Diffusion-encoding Directions Research Articles

Model‐based reconstruction for simultaneous multislice and parallel imaging accelerated multishot diffusion tensor imaging

Multishot interleaved echo-planar imaging (iEPI) can achieve higher image resolution than single-shot EPI for diffusion tensor imaging (DTI), but its application is limited by the prolonged acquisition time. To reduce the acquisition time, a novel model-based reconstruction for simultaneous multislice (SMS) and parallel imaging (PI) accelerated iEPI DTI is proposed. DTI datasets acquired by iEPI with SMS and PI acceleration can be regarded as 3D k-space data, which is undersampled along both the slice and phase encoding directions. Instead of reconstruction of individual diffusion-weighted image, diffusion tensors are directly estimated by the joint reconstruction of undersampled 3D k-space from all diffusion-encoding directions using a model-based formulation to exploit the correlation across different directions. DTI simulation and in vivo acquisition were used to demonstrate the superior performance of the proposed method. The proposed method reduced the estimation errors and artifacts than traditional parallel imaging reconstruction in DTI simulation. In the in vivo DTI experiment, the acquisition time of 4-shot iEPI was reduced from 11 min 7 s to 3 min 53 s with an acceleration factor of 4, and the image quality and precision of quantitative parameters were comparable with the fully sampled acquisition. The proposed model-based reconstruction for iEPI DTI with SMS and PI can achieve fourfold acceleration while maintaining high accuracy for tensor measurements.

Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T2 relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T1 -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79±0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200s/mm2 and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC=0.79±0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.

Effect of cardiac-related translational motion in diffusion MRI of the spinal cord

Cardiac-related spinal cord motion affects diffusion-weighted (DWI) signal. The goal of this study was to further quantify the specific detrimental effect of cord translational motion on the DWI signal in order to make better informed decisions about the cost-benefit of cardiac gating.We designed an MRI-compatible phantom mimicking the spinal cord translational motion. Cardiac-gated DWI data were acquired by varying the trigger delay and the b-values. Evaluation of the effect of motion on the DWI signal was done by computing the apparent diffusion coefficient (ADC) along (z-direction) and orthogonal (y- and x-directions) to the phantom.The computed ADCs of the phantom moving along Z were similar for the three orthogonal diffusion-encoding directions, with an average value of 1.65·10−9 , 1.66·10−9 and 1.65·10−9 m2/s along X, Y and Z respectively. DW phase images on the other hand showed the expected linear relationship with phantom velocity.Pure translational motion has minor effect on the diffusion-weighted magnitude signal. The sudden signal drop typically observed in in vivo spinal cord DWI is likely not caused by translational motion of the spinal cord, and possibly originates from non-rigid compression/stretching of the cord and/or from intra-voxel incoherent motion (IVIM).

Cardiac-gated intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for the investigation of intracranial cerebrospinal fluid dynamics in the lateral ventricle: a feasibility study.

Intravoxel incoherent motion (IVIM) in diffusion-weighted magnetic resonance imaging (DW-MRI) attributes the signal attenuation to the molecular diffusion and to a faster pseudo-diffusion. Purpose of the study was to demonstrate the feasibility of IVIM for the investigation of intracranial cerebrospinal fluid (CSF) dynamics. Cardiac-gated DW-MRI images with fifteen b-values (0-1300s/mm2) along three orthogonal directions (mediolateral (ML), anteroposterior (AP), and craniocaudal (CC)) were acquired during maximum systole and diastole in 10 healthy volunteers (6 males, mean age 36 ± 15years). A pixel-wise bi-exponential fitting with an iterative nonparametric algorithm was carried out to calculate the following parameters: diffusion coefficient (D), fast diffusion coefficient (D*), and fraction of fast diffusion (f). Region of interest measurements were performed in both lateral ventricles. Comparison of IVIM parameters was performed among two cardiac cycle acquisitions and among the diffusion-encoding directions using a paired Student's t test. f significantly (p < 0.05) depended on the diffusion-encoding direction and on the cardiac cycle (diastole AP 0.30 ± 0.13, ML 0.22 ± 0.12, CC 0.26 ± 0.17; systole AP 0.45 ± 0.17, ML 0.34 ± 0.15, CC 0.40 ± 0.21). Neither a cardiac cycle nor a direction dependency was found among mean D values (which is in line with the expected intraventricular isotropic diffusion) and D* values (p > 0.05 each). The fraction of fast diffusion from IVIM is feasible to detect a direction-dependent and cardiac-dependent pulsatile CSF flow within the lateral ventricles allowing for quantitative monitoring of CSF dynamics. This technique might provide opportunities to further investigate the pathophysiology of various neurological disorders involving altered CSF dynamics.

Bilateral cingulum fiber reductions in temporal lobe epilepsy with unilateral hippocampal sclerosis

PurposeTo evaluate whether white matter tracts within the Papez circuit are altered in patients with unilateral hippocampal sclerosis (HS). MethodsTwenty patients with histologically proven unilateral HS and 20 age-matched controls were studied with a 3T Epilepsy-dedicated MRI protocol including a MPRAGE sequence for hippocampus volumetry and a diffusion tensor imaging (DTI) sequence (61 diffusion-encoding directions, 2×2×2mm3 voxels) for diffusion tensor tractography (DTT). An energy-based global tracking algorithm was used to calculate streamline counts (SC) and fractional anisotropy (FA) of cingulate, fornix, and mammillo-thalamic tracts, respectively. ResultsSclerotic hippocampi were significantly smaller compared to the contralateral side and to age-matched controls. Cingulum SC but not FA were reduced on the hippocampal sclerosis (258+81.0) and contralateral side (271+85.6) compared to age-matched controls (447+138). ConclusionFocusing on white matter tracts of the Papez circuit we showed that in patients with intractable temporal lobe epilepsy unilateral hippocampal sclerosis is associated with a bilateral reduction of cingulum association fibers projecting from the cingulate gyrus to the parahippocampal gyrus.

Diffusion-weighted tractography in the common marmoset monkey at 9.4T.

The common marmoset (Callithrix jacchus) is a small New World primate that is becoming increasingly popular in the neurosciences as an animal model of preclinical human disease. With several major disorders characterized by alterations in neural white matter (e.g., multiple sclerosis, Alzheimer's disease, schizophrenia), proposed to be transgenically modeled using marmosets, the ability to isolate and characterize reliably major white matter fiber tracts with MRI will be of use for evaluating structural brain changes related to disease processes and symptomatology. Here, we propose protocols for isolating major white matter fiber tracts in the common marmoset using in vivo ultrahigh-field MRI (9.4T) diffusion-weighted imaging (DWI) data. With the use of a high angular-resolution DWI (256 diffusion-encoding directions) sequence, collected on four anesthetized marmosets, we provide guidelines for manually drawing fiber-tracking regions of interest, based on easily identified anatomical landmarks in DWI native space. These fiber-tract isolation protocols are expected to be experimentally useful for visualization and quantification of individual white matter fiber tracts in both control and experimental groups of marmosets (e.g., transgenic models). As disease models in the marmoset advance, the determination of how macroscopic white matter anatomy is altered as a function of disease state will be relevant in bridging the existing translational gap between preclinical rodent models and human patients.NEW & NOTEWORTHY Although significant progress has been made in mapping white matter connections in the marmoset brain using ex vivo tracing techniques, the application of in vivo virtual dissection of major white matter fiber tracts has been established by few studies in the marmoset literature. Here, we demonstrate the feasibility of whole-brain diffusion-weighted tractography in anesthetized marmosets at ultrahigh-field MRI (9.4T) and propose protocols for isolating nine major white matter fiber tracts in the marmoset brain.

Regional Susceptibility to Chronic Anemia in WM Microstructure Using Diffusion Tensor Imaging

Introduction: White matter strokes are a major concern for patients with sickle cell disease and other forms of anemia. SCD patients are known to have diffuse white matter volume loss, abnormal WM microstructure, silent strokes, and poor neurocognitive function. Similar neurological abnormalities have been described in thalassemia intermedia. Low levels of hemoglobin have been associated with risk of silent cerebral infarctions across patient populations with chronic anemia and severe anemia may increase the risk for cerebral vascular accidents. In this current study, we use MRI brain mapping techniques to explore the relationship between a wide range of hemoglobin levels and WM neural tissue properties.Methods: 16 SCD patients (age=26.1 ± 10.3; F=9, M=7; hemoglobin=9.4 ± 2.1), 8 patients with non-sickle anemia (age=23.6 ± 7.4; F=4, M=4; hemoglobin=12.2 ± 3.2), and 15 control subjects (age=22.8 ± 7.2; F=10, M=5; hemoglobin=13.2 ± 1.3) were recruited with informed consent or assent; the study was approved by the Institutional Review Board at Children’s Hospital Los Angeles (CCI#11-00083). Exclusion criteria included pregnancy, previous overt stroke, acute chest, or pain crisis hospitalization within one month. MRI data were acquired on a 3T Philips Achieva (v.3.2.1) using an 8-channel head coil.3D T1-weighted images (TE =3.8ms TR =8.3ms; resolution = 1mm3) were pre-processed using BrainSuite (brainsuite.org) and registered to a common atlas space using Surface Volume Registration (SVReg15a). Diffusion weighted images (TE = 2.5ms; TR = 4.8ms; 2.5mm isotropic voxels) included 30 diffusion-encoding directions at b-value=1000m/s2 and a reverse-gradient b=0. Fieldmaps were estimated using FSL's topup module. BrainSuite Diffusion Pipeline (BDP15a) performed distortion correction, co-registration to T1-weighted images, and diffusion tensor modeling. Voxel-wise calculations of DTI metrics including fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were transferred to the common atlas space. Pearson correlations were performed using BrainSuite Statistics Toolbox.Results: Hemoglobin was correlated to FA, AD, and, RD in multiple brain regions after controlling for age and sex (Figure 1) The colorbar represents the r-value for the correlations (red positive, blue negative) and only statistically significant values are displayed. Anemia was associated with increased RD and AD broadly over the brain (blue), including corpus callosum, subcortical nuclei, and frontal and parietal white matter. These regions represent classic "watershed" areas that are the primary targets of silent strokes in sickle cell disease, hypertension, aging, and obstructive sleep apnea. FA changes were more modest in extent, but also occurred in areas classically associated with silent stroke.Conclusion: Our study suggests that the severity of anemia, rather than the type of anemia, is the primary risk factor for white matter damage. Low hemoglobin concentration correlated to increases in RD and AD, indicative of demyelination and axonal degeneration respectively. Interestingly, the regions of abnormal diffusivity parallel the pattern of regional vulnerability to stroke in SCD patients in previous literature (frontal lobe > parietal lobe, subcortical nuclei, > temporal lobe, >> occipital lobe and cerebellum). While silent strokes and abnormal diffusivity co-localize, the volume of infarcted tissue is much smaller, suggesting that changes in RD and AD are detecting preclinical white matter injury in anemic patients. We postulate that anemia, by depleting cerebrovascular reserve, leaves patients vulnerable to acute interruptions in supply or increases in metabolic demand. Our observations may have important implications for choosing set points for chronic transfusion therapy in anemic patients. [Display omitted] DisclosuresWood:Biomed Informatics: Consultancy; Biomed Informatics: Consultancy; Apopharma: Consultancy; Apopharma: Consultancy; AMAG: Consultancy; AMAG: Consultancy; Celgene: Consultancy; Celgene: Consultancy; World Care Clinical: Consultancy; World Care Clinical: Consultancy; Vifor: Consultancy; Vifor: Consultancy; Ionis Pharmaceuticals: Consultancy; Ionis Pharmaceuticals: Consultancy.

Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain

PurposeThe dependence of the direction-averaged diffusion-weighted imaging (DWI) signal in brain was studied as a function of b-value in order to help elucidate the relationship between diffusion weighting and brain microstructure. MethodsHigh angular resolution diffusion imaging (HARDI) data were acquired from two human volunteers with 128 diffusion-encoding directions and six b-value shells ranging from 1000 to 6000s/mm2 in increments of 1000s/mm2. The direction-averaged signal was calculated for each shell by averaging over all diffusion-encoding directions, and the signal was plotted as a function of b-value for selected regions of interest. As a supplementary analysis, similar methods were also applied to retrospective DWI data obtained from the human connectome project (HCP), which includes b-values up to 10,000s/mm2. ResultsFor all regions of interest, a simple power law relationship accurately described the observed dependence of the direction-averaged signal as a function of the diffusion weighting. In white matter, the characteristic exponent was 0.56±0.05, while in gray matter it was 0.88±0.11. Comparable results were found with the HCP data. ConclusionThe direction-averaged DWI signal varies, to a good approximation, as a power of the b-value, for b-values between 1000 and 6000s/mm2. The exponents characterizing this power law behavior were markedly different for white and gray matter, indicative of sharply contrasting microstructural environments. These results may inform the construction of microstructural models used to interpret the DWI signal.

Diffusion Tractography of the Entire Left Ventricle by Using Free-breathing Accelerated Simultaneous Multisection Imaging.

Purpose To develop a clinically feasible whole-heart free-breathing diffusion-tensor (DT) magnetic resonance (MR) imaging approach with an imaging time of approximately 15 minutes to enable three-dimensional (3D) tractography. Materials and Methods The study was compliant with HIPAA and the institutional review board and required written consent from the participants. DT imaging was performed in seven healthy volunteers and three patients with pulmonary hypertension by using a stimulated echo sequence. Twelve contiguous short-axis sections and six four-chamber sections that covered the entire left ventricle were acquired by using simultaneous multisection (SMS) excitation with a blipped-controlled aliasing in parallel imaging readout. Rate 2 and rate 3 SMS excitation was defined as two and three times accelerated in the section axis, respectively. Breath-hold and free-breathing images with and without SMS acceleration were acquired. Diffusion-encoding directions were acquired sequentially, spatiotemporally registered, and retrospectively selected by using an entropy-based approach. Myofiber helix angle, mean diffusivity, fractional anisotropy, and 3D tractograms were analyzed by using paired t tests and analysis of variance. Results No significant differences (P > .63) were seen between breath-hold rate 3 SMS and free-breathing rate 2 SMS excitation in transmural myofiber helix angle, mean diffusivity (mean ± standard deviation, [0.89 ± 0.09] × 10-3 mm2/sec vs [0.9 ± 0.09] × 10-3 mm2/sec), or fractional anisotropy (0.43 ± 0.05 vs 0.42 ± 0.06). Three-dimensional tractograms of the left ventricle with no SMS and rate 2 and rate 3 SMS excitation were qualitatively similar. Conclusion Free-breathing DT imaging of the entire human heart can be performed in approximately 15 minutes without section gaps by using SMS excitation with a blipped-controlled aliasing in parallel imaging readout, followed by spatiotemporal registration and entropy-based retrospective image selection. This method may lead to clinical translation of whole-heart DT imaging, enabling broad application in patients with cardiac disease. © RSNA, 2016 Online supplemental material is available for this article.

Whole brain white matter integrity measures on patients with moderate to severe traumatic brain injury (TBI). FA and entropy analysis

Introduction Mean Fractional Anisotropy (FA) along with Longitudinal and Transverse Apparent Diffusion Coefficients (ADC) are the established measures in assessing white matter (WM) integrity in TBI patients. A question is raised whether mean FA performs better than Shannon Entropy (SE) as a measure of discrimination between controls and patients with moderate to severe TBI. Purpose To assess WM integrity in moderate to severe TBI patients utilizing the established diffusion tensor measures, and compare FA with Shannon Entropy as measures of assessment of WM integrity. Materials and methods A group of 13 patients with moderate to severe TBI, 2–8 years post-ictus, has been compared with a group of 14 matched healthy controls. Diffusion Tensor Imaging (DTI) data were obtained at 3T with a pulse sequence employing 32 diffusion-encoding gradient directions. After data preprocessing, Tract Based Spatial Statistics (FSL-FMRIB) for FA, whole-brain histogram analysis for SE and Receiver Operating Curve (ROC) analysis for both FA and SE have been performed. Results Higher axial and transverse diffusivity and lower FA have been observed in TBI patients ( p P = 0.007), Global mean FA (0.8, P = 0.0066) and SE (0.8, p = 0.008). Conclusion Axial and transverse diffusivity are increased in TBI patients compared to controls. Diffuse Axonal Injury (DAI) is evident as global reduction of FA in TBI patients, which is driven by increased transverse diffusion changes. No significant difference is observed between FA and SE in TBI patient discrimination. Disclosure The authors declare no conflicts of interest including financial, consultant, institutional, and other relationships that might lead to bias or a any other conflict of interest. This work was supported by the Cyprus Research Promotion Foundation through a grant co-funded by the Cyprus Government and the European Regional Development Fund (F.C., PI; NEW INFRASTRUCTURE/STRATEGIC/0309/37). The authors would like to thank the study volunteers who participated in the project.

Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging.

Diffusion tensor imaging (DTI) is used to quantify myocardial fiberorientation based on helicalangles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, wepropose to reduce TA by implementinga 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivohealthy and infarcted porcine hearts. DTI was performed onex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering(AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering(AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure.

Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis.

ObjectivesA new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.MethodsFourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm2) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).ResultsError-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.ConclusioneDKI might be useful for assessing DKI in clinical settings.Key Points• A method to practically estimate axial/radial DKI from limited data was developed.• The high robustness of the proposed method can greatly improve map images.• The accuracy of the proposed method was high.• Axial/radial K maps can be calculated from limited diffusion-encoding directions.• The proposed method might be useful for assessing DKI in clinical settings.

Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma.

ObjectivesTo evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI).Material and MethodsAll subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.ResultsThere were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P <0.05). Good IQ, acceptable scan time and reasonable FA/ADC values were acquired using NED = 9 with b-value of (0,300) s/mm2. Using the optimized DTI sequence, ADC value of the tumor lesion was significantly lower than that of the healthy liver region (1.30 ± 0.34×10−3 vs 1.52 ± 0.27×10−3 mm2/s, P = 0.013), whereas the mean FA value of the tumor lesion (0.42 ± 0.11) was significantly higher than the normal liver region (0.32 ± 0.10) (P = 0.004).ConclusionEither FA or ADC value from DTI can be used to differentiate HCC from healthy liver. HCC lead to higher FA value and lower ADC value on DTI than healthy liver.

In vivo high angular resolution diffusion-weighted imaging of mouse brain at 16.4 Tesla.

Magnetic Resonance Imaging (MRI) of the rodent brain at ultra-high magnetic fields (> 9.4 Tesla) offers a higher signal-to-noise ratio that can be exploited to reduce image acquisition time or provide higher spatial resolution. However, significant challenges are presented due to a combination of longer T 1 and shorter T 2/T2* relaxation times and increased sensitivity to magnetic susceptibility resulting in severe local-field inhomogeneity artefacts from air pockets and bone/brain interfaces. The Stejskal-Tanner spin echo diffusion-weighted imaging (DWI) sequence is often used in high-field rodent brain MRI due to its immunity to these artefacts. To accurately determine diffusion-tensor or fibre-orientation distribution, high angular resolution diffusion imaging (HARDI) with strong diffusion weighting (b >3000 s/mm2) and at least 30 diffusion-encoding directions are required. However, this results in long image acquisition times unsuitable for live animal imaging. In this study, we describe the optimization of HARDI acquisition parameters at 16.4T using a Stejskal-Tanner sequence with echo-planar imaging (EPI) readout. EPI segmentation and partial Fourier encoding acceleration were applied to reduce the echo time (TE), thereby minimizing signal decay and distortion artefacts while maintaining a reasonably short acquisition time. The final HARDI acquisition protocol was achieved with the following parameters: 4 shot EPI, b = 3000 s/mm2, 64 diffusion-encoding directions, 125×150 μm2 in-plane resolution, 0.6 mm slice thickness, and 2h acquisition time. This protocol was used to image a cohort of adult C57BL/6 male mice, whereby the quality of the acquired data was assessed and diffusion tensor imaging (DTI) derived parameters were measured. High-quality images with high spatial and angular resolution, low distortion and low variability in DTI-derived parameters were obtained, indicating that EPI-DWI is feasible at 16.4T to study animal models of white matter (WM) diseases.

A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints.

To accelerate denoising of magnitude diffusion-weighted images subject to joint rank and edge constraints. We extend a previously proposed majorize-minimize method for statistical estimation that involves noncentral χ distributions to incorporate joint rank and edge constraints. A new algorithm is derived which decomposes the constrained noncentral χ denoising problem into a series of constrained Gaussian denoising problems each of which is then solved using an efficient alternating minimization scheme. The performance of the proposed algorithm has been evaluated using both simulated and experimental data. Results from simulations based on ex vivo data show that the new algorithm achieves about a factor of 10 speed up over the original Quasi-Newton-based algorithm. This improvement in computational efficiency enabled denoising of large datasets containing many diffusion-encoding directions. The denoising performance of the new efficient algorithm is found to be comparable to or even better than that of the original slow algorithm. For an in vivo high-resolution Q-ball acquisition, comparison of fiber tracking results around hippocampus region before and after denoising will also be shown to demonstrate the denoising effects of the new algorithm. The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm.

Probing region-specific microstructure of human cortical areas using high angular and spatial resolution diffusion MRI

Regional heterogeneity in cortical cyto- and myeloarchitecture forms the structural basis of mapping of cortical areas in the human brain. In this study, we investigate the potential of diffusion MRI to probe the microstructure of cortical gray matter and its region-specific heterogeneity across cortical areas in the fixed human brain. High angular resolution diffusion imaging (HARDI) data at an isotropic resolution of 92-μm and 30 diffusion-encoding directions were acquired using a 3D diffusion-weighted gradient-and-spin-echo sequence, from prefrontal (Brodmann area 9), primary motor (area 4), primary somatosensory (area 3b), and primary visual (area 17) cortical specimens (n=3 each) from three human subjects. Further, the diffusion MR findings in these cortical areas were compared with histological silver impregnation of the same specimens, in order to investigate the underlying architectonic features that constitute the microstructural basis of diffusion-driven contrasts in cortical gray matter. Our data reveal distinct and region-specific diffusion MR contrasts across the studied areas, allowing delineation of intracortical bands of tangential fibers in specific layers—layer I, layer VI, and the inner and outer bands of Baillarger. The findings of this work demonstrate unique sensitivity of diffusion MRI to differentiate region-specific cortical microstructure in the human brain, and will be useful for myeloarchitectonic mapping of cortical areas as well as to achieve an understanding of the basis of diffusion NMR contrasts in cortical gray matter.

Reproducibility of diffusion tensor imaging in normal subjects: an evaluation of different gradient sampling schemes and registration algorithm

Diffusion tensor imaging (DTI) is very useful for investigating white matter integrity in ageing and neurological disorders; thus, evaluating its reproducibility under different acquisition protocols and analysis methods may assist in the design of clinical studies. To measure the reproducibility of DTI in normal subjects, this study include (1) depicting the reproducibility of DTI measurements in commonly used regions-of-interest analysis by intraclass correlation coefficient (ICC) and coefficient of variation (CV), (2) evaluating and comparing inter and intrasession test-retest reproducibility, and (3) illustrating the effect of the number of diffusion-encoding directions (NDED) and registration algorithms on measurement reproducibility. DTI measurements exhibit high reproducibility, with overall (430/480) ICC ≥ 0.70, (478/480) within-subject CV (CVws) ≤10.00 % and between-subject CV (CVbs) ranging from 1.32 to 13.63 %. Repeated measures ANOVAs and paired t tests were conducted to compare inter and intrasession reproducibility with different diffusion sampling schemes and registration algorithms. Our results also confirmed that increasing the NDED could improve the accuracy and reproducibility of DTI measurements. In addition, we compared reproducibility indices that were derived using different registration algorithms, and a tensor-based deformable registration yielded the most reproducible results. Finally, we found that increasing the NDED could reduce the difference between the reproducibility of measurement derived using different registration algorithms and between the reproducibility of intersession and intrasession. Our results suggest that the choice of DTI acquisition protocol and post-processing methods can influence the accurate estimation and reproducibility of DTI measurements and should be considered carefully for clinical applications.

Estimation of helical angle of the left ventricle using diffusion tensor imaging with minimum acquisition time

Background Myocardial fiber structure exhibits a helical geometry, characterized by the helical angle (HA). HA transitions smoothly from a negative helix in the epicardium to a positive helix in the endocardium. HA plays an important role in understanding the electrical and mechanical properties of the heart. Therefore, there exists a need to non-invasively quantify HA which can be determined using diffusion tensor imaging (DTI). The evaluation of HA measured from DTI depends on the accuracy of the diffusion tensors which in turn depend on the number of diffusion-encoding directions (DED) and the signal to noise ratio (SNR, which can be increased by increasing the total number of excitations (NEX)). However, increasing SNR and DED also increases the total acquisition time (TA). The aim of the study is to optimize the acquisition protocol (NEX and DED) to robustly estimate HA in the shortest possible TA.

The Effect of Respiratory and Cardiac Motion in Liver Diffusion Tensor Imaging (DTI)

To evaluate the effect of respiratory and cardiac motion on diffusion tensor imaging (DTI) metrics in healthy human liver. Fifteen healthy subjects, participating in either part of this study, were scanned using a 1.5-T magnetic resonance imaging (MRI) device. Coronal liver DTI (6 diffusion-encoding directions; b, 300 mm/s) during breath holding was compared to free breathing. Cardiac motion effects were evaluated by comparing breath-held DTI scans acquired during both diastole and systole. Free breathing resulted in a significantly increased mean diffusivity (P < 0.05), λ1 (P < 0.01), λ2 (P < 0.05), and λ3 (P < 0.01) compared to breath holding. During systole significant increases in fractional anisotropy (P < 0.05), mean diffusivity (P < 0.05), and λ1 (P < 0.05), compared to systole, were found in the left lobe. The right lobe, which is less affected by cardiac motion, showed no significant change in DTI metrics over the cardiac cycle. Respiratory and cardiac motion tends to increase liver DTI metrics.

Diffusion Tensor Imaging of the Kidneys: Influence of b-Value and Number of Encoding Directions on Image Quality and Diffusion Tensor Parameters.

Objectives:The purpose of this study was to evaluate to which degree investment of acquisition time in more encoding directions leads to better image quality (IQ) and what influence the number of encoding directions and the choice of b-values have on renal diffusion tensor imaging (DTI) parameters.Material and Methods:Eight healthy volunteers (32.3 y ± 5.1 y) consented to an examination in a 1.5T whole-body MR scanner. Coronal DTI data sets of the kidneys were acquired with systematic variation of b-values (50, 150, 300, 500, and 700 s/mm2) and number of diffusion-encoding directions (6, 15, and 32) using a respiratory-triggered echo-planar sequence (TR/TE 1500 ms/67 ms, matrix size 128 × 128). Additionally, two data sets with more than two b-values were acquired (0, 150, and 300 s/mm2 and all six b-values). Parametrical maps were calculated on a pixel-by-pixel basis. Image quality was determined with a reader score.Results:Best IQ was visually assessed for images acquired with 15 and 32 encoding directions, whereas images acquired with six directions had significantly lower IQ ratings. Image quality, fractional anisotropy, and mean diffusivity only varied insignificantly for b-values between 300 and 500 s/mm2. In the renal medulla fractional anisotropy (FA) values between 0.43 and 0.46 and mean diffusivity (MD) values between 1.8-2.1 × 10-3 mm2/s were observed. In the renal cortex, the corresponding ranges were 0.24-0.25 (FA) and 2.2-2.8 × 10-3 mm2/s (MD). Including b-values below 300 s/mm2, notably higher MD values were observed, while FA remained constant. Susceptibility artifacts were more prominent in FA maps than in MD maps.Conclusion:In DTI of the kidneys at 1.5T, the best compromise between acquisition time and resulting image quality seems the application of 15 encoding directions with b-values between 300 and 500 s/mm2. Including lower b-values allows for assessment of fast diffusing spin components.