A Basic Introduction to Diffusion Tensor Imaging Mathematics and Image Processing Steps

Rajagopalan, Venkateswaran; Jiang, Zhiguo; Yue, Guang H.; Stojanovic-Radic, Jelena; Pioro, Erik P.; Wylie, Glenn R.; Das, and Abhijit

doi:10.4172/2168-975x.1000229

Cited by 14 publications

(15 citation statements)

References 34 publications

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“…The pattern of change across tissue types observed in MD was very similar for AD and RD (Figures 3(b)-3(d)) which was confirmed by linear regression (Figure 4). The minimum values of MD and RD were observed in NAWM as expected [80,81]. MD values of ~0.79 in NAWM and ~1.14 in dWMH (Figure 3(b)) were consistent with the MD in NAWM of 0.78 and the MD of 1.00 in WMH of people with minor stroke [71].…”

Section: Discussionsupporting

confidence: 77%

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Haddad

Scott

Ozzoude

et al. 2022

International Journal of Biomedical Imaging

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Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

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Section: Discussionsupporting

confidence: 77%

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Haddad

Scott

Ozzoude

et al. 2022

International Journal of Biomedical Imaging

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“…Another popular scalar measure in DTI is mean diffusivity (MD), which represents the overall amount of diffusion. Many other scalar measures have been proposed based on the more complex behaviour of molecular diffusion and are explained in detail in surveys by Novikov et al [75], Rajagopalan et al [83] and Vilanova et al [99].…”

Section: Diffusion Tensormentioning

confidence: 99%

Uncertainty in the DTI Visualization Pipeline

Siddiqui

Höllt

Vilanova

2021

Mathematics and Visualization

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Diffusion-Weighted Magnetic Resonance Imaging (DWI) enables the in-vivo visualization of fibrous tissues such as white matter in the brain. Diffusion-Tensor Imaging (DTI) specifically models the DWI diffusion measurements as a second order-tensor. The processing pipeline to visualize this data, from image acquisition to the final rendering, is rather complex. It involves a considerable amount of measurements, parameters and model assumptions, all of which generate uncertainties in the final result which typically are not shown to the analyst in the visualization. In recent years, there has been a considerable amount of work on the visualization of uncertainty in DWI, and specifically DTI. In this chapter, we primarily focus on DTI given its simplicity and applicability, however, several aspects presented are valid for DWI as a whole. We explore the various sources of uncertainties involved, approaches for modeling those uncertainties, and, finally, we survey different strategies to visually represent them. We also look at several related methods of uncertainty visualization that have been applied outside DTI and discuss how these techniques can be adopted to the DTI domain. We conclude our discussion with an overview of potential research directions.

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“…Water diffuses freely and isotropically in most conditions. When impenetrable barriers are encountered, it becomes anisotropic with an ellipsoidal shape [ 23 ], with rapid, unrestricted diffusion by the boundaries and slower, constrained diffusion vertical to the boundaries, based on the cell size and density. Diffusion in the ductal/glandular system is rapid due to the breast’s high water content and low cell density.…”

Section: Introductionmentioning

confidence: 99%

“…Although DWI does not detect this ellipsoidal shape, DTI has overcome this restriction. Theoretically, in DTI, at least six independent diffusion gradients measurements accompanied by six non-collinear directions are needed to evaluate all diffusion tensor elements [ 23 ]. At present, the most commonly used invariant anisotropy test is fractional anisotropy (FA) [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

et al. 2022

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Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.

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A Basic Introduction to Diffusion Tensor Imaging Mathematics and Image Processing Steps

Cited by 14 publications

References 34 publications

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Uncertainty in the DTI Visualization Pipeline

Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

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