B-matrix Spatial Distribution Research Articles

The added value of diffusion tensor imaging with systematic bias correction for the assessment of liver morphology and physiology.

Diffusion-weighted images of the human liver are prone to artifacts from bulk motions, poor SNR, non-uniformity of magnetic field gradients, and non-optimal choice of diffusion weightings. These factors markedly affect diffusion tensor imaging (DTI) metrics such as mean diffusivity (MD) and fractional anisotropy (FA). This work presents a simple preprocessing pipeline for enhanced magnetic field gradient non-uniformity calibration and analysis of the systematic bias removal attained in each correction step. Liver DTI scans were conducted in two isotropic phantoms and one healthy volunteer. Diffusion tensor was calculated for the original data and after denoising, B1 correction, rigid body registration, and magnetic field gradient non-uniformity correction applying the B-matrix spatial distribution (BSD) method and then, compared with the standard approach (sDTI). MD and FA were determined in three segments of the right lobe from DTI using four different combinations of b-values from the set 0, 400, and 800 s/mm2. Results showed that the proposed preprocessing and BSD methods have a significant impact on MD and FA values in off- and iso-centered isotropic phantoms. The applied corrections applied to the human liver resulted in a 11% change in MD and a - 64% change in FA. By manipulating the b-values used in the diffusion tensor calculation, DTI metrics that reflect only morphology or additional information about liver tissue physiology can be obtained. Accurate quantification of the human liver by diffusion requires appropriate preprocessing and carefully chosen b-value. Noise, B1 inhomogeneity, mis-registration, and non-uniform magnetic field gradients significantly change distributions of DTI metrics in isotropic phantoms and the human liver. Basic preprocessing and the B-matrix spatial distribution (BSD) method perform differently for off-center and isocenter locations. In the human liver, they removed systematic bias of FA and MD by up to -63% and 11%, respectively. Visible variability of FA and MD among b-value sets indicates the possibility of DTI sensitization to different liver compartments.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even − 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and − 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score

Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1–3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.

Diffusion-tensor magnetic resonance imaging of the human heart in health and in acute myocardial infarction using diffusion-weighted echo-planar imaging technique with spin-echo signals.

Originally thought unsuitable due to proneness to myocardial motion and susceptibility artefacts, spin-echo echo planar imaging (SE-EPI) has gained attention for the cardiac diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) offering higher SNR and lower achievable echo time (TE). The application of DTI for patients with acute myocardial infarction (AMI) using our methodology developed on the basis of the SE-EPI sequence. Twelve patients with AMI and six healthy controls were enrolled in the preliminary DTI study within the CIRCULATE STRATEGMED 2 project. Our method relied on a pilot ECG-triggered DTI examination, based on which the initial evaluation was possible and allowed proper manipulation of TE (64/47 ms for patients/control), repetition time (TR) and ECG trigger delay in the consecutive DTI. The study demonstrated that by using our algorithm it was possible to obtain DWI images showing infarct zones identified on T1-weighted images with late gadolinium-enhancement (LGE) with division into subtle and severe damage. Quantitative DTI showed increased mean diffusivity (MD) and decreased fractional anisotropy (FA) in the infarct compared to remote tissue. The application of B-matrix spatial distribution (BSD) calibration allowed the improvement of FA. Our algorithm is suitable for qualitative assessment of infarction zones with different severity. The analysis of the quantitative DTI showed that despite the lack of motion compensation blocks in the applied SE-EPI sequence, it was possible to approach the diffusion tensor parameter values reported for the myocardium.

Assessment of the systematic errors caused by diffusion gradient inhomogeneity in DTI-computer simulations.

Diffusion tensor imaging (DTI) is a powerful MRI modality that allows the investigation of the microstructure of tissues both in vivo and noninvasively. Its reliability is strictly dependent on the performance of diffusion-sensitizing gradients, of which spatial nonuniformity is a known issue in the case of virtually all clinical MRI scanners. The influence of diffusion gradient inhomogeneity on the accuracy of the diffusion tensor imaging was investigated by means of computer simulations supported by an MRI experiment performed at the isocenter and 15cm away. The DTI measurements of two diffusion phantoms were simulated assuming a nonuniform diffusion-sensitizing gradient and various levels of noise. Thereafter, the tensors were calculated by two methods: (i) assuming a spatially constant b-matrix (standard DTI) and (ii) applying the b-matrix spatial distribution in the DTI (BSD-DTI) technique, a method of indicating the b-matrix for each voxel separately using an anisotropic phantom as a standard of diffusion. The average eigenvalues and fractional anisotropy across the homogeneous region of interest were calculated and compared with the expected values. Diffusion gradient inhomogeneity leads to overestimation of the largest eigenvalue, underestimation of the smallest one and thus overestimation of fractional anisotropy. The effect is similar to that caused by noise; however, it could not be corrected by increasing SNR. The MRI measurements, performed using a 3T clinical scanner, revealed that the split of the eigenvalues measured 15cm away from the isocenter is significant (up to 25%). The BSD-DTI calibration allowed the reduction of the measured fractional anisotropy of the isotropic medium from 0.174 to 0.031, suggesting that gradient inhomogeneity was the main cause of this error. For the phantom measured at the isocenter, however, the split was almost not observed; the average eigenvalues were shifted from the expected value by ~ 5%.

Analysis and correction of errors in DTI-based tractography due to diffusion gradient inhomogeneity

The DTI-based tractography, despite its restrictions, is the most widely utilized fiber tracking method in clinical practice. Its fidelity is strictly dependent on the precision and accuracy of the DTI measurement, which in turn is limited by the linearity of the diffusion sensitizing gradient.The influence of the gradient distortions on the differences between the real and measured orientation of fibers was investigated by computer simulations. In addition, the potential of the b-matrix Spatial Distribution in DTI (BSD-DTI) technique in correcting such kind of errors was demonstrated experimentally.The simulations revealed that the diffusion gradient inhomogeneity, if not corrected, leads to the erroneous indication of the fiber direction. The average and maximum deviations were about 1° and 15°, respectively. Remarkably, the deviation between the real and measured orientation of fibers is directionally dependent, what was confirmed in MRI measurement. The deviation errors can be effectively corrected by preceding the DTI measurement with the BSD-DTI calibration.

High-resolution computed microtomography for the characterization of a diffusion tensor imaging phantom

This paper addresses the issue of the quantitative characterization of the structure of the calibration model (phantom) for b-matrix spatial distribution diffusion tensor imaging (BSD-DTI) scanners. The aim of this study was to verify manufacturing assumptions of the structure of materials, since phantoms are used for BSD-DTI calibration directly after manufacturing. Visualization of the phantoms’ structure was achieved through optical microscopy and high-resolution computed microtomography (µCT). Using µCT images, a numerical model of the materials structure was developed for further quantitative analysis. 3D image characterization was performed to determine crucial structural parameters of the phantom: porosity, uniformity and distribution of equivalent diameter of capillary bundles. Additionally calculations of hypothetical flow streamlines were also performed based on the numerical model that was developed. The results obtained in this study can be used in the calibration of DTI-BST measurements. However, it was found that the structure of the phantom exhibits flaws and discrepancies from the assumed geometry which might affect BSD-DTI calibration.

A theoretical validation of the B-matrix spatial distribution approach to diffusion tensor imaging

The recently presented B-matrix Spatial Distribution (BSD) approach is a calibration technique which derives the actual distribution of the B-matrix in space. It is claimed that taking into account the spatial variability of the B-matrix improves the accuracy of diffusion tensor imaging (DTI). The purpose of this study is to verify this approach theoretically through computer simulations.Assuming three different spatial distributions of the B-matrix, diffusion weighted signals were calculated for the six orientations of a model anisotropic phantom. Subsequently two variants of the BSD calibration were performed for each of the three cases; one with the assumption of high uniformity of the model phantom (uBSD-DTI) and the other taking into account imperfections in phantom structure (BSD-DTI). Several cases of varying degrees of phantom uniformity were analyzed and the distributions of the B-matrix obtained were used for the calculation of the diffusion tensor of a model isotropic phantom. The results were compared with standard diffusion tensor calculation.The simulations confirmed the improvement of accuracy in the determination of the diffusion tensor after the calibration. BSD-DTI improves accuracy independent of both the degree of uniformity of the phantom and the inhomogeneity of the B-matrix. In cases of a relatively good uniformity of the phantom and minor distortions in the spatial distribution of the B-matrix, the uBSD-DTI approach is sufficient.

Innovative anisotropic phantoms for calibration of diffusion tensor imaging sequences

The paper describes a novel type of anisotropic phantoms designed for b-matrix spatial distribution diffusion tensor imaging (BSD-DTI). Cubic plate anisotropic phantom, cylinder capillary phantom and water reference phantom are described as a complete set necessary for calibration, validation and normalization of BSD-DTI.An innovative design of the phantoms basing on enclosing the anisotropic cores in glass balls filled with liquid made for the first time possible BSD calibration with usage of echo planar imaging (EPI) sequence. Susceptibility artifacts prone to occur in EPI sequences were visibly reduced in the central region of the phantoms.The phantoms were designed for usage in a clinical scanner's head coil, but can be scaled for other coil or scanner types. The phantoms can be also used for a pre-calibration of imaging of other types of phantoms having more specific applications.

The b matrix calculation using the anisotropic phantoms for DWI and DTI experiments.

B-matrix Spatial Distribution Diffusion Tensor Imaging (BSD-DTI) is a novel approach to imaging of the diffusion tensor. By means of the method a spatial distribution of the b matrices is determined and subsequently incorporated into diffusion tensor calculation. This paper presents experimental verification of the method. Statistical analysis of the results shows significant variation of the b matrix components through the slices and spatial b matrix maps depict inhomogeneity of the gradient distribution for MRI scanners equipped with wide bore magnets. The accuracy of the diffusion tensor determination was improved by the factor of about 3 for the isotropic phantom using the Spin Echo Diffusion sequence.

Anisotropic phantoms in Magnetic Resonance Imaging.

Even though Magnetic Resonance Imaging (MRI) gives possibility to obtain qualitatively very good images, most quantitative results obtained by means of MRI are biased with high dependence on particular hardware parameters, imaging sequence used, and properties of analysed sample. Thus to enable comparison between results obtained on different scanners a calibration is needed. In one of the approaches to Diffusion Tensor Imaging (DTI), a B-matrix Spatial Distribution DTI (BSD-DTI) anisotropic phantoms are crucial in precise determination of the diffusion tensor. Anisotropic phantoms can be also useful as a porosity models or rock models in geology. The paper focuses on characterization of several anisotropic phantoms and describes their applications in DTI, and other domains related to MRI.

Improving the accuracy of PGSE DTI experiments using the spatial distribution of b matrix

A novel method for improving the accuracy of diffusion tensor imaging (DTI) is proposed. It takes into account the b matrix spatial variations, which can be easily determined using a simple anisotropic diffusion phantom. In opposite to standard numerical procedure of the b matrix calculation that requires the exact knowledge of amplitudes, shapes and time dependencies of diffusion gradients, the new method, which we call BSD-DTI (B-matrix spatial distribution in DTI), relies on direct measurements of its space-dependent components. The proposed technique was demonstrated on the Bruker Biospec 94/20USR system, using the spin echo diffusion sequence to image an isotropic water phantom and an anisotropic capillary phantom. The accuracy of the diffusion tensor determination was improved by an overall factor of about 8 for the isotropic water phantom.