High Angular Resolution Diffusion Imaging Data Research Articles

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging

PurposeTo investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). MethodsWe included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. ResultsIn the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. ConclusionsWhite matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

An interactive triangle-based ODF glyph rendering for high angular resolution diffusion imaging

Diffusion magnetic resonance imaging quantifies the diffusion of water molecules. It is unique in showing white matter fiber structures and connectivity in-vivo, relying on the fact that the water molecules’ displacements are more significant along with the fiber orientation than in other directions due to biological barriers. High angular resolution diffusion imaging (HARDI) acquisition enables the differentiation of multiple fiber signals passing through a voxel. Advanced methods for processing diffusion data were developed to synthesize the HARDI data of each voxel into an orientation distribution function (ODF) that relates each direction sample to its probability of water diffusion. Visualizing ODFs through glyphs helps gain insight into the local structure of the brain white matter. Because of the high dimensionality of the data, interactive glyph exploration remains a challenge for a volume containing millions of voxels. This work presents a 3D triangle-based multi-resolution rendering scheme for the ODF glyph. We explored data properties and GPU features to make the triangle-based rendering interactive. Linear performance relative to some fixed-resolution rendered glyphs was assessed using NVIDIA® Nsight Visual StudioTM. A control volume, available in the Retest dataset of the WU-Minn Human Connectome Project, was used to assess the effectiveness of interactive 3D visualization over a multiplanar and curvilinear reformatted volume of ODF glyphs in voxels with multiple fibers, namely the triple crossing at the centrum semiovale, the fanning at the corona radiata, and the crossing of the cingulum and corpus callosum fiber bundles.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

CSR-PERT: Joint framework for MRI and HARDI data reconstruction using perturbed radial trajectory estimated from compressively sensed measurements

Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. These advantages of radial sampling can be exploited in acquisition of multidimensional signals such as High Angular Resolution Diffusion Imaging (HARDI), with tremendous scope of acceleration. Despite such benefits, gradient delays lead to samples being acquired from unknown miscentered radial trajectories, severely degrading the image reconstruction quality. In the present work, we propose Csr-Pert that is a joint framework, wherein these perturbed radial trajectories are estimated and utilized for image reconstruction from the compressively sensed measurements of (i) MRI data and (ii) HARDI data. The proposed Csr-Pert method is tested on one real MRI dataset with trajectory deviations and is observed to perform better than the existing state-of-the-art method at high acceleration factors up to 8. To the best of our knowledge, this is the first work to address the problem of estimating perturbed trajectories using the compressively sensed MRI and HARDI data. The method is also tested for varying combinations of trajectory deviations and sampling proportions. It is observed to yield very good quality HARDI reconstruction for a wide variety of scenarios. We have also demonstrated the robustness of the proposed method on real datasets in clinical settings assuming perturbed as well as noisy trajectories.

TL-HARDI: Transform learning based accelerated reconstruction of HARDI data

Diffusion magnetic resonance imaging (dMRI) is being extensively used to study the neural architecture of the brain. High angular resolution diffusion imaging (HARDI), a variant of diffusion MRI, measures the diffusion of water molecules along the angular gradient directions in the q-space. It provides better estimates of fiber orientations compared to the traditionally used diffusion tensor imaging (DTI). However, HARDI requires acquisition of relatively large number of samples leading to longer scanning times. Several approaches based on compressive sensing (CS) have been proposed to accelerate HARDI acquisition, leveraging on the sparse representation of the HARDI signal in a pre-specified sparsifying basis. In this paper, we propose to carry out reconstruction of compressively sensed HARDI data using an adaptively learned transform. The transform is learned (i) from the compressive measurements on-the-fly, thereby, eliminating the overhead of choosing fixed sparsifying transforms, and (ii) on overlapping patches of the data, thereby, capturing local image structure effectively. Experiments are conducted on multiple real HARDI data for varying sampling ratios and sampling schemes. The performance of the proposed “TL-HARDI” method is compared with the state-of-the-art methods on various known image quality metrics as well as on dMRI feature maps derived from the reconstructed images. The proposed method is observed to yield better reconstruction than the existing state-of-the-art methods in both quantitative and qualitative comparisons.

The unique role of the frontal aslant tract in speech and language processing

The frontal aslant tract (FAT) is a recently described intralobar tract that connects the superior and inferior frontal gyri. The FAT has been implicated in various speech and language processes and disorders, including motor speech impairments, stuttering disorders, opercular syndrome, and verbal fluency, but the specific function(s) of the FAT have yet to be elucidated. In the current study, we aimed to address this knowledge gap by investigating the underlying role that the FAT plays in motor aspects of speech and language abilities in post-stroke aphasia. Our goals were three-fold: 1) To identify which specific motor speech or language abilities are impacted by FAT damage by utilizing a powerful imaging analysis method, High Angular Resolution Diffusion Imaging (HARDI) tractography; 2) To determine whether damage to the FAT is associated with functional deficits on a range of motor speech and language tasks even when accounting for cortical damage to adjacent cortical regions; and 3) To explore whether subsections of the FAT (lateral and medial segments) play distinct roles in motor speech performance. We hypothesized that damage to the FAT would be most strongly associated with motor speech performance in comparison to language tasks. We analyzed HARDI data from thirty-three people with aphasia (PWA) with a history of chronic left hemisphere stroke. FAT metrics were related to scores on several speech and language tests: the Motor Speech Evaluation (MSE), the Western Aphasia Battery (WAB) aphasia quotient and subtests, and the Boston Naming Test (BNT). Our results indicated that the integrity of the FAT was strongly associated with the MSE as predicted, and weakly negatively associated with WAB subtest scores including Naming, Comprehension, and Repetition, likely reflecting the fact that performance on these WAB subtests is associated with damage to posterior areas of the brain that are unlikely to be damaged with a frontal lesion. We also performed hierarchical stepwise regressions to predict language function based on FAT properties and lesion load to surrounding cortical areas. After accounting for the contributions of the inferior frontal gyrus, the ventral precentral gyrus, and the superior precentral gyrus of the insula, the FAT still remained a significant predictor of MSE apraxia scores. Our results further showed that the medial and lateral subsections of the FAT did not appear to play distinct roles but rather may indicate normal anatomical variations of the FAT. Overall, current results indicate that the FAT plays a specific and unique role in motor speech. These results further our understanding of the role that white matter tracts play in speech and language.

Nonparametric model for a tensor field based on high angular resolution diffusion imaging (HARDI)

We develop a nonparametric technique for the estimation of curve trajectories using HARDI data. For various regions of the brain, we consider the imaging signal process and apply multivariate kernel smoothing techniques to a general function f describing the signal process obtained from the MRI image. At each location in the brain we search for the direction of maximum diffusion on the unit sphere, and then trace the integral curve driven by the vector field to obtain the estimates of curve trajectories. We establish the convergence of the properly normalized curve estimators to a Gaussian process. This method is computationally efficient as with each step of the curve tracing we construct a pointwise confidence ellipsoid region as opposed to exhaustive iterative sampling methods. These curve trajectories are models of axonal fibers whose location and geometry are important in neuroscience.

Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.

Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing.

Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing. Compressed sensing was applied in either q-space or simultaneously in k-space and q-space, by undersampling the k-space in the phase-encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole-brain tractography. The apparent fiber density and complexity metric showed relatively minor differences when only q-space undersampling was used, but deteriorate when k-space undersampling was applied. Likewise, the tract density weighted image showed good results when only q-space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k-space undersampling were used. It was found that acquiring 15 to 20 diffusion directions with a full k-space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss.

Projections of Brodmann Area 6 to the Pyramidal Tract in Humans: Quantifications Using High Angular Resolution Data.

Primate studies indicate that the pyramidal tract (PyT) could originate from Brodmann area (BA) 6. However, in humans, the accurate origin of PyT from BA 6 is still uncertain owing to difficulties in visualizing anatomical features such as the fanning shape at the corona radiata and multiple crossings at the semioval centrum. High angular-resolution diffusion imaging (HARDI) could reliably replicate these anatomical features. We explored the origin of the human PyT from BA 6 using HARDI. With HARDI data of 30 adults from the Massachusetts General Hospital-Human Connectome Project (MGH-HCP) database and the HCP 1021 template (average of 1021 HCP diffusion data), we visualized the PyT at the 30-averaged group level and the 1021 large-sample level and validated the observations in each of the individuals. Endpoints of the fibers within each subregion were quantified. PyT fibers originating from the BA 6 were consistently visualized in all images. Specifically, the bilateral supplementary motor area (SMA) and dorsal premotor area (dPMA) were consistently found to contribute to the PyT. PyT fibers from BA 6 and those from BA 4 exhibited a twisting topology. The PyT contains fibers originating from the SMA and dPMA in BA 6. Infarction of these regions or aging would result in incomplete provision of information to the PyT and concomitant decreases in motor planning and coordination abilities.

Optimization of data acquisition and analysis for fiber ball imaging

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm2, for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm2 with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography.

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language.

Age-related changes in fiber tracts in healthy adult brains: A generalized q-sampling and connectometry study.

Generalized q-sampling (GQI) and connectometry analysis provide new indices, i.e., quantitative anisotropy (QA) and spin distribution function (SDF) in comparison with diffusion tensor imaging (DTI). They may provide more age-related changes in white matter (WM) in aging. To investigate the feasibility of using GQI and connectometry analysis to determine WM properties changes in aging. Cross-cohort research. Fifty normal adults (27 females) aged 21-71 years. T1 -weighted images (T1 WI) and high angular resolution diffusion imaging (HARDI) images were acquired at 1.5T. HARDI data were analyzed using DTI and GQI to obtain fractional anisotropy (FA), QA, fiber numbers, and fiber lengths for tract analysis and using q-space diffeomorphic reconstruction (QSDR) for the connectometry analysis. We compared differences of DTI, GQI, and connectometry analysis to reflect WM changes in aging. Associations between FA, QA, and fiber numbers and lengths and age were analyzed using Pearson's correlation coefficients. The connectometry analysis was conducted using a multiple linear regression analysis, including age and gender as factors. Uncorrected P-value/false discovery rate (FDR) (corrected for multiple comparisons) < 0.05 was considered statistically significant. More regional changes were detected in FA related to age than changes in QA (17 > 6 regions, P < 0.05). Fewer regional changes in fiber numbers and more changes of fiber lengths were observed for DTI than for GQI (5 < 8/10 > 7 regions, P < 0.05). However, DTI and GQI analyses revealed consistent results in some regions, including the genu of the corpus callosum (GCC), body of the corpus callosum (BCC), fornix (Fx), and anterior coronal radiation (ACR) (P < 0.05). The connectometry analysis showed more tract changes associated with age at an FDR of 0.05, which partially overlapped with the FA and QA. GQI and connectometry provide more information about age-related tracts and complement the DTI findings. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:369-381.

Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in Diffusion MRI.

In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: (1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction. (2) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in a single voxel or in a region, respectively; (3) We also show how to construct a local orthogonal coordinate frame in each voxel exhibiting anisotropic diffusion; (4) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to quantitatively describe orientational distortion (splay, bend, and twist) in general spherical function fields from DTI or HARDI data. The proposed DFA and its related mathematical tools can be used to process not only diffusion MRI data but also general director field data, and the proposed scalar indices are useful for detecting local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The related codes and a tutorial for DFA will be released in DMRITool.

Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data.

High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50% without losing signal's quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.

Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions.

Cerebello-cerebral connectivity in the developing brain

Disrupted cerebellar development and injury is associated with impairments in both motor and non-motor domains. Methods to non-invasively characterize cerebellar afferent and efferent connections during early development are lacking. The aim of this study was to assess the feasibility of delineating cortico-ponto-cerebellar (CPC) and cerebello-thalamo-cortical (CTC) white matter tracts during brain development using high angular resolution diffusion imaging (HARDI). HARDI data were obtained in 24 infants born between 24+6 and 39 weeks gestational age (median 33+4 weeks) and scanned between 29+1 and 44 weeks postmenstrual age (PMA) (median 37+1 weeks). Probabilistic tractography of CPC and CTC fibers was performed using constrained spherical deconvolution. Connections between cerebellum and contralateral cerebral hemisphere were identified in all infants studied. Fractional anisotropy (FA) values of CTC and CPC pathways increased with increasing PMA at scan (p < 0.001). The supratentorial regions connecting to contralateral cerebellum in most subjects, irrespective of PMA at scan, included the precentral cortex, superior frontal cortex, supplementary motor area, insula, postcentral cortex, precuneus, and paracentral lobule. This study demonstrates the feasibility of assessing CTC and CPC white matter connectivity in vivo during the early stages of development. The ability to assess cerebellar connectivity during this critical developmental period may help improve our understanding of the role of the cerebellum in a wide range of neuromotor and neurocognitive disorders.

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone–air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.

A localized Richardson-Lucy algorithm for fiber orientation estimation in high angular resolution diffusion imaging.

Diffusion tensor imaging is widely used for studying neural fiber trajectories in white matter and for quantifying changes in tissue using diffusion properties at each voxel in the brain. To better model the nature of crossing fibers within complex architectures, rather than using a simplified tensor model that assumes only a single fiber direction at each image voxel, a model mixing multiple diffusion tensors is used to profile diffusion signals from high angular resolution diffusion imaging (HARDI) data. Based on the HARDI signal and a multiple tensors model, spherical deconvolution methods have been developed to overcome the limitations of the diffusion tensor model when resolving crossing fibers. The Richardson-Lucy algorithm is a popular spherical deconvolution method used in previous work. However, it is based on a Gaussian distribution, while HARDI data are always very noisy, and the distribution of HARDI data follows a Rician distribution. This current work aims to present a novel solution to address these issues. By simultaneously considering both the Rician bias and neighbor correlation in HARDI data, the authors propose a localized Richardson-Lucy (LRL) algorithm to estimate fiber orientations for HARDI data. The proposed method can simultaneously reduce noise and correct the Rician bias. Mean angular error (MAE) between the estimated Fiber orientation distribution (FOD) field and the reference FOD field was computed to examine whether the proposed LRL algorithm offered any advantage over the conventional RL algorithm at various levels of noise. Normalized mean squared error (NMSE) was also computed to measure the similarity between the true FOD field and the estimated FOD filed. For MAE comparisons, the proposed LRL approach obtained the best results in most of the cases at different levels of SNR and b-values. For NMSE comparisons, the proposed LRL approach obtained the best results in most of the cases at b-value = 3000 s/mm(2), which is the recommended schema for HARDI data acquisition. In addition, the FOD fields estimated by the proposed LRL approach in regions of fiber crossing regions using real data sets also showed similar fiber structures which agreed with common acknowledge in these regions. The novel spherical deconvolution method for improved accuracy in investigating crossing fibers can simultaneously reduce noise and correct Rician bias. With the noise smoothed and bias corrected, this algorithm is especially suitable for estimation of fiber orientations in HARDI data. Experimental results using both synthetic and real imaging data demonstrated the success and effectiveness of the proposed LRL algorithm.

Assessment of white matter microstructure in stroke patients using NODDI.

Diffusion weighted imaging (DWI) is widely used to study changes in white matter following stroke. In various studies employing diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) modalities, it has been shown that fractional anisotropy (FA), mean diffusivity (MD), and generalized FA (GFA) can be used as measures of white matter tract integrity in stroke patients. However, these measures may be non-specific, as they do not directly delineate changes in tissue microstructure. Multi-compartment models overcome this limitation by modeling DWI data using a set of indices that are directly related to white matter microstructure. One of these models which is gaining popularity, is neurite orientation dispersion and density imaging (NODDI). This model uses conventional single or multi-shell HARDI data to describe fiber orientation dispersion as well as densities of different tissue types in the imaging voxel. In this paper, we apply for the first time the NODDI model to 4-shell HARDI stroke data. By computing NODDI indices over the entire brain in two stroke patients, and comparing tissue regions in ipsilesional and contralesional hemispheres, we demonstrate that NODDI modeling provides specific information on tissue microstructural changes. We also introduce an information theoretic analysis framework to investigate the non-local effects of stroke in the white matter. Our initial results suggest that the NODDI indices might be more specific markers of white matter reorganization following stroke than other measures previously used in studies of stroke recovery.