Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning

Gong, Shun; Zhang, Fan; Norton, Isaiah; Essayed, Walid Ibn; Unadkat, Prashin; Rigolo, Laura; Pasternak, Ofer; Rathi, Yogesh; Hou, Lingling; Golby, Alexandra J.; O’Donnell, Lauren J.

doi:10.1371/journal.pone.0197056

Cited by 42 publications

(59 citation statements)

References 71 publications

(114 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods, similarly to q-ball tractography, were shown to improve the depiction of complex fiber orientations (Tournier et al, 2007;Malcolm et al, 2010;Fillard et al, 2011;Dell'Acqua and Tournier, 2019). Besides, recent data illustrates how multitensor models can increase tracking sensitivity through tissue affected by edema or tumor infiltration by including an additional isotropic tensor reflecting the free water compartment (Gong et al, 2018). Similarly, other multi-compartimental models like NODDI may be employed to improve the tracking of complex fiber configurations (Reddy and Rathi, 2016).…”

Section: Discussionmentioning

confidence: 98%

“…Future tractography studies may also employ alternative tracking algorithms that have already been proven feasible on brain tumor patients, such as spherical deconvolution tractography (Mormina et al, 2016;Becker et al, 2019) and multi-fiber tractography (Chen et al, 2015;Gong et al, 2018). These methods, similarly to q-ball tractography, were shown to improve the depiction of complex fiber orientations (Tournier et al, 2007;Malcolm et al, 2010;Fillard et al, 2011;Dell'Acqua and Tournier, 2019).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients

et al. 2020

View full text Add to dashboard Cite

Sanvito et al. fMRI-Targeted Language HARDI Tractography Results: MNI Atlases showed that fMRI-T is a subset of Anatomical-T, and that different task-specific fMRI-T involve both shared subsets and task-specific subsetse.g., verbal fluency fMRI-T strongly involves dorsal frontal tracts, consistently with the phonogical-articulatory features of this task. A quantitative analysis in patients revealed that Anatomical-T removed portions of AF-SLF and IFOF were significantly greater than verbal fluency fMRI-T ones, suggesting that fMRI-T is a more specific approach. In addition, qualitative analyses showed that fMRI-T AF-SLF and IFOF predict the exact functional limits of resection with increased specificity when compared to Anatomical-T counterparts, especially the superior frontal portion of IFOF, in a subcohort of patients. Conclusion: These results suggest that performing fMRI-T in addition to the 'classic' Anatomical-T may be useful in a preoperative setting to identify the 'high-risk subsets' that should be spared during the surgical procedure.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The Advanced Normalization Tools (ANTS) package (https://github.com/ANTsX/ANTs) (Avants, Tustison, & Song, 2009) was used to perform the registration, following the default steps in the software including a rigid, an affine, then a deformable transformation to reach a good intra-subject alignment. Here, we chose an FA-based registration because the FA image is sensitive to white matter fiber tracts and can provide a good correspondence between the registered fiber tracts (Goodlett, Davis, Jean, Gilmore, & Gerig, 2006), and it has been applied in many studies for registering between dMRI datasets (Besseling et al, 2012;Papinutto et al, 2013;Vollmar et al, 2010). In details, this registration was performed by aligning the FA image from the second scan to that from the first scan.…”

Section: Datasets Data Processing and Tractographymentioning

confidence: 99%

“…White matter parcellation can enable the study of fiber parcels from the entire white matter to identify between‐population differences (e.g., between patients harboring disease and healthy subjects) using machine learning or statistical analyses (Ingalhalikar et al, ; Sporns, Tononi, & Kötter, ; Zalesky, Cocchi, Fornito, Murray, & Bullmore, ; Zhang, Savadjiev, et al, ; Zhang, Wu, Ning, ). White matter parcellation is also important for identifying anatomical fiber tracts for clinical visualization (Golby et al, ; Gong et al, ; Nimsky, Ganslandt, Dorit, Gregory Sorensen, & Fahlbusch, ; O'Donnell et al, ) or hypothesis‐driven research (Alexander et al, ; Shany et al, ; Wu et al, , ; Yeo, Jang, & Son, ). Automated and robust white matter parcellation can enable the analysis of new, large dMRI datasets that are being acquired to study complex neural systems across the lifespan and across brain disorders (Alexander et al, ; Casey et al, ; Thompson et al, ).…”

Section: Introductionmentioning

confidence: 99%

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Zhang

Norton

et al. 2019

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical‐parcellation‐based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test–retest reproducibility of automated white matter parcellations using cortical‐parcellation‐based strategies, there are no existing studies of test–retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test–retest reproducibility. The assessment is performed on three test–retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5–82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical‐parcellation‐based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test–retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical‐parcellation‐based method.

show abstract

“…This highlights how care should be taken when applying such thresholds in a clinical population where the presence of oedema can have a significant influence. A number of approaches have attempted to address this issue through modelling the free water component of the diffusion signal [56,57] or through applying a generalised q-sampling acquisition scheme which allows for better modelling of both magnitude and direction of crossing fibres within a voxel [27]. These techniques identify regions of peritumoral oedema and improve the visualisation of tracts often though at the sacrifice of increased acquisition time.…”

Section: Plos Onementioning

confidence: 99%

Implementation of clinical tractography for pre-surgical planning of space occupying lesions: An investigation of common acquisition and post-processing methods compared to dissection studies

Ashmore¹,

Pemberton²,

Crum³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Background and purpose There is limited standardization of acquisition and processing methods in diffusion tractography for pre-surgical planning, leading to a range of approaches. In this study, a number of representative acquisition variants and post processing methods are considered, to assess their importance when implementing a clinical tractography program. Methods Diffusion MRI was undertaken in ten healthy volunteers, using protocols typical of clinical and research scanning: a 32-direction diffusion acquisition with and without peripheral gating, and a non-gated 64 diffusion direction acquisition. All datasets were post-processed using diffusion tensor reconstruction with streamline tractography, and with constrained spherical deconvolution (CSD) with both streamline and probabilistic tractography, to delineate the cortico-spinal tract (CST) and optic radiation (OR). The accuracy of tractography results was assessed against a histological atlas using a novel probabilistic Dice overlap technique, together with direct comparison to tract volumes and distance of Meyer's loop to temporal pole (ML-TP) from dissections studies. Three clinical case studies of patients with space occupying lesions were also investigated. Results Tracts produced by CSD with probabilistic tractography provided the greatest overlap with the histological atlas (overlap scores of 44% and 52% for the CST and OR, respectively) and best matched tract volume and ML-TP distance from dissection studies. The acquisition protocols investigated had limited impact on the accuracy of the tractography. In all patients,

show abstract

Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning

Cited by 42 publications

References 71 publications

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

Implementation of clinical tractography for pre-surgical planning of space occupying lesions: An investigation of common acquisition and post-processing methods compared to dissection studies

Contact Info

Product

Resources

About