Diffusion tensor imaging of the human kidney: Does image registration permit scanning without respiratory triggering?

PURPOSE To investigate if image registration of diffusion tensor imaging (DTI) allows omitting respiratory triggering for both transplanted and native kidneys MATERIALS AND METHODS: Nine kidney transplant recipients and eight healthy volunteers underwent renal DTI on a 3T scanner with and without respiratory triggering. DTI images were registered using a multimodal nonrigid registration algorithm. Apparent diffusion coefficient (ADC), the contribution of perfusion (FP ), and the fractional anisotropy (FA) were determined. Relative root mean square errors (RMSE) of the fitting and the standard deviations of the derived parameters within the regions of interest (SDROI ) were evaluated as quality criteria. RESULTS Registration significantly reduced RMSE in all DTI-derived parameters of triggered and nontriggered measurements in cortex and medulla of both transplanted and native kidneys (P < 0.05 for all). In addition, SDROI values were lower with registration for all 16 parameters in transplanted kidneys (14 of 16 SDROI values were significantly reduced, P < 0.04) and for 15 of 16 parameters in native kidneys (9 of 16 SDROI values were significantly reduced, P < 0.05). Comparing triggered versus nontriggered DTI in transplanted kidneys revealed no significant difference for RMSE (P > 0.14) and for SDROI (P > 0.13) of all parameters. In contrast, in native kidneys relative RMSE from triggered scans were significantly lower than those from nontriggered scans (P < 0.02), while SDROI was slightly higher in triggered compared to nontriggered measurements in 15 out of 16 comparisons (significantly for two, P < 0.05). CONCLUSION Registration improves the quality of DTI in native and transplanted kidneys. Diffusion parameters in renal allografts can be measured without respiratory triggering. In native kidneys, respiratory triggering appears advantageous. J. Magn. Reson. Imaging 2016.

In-vivo diffusion tensor imaging of rat spinal cord with a phased array coil at 7T

Magnetic resonance imaging (MRI) is a non-invasive technique that offers excellent soft tissue contrast for characterizing soft tissue pathologies. Diffusion tensor imaging (DTI) is an MRI technique that has shown to have the sensitivity to detect subtle pathology that is not evident on conventional MRI. ^ Rats are commonly used as animal models in characterizing the spinal cord pathologies including spinal cord injury (SCI), cancer, multiple sclerosis, etc. These pathologies could affect both thoracic and cervical regions and complete characterization of these pathologies using MRI requires DTI characterization in both the thoracic and cervical regions. Prior to the application of DTI for investigating the pathologic changes in the spinal cord, it is essential to establish DTI metrics in normal animals. ^ To date, in-vivo DTI studies of rat spinal cord have used implantable coils for high signal-to-noise ratio (SNR) and spin-echo pulse sequences for reduced geometric distortions. Implantable coils have several disadvantages including: (1) the invasive nature of implantation, (2) loss of SNR due to frequency shift with time in the longitudinal studies, and (3) difficulty in imaging the cervical region. While echo planar imaging (EPI) offers much shorter acquisition times compared to spin-echo imaging, EPI is very sensitive to static magnetic field inhomogeneities and the existing shimming techniques implemented on the MRI scanner do not perform well on spinal cord because of its geometry. ^ In this work, an integrated approach has been implemented for in-vivo DTI characterization of rat spinal cord in the thoracic and cervical regions. A three element phased array coil was developed for improved SNR and extended spatial coverage. A field-map shimming technique was developed for minimizing the geometric distortions in EPI images. Using these techniques, EPI based DWI images were acquired with optimized diffusion encoding scheme from 6 normal rats and the DTI-derived metrics were quantified. ^ The phantom studies indicated higher SNR and smaller bias in the estimated DTI metrics than the previous studies in the cervical region. In-vivo results indicated no statistical difference in the DTI characteristics of either gray matter or white matter between the thoracic and cervical regions. ^

Diffusion Tensor Imaging in Alzheimer's disease

Attentional control and Information processing speed are central concepts in cognitive psychology and neuropsychology. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. One of its component processes, Attentional set-shifting ability, is commonly assessed using the Trail Making Test (TMT). Performance in the TMT decreases with increasing age in adults, Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Besides, speed of information processing (SIP) seems to modulate attentional performance. While neural correlates of attentional control have been widely studied, there are few evidences about the neural substrates of SIP in these groups of patients. Different authors have suggested that it could be a property of cerebral white matter, thus, deterioration of the white matter tracts that connect brain regions related to set-shifting may underlie the age-related, MCI and AD decrease in performance. The aim of this study was to study the anatomical dissociation of attentional and speed mechanisms. Diffusion tensor imaging (DTI) provides a unique insight into the cellular integrity of the brain, offering an in vivo view into the microarchitecture of cerebral white matter. At the same time, the study of ageing, characterized by white matter decline, provides the opportunity to study the anatomical substrates speeded or slowed information processing. We hypothesized that FA values would be inversely correlated with time to completion on Parts A and B of the TMT, but not the derived scores B/A and B-A.

Three-dimensional brain fiber tracking modeling in diffusion tensor imaging

This dissertation establishes the foundation for a new 3-D visual interface integrating Magnetic Resonance Imaging (MRI) to Diffusion Tensor Imaging (DTI). The need for such an interface is critical for understanding brain dynamics, and for providing more accurate diagnosis of key brain dysfunctions in terms of neuronal connectivity. ^ This work involved two research fronts: (1) the development of new image processing and visualization techniques in order to accurately establish relational positioning of neuronal fiber tracts and key landmarks in 3-D brain atlases, and (2) the obligation to address the computational requirements such that the processing time is within the practical bounds of clinical settings. The system was evaluated using data from thirty patients and volunteers with the Brain Institute at Miami Children's Hospital. ^ Innovative visualization mechanisms allow for the first time white matter fiber tracts to be displayed alongside key anatomical structures within accurately registered 3-D semi-transparent images of the brain. ^ The segmentation algorithm is based on the calculation of mathematically-tuned thresholds and region-detection modules. The uniqueness of the algorithm is in its ability to perform fast and accurate segmentation of the ventricles. In contrast to the manual selection of the ventricles, which averaged over 12 minutes, the segmentation algorithm averaged less than 10 seconds in its execution. ^ The registration algorithm established searches and compares MR with DT images of the same subject, where derived correlation measures quantify the resulting accuracy. Overall, the images were 27% more correlated after registration, while an average of 1.5 seconds is all it took to execute the processes of registration, interpolation, and re-slicing of the images all at the same time and in all the given dimensions. ^ This interface was fully embedded into a fiber-tracking software system in order to establish an optimal research environment. This highly integrated 3-D visualization system reached a practical level that makes it ready for clinical deployment. ^

Longitudinal diffusion tensor imaging in dementia with Lewy bodies and Alzheimer's disease

Copyright © 2016 Elsevier Ltd. All rights reserved. Acknowledgements The study was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

The present study characterized two fiber pathways important for language, the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) and the frontal aslant tract (FAT), and related these tracts to speech, language, and literacy skill in children five to eight years old. We used Diffusion Tensor Imaging (DTI) to characterize the fiber pathways and administered several language assessments. The FAT was identified for the first time in children. Results showed no age-related change in integrity of the FAT, but did show age-related change in the left (but not right) SLF/AF. Moreover, only the integrity of the right FAT was related to phonology but not audiovisual speech perception, articulation, language, or literacy. Both the left and right SLF/AF related to language measures, specifically receptive and expressive language, and language content. These findings are important for understanding the neurobiology of language in the developing brain, and can be incorporated within contemporary dorsal-ventral-motor models for language.

Diffusion tensor of water in model articular cartilage

We used Monte Carlo simulations of Brownian dynamics of water to study anisotropic water diffusion in an idealised model of articular cartilage. The main aim was to use the simulations as a tool for translation of the fractional anisotropy of the water diffusion tensor in cartilage into quantitative characteristics of its collagen fibre network. The key finding was a linear empirical relationship between the collagen volume fraction and the fractional anisotropy of the diffusion tensor. Fractional anisotropy of the diffusion tensor is potentially a robust indicator of the microstructure of the tissue because, in the first approximation, it is invariant to the inclusion of proteoglycans or chemical exchange between free and collagen-bound water in the model. We discuss potential applications of Monte Carlo diffusion-tensor simulations for quantitative biophysical interpretation of MRI diffusion-tensor images of cartilage. Extension of the model to include collagen fibre disorder is also discussed.

Digital processing of diffusion-tensor images of avascular tissues

Diffusion is the process that leads to the mixing of substances as a result of spontaneous and random thermal motion of individual atoms and molecules. It was first detected by the English botanist Robert Brown in 1827, and the phenomenon became known as ‘Brownian motion’. More specifically, the motion observed by Brown was translational diffusion – thermal motion resulting in random variations of the position of a molecule. This type of motion was given a correct theoretical interpretation in 1905 by Albert Einstein, who derived the relationship between temperature, the viscosity of the medium, the size of the diffusing molecule, and its diffusion coefficient. It is translational diffusion that is indirectly observed in MR diffusion-tensor imaging (DTI). The relationship obtained by Einstein provides the physical basis for using translational diffusion to probe the microscopic environment surrounding the molecule.

Langevin dynamics modeling of the water diffusion tensor in partially aligned collagen networks

In this work, a Langevin dynamics model of the diffusion of water in articular cartilage was developed. Numerical simulations of the translational dynamics of water molecules and their interaction with collagen fibers were used to study the quantitative relationship between the organization of the collagen fiber network and the diffusion tensor of water in model cartilage. Langevin dynamics was used to simulate water diffusion in both ordered and partially disordered cartilage models. In addition, an analytical approach was developed to estimate the diffusion tensor for a network comprising a given distribution of fiber orientations. The key findings are that (1) an approximately linear relationship was observed between collagen volume fraction and the fractional anisotropy of the diffusion tensor in fiber networks of a given degree of alignment, (2) for any given fiber volume fraction, fractional anisotropy follows a fiber alignment dependency similar to the square of the second Legendre polynomial of cos(θ), with the minimum anisotropy occurring at approximately the magic angle (θMA), and (3) a decrease in the principal eigenvalue and an increase in the transverse eigenvalues is observed as the fiber orientation angle θ progresses from 0◦ to 90◦. The corresponding diffusion ellipsoids are prolate for θ < θMA, spherical for θ ≈ θMA, and oblate for θ > θMA. Expansion of the model to include discrimination between the combined effects of alignment disorder and collagen fiber volume fraction on the diffusion tensor is discussed.

Diffusion tensor of water in partially aligned fibre networks

Monte Carlo simulations were used to investigate the relationship between the morphological characteristics and the diffusion tensor (DT) of partially aligned networks of cylindrical fibres. The orientation distributions of the fibres in each network were approximately uniform within a cone of a given semi-angle (θ0). This semi-angle was used to control the degree of alignment of the fibres. The networks studied ranged from perfectly aligned (θ0 = 0) to completely disordered (θ0 = 90°). Our results are qualitatively consistent with previous numerical models in the overall behaviour of the DT. However, we report a non-linear relationship between the fractional anisotropy (FA) of the DT and collagen volume fraction, which is different to the findings from previous work. We discuss our results in the context of diffusion tensor imaging of articular cartilage. We also demonstrate how appropriate diffusion models have the potential to enable quantitative interpretation of the experimentally measured diffusion-tensor FA in terms of collagen fibre alignment distributions.

Characterisation of the micro-architecture of direct writing melt electrospun tissue engineering scaffolds using diffusion tensor and computed tomography microimaging

This article describes the first steps toward comprehensive characterization of molecular transport within scaffolds for tissue engineering. The scaffolds were fabricated using a novel melt electrospinning technique capable of constructing 3D lattices of layered polymer fibers with well - defined internal microarchitectures. The general morphology and structure order was then determined using T 2 - weighted magnetic resonance imaging and X - ray microcomputed tomography. Diffusion tensor microimaging was used to measure the time - dependent diffusivity and diffusion anisotropy within the scaffolds. The measured diffusion tensors were anisotropic and consistent with the cross - hatched geometry of the scaffolds: diffusion was least restricted in the direction perpendicular to the fiber layers. The results demonstrate that the cross - hatched scaffold structure preferentially promotes molecular transport vertically through the layers ( z - axis), with more restricted diffusion in the directions of the fiber layers ( x – y plane). Diffusivity in the x – y plane was observed to be invariant to the fiber thickness. The characteristic pore size of the fiber scaffolds can be probed by sampling the diffusion tensor at multiple diffusion times. Prospective application of diffusion tensor imaging for the real - time monitoring of tissue maturation and nutrient transport pathways within tissue engineering scaffolds is discussed.

Fluid registration of diffusion tensor images using information theory

We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or $J$-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the $J$-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.

Active fibers: Matching deformable tract templates to diffusion tensor images

Reliable quantitative analysis of white matter connectivity in the brain is an open problem in neuroimaging, with common solutions requiring tools for fiber tracking, tractography segmentation and estimation of intersubject correspondence. This paper proposes a novel, template matching approach to the problem. In the proposed method, a deformable fiber-bundle model is aligned directly with the subject tensor field, skipping the fiber tracking step. Furthermore, the use of a common template eliminates the need for tractography segmentation and defines intersubject shape correspondence. The method is validated using phantom DTI data and applications are presented, including automatic fiber-bundle reconstruction and tract-based morphometry. © 2009 Elsevier Inc. All rights reserved.

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.

Le metodiche di Diffusion Weighted Imaging per la caratterizzazione della malattia policistica renale

La tecnica di Diffusion Weighted Imaging (DWI) si basa sullo studio del moto diffusivo delle molecole d’acqua nei tessuti biologici ed è in grado di fornire informazioni sulla struttura dei tessuti e sulla presenza di eventuali alterazioni patologiche. Il più recente sviluppo della DWI è rappresentato dal Diffusion Tensor Imaging (DTI), tecnica che permette di determinare non solo l’entità, ma anche le direzioni principali della diffusione. Negli ultimi anni, grazie ai progressi nella tecnica di risonanza magnetica, l’imaging di diffusione è stato anche applicato ad altri distretti anatomici tra cui quello renale, per sfruttarne le potenzialità diagnostiche. Tuttavia, pochi sono ancora gli studi relativi all’applicazione delle metodiche di diffusione per la valutazione della malattia policistica renale autosomica dominante (ADPKD). ADPKD è una delle malattie ereditarie più comuni ed è la principale causa genetica di insufficienza renale dell’adulto. La caratteristica principale consiste nella formazione di cisti in entrambi i reni, che progressivamente aumentano in numero e dimensioni fino a causare la perdita della funzionalità renale nella metà circa dei pazienti. Ad oggi non sono disponibili terapie capaci di arrestare o rallentare l’evoluzione di ADPKD; è possibile controllare le complicanze per evitare che costituiscano componenti peggiorative. Il lavoro di tesi nasce dalla volontà di indagare se la tecnica dell’imaging di diffusione possa essere utile per fornire informazioni sullo stato della malattia e sul suo grado di avanzamento. L’analisi di studio è concentrata sul calcolo del coefficiente di diffusione apparente (ADC), derivato dalle immagini DWI e valutato nella regione della midollare. L’obiettivo di questo lavoro è verificare se tale valore di ADC sia in grado di caratterizzare la malattia policistica renale e possa essere utilizzato in ambito clinico-diagnostico come indicatore prognostico nella progressione di questa patologia.