GM, we investigated the use of a simplified 2-tissue (WM + CSF only) CSD approach, which we hypothesized would improve sensitivity to fibre orientations in oedematous regions . Finally, by incorporating FWC with single-tissue CSD, we isolated the CSF signal through multi-tissue response estimation and multi-tissue forward modelling to remove CSF contributions to FOD estimation. Streamline count, average streamline length, and tract volume were computed within perilesional oedematous regions and compared across models using paired t-tests.
Results:
Oedema volumes ranged from 3,046 mm³ to 132,644 mm³ (mean = 49,499 mm³). There were no significant differences in tract metrics between the 1-tissue CSD with or without FWC (Fig. 2a), suggesting that FWC had little to no effect on the outcome, as the b-value was likely sufficiently large to suppress any free water signal in the shell. The 2-tissue MSMT-CSD model outperformed the 3-tissue CSD, showing significantly higher average streamline length (Mean Difference = 6.79 mm; Cohen's d = 1.32) and a significantly greater proportion of tracts in the peri-lesional oedematous region (Mean Difference = 0.45 mm3; Cohen's d = 2.22) (Fig. 2b). This is likely because some FODs initially mislabelled as grey matter were classified as white matter in the 2-tissue CSD model (Fig. 1).
Conclusions:
Improvements to tractography within perilesional oedema can be made by modifying standard MSMT-CSD approaches. The 2-tissue MSMT-CSD model outperformed the 3-tissue approach by generating longer streamline reconstructions and larger tract volumes in oedematous tissue. In contrast, adding FWC to single-tissue CSD did not enhance tractography performance, indicating limited additional benefit. These improvements to probabilistic tractography have implications for presurgical planning in cases involving peri-lesional oedema. Further research in larger patient cohorts with postoperative and functional brain mapping data is needed to validate functional relevance and clinical impact of these results.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Methods Development 2
Keywords:
Epilepsy
MRI
PEDIATRIC
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kamagata, K. (2023). Advancements in diffusion MRI tractography for neurosurgery. Investigative Radiology, 59(1), 13–25. https://doi.org/10.1097/rli.0000000000001015
Jeurissen, B. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 103, 411–426. https://doi.org/10.1016/j.neuroimage.2014.07.061
Mormina, E. (2015). MRI tractography of corticospinal tract and arcuate fasciculus in High-Grade Gliomas performed by constrained spherical deconvolution: Qualitative and Quantitative analysis. American Journal of Neuroradiology, 36(10), 1853–1858. https://doi.org/10.3174/ajnr.a4368
Parker, D. (2020). Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data. PLOS ONE, 15(5), e0233645. https://doi.org/10.1371/journal.pone.0233645