Optimized diffusion gradient orientation schemes for corrupted clinical DTI data sets

Impact of truncating diffusion MRI scans on diffusional kurtosis imaging

OBJECTIVE

Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (OptEEM); 2) spherical codes (OptSC); 3) random (RandomTRUNC).

MATERIALS AND METHODS

Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively.

RESULTS

Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). RandomTRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (OptEEM: up to 5% error; OptSC: up to 7% error) and peak height (OptEEM: up to 8% error; OptSC: up to 11% error) the most affected.

CONCLUSION

The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Optimal Diffusion Gradient Encoding Scheme for Diffusion Tensor Imaging Based on Golden Ratio

BACKGROUND

The accuracy of the estimated diffusion tensor elements can be improved by using a well-chosen magnetic resonance imaging (MRI) diffusion gradient encoding scheme (DGES). Conversely, diffusion tensor imaging (DTI) is typically challenged by the subject's motion during data acquisition and results in corrupted image data.

PURPOSE

To identify a reliable DGES based on the golden ratio (GR) that can generate an arbitrary number of uniformly distributed directions to precisely estimate the DTI parameters of partially acquired datasets owing to subject motion.

STUDY TYPE

Prospective.

POPULATION

Simulations study; three healthy volunteers.

FIELD STRENGTH/SEQUENCE

3 T/DTI data were obtained using a single-shot echo planar imaging sequence.

STATISTICAL TESTS

A paired sample t-test and the Wilcoxon test were used, P < 0.05 was considered statistically significant.

ASSESSMENT

Two corrupted scenarios A and B were considered and evaluated. For the simulation study, the GR DGES and generated subsets were compared with the Jones and spiral DGESs by electric potential (EP) and condition number (CN). For the human study, the specific subsets A and B selected from scenarios A and B were used for MRI to evaluate fractional anisotropic (FA) map.

RESULTS

For the simulation study, the EPs of the GR (14034.25 ± 12957.24) DGES were significantly lower than the Jones (15112.81 ± 13926.08) and spiral (14297.49 ± 13232.94) DGESs. CN variations of GR (1.633 ± 0.024) DGES were significantly lower than Jones (1.688 ± 0.119) and spiral (4.387 ± 2.915) DGESs. For the human study, GR (0.008 ± 0.020) DGES performed similarly with Jones (0.008 ± 0.022) DGES and was superior to spiral (0.022 ± 0.054) DGES in the FA map error.

DATA CONCLUSION

The GR DGES ensured that directions of the complete sets and subsets were uniform. The GR DGES had lower error propagation sensitivity, which can help image infants or patients who cannot stay still during scanning.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances

In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

Diffusion tensor imaging in schizophrenia

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is increasingly being used for the non-invasive evaluation of brain white matter abnormalities. In this review, we discuss the basic principles of DTI, its roots and the contribution of European centres in its development, and we review the findings from DTI studies in schizophrenia. We searched EMBASE, PubMed, PsychInfo, and Medline from February 1998 to December 2006 using as keywords ‘schizophrenia’, ‘diffusion’, ‘tensor’, and ‘DTI’. Forty studies fulfilling the inclusion criteria of this review were included and systematically reviewed. White matter abnormalities in many diverse brain regions were identified in schizophrenia. Although the findings are not completely consistent, frontal and temporal white matter seems to be more commonly affected. Limitations and future directions of this method are discussed.

Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm

Mean Apparent Propagator (MAP) MRI is a recently introduced technique to estimate the diffusion probability density function (PDF) robustly. Using the estimated PDF, MAP MRI then calculates zero-displacement and non-Gaussianity metrics, which might better characterize tissue microstructure compared to diffusion tensor imaging or diffusion kurtosis imaging. However, intensive q-space sampling required for MAP MRI limits its widespread adoption. A reduced q-space sampling scheme that maintains the accuracy of the derived metrics would make it more practical. A heuristic approach for acquiring MAP MRI with fewer q-space samples has been introduced earlier with scan duration of less than 10 minutes. However, the sampling scheme was not optimized systematically to preserve the accuracy of the model metrics. In this work, a genetic algorithm is implemented to determine optimal q-space subsampling schemes for MAP MRI that will keep total scan time under 10 min. Results show that the metrics derived from the optimized schemes more closely match those computed from the full set, especially in dense fiber tracts such as the corpus callosum.

Clinical application of advanced MR methods in children: points to consider

The application of both functional MRI and diffusion MR tractography prior to a neurosurgical operation is well established in adults, but less so in children, for several reasons. For this review, we have identified several aspects (task design, subject preparation, actual scanning session, data processing, interpretation of results, and decision-making) where pediatric peculiarities should be taken into account. Further, we not only systematically identify common issues, but also provide solutions, based on our experience as well as a review of the pertinent literature. The aim is to provide the clinician as well as the imaging scientist with information that helps to plan, conduct, and interpret such a clinically-indicated exam in a way that maximizes benefit for, and minimizes the burden on the individual child.

White matter network connectivity deficits in developmental dyslexia

A number of studies have shown an abnormal connectivity of certain white matter pathways in developmental dyslexia, as well as correlations between these white matter pathways and behavioral deficits. However, whether developmental dyslexia presents broader white matter network connectivity disruption is currently unknown. The present study reconstructed white matter networks for 26 dyslexic children (11.61 ± 1.31 years) and 31 age-matched controls (11.49 ± 1.36 years) using constrained spherical deconvolution tractography. Network-based statistics (NBS) analysis was performed to identify network connectivity deficits in dyslexic individuals. Network topological features were measured based on graph theory to examine whether these parameters correlate with literacy skills, and whether they explain additional variance over previously established white matter connectivity abnormalities in dyslexic children. The NBS analysis identified a network connecting the left-occipital-temporal cortex and temporo-parietal cortex that had decreased streamlines in dyslexic children. Four network topological parameters (clustering coefficient, local efficiency, transitivity, and global efficiency) were positively correlated with literacy skills of dyslexic children, and explained a substantial proportion of additional variance in literacy skills beyond connectivity measures of white matter pathways. This study for the first time reports a disconnection in a local subnetwork in the left hemisphere in dyslexia and shows that the global white matter network topological properties contribute to reduced literacy skills in dyslexic children.

Time-efficient and flexible design of optimized multishell HARDI diffusion

PURPOSE

Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time-efficient and flexible diffusion acquisition capability with built-in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible.

METHODS

Complete flexibility in the sampling of diffusion space combined with free choice of phase-encode-direction and the temporal ordering of the sampling scheme was developed taking into account motion robustness, internal consistency, and hardware limits. A split-diffusion-gradient preparation, multiband acceleration, and a restart capacity were added.

RESULTS

The framework was used to explore different parameters choices for the desired high angular resolution diffusion imaging diffusion sampling. For the developing Human Connectome Project, a high-angular resolution, maximally time-efficient (20 min) multishell protocol with 300 diffusion-weighted volumes was acquired in >400 neonates. An optimal design of a high-resolution (1.2 × 1.2 mm2 ) two-shell acquisition with 54 diffusion weighted volumes was obtained using a split-gradient design.

CONCLUSION

The presented framework provides flexibility to generate time-efficient and motion-robust diffusion magnetic resonance imaging acquisitions taking into account hardware constraints that might otherwise result in sub-optimal choices. Magn Reson Med 79:1276-1292, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Track-weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

BACKGROUND

The use of MRI-tractography to explore the human neuroretina is yet to be reported. Track-weighted imaging (TWI) was recently introduced as a qualitative tractography-based method with high anatomical contrast.

PURPOSE

To explore the human retina in healthy volunteers and patients with anterior ischemic optic neuropathy (AION) using TWI reconstructions.

STUDY TYPE

Prospective.

POPULATION

Twenty AION patients compared with 20 healthy volunteers.

FIELD STRENGTH/SEQUENCE

3.0T MRI diffusion-weighted imaging (DWI) with b-value of 1000 s/mm2 and 60 diffusion-weighting noncollinear directions.

ASSESSMENT

We performed constrained spherical deconvolution from the diffusion-weighted signal and volumetric tractography method, whereby 10 million streamlines are initiated from seed points randomly distributed throughout the orbital area. We then reconstructed TWI maps with isotropic voxel size of 300 μm.

STATISTICAL TESTS

We tested the effect of the number of diffusion-weighting directions, ocular laterality, and ocular dominance on healthy retinal fascicles distribution. We then performed factorial analysis of variance to test the effects of the presence/absence of the fascicles on the visual field defect in patients.

RESULTS

In healthy volunteers, we found more temporal fascicle in right eyes (P = 0.001), more superior fascicles in dominant eyes (P = 0.014), and fewer fascicles with tractography maps based on 30 directions than those based on 45 directions (P = 9 × 10-8 ) and 60 directions (P = 6 × 10-7 ). Eight out of 20 AION patients presented with complete absence of neuroretinal fascicle, side of the disease, which was correlated with visual field mean deviation at the 6-month visit [F(1,17) = 6.97, P = 0.016]. Seven patients presented with a temporal fascicle in the injured eye; this fascicle presence was linked to visual field mean deviation at the 6-month visit [F(1,17) = 8.43, P = 0.009].

DATA CONCLUSION

In AION patients, the presence of the temporal neuroretinal fascicle in the affected eye provides an objective outcome radiological sign correlated with visual performance.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes

In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called Spherical Code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear Programming (MILP) method, and a Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.

A novel measure of reliability in Diffusion Tensor Imaging after data rejections due to subject motion

Diffusion Tensor Imaging (DTI) is commonly challenged by subject motion during data acquisition, which often leads to corrupted image data. Currently used procedure in DTI analysis is to correct or completely reject such data before tensor estimations, however assessing the reliability and accuracy of the estimated tensor in such situations has evaded previous studies. This work aims to define the loss of data accuracy with increasing image rejections, and to define a robust method for assessing reliability of the result at voxel level. We carried out simulations of every possible sub-scheme (N=1,073,567,387) of Jones30 gradient scheme, followed by confirming the idea with MRI data from four newborn and three adult subjects. We assessed the relative error of the most commonly used tensor estimates for DTI and tractography studies, fractional anisotropy (FA) and the major orientation vector (V1), respectively. The error was estimated using two measures, the widely used electric potential (EP) criteria as well as the rotationally variant condition number (CN). Our results show that CN and EP are comparable in situations with very few rejections, but CN becomes clearly more sensitive to depicting errors when more gradient vectors and images were rejected. The error in FA and V1 was also found depend on the actual FA level in the given voxel; low actual FA levels were related to high relative errors in the FA and V1 estimates. Finally, the results were confirmed with clinical MRI data. This showed that the errors after rejections are, indeed, inhomogeneous across brain regions. The FA and V1 errors become progressively larger when moving from the thick white matter bundles towards more superficial subcortical structures. Our findings suggest that i) CN is a useful estimator of data reliability at voxel level, and ii) DTI preprocessing with data rejections leads to major challenges when assessing brain tissue with lower FA levels, such as all newborn brain, as well as the adult superficial, subcortical areas commonly traced in precise connectivity analyses between cortical regions.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm^-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

Trade-off between angular and spatial resolutions in in vivo fiber tractography

Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angular resolutions to determine which of these factors is most worth investing scan time in. We created a unique diffusion MRI dataset with 1.0 mm isotropic resolution and a high angular resolution (100 directions) using an advanced 3D diffusion-weighted multi-slab EPI acquisition. This dataset was reconstructed to create subsets of lower angular (75, 50, and 25 directions) and lower spatial (1.5, 2.0, and 2.5 mm) resolution. Using all subsets, we investigated the effects of angular and spatial resolutions in three fiber bundles-the corticospinal tract, arcuate fasciculus and corpus callosum-by analyzing the volumetric bundle overlap and anatomical correspondence between tracts. Our results indicate that the subsets of 25 and 50 directions provided inferior tract reconstructions compared with the datasets with 75 and 100 directions. Datasets with spatial resolutions of 1.0, 1.5, and 2.0 mm were comparable, while the lowest resolution (2.5 mm) datasets had discernible inferior quality. In conclusion, we found that angular resolution appeared to be more influential than spatial resolution in improving tractography results. Spatial resolutions higher than 2.0 mm only appear to benefit multi-fiber tractography methods if this is not at the cost of decreased angular resolution.

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging

The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.

Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing

PURPOSE

The composite hindered and restricted model of diffusion provides microstructural indices that are potentially more specific than those from diffusion tensor imaging. However, in comparison to diffusion tensor imaging, the acquisition time is longer, limiting clinical applications. Moreover, the model requires several parameters to be estimated whose confidence intervals can be large. Here, the composite hindered and restricted model of diffusion acquisition and data processing pipelines are optimized to extend the utility of this approach.

METHODS

A multishell sampling scheme was optimized using the electrostatic repulsion algorithm, combined with optimal ordering. The optimal protocol, using as few measurements as possible, was determined through leave-n-out analyses. Parsimonious model selection criteria were used to select between nested models, comprising up to three restricted compartments. The schemes were evaluated using both through Monte-Carlo simulations and in vivo data.

RESULTS

The optimization/model selection procedure resulted in increased accuracy and precision on the estimated parameters, allowing for a reduction in acquisition time and marked improvements in data quality. The final protocol provided whole brain coverage data in only 12 min.

CONCLUSION

Through careful optimization of the acquisition and analysis pipeline for the composite hindered and restricted model of diffusion, it is possible to reduce acquisition time for whole brain datasets to a time that is clinically applicable.

Pseudometrically constrained centroidal voronoi tessellations: Generating uniform antipodally symmetric points on the unit sphere with a novel acceleration strategy and its applications to diffusion and three-dimensional radial MRI

PURPOSE

The purpose of this work is to investigate the hypothesis that uniform sampling measurements that are endowed with antipodal symmetry play an important role in image quality when the raw data and image data are related through the Fourier relationship. Currently, it is extremely challenging to generate large and uniform antipodally symmetric point sets suitable for three-dimensional radial MRI. A novel approach is proposed to solve this long-standing problem in a unique and optimal way.

METHODS

The proposed method is based on constrained centroidal Voronoi tessellations of the upper hemisphere with a novel pseudometric.

RESULTS

The time complexity of the proposed tessellations was shown to be effectively linear, i.e., on the order of the number of sampling measurements. For small sample size, the proposed method was comparable with the state-of-the-art method (a direct iterative minimization of the electrostatic potential energy of a collection of electrons antipodal-symmetrically distributed on the unit sphere) in terms of the sampling uniformity. For large sample size, in which the state-of-the-art method is infeasible, the reconstructed images from the proposed method has less streak and ringing artifacts, when compared with those of the commonly used methods.

CONCLUSION

This work proposed a unique and optimal approach to solving a long-standing problem in generating uniform sampling points for three-dimensional radial MRI.

Anatomical connections of the visual word form area

The visual word form area (VWFA), a region systematically involved in the identification of written words, occupies a reproducible location in the left occipitotemporal sulcus in expert readers of all cultures. Such a reproducible localization is paradoxical, given that reading is a recent invention that could not have influenced the genetic evolution of the cortex. Here, we test the hypothesis that the VWFA recycles a region of the ventral visual cortex that shows a high degree of anatomical connectivity to perisylvian language areas, thus providing an efficient circuit for both grapheme-phoneme conversion and lexical access. In two distinct experiments, using high-resolution diffusion-weighted data from 75 human subjects, we show that (1) the VWFA, compared with the fusiform face area, shows higher connectivity to left-hemispheric perisylvian superior temporal, anterior temporal and inferior frontal areas; (2) on a posterior-to-anterior axis, its localization within the left occipitotemporal sulcus maps onto a peak of connectivity with language areas, with slightly distinct subregions showing preferential projections to areas respectively involved in grapheme-phoneme conversion and lexical access. In agreement with functional data on the VWFA in blind subjects, the results suggest that connectivity to language areas, over and above visual factors, may be the primary determinant of VWFA localization.

Automated assessment of the quality of diffusion tensor imaging data using color cast of color-encoded fractional anisotropy images

Diffusion tensor imaging (DTI) data often suffer from artifacts caused by motion. These artifacts are especially severe in DTI data from infants, and implementing tight quality controls is therefore imperative for DTI studies of infants. Currently, routine procedures for quality assurance of DTI data involve the slice-wise visual inspection of color-encoded, fractional anisotropy (CFA) images. Such procedures often yield inconsistent results across different data sets, across different operators who are examining those data sets, and sometimes even across time when the same operator inspects the same data set on two different occasions. We propose a more consistent, reliable, and effective method to evaluate the quality of CFA images automatically using their color cast, which is calculated on the distribution statistics of the 2D histogram in the color space as defined by the International Commission on Illumination (CIE) on lightness and a and b (LAB) for the color-opponent dimensions (also known as the CIELAB color space) of the images. Experimental results using DTI data acquired from neonates verified that this proposed method is rapid and accurate. The method thus provides a new tool for real-time quality assurance for DTI data.

Design of multishell sampling schemes with uniform coverage in diffusion MRI

PURPOSE

In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques.

METHODS

The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell.

RESULTS

We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination.

DISCUSSION

We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI.

Acquisition guidelines and quality assessment tools for analyzing neonatal diffusion tensor MRI data

Diffusion tensor imaging is a valuable measure in clinical settings to assess diagnosis and prognosis of neonatal brain development. However, obtaining reliable images is not straightforward because of the tissue characteristics of the neonatal brain and the high likelihood of motion artifacts. In this review, we present guidelines on how to acquire DTI data of the neonatal brain and recommend high-quality data acquisition and processing as an essential means to obtain accurate and robust parametric maps. Sudden head movements are problematic for DTI in neonates, and these may lead to incorrect values. We describe strategies to minimize the corrupting effects both in terms of acquisition (eg, more gradient directions) and postprocessing (eg, tensor estimation methods). In addition, tools are described that can help assess whether a dataset is of sufficient quality for further assessment.

Multimodality evaluation of the pediatric brain: DTI and its competitors

The development of the human brain, from the fetal period until childhood, happens in a series of intertwined neurogenetical and histogenetical events that are influenced by environment. Neuronal proliferation and migration, cell aggregation, axonal ingrowth and outgrowth, dendritic arborisation, synaptic pruning and myelinisation contribute to the 'plasticity of the developing brain'. These events taken together contribute to the establishment of adult-like neuroarchitecture required for normal brain function. With the advances in technology today, mostly due to the development of non-invasive neuroimaging tools, it is possible to analyze these structural events not only in anatomical space but also longitudinally in time. In this review we have highlighted current 'state of the art' neuroimaging tools. Development of the new MRI acquisition sequences (DTI, CHARMED and phase imaging) provides valuable insight into the changes of the microstructural environment of the cortex and white matter. Development of MRI imaging tools dedicated for analysis of the acquired images (i) TBSS and ROI fiber tractography, (ii) new tissue segmentation techniques and (iii) morphometric analysis of the cortical mantle (cortical thickness and convolutions) allows the researchers to map the longitudinal changes in the macrostructure of the developing brain that go hand-in-hand with the acquisition of cognitive skills during childhood. Finally, the latest and the newest technologies, like connectom analysis and resting state fMRI connectivity analysis, today, for the first time provide the opportunity to study the developing brain through the prism of maturation of the systems and networks beyond individual anatomical areas. Combining these methods in the future and modeling the hierarchical organization of the brain might ultimately help to understand the mechanisms underlying complex brain structure function relationships of normal development and of developmental disorders.

Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements

Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.

Sparse and optimal acquisition design for diffusion MRI and beyond

PURPOSE

Diffusion magnetic resonance imaging (MRI) in combination with functional MRI promises a whole new vista for scientists to investigate noninvasively the structural and functional connectivity of the human brain-the human connectome, which had heretofore been out of reach. As with other imaging modalities, diffusion MRI data are inherently noisy and its acquisition time-consuming. Further, a faithful representation of the human connectome that can serve as a predictive model requires a robust and accurate data-analytic pipeline. The focus of this paper is on one of the key segments of this pipeline-in particular, the development of a sparse and optimal acquisition (SOA) design for diffusion MRI multiple-shell acquisition and beyond.

METHODS

The authors propose a novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and a novel and effective semistochastic and moderately greedy combinatorial search strategy with simulated annealing to locate the optimum design or configuration. The goal of the optimality criteria is threefold: first, to maximize uniformity of the diffusion measurements in each shell, which is equivalent to maximal incoherence in angular measurements; second, to maximize coverage of the diffusion measurements around each radial line to achieve maximal incoherence in radial measurements for multiple-shell acquisition; and finally, to ensure maximum uniformity of diffusion measurement directions in the limiting case when all the shells are coincidental as in the case of a single-shell acquisition. The approach taken in evaluating the stability of various acquisition designs is based on the condition number and the A-optimal measure of the design matrix.

RESULTS

Even though the number of distinct configurations for a given set of diffusion gradient directions is very large in general-e.g., in the order of 10(232) for a set of 144 diffusion gradient directions, the proposed search strategy was found to be effective in finding the optimum configuration. It was found that the square design is the most robust (i.e., with stable condition numbers and A-optimal measures under varying experimental conditions) among many other possible designs of the same sample size. Under the same performance evaluation, the square design was found to be more robust than the widely used sampling schemes similar to that of 3D radial MRI and of diffusion spectrum imaging (DSI).

CONCLUSIONS

A novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and an effective search strategy for finding the best configuration have been developed. The results are very promising, interesting, and practical for diffusion MRI acquisitions.

Nonlinear microstructural changes in the right superior temporal sulcus and lateral occipitotemporal gyrus between 35 and 43 weeks in the preterm brain

Using diffusion tensor imaging (DTI), we explored microstructural brain maturation in a population of 65 preterm neonates who underwent magnetic resonance imaging between 35 and 43 weeks of corrected gestational age. A voxel-based analysis approach, statistical parametric mapping (SPM8), was used to evidence the nonlinear changes with the corrected gestational age in the regional distribution of mean diffusivity (MD), fractional anisotropy (FA), longitudinal and transverse diffusivities (λ//and λ⊥). We found that FA changes nonlinearly with age around the right superior temporal sulcus and in the right lateral occipitotemporal gyrus, with FA decrease between 34 and 39 weeks followed by FA increase from 40 weeks to 43 weeks. Considering the key role of these brain areas in verbal and non-verbal communicative behaviors, the effect of these microstructural changes in terms of early social network functional maturation needs to be assessed by joint functional and anatomical studies.

Optimal real-time estimation in diffusion tensor imaging

Diffusion tensor imaging (DTI) constitutes the most used paradigm among the diffusion-weighted magnetic resonance imaging (DW-MRI) techniques due to its simplicity and application potential. Recently, real-time estimation in DW-MRI has deserved special attention, with several proposals aiming at the estimation of meaningful diffusion parameters during the repetition time of the acquisition sequence. Specifically focusing on DTI, the underlying model of the noise present in the acquired data is not taken into account, leading to a suboptimal estimation of the diffusion tensor. In this paper, we propose an optimal real-time estimation framework for DTI reconstruction in single-coil acquisitions. By including an online estimation of the time-changing noise variance associated to the acquisition process, the proposed method achieves the sequential best linear unbiased estimator. Results on both synthetic and real data show that our method outperforms those so far proposed, reaching the best performance of the existing proposals by processing a substantially lower number of diffusion images.

Correction of vibration artifacts in DTI using phase-encoding reversal (COVIPER)

Diffusion tensor imaging is widely used in research and clinical applications, but still suffers from substantial artifacts. Here, we focus on vibrations induced by strong diffusion gradients in diffusion tensor imaging, causing an echo shift in k-space and consequential signal-loss. We refined the model of vibration-induced echo shifts, showing that asymmetric k-space coverage in widely used Partial Fourier acquisitions results in locally differing signal loss in images acquired with reversed phase encoding direction (blip-up/blip-down). We implemented a correction of vibration artifacts in diffusion tensor imaging using phase-encoding reversal (COVIPER) by combining blip-up and blip-down images, each weighted by a function of its local tensor-fit error. COVIPER was validated against low vibration reference data, resulting in an error reduction of about 72% in fractional anisotropy maps. COVIPER can be combined with other corrections based on phase encoding reversal, providing a comprehensive correction for eddy currents, susceptibility-related distortions and vibration artifact reduction.

Extremely efficient and deterministic approach to generating optimal ordering of diffusion MRI measurements

PURPOSE

Diffusion MRI measurements are typically acquired sequentially with unit gradient directions that are distributed uniformly on the unit sphere. The ordering of the gradient directions has significant effect on the quality of dMRI-derived quantities. Even though several methods have been proposed to generate optimal orderings of gradient directions, these methods are not widely used in clinical studies because of the two major problems. The first problem is that the existing methods for generating highly uniform and antipodally symmetric gradient directions are inefficient. The second problem is that the existing methods for generating optimal orderings of gradient directions are also highly inefficient. In this work, the authors propose two extremely efficient and deterministic methods to solve these two problems.

METHODS

The method for generating nearly uniform point set on the unit sphere (with antipodal symmetry) is based upon the notion that the spacing between two consecutive points on the same latitude should be equal to the spacing between two consecutive latitudes. The method for generating optimal ordering of diffusion gradient directions is based on the idea that each subset of incremental sample size, which is derived from the prescribed and full set of gradient directions, must be as uniform as possible in terms of the modified electrostatic energy designed for antipodally symmetric point set.

RESULTS

The proposed method outperformed the state-of-the-art method in terms of computational efficiency by about six orders of magnitude.

CONCLUSIONS

Two extremely efficient and deterministic methods have been developed for solving the problem of optimal ordering of diffusion gradient directions. The proposed strategy is also applicable to optimal view-ordering in three-dimensional radial MRI.

[Brain development of infant and MRI by diffusion tensor imaging]

Studying how the brain develops and becomes functional is important to understand how the man has been able to develop specific cognitive abilities, and to comprehend the complexity of some developmental pathologies. Thanks to magnetic resonance imaging (MRI), it is now possible to image the baby's immature brain and to consider subtle correlations between the brain anatomical development and the early acquisition of cognitive functions. Dedicated methodologies for image acquisition and post-treatment must then be used because the size of cerebral structures and the image contrast are very different in comparison with the adult brain, and because the examination length is a major constraint. Two recent studies have evaluated the developing brain under an original perspective. The first one has focused on cortical folding in preterm newborns, from 6 to 8 months of gestational age, assessed with T2-weighted conventional MRI. The second study has mapped the organization and maturation of white matter fiber bundles in 1- to 4-month-old healthy infants with diffusion tensor imaging (DTI). Both studies have enabled to highlight spatio-temporal differences in the brain regions' maturation, as well as early anatomical asymmetries between cerebral hemispheres. These studies emphasize the potential of MRI to evaluate brain development compared with the infant's psychomotor acquisitions after birth.

Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain

Diffusion-weighting in magnetic resonance imaging (MRI) increases the sensitivity to molecular Brownian motion, providing insight in the micro-environment of the underlying tissue types and structures. At the same time, the diffusion weighting renders the scans sensitive to other motion, including bulk patient motion. Typically, several image volumes are needed to extract diffusion information, inducing also inter-volume motion susceptibility. Bulk motion is more likely during long acquisitions, as they appear in diffusion tensor, diffusion spectrum and q-ball imaging. Image registration methods are successfully used to correct for bulk motion in other MRI time series, but their performance in diffusion-weighted MRI is limited since diffusion weighting introduces strong signal and contrast changes between serial image volumes. In this work, we combine the capability of free induction decay (FID) navigators, providing information on object motion, with image registration methodology to prospectively--or optionally retrospectively--correct for motion in diffusion imaging of the human brain. Eight healthy subjects were instructed to perform small-scale voluntary head motion during clinical diffusion tensor imaging acquisitions. The implemented motion detection based on FID navigator signals is processed in real-time and provided an excellent detection performance of voluntary motion patterns even at a sub-millimetre scale (sensitivity≥92%, specificity>98%). Motion detection triggered an additional image volume acquisition with b=0 s/mm2 which was subsequently co-registered to a reference volume. In the prospective correction scenario, the calculated motion-parameters were applied to perform a real-time update of the gradient coordinate system to correct for the head movement. Quantitative analysis revealed that the motion correction implementation is capable to correct head motion in diffusion-weighted MRI to a level comparable to scans without voluntary head motion. The results indicate the potential of this method to improve image quality in diffusion-weighted MRI, a concept that can also be applied when highest diffusion weightings are performed.

Diffusion imaging with prospective motion correction and reacquisition

A major source of artifacts in diffusion-weighted imaging is subject motion. Slow bulk subject motion causes misalignment of data when more than one average or diffusion gradient direction is acquired. Fast bulk subject motion can cause signal dropout artifacts in diffusion-weighted images and results in erroneous derived maps, e.g., fractional anisotropy maps. To address both types of artifacts, a fully automatic method is presented that combines prospective motion correction with a reacquisition scheme. Motion correction is based on the prospective acquisition correction method modified to work with diffusion-weighted data. The images to reacquire are determined automatically during the acquisition from the imaging data, i.e., no extra reference scan, navigators, or external devices are necessary. The number of reacquired images, i.e., the additional scan duration can be adjusted freely. Diffusion-weighted prospective acquisition correction corrects slow bulk motion well and reduces misalignment artifacts like image blurring. Mean absolute residual values for translation and rotation were <0.6 mm and 0.5°. Reacquisition of images affected by signal dropout artifacts results in diffusion maps and fiber tracking free of artifacts. The presented method allows the reduction of two types of common motion related artifacts at the cost of slightly increased acquisition time.

Minimization of imaging gradient effects in diffusion tensor imaging

A new, sample independent optimization criterion for minimizing the effect of the imaging gradients, including the directional awareness they create, is defined for diffusion tensor imaging (DTI) experiments. The DTI linear algebraic framework is expanded to a normed space to design optimal diffusion gradient schemes (DGS) in an integral fashion without separating the magnitude and direction of the DGS vectors. The feasible space of DGS vectors, for which the estimation equations are determinate, thus a hard constraint for the optimization, is parametrized. Newly generated optimal DGSs demonstrate on an isotropic sample and an ex-vivo baboon brain that the optimization goals are reached as well as a significant increase in estimation performance.

Diffusion tensor imaging

Diffusion tensor MRI (DT-MRI) is the only non-invasive method for characterising the microstructural organization of tissue in vivo. Generating parametric maps that help to visualise different aspects of the tissue microstructure (mean diffusivity, tissue anisotropy and dominant fibre orientation) involves a number of steps from deciding on the optimal acquisition parameters on the scanner, collecting the data, pre-processing the data and fitting the model to generating final parametric maps for entry into statistical data analysis. Here, we describe an entire protocol that we have used on over 400 subjects with great success in our laboratory. In the 'Notes' section, we justify our choice of the various parameters/choices along the way so that the reader may adapt/modify the protocol to their own time/hardware constraints.

Episodic memory in detoxified alcoholics: contribution of grey matter microstructure alteration

Even though uncomplicated alcoholics may likely have episodic memory deficits, discrepancies exist regarding to the integrity of brain regions that underlie this function in healthy subjects. Possible relationships between episodic memory and 1) brain microstructure assessed by magnetic resonance diffusion tensor imaging (DTI), 2) brain volumes assessed by voxel-based morphometry (VBM) were investigated in uncomplicated, detoxified alcoholics.

Diffusion and morphometric analyses were performed in 24 alcohol dependent men without neurological or somatic complications and in 24 healthy men. The mean apparent coefficient of diffusion (ADC) and grey matter volumes were measured in the whole brain. Episodic memory performance was assessed using a French version of the Free and Cued Selective Reminding Test (FCSRT). Correlation analyses between verbal episodic memory, brain microstructure, and brain volumes were carried out using SPM2 software.

In those with alcohol dependence, higher ADC was detected mainly in frontal, temporal and parahippocampal regions, and in the cerebellum. In alcoholics, regions with higher ADC typically also had lower grey matter volume. Low verbal episodic memory performance in alcoholism was associated with higher mean ADC in parahippocampal areas, in frontal cortex and in the left temporal cortex; no correlation was found between regional volumes and episodic memory scores. Regression analyses for the control group were not significant.

These findings support the hypothesis that regional microstructural but no macrostructural alteration of the brain might be responsible, at least in part, for episodic memory deficits in alcohol dependence.

[Influence exerted by MPG-directions on diffusion tensor imaging (DTI)]

When diffusion tensor was analyzed, increasing motion probing gradient (MPG) directions and increasing signal-to-noise ratio (SNR) as result might influence diffusion tensor calculation. This study determined that mean diffusivity (MD) and fractional anisotropy (FA) are calculated by changing MPG-directions or SNR in the volunteer. There were no statistically significant differences in MD, which is calculated from all scanning parameters (p>0.05). FA of the caudate nucleus was similar as well. However, there were statistically significant differences between FA calculated from 31 MPG-directions and from 13 MPG-directions or less for the frontal lobe and caudate nucleus (p<0.05). If SNR of a trace image is 30 or more, FA wasn't affected by SNR (p>0.05). To calculate FA, minimum MPG-directions that were not statistically different compared with 31 MPG-directions were 15 MPG-directions (p<0.05).

Maturation of thalamic radiations between 34 and 41 weeks' gestation: a combined voxel-based study and probabilistic tractography with diffusion tensor imaging

BACKGROUND AND PURPOSE

This study aimed to investigate brain maturation along gestational age with diffusion tensor imaging in healthy preterm and term neonates. Therefore, a voxel-based study of fractional anisotropy (FA) and mean diffusivity (D(av)) was performed to reveal the brain regions experiencing microstructural changes with age. With tractography, the authors intended to identify which fiber tracts were included in these significant voxels.

MATERIALS AND METHODS

There were 22 healthy preterm and 6 healthy term infants who underwent MR imaging between 34 and 41 weeks of gestation. A statistical parametric approach was used to evidence the effect of age on regional distribution of FA and D(av) values. The fiber tracts suspected to be included in the significant clusters of voxels were identified with neuroanatomy and tractography atlases, reconstructed with probabilistic tractography, and superimposed on the parametric maps.

RESULTS

Parametric analysis showed that FA increases with age in the subcortical projections from the frontal (motor and premotor areas) and parietal cortices, the centrum semiovale, the anterior and posterior arms of the internal capsules, the optic radiations, the corpus callosum, and the thalami (P < .05, corrected). Superimposition of the parametric maps on tractography showed that the corticospinal tract (CST); the callosal radiations (CR); and the superior, anterior, and posterior thalamic radiations were included in the significant voxels. No statistically significant results were found for D(av) maps.

CONCLUSIONS

These results highlight that, besides the already-evidenced FA increase in the CST and CR, the thalami and the thalamic radiations experience microstructural changes in the early development of the human brain.

Diffusion tensor tractography in mesencephalic bundles: relation to mental flexibility in detoxified alcohol-dependent subjects

Components of the corticocerebellar circuit and the midbrain individually play a central role in addictive processes and have been associated with altered volumes and impairment of cognitive flexibility in alcohol-dependent subjects. The microstructure of white matter bundles composing the corticocerebellar network and passing through the midbrain was studied using diffusion tensor imaging in a group of detoxified alcohol-dependent men (n=20) and a group of healthy men (n=24). The relationship between properties of these white matter bundles and cognitive flexibility performance was investigated in alcohol-dependent subjects. Bundles connecting two regions of interest were analyzed using a fiber-tracking quantitative approach, which provided estimates of the fractional anisotropy and the apparent diffusion coefficient, as well as the number of tracked fibers normalized by the volume of regions of interest. Within the bundles running between the midbrain and pons, a mean of 18% fewer fibers per unit volume were tracked in alcohol-dependent men than in healthy controls. In addition, the normalized number of these fibers correlated with the performance in the Trail-Making Test part-B. Even though the alcohol-dependent subjects were detoxified and apparently neurologically intact, their earlier excessive use of alcohol seems to be associated with altered neural microstructure of mesencephalic white matter bundles, which may contribute to their cognitive flexibility impairment.

Comparison of gradient encoding directions for higher order tensor diffusion data

Recently, higher order tensors were proposed for a more advanced representation of non-Gaussian diffusion. These advanced diffusion models have new requirements for the gradient encoding schemes used in the diffusion weighted image acquisition. The influence of the gradient encoding schemes on the estimated standard second order diffusion tensor was previously investigated. Here, we focus on the suitability of different encoding scheme types for higher order tensor models. Two quality measures for the gradient encoding schemes, the condition number of the estimation matrix and a new measure that evaluates the signal deviation on simulated data, are used to determine which gradient encoding is suited best for higher order tensor estimations. Six different gradient encoding scheme types were investigated. A certain force-minimizing scheme type gave the best results in the evaluations presented here.

Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering

Diffusion magnetic resonance imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real-time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet. First, we introduce the general linear models corresponding to the two diffusion tensor and analytical Q-ball models of interest. Then, we present the Kalman filtering framework and we focus on the optimization of the diffusion orientation sets in order to speed up the convergence of the online processing. Last, we give some results on a healthy volunteer for the online tensor and the Q-ball model, and we make some comparisons with the conventional offline techniques used in the literature. We could achieve full real-time for diffusion tensor imaging and deferred time for Q-ball imaging, using a single workstation.