Evaluating the performance of 3-tissue constrained spherical deconvolution pipelines for within-tumor tractography

AbstractThe use of diffusion MRI (dMRI) for assisting in the planning of neurosurgery has become increasingly common practice, allowing to non-invasively map white matter pathways via tractography techniques. Limitations of earlier pipelines based on the diffusion tensor imaging (DTI) model have since been revealed and improvements were made possible by constrained spherical deconvolution (CSD) pipelines. CSD allows to resolve a full white matter (WM) fiber orientation distribution (FOD), which can describe so-called “crossing fibers”: complex local geometries of WM tracts, which DTI fails to model. This was found to have a profound impact on tractography results, with substantial implications for presurgical decision making and planning. More recently, CSD itself has been extended to allow for modeling of other tissue compartments in addition to the WM FOD, typically resulting in a 3-tissue CSD model. It seems likely this may improve the capability to resolve WM FODs in the presence of infiltrating tumor tissue. In this work, we evaluated the performance of 3-tissue CSD pipelines, with a focus on within-tumor tractography. We found that a technique named single-shell 3-tissue CSD (SS3T-CSD) successfully allowed tractography within infiltrating gliomas, without increasing existing single-shell dMRI acquisition requirements.

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Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

10.1101/2021.04.07.438845 ◽

2021 ◽

Author(s):

Philippe Karan ◽

Alexis Reymbaut ◽

Guillaume Gilbert ◽

Maxime Descoteaux

Keyword(s):

Diffusion Mri ◽

Diffusion Tensor ◽

Simulated Data ◽

Orientation Distribution ◽

Variance Decomposition ◽

Distribution Functions ◽

Constrained Spherical Deconvolution ◽

Spherical Deconvolution ◽

Fiber Orientation Distribution

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of HARDI data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). However, tensor-valued diffusion MRI data is not compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

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Test-retest reliability and long-term stability of 3-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data

10.1101/764506 ◽

2019 ◽

Cited By ~ 1

Author(s):

Benjamin T. Newman ◽

Thijs Dhollander ◽

Kristen A. Reynier ◽

Matthew B. Panzer ◽

T. Jason Druzgal

Keyword(s):

White Matter ◽

Grey Matter ◽

Tissue Microstructure ◽

Term Stability ◽

Long Term Stability ◽

Constrained Spherical Deconvolution ◽

Spherical Deconvolution ◽

Brain Microstructure ◽

Tissue Compartments

AbstractPurposeSeveral recent studies have utilized a 3-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, comprising white matter-, grey matter-, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics has not yet been evaluated.MethodsThis study examined estimates of whole brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort has different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to three months in the longest interval. Each cohort was also collected with different acquisition parameters.ResultsThe CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and grey matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC>0.90), however this declined in the 3 month interval cohort to both compartments having ICC>0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC>0.80 in each cohort.ConclusionThe 3-tissue CSD techniques provide reliable and stable estimates of tissue microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations utilizing 3-tissue CSD techniques to track changes in microstructure across a variety of brain pathologies.

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An atlas of white matter anatomy, its variability, and reproducibility based on Constrained Spherical Deconvolution of diffusion MRI

10.1101/2021.10.13.464139 ◽

2021 ◽

Author(s):

Ahmed M. Radwan ◽

Stefan Sunaert ◽

Kurt G. Schilling ◽

Maxime Descoteaux ◽

Bennett A. Landman ◽

...

Keyword(s):

White Matter ◽

Diffusion Mri ◽

Diffusion Tensor ◽

Single Subject ◽

Constrained Spherical Deconvolution ◽

Spherical Deconvolution ◽

Pyramidal Tracts ◽

Human Connectome Project ◽

Subject Specific ◽

Virtual Dissection

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals.

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Recent advances in diffusion neuroimaging: applications in the developing preterm brain

F1000Research ◽

10.12688/f1000research.15073.1 ◽

2018 ◽

Vol 7 ◽

pp. 1326 ◽

Cited By ~ 14

Author(s):

Diliana Pecheva ◽

Christopher Kelly ◽

Jessica Kimpton ◽

Alexandra Bonthrone ◽

Dafnis Batalle ◽

...

Keyword(s):

White Matter ◽

Neonatal Period ◽

Diffusion Tensor ◽

Neurodevelopmental Outcome ◽

Mean Diffusivity ◽

Spherical Deconvolution ◽

White Matter Microstructure ◽

Recent Developments ◽

White Matter Development ◽

Diffusion Tensor Imaging Dti

Measures obtained from diffusion-weighted imaging provide objective indices of white matter development and injury in the developing preterm brain. To date, diffusion tensor imaging (DTI) has been used widely, highlighting differences in fractional anisotropy (FA) and mean diffusivity (MD) between preterm infants at term and healthy term controls; altered white matter development associated with a number of perinatal risk factors; and correlations between FA values in the white matter in the neonatal period and subsequent neurodevelopmental outcome. Recent developments, including neurite orientation dispersion and density imaging (NODDI) and fixel-based analysis (FBA), enable white matter microstructure to be assessed in detail. Constrained spherical deconvolution (CSD) enables multiple fibre populations in an imaging voxel to be resolved and allows delineation of fibres that traverse regions of fibre-crossings, such as the arcuate fasciculus and cerebellar–cortical pathways. This review summarises DTI findings in the preterm brain and discusses initial findings in this population using CSD, NODDI, and FBA.

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White matter fiber tractography: why we need to move beyond DTI

Journal of Neurosurgery ◽

10.3171/2013.2.jns121294 ◽

2013 ◽

Vol 118 (6) ◽

pp. 1367-1377 ◽

Cited By ~ 228

Author(s):

Shawna Farquharson ◽

J.-Donald Tournier ◽

Fernando Calamante ◽

Gavin Fabinyi ◽

Michal Schneider-Kolsky ◽

...

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Diffusion Tensor ◽

Neurosurgical Procedures ◽

Fiber Tractography ◽

Constrained Spherical Deconvolution ◽

Spherical Deconvolution ◽

Corticospinal Tracts ◽

Diffusion Weighted ◽

Weighted Data

Object Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe. Methods Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution–based tractography methodologies were applied to both patients and controls. Results Diffusion tensor imaging–based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution–based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution–based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex. Conclusions This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging–based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.

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