scholarly journals Freewater EstimatoR using iNtErpolated iniTialization (FERNET): Toward Accurate Estimation of Free Water in Peritumoral Region Using Single-Shell Diffusion MRI Data

2019 ◽  
Author(s):  
Abdol Aziz Ould Ismail ◽  
Drew Parker ◽  
Moises Hernandez-Fernandez ◽  
Ronald Wolf ◽  
Steven Brem ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Free Water ◽  
Simulated Data ◽  
Compartment Model ◽  
Accurate Estimation ◽  
Imaging Data ◽  
Tissue Microstructure ◽  
Single Shell

ABSTRACTCharacterization of healthy versus pathological tissue is a key concern when modeling tissue microstructure in the peritumoral area, confounded by the presence of free water (e.g., edema). Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic, or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy and brain tumor datasets, demonstrating its applicability on clinically acquired data. Additionally, it has been applied to data from brain tumor patients to demonstrate the improvement in tractography in the peritumoral region.

scholarly journals [Re] Optimization of a free water elimination two-compartment model for diffusion tensor imaging

2017 ◽  
Author(s):  
Rafael Neto Henriques ◽  
Ariel Rokem ◽  
Eleftherios Garyfallidis ◽  
Samuel St-Jean ◽  
Eric Thomas Peterson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Free Water ◽  
Compartment Model ◽  
B Values ◽  
Brain White Matter ◽  
Diffusion Weighted ◽  
Two Compartment Model ◽  
Water Elimination

Typical diffusion-weighted imaging (DWI) is susceptible to partial volume effects: different types of tissue that reside in the same voxel are inextricably mixed. For instance, in regions near the cerebral ventricles or parenchyma, fractional anisotropy (FA) from diffusion tensor imaging (DTI) may be underestimated, due to partial volumes of cerebral spinal fluid (CSF). Free-water can be suppressed by adding parameters to diffusion MRI models. For example, the DTI model can be extended to separately take into account the contributions of tissue and CSF, by representing the tissue compartment with an anisotropic diffusion tensor and the CSF compartment as an isotropic free water diffusion coefficient. Recently, two procedures were proposed to fit this two-compartment model to diffusion-weighted data acquired for at least two different non-zero diffusion MRI b-values. In this work, the first open-source reference implementation of these procedures is provided. In addition to presenting some methodological improvements that increase model fitting robustness, the free water DTI procedures are re-evaluated using Monte-Carlo multicompartmental simulations. Analogous to previous studies, our results show that the free water elimination DTI model is able to remove confounding effects of fast diffusion for typical FA values of brain white matter. In addition, this study confirms that for a fixed scanning time the fwDTI fitting procedures have better performance when data is acquired for diffusion gradient direction evenly distributed along two b-values of 500 and 1500 s/mm2.

scholarly journals Disruption of neurite morphology parallels MS progression

2018 ◽  
Vol 5 (6) ◽  
pp. e502 ◽  
Author(s):  
Barbara Spanò ◽  
Giovanni Giulietti ◽  
Valerio Pisani ◽  
Manuela Morreale ◽  
Elisa Tuzzi ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Disease Severity ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Compartment Model ◽  
Dispersion Index ◽  
Brain Areas ◽  
Neurite Density ◽  
Relapsing Remitting ◽  
Diffusion Tensor Imaging Dti

ObjectivesTo apply advanced diffusion MRI methods to the study of normal-appearing brain tissue in MS and examine their correlation with measures of clinical disability.MethodsA multi-compartment model of diffusion MRI called neurite orientation dispersion and density imaging (NODDI) was used to study 20 patients with relapsing-remitting MS (RRMS), 15 with secondary progressive MS (SPMS), and 20 healthy controls. Maps of NODDI were analyzed voxel-wise to assess the presence of abnormalities within the normal-appearing brain tissue and the association with disease severity. Standard diffusion tensor imaging (DTI) parameters were also computed for comparing the 2 techniques.ResultsPatients with MS showed reduced neurite density index (NDI) and increased orientation dispersion index (ODI) compared with controls in several brain areas (p < 0.05), with patients with SPMS having more widespread abnormalities. DTI indices were also sensitive to some changes. In addition, patients with SPMS showed reduced ODI in the thalamus and caudate nucleus. These abnormalities were associated with scores of disease severity (p < 0.05). The association with the MS functional composite score was higher in patients with SPMS compared with patients with RRMS.ConclusionsNODDI and DTI findings are largely overlapping. Nevertheless, NODDI helps interpret previous findings of increased anisotropy in the thalamus of patients with MS and are consistent with the degeneration of selective axon populations.

Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI

2009 ◽  
pp. 90-107 ◽  
Author(s):  
Dimitrios C. Karampinos ◽  
Robert Dawe ◽  
Konstantinos Arfanakis ◽  
John G. Georgiadis
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Cerebral White Matter ◽  
Tissue Microstructure ◽  
High Order Schemes ◽  
Selection Strategies ◽  
Diffusion Characteristics ◽  
Encoding Strategies ◽  
White Matter Connectivity ◽  
Selection Of

Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water molecules in a biological tissue. Although originally proposed for the characterization of cerebral white matter connectivity and pathologies, its implementation has extended to many other areas of the human body. In a parallel development, a number of diffusion models have been proposed in order to extract the underlying tissue microstructural properties from the diffusion MRI signal. The present study reviews the basic considerations that have to be taken into account in the selection of the diffusion encoding parameters in diffusion MRI acquisition. Both diffusion tensor imaging (DTI) and high-order schemes are reviewed. The selection of these parameters relies strongly on requirements of the adopted diffusion model and the diffusion characteristics of the tissue under study. The authors review several successful parameter selection strategies for the imaging of the human brain, and conclude with the basics of parameter optimization on promising applications of the technique on other tissues, such as the spinal cord, the myocardium, and the skeletal muscles.

scholarly journals Probing tissue microstructure by diffusion skewness tensor imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lipeng Ning ◽  
Filip Szczepankiewicz ◽  
Markus Nilsson ◽  
Yogesh Rathi ◽  
Carl-Fredrik Westin
Keyword(s):  
Diffusion Processes ◽  
Diffusion Tensor ◽  
Fundamental Problem ◽  
Medical Science ◽  
Synthetic Data ◽  
Full Rank ◽  
Imaging Data ◽  
Tissue Microstructure ◽  
Diffusion Tensors

AbstractProbing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

scholarly journals Dimensionality Reduction of Diffusion MRI Measures for Improved Tractometry of the Human Brain

2019 ◽  
Author(s):  
Maxime Chamberland ◽  
Erika P. Raven ◽  
Sila Genc ◽  
Kate Duffy ◽  
Maxime Descoteaux ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Tissue Microstructure ◽  
Age Related ◽  
Diffusion Tensor Imaging Dti

AbstractVarious diffusion MRI measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different diffusion measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. In this work, we first demonstrate redundancies in the amount of information captured by 10 diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) measures. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in commonly-used DTI and HARDI measures profiled along 22 brain pathways extracted from typically developing children aged 8 - 18 years (n = 36). The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. Our results also suggest that HARDI measures are more sensitive at detecting age-related changes in tissue microstructure than DTI measures.

BRMP-04. AI-based biomarker of the peritumoral region using tissue microstructure

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi223-vi224
Author(s):  
Zahra Riahi Samani ◽  
Drew Parker ◽  
Ronald Wolf ◽  
Steven Brem ◽  
Ragini Verma
Keyword(s):  
Diffusion Tensor ◽  
Unmet Need ◽  
Clinical Importance ◽  
Free Water ◽  
Brain Structures ◽  
Connected Components ◽  
Surgical Biopsy ◽  
Significant Group ◽  
Tissue Microstructure ◽  
Clinically Significant

Abstract PURPOSE Glioblastomas, the most common malignant brain tumor [BS1], infiltrate into peritumoral brain structures, making clinical management challenging. An unmet need is to develop a biomarker that reliably characterize infiltration in the peritumoral region, where surgical biopsy or resection may be hazardous. Diffusion tensor imaging (DTI) with multicompartment modeling can characterize extracellular free water, providing unique information of the tissue microstructure that is able to capture this heterogeneity. We propose a novel biomarker based on peritumoral tissue microstructure, using deep-learning on DTI-based free water map. METHOD Peritumoral regions were automatically segmented for 136 patients with brain tumors (86 glioblastomas and 50 metastases, ages 23–87 years, 65 females). We trained a Convolutional Neural Network (CNN) on free-water maps using automatically defined patches in the peritumoral area from glioblastomas and metastases, labeled as low free-water and high free-water to extract a microstructural index for each voxel. To extract the biomarker, we grouped peritumoral voxels into connected components (CCs) where adjacent voxels have high (&gt;0.9) microstructural index values. Two independent test sets related to two clinically significant problems were evaluated: i) metastases vs. glioblastomas; ii) glioma patients categorized into short and long survival groups and the number of CCs were statistically compared. RESULT Two-sample t-tests showed significant group difference in the number of CCs between metastases and glioblastomas (p&lt; 0.05), and short and long-survivals (p&lt;0.05) with higher number of CCs in metastases and long-survivals, which suggests smaller number of voxels in CCs. CONCLUSION The proposed biomarker based on CCs of microstructural index captures the differences in infiltration of the peritumoral region, showing larger CCs in glioblastomas and short-survivals corresponding to higher infiltration. CLINICAL IMPORTANCE The proposed biomarker provides a novel insight into the peritumoral microenvironment and can be derived from clinically feasible DTI data, providing new possibilities for the diagnosis and treatment of glioblastoma.

scholarly journals Neurite orientation and dispersion density imaging (NODDI) detects cortical and corticospinal tract degeneration in ALS

2018 ◽  
Vol 90 (4) ◽  
pp. 404-411 ◽  
Author(s):  
Rebecca J Broad ◽  
Matt C Gabel ◽  
Nicholas G Dowell ◽  
David J Schwartzman ◽  
Anil K Seth ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Corticospinal Tract ◽  
Diffusion Tensor ◽  
Free Water ◽  
Cortical Atrophy ◽  
Precentral Gyrus ◽  
Water Fraction ◽  
Sporadic Als ◽  
The Right

BackgroundCorticospinal tract (CST) degeneration and cortical atrophy are consistent features of amyotrophic lateral sclerosis (ALS). We hypothesised that neurite orientation dispersion and density imaging (NODDI), a multicompartment model of diffusion MRI, would reveal microstructural changes associated with ALS within the CST and precentral gyrus (PCG) ‘in vivo’.Methods23 participants with sporadic ALS and 23 healthy controls underwent diffusion MRI. Neurite density index (NDI), orientation dispersion index (ODI) and free water fraction (isotropic compartment (ISO)) were derived. Whole brain voxel-wise analysis was performed to assess for group differences. Standard diffusion tensor imaging (DTI) parameters were computed for comparison. Subgroup analysis was performed to investigate for NODDI parameter differences relating to bulbar involvement. Correlation of NODDI parameters with clinical variables were also explored. The results were accepted as significant where p<0.05 after family-wise error correction at the cluster level, clusters formed with p<0.001.ResultsIn the ALS group NDI was reduced in the extensive regions of the CST, the corpus callosum and the right PCG. ODI was reduced in the right anterior internal capsule and the right PCG. Significant differences in NDI were detected between subgroups stratified according to the presence or absence of bulbar involvement. ODI and ISO correlated with disease duration.ConclusionsNODDI demonstrates that axonal loss within the CST is a core feature of degeneration in ALS. This is the main factor contributing to the altered diffusivity profile detected using DTI. NODDI also identified dendritic alterations within the PCG, suggesting microstructural cortical dendritic changes occur together with CST axonal damage.

scholarly journals Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1606
Author(s):  
Isaac Daimiel Naranjo ◽  
Alexis Reymbaut ◽  
Patrik Brynolfsson ◽  
Roberto Lo Gullo ◽  
Karin Bryskhe ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Diffusion Tensor ◽  
Free Water ◽  
Specific Cell ◽  
Tissue Microstructure ◽  
Mean Values ◽  
Apparent Diffusion Coefficients ◽  
Multidimensional Diffusion ◽  
Invasive Carcinomas

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

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

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.

scholarly journals Evaluation of six phase encoding based susceptibility distortion correction methods for diffusion MRI

2019 ◽  
Author(s):  
Xuan Gu ◽  
Anders Eklund
Keyword(s):  
Standard Deviation ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Simulated Data ◽  
Ground Truth ◽  
Distortion Correction ◽  
The Other ◽  
Phase Encoding ◽  
Corrected Data ◽  
The Difference

PurposeSusceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.MethodsIn this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP and PA data.ResultsWe found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.ConclusionWe suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. We also suggest that indirect metrics must be interpreted cautiously when evaluating methods for correcting susceptibility distortions in diffusion MRI data.

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