scholarly journals White Matter Tissue Quantification at Low b-Values Within Constrained Spherical Deconvolution Framework

2018 ◽  
Vol 9 ◽  
Author(s):  
Alessandro Calamuneri ◽  
Alessandro Arrigo ◽  
Enricomaria Mormina ◽  
Demetrio Milardi ◽  
Alberto Cacciola ◽  
...  
Keyword(s):  
White Matter ◽  
B Values ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Tissue Quantification

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

2019 ◽  
Author(s):  
Hannelore Aerts ◽  
Thijs Dhollander ◽  
Daniele Marinazzo
Keyword(s):  
White Matter ◽  
Tumor Tissue ◽  
Diffusion Tensor ◽  
Orientation Distribution ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution ◽  
Tissue Compartments ◽  
Diffusion Tensor Imaging Dti ◽  
Single Shell

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.

scholarly journals Isotropic non-white matter partial volume effects in constrained spherical deconvolution

2014 ◽  
Vol 8 ◽  
Author(s):  
Timo Roine ◽  
Ben Jeurissen ◽  
Daniele Perrone ◽  
Jan Aelterman ◽  
Alexander Leemans ◽  
...  
Keyword(s):  
White Matter ◽  
Partial Volume ◽  
Constrained Spherical Deconvolution ◽  
Partial Volume Effects ◽  
Spherical Deconvolution ◽  
Volume Effects

White Matter and Visuospatial Processing in Autism: A Constrained Spherical Deconvolution Tractography Study

2013 ◽  
Vol 6 (5) ◽  
pp. 307-319 ◽  
Author(s):  
Jane McGrath ◽  
Katherine Johnson ◽  
Erik O'Hanlon ◽  
Hugh Garavan ◽  
Louise Gallagher ◽  
...  
Keyword(s):  
White Matter ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Visuospatial Processing

scholarly journals Magnetic resonance tractography based on the constrained spherical deconvolution in patients with gliomas of the optic pathway

2021 ◽  
Vol 49 (1) ◽  
pp. 11-20
Author(s):  
A. A. Baev ◽  
E. L. Pogosbekian ◽  
N. E. Zakharova ◽  
D. I. Pitskhelauri ◽  
A. I. Batalov ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Angular Resolution ◽  
Complex Structure ◽  
Brain Mri ◽  
High Angular Resolution ◽  
Optic Radiation ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Perifocal Edema

Background: The use of magnetic resonance (MR) tractography in neurosurgery is becoming an increasingly common practice for noninvasive imaging of white matter pathways. The most common method of tract reconstruction is the deterministic algorithm of diffusion tensor magnetic resonance imaging (MRI). However, this method of reconstructing pathways has a  number of significant limitations. The most important of them are the lack of the possibility of visualizing the intersecting fibers, the complexity of building tracts in the area of perifocal edema and in the immediate vicinity of the tumor borders. The method of MR tractography, based on obtaining a  diffusion image with a  high angular resolution (High Angular Resolution Diffusion Imaging, HARDI), using the constrained spherical deconvolution (CSD) algorithm for post-processing of data, makes it possible to avoid these disadvantages. Relatively recently, a new algorithm, Single-Shell 3-Tissue CSD (SS3TCSD), has been proposed for processing HARDI data, which has the potential to improve the reconstructing of pathways in the area of perifocal edema or edema-infiltration.Aim: To evaluate the potential of the new SS3TCSD algorithm compared to ST-CSD (Single-Tissue CSD) in the imaging of the optic radiation and visual tracts in patients with gliomas.Materials and methods: Diffusion and routine brain MRI was performed in 10 patients with newly diagnosed cerebral gliomas, followed by reconstruction of the optic radiation and visual tracts. We compared new algorithms for postprocessing MR tractography (ST-CSD and SS3TCSD) in imaging of the optic tract and visual radiation in patients with brain gliomas affecting various parts of the visual system.Results: The SS3T-CSD method showed a  lower mean percentage of false positive tracts compared to the ST-CSD method: 19.75% for the SS3T-CSD method and 80.32% for the ST-CSD method in cases of proximity of the tumor to the tracts, 5.27% for the SS3T-CSD method and 25.27% for the STCSD method in cases of reconstructing tracts in healthy white matter.Conclusion: The SS3T-CSD method has a number of advantages over ST-CSD and allows for successful imaging of the optic pathways that have a complex structure and repeatedly change direction along their course.

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

2019 ◽  
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.

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

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.

scholarly journals Insular gliomas and tractographic visualization of the connectome

2022 ◽  
Vol 6 (1) ◽  
pp. V4
Keyword(s):  
Artificial Intelligence ◽  
White Matter ◽  
Surgical Resection ◽  
Brain Mapping ◽  
Image Guidance ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Brain Connectome ◽  
Mapping Software ◽  
Cortical Regions

In this video, the authors present a connectome-guided surgical resection of an insular glioma in a 39-year-old woman. Preoperative study with constrained spherical deconvolution (CSD)–based tractography revealed the surrounding brain connectome architecture around the tumor relevant for safe surgical resection. Connectomic information provided detailed maps of the surrounding language and salience networks, including eloquent white matter fibers and cortical regions, which were visualized intraoperatively with image guidance and artificial intelligence (AI)–based brain mapping software. Microsurgical dissection is presented with detailed discussion of the safe boundaries and angles of resection when entering the insular operculum defined by connectomic information. The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21194

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

2013 ◽  
Vol 118 (6) ◽  
pp. 1367-1377 ◽  
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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