Euclidian distance-weighted smoothing for quantitative MRI: application to intervoxel anisotropy index mapping with DTI

During the computation of intervoxel anisotropy features, the inclusion of both eigenvalues and eigenvectors reduces the effect of noise, but spatial averaging blurs the resulting maps. We propose a new adaptive technique that uses data-dependent weights in the averaging process so that the influence of each neighbor in the local window is proportional to the similarity of characteristics of the neighbor considered to those of the reference central voxel. This likeness criterion is based on the multidimensional Euclidian distance using the entire available multispectral information contained in the diffusion-weighted images. This solution is controlled by a single parameter beta that results from a compromise between edge-preserving and noise-smoothing abilities. This Euclidian distance-weighted technique is a generic solution for filtering noise during parametric reconstruction. It was applied to map anisotropy using an intervoxel lattice index (LI) from experimental images of mouse brain in vivo and achieves noise reduction without distorting small anatomical structures. We also show how to employ in the discrimination scheme the images not used in the estimation of the considered feature.     

Analyzing diffusion tensor images with ghosting artifacts: the effects of direct and indirect normalization

The current study aims to assess the applicability of direct or indirect normalization for the analysis of fractional anisotropy (FA) maps in the context of diffusion-weighted images (DWIs) contaminated by ghosting artifacts. We found that FA maps acquired by direct normalization showed generally higher anisotropy than indirect normalization, and the disparities were aggravated by the presence of ghosting artifacts in DWIs. The voxel-wise statistical comparisons demonstrated that indirect normalization reduced the influence of artifacts and enhanced the sensitivity of detecting anisotropy differences between groups. This suggested that images contaminated with ghosting artifacts can be sensibly analyzed using indirect normalization.     

Application of a probabilistic double-fibre structure model to diffusion-weighted MR images of the human brain

A Markov chain Monte Carlo (MCMC) algorithm has been reported which is capable of determining the probabilistic orientation of two-fibre populations from high angular resolution diffusion-weighted data (HARDI). We present and critically discuss the application of this algorithm to in vivo human datasets acquired in clinically realistic times. We show that by appropriate model selection areas of multiple fibre populations can be identified that correspond with those predicted from known anatomy. Quantitative maps of fibre orientation probability are derived and shown for one- and two-fibre models of neural architecture. Fibre crossings in the pons, the internal capsule and the corona radiata are shown. In addition, we demonstrate that the relative proportion of anisotropic signal may be a more appropriate measure of anisotropy than summary measures derived from the tensor model such as fractional anisotropy in areas with multi-fibre populations.     

White matter maturation in the brains of Long Evans shaker myelin mutant rats by ex-vivo QSI and DTI

The brains of Long Evans shaker (les) rats, a model of dysmyelination, and their age- matched controls were studied by ex-vivo q-space diffusion imaging (QSI) and diffusion tensor imaging (DTI). The QSI and DTI indices were computed from the same acquisition. The les and the control brains were studied at different stages of maturation and disease progression. The mean displacement, the probability for zero displacement and kurtosis were computed from QSI data while the fractional anisotropy (FA) and the eigenvalues were computed from DTI. It was found that all QSI indices detect the les pathology, at all stages of maturation, while only some of the DTI indices could detect the les pathology. The QSI mean displacement was larger in the les group as compared with their age-matched controls while the probability for zero displacement and the kurtosis were both lower all indicating higher degree of restriction in the control brains. Since all the DTI eigenvalues were higher in the les brains as compared to controls, the less efficient DTI measure for discerning the les pathology was found to be the FA. Clearly, the most sensitive DTI parameter to the les pathology is λ₃, i.e. the minimal diffusivity. Since the QSI and DTI data were obtained from the same acquisition, despite the somewhat higher SNR of the QSI data compared to the DTI data, it seems that the higher diagnostic capacity of the QSI data in this experimental model of dysmyelination, originates mainly from the higher diffusing weighting of the QSI data.     

MRI with TRELLIS: a novel approach to motion correction

A motion-correcting pulse sequence and reconstruction algorithm, termed TRELLIS, is presented. k-Space is filled using orthogonal overlapping strips and the directions for phase- and frequency-encoding are alternated such that the frequency-encode direction always runs lengthwise along each strip. The overlap between strips is used both for signal averaging and to produce a system of equations that, when solved, quantifies the rotational and translational motion of the object. Results obtained from simulations with computer-generated phantoms, a purpose-built moving phantom, and in human subjects show the method is effective. TRELLIS offers some advantages over existing techniques in that k-space is sampled uniformly and all acquired data are used for both motion detection and image reconstruction.     

Multiple sclerosis: Benefits of q-space imaging in evaluation of normal-appearing and periplaque white matter

Introduction

Diffusion tensor imaging (DTI) reveals white matter pathology in patients with multiple sclerosis (MS). A recent non-Gaussian diffusion imaging technique, q-space imaging (QSI), may provide several advantages over conventional MRI techniques in regard to in vivo evaluation of the disease process in patients with MS. The purpose of this study is to investigate the use of root mean square displacement (RMSD) derived from QSI data to characterize plaques, periplaque white matter (PWM), and normal-appearing white matter (NAWM) in patients with MS.

Methods

We generated apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps by using conventional DTI data from 21 MS patients; we generated RMSD maps by using QSI data from these patients. We used the Steel–Dwass test to compare the diffusion metrics of regions of interest in plaques, PWM, and NAWM.

Results

ADC differed (P < 0.05) between plaques and PWM and between plaques and NAWM. FA differed (P < 0.05) between plaques and NAWM. RMSD differed (P < 0.05) between plaques and PWM, plaques and NAWM, and PWM and NAWM.

Conclusion

RMSD values from QSI may reflect microstructural changes and white-matter damage in patients with MS with higher sensitivity than do conventional ADC and FA values.     

Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques

White matter loss, ventricular enlargement and white matter lesions are common findings on brain scans of older subjects. Accurate assessment of these different features is therefore essential for normal aging research. Recently, we developed a novel unsupervised classification method, named ‘Multispectral Coloring Modulation and Variance Identification’ (MCMxxxVI), that fuses two different structural magnetic resonance imaging (MRI) sequences in red/green color space and uses Minimum Variance Quantization (MVQ) as the clustering technique to segment different tissue types. Here we investigate how this method performs compared with several commonly used supervised image classifiers in segmenting normal-appearing white matter, white matter lesions and cerebrospinal fluid in the brains of 20 older subjects with a wide range of white matter lesion load and brain atrophy. The three tissue classes were segmented from T₁-, T₂-, T₂?- and fluid attenuation inversion recovery (FLAIR)-weighted structural MRI data using MCMxxxVI and the four supervised multispectral classifiers available in the Analyze package, namely, Back-Propagated Neural Networks, Gaussian classifier, Nearest Neighbor and Parzen Windows. Bland–Altman analysis and Jaccard index values indicated that, in general, MCMxxxVI performed better than the supervised multispectral classifiers in identifying the three tissue classes, although final manual editing was still required to deliver radiologically acceptable results. These analyses show that MVQ, as implemented in MCMxxxVI, has the potential to provide quick and accurate white matter segmentations in the aging brain, although further methodological developments are still required to automate fully this technique.     

Quantitative assessment of motion correction for high angular resolution diffusion imaging

Several methods have been proposed for motion correction of high angular resolution diffusion imaging (HARDI) data. There have been few comparisons of these methods, partly due to a lack of quantitative metrics of performance. We compare two motion correction strategies using two figures of merit: displacement introduced by the motion correction and the 95% confidence interval of the cone of uncertainty of voxels with prolate tensors. What follows is a general approach for assessing motion correction of HARDI data that may have broad application for quality assurance and optimization of postprocessing protocols. Our analysis demonstrates two important issues related to motion correction of HARDI data: (1) although neither method we tested was dramatically superior in performance, both were dramatically better than performing no motion correction, and (2) iteration of motion correction can improve the final results. Based on the results demonstrated here, iterative motion correction is strongly recommended for HARDI acquisitions.     

(Mathematical) Necessary conditions for the selection of gradient vectors in DTI

A set of necessary conditions for the choice of diffusion gradient vectors to make the linear equations nonsingular for the estimation of the diffusion matrix are given in a coordinate free manner. The conditions assert that the initial step in the design of a DTI experiment with six or more acquisitions must be to select six valid diffusion gradients first and then add new ones.     

Principal eigenvector field segmentation for reproducible diffusion tensor tractography of white matter structures

The study was aimed to test the feasibility of utilizing an algorithmically determinable stable fiber mass (SFM) map obtained by an unsupervised principal eigenvector field segmentation (PEVFS) for automatic delineation of 18 white matter (WM) tracts: (1) corpus callosum (CC), (2) tapetum (TP), (3) inferior longitudinal fasciculus (ILF), (4) uncinate fasciculus (UNC), (5) inferior fronto-occipital fasciculus (IFO), (6) optic pathways (OP), (7) superior longitudinal fasciculus (SLF), (8) arcuate fasciculus (AF), (9) fornix (FX), (10) cingulum (CG), (11) anterior thalamic radiation (ATR), (12) superior thalamic radiation (STR), (13) posterior thalamic radiation (PTR), (14) corticospinal/corticopontine tract (CST/CPT), (15) medial lemniscus (ML), (16) superior cerebellar peduncle (SCP), (17) middle cerebellar peduncle (MCP) and (18) inferior cerebellar peduncle (ICP). Diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) and the principal eigenvector field have been used to create the SFM consisting of a collection of linear voxel structures which are grouped together by color-coding them into seven natural classes to provide PEVFS signature segments which greatly facilitate the selection of regions of interest (ROIs) for fiber tractography using just a single mouse click, as compared with a manual drawing of ROIs in the classical approach. All the 18 fiber bundles have been successfully reconstructed, in all the subjects, using the single ROIs provided by the SFM approach, with their reproducibility characterized by the fact that the ROI selection is user independent. The essentially automatic PEVFS method is robust, efficient and compares favorably with the classical ROI methods for diffusion tensor tractography (DTT).     

MR evaluation of breast lesions obtained by diffusion-weighted imaging with background body signal suppression (DWIBS) and correlations with histological findings

Objectives

Diffusion imaging represents a new imaging tool for the diagnosis of breast cancer. This study aims to investigate the role of diffusion-weighted MRI with background body signal suppression (DWIBS) for evaluating breast lesions.

Methods

90 patients were prospectively evaluated by MRI with STIR, TSE-T2, contrast enhanced THRIVE-T1 and DWIBS sequences. DWIBS were analyzed searching for the presence of breast lesions and calculating the ADC value. ADC values of ≤ 1.44 × 10^- 3 mm²/s were considered suspicious for malignancy. This analysis was then compared with the histological findings. Sensitivity, specificity, diagnostic accuracy (DA), positive predictive value (PPV) and negative (NPV) were calculated.

Results

In 53/90 (59%) patients, DWIBS indicated the presence of breast lesions, 16 (30%) with ADC values of  > 1.44 and 37 (70%) with ADC ≤ 1.44. The comparison with histology showed 25 malignant and 28 benign lesions. DWIBS sequences obtained sensitivity, specificity, DA, PPV and NPV values of 100, 82, 87, 68 and 100%, respectively.

Conclusion

DWIBS can be proposed in the MRI breast protocol representing an accurate diagnostic complement.     

Abdominal apparent diffusion coefficient measurements: effect of diffusion-weighted image quality and usefulness of anisotropic images

This study aimed to assess the effect of diffusion-weighted image (DWI) quality on abdominal apparent diffusion coefficient (ADC) measurements and the usefulness of anisotropic images. Twenty-six patients (10 men and 16 women; mean, 58.1 years) who underwent DW imaging and were diagnosed not to have any abdominal diseases were analyzed. Single-shot spin-echo echo-planar DW imaging was performed, and one isotropic and three orthogonal anisotropic images were created. ADCs were calculated for liver (four segments), spleen, pancreas (head, body, tail) and renal parenchyma. Image quality for each organ part was scored visually. We estimated the correlation between ADC and image quality and evaluated the feasibility of using anisotropic images. ADCs and image quality were affected by motion probing gradient directions in the liver and pancreas. A significant inverse correlation was found between ADC and image quality. The r values for isotropic images were −.46, −.48, −.70 and −.28 for the liver, spleen, pancreas and renal parenchyma, respectively. Anisotropic images had the best quality and lowest ADC in at least one organ part in 17 patients. DWIs with the best quality among isotropic and anisotropic images should be used in the liver and pancreas.     

Correlation of CSF neuroinflammatory molecules with leptomeningeal cortical subcortical white matter fractional anisotropy in neonatal meningitis

It has been previously hypothesized that the high fractional anisotropy (FA) values in leptomeningeal cortical subcortical white matter (LCSWM) regions of neonatal brain with bacterial meningitis is due to the presence of adhesion molecules in the subarachnoid space, which are responsible for adherence of inflammatory cells over the subarachnoid membrane. The aim of this study was to look for any relationship between FA values in LCSWM regions and various neuroinflammatory molecules (NMs) in cerebrospinal fluid (CSF) measured in neonates with bacterial meningitis. Diffusion tensor imaging was performed on 18 term neonates (median age, 10.5 days) having bacterial meningitis and 10 age-/sex-matched healthy controls. CSF enzyme-linked immunosorbent assay was performed to quantify NMs [soluble intracellular adhesion molecules (sICAM), tumor necrosis factor-alpha (TNF-alpha) and interleukin-1beta (IL-1beta)]. Significantly increased FA values were observed in LCSWM regions of the patients compared to controls. A significant positive correlation was observed between FA values in LCSWM regions and NMs [sICAM (r=0.67, P=.006), TNF-alpha (r=0.69, P=.005) and IL-1beta (r=0.82, P=.000)] in CSF of these patients. No difference in FA values (P=.99) in LCSWM regions was observed between patients with sterile (0.12+/-0.02) and culture-positive CSF study (0.12+/-0.02). FA may be used as noninvasive surrogate marker of NMs in neonatal meningitis in assessing therapeutic response in future.     

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit.     

Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection

An evaluative methodology and five accompanying performance measures were developed to quantitatively assess the performance of the skeleton projection algorithm constituting the heart of tract-based spatial statistics (TBSS). The performance measures were designed to quantify the accuracy of skeleton projection in its indented task of alleviating any residual misalignment that may remain after image registration. A ground truth fractional anisotropy (FA) image was slightly warped using a realistic warp field that served to model post-registration residual misalignment of varying magnitudes. Skeleton projection was then used to register the warped FA image to the ground truth. Performing skeleton projection was found to yield up to 50% better correspondence between the values of FA compared to smoothing, despite the fact that less than 10% of post-registration misalignment was corrected. The align-max-with-max strategy underlying TBSS was posited as a potential explanation for this high correspondence in the values of FA, at the expense of lesser alignment between anatomically concordant voxels.     

An investigation of motion correction algorithms for pediatric spinal cord DTI in healthy subjects and patients with spinal cord injury

Patient and physiological motion can cause artifacts in DTI of the spinal cord which can impact image quality and diffusion indices. The purpose of this investigation was to determine a reliable motion correction method for pediatric spinal cord DTI and show effects of motion correction on DTI parameters in healthy subjects and patients with spinal cord injury. Ten healthy subjects and ten subjects with spinal cord injury were scanned using a 3 T scanner. Images were acquired with an inner field-of-view DTI sequence covering cervical spine levels C1 to C7. Images were corrected for motion using two types of transformation (rigid and affine) and three cost functions. Corrected images and transformations were examined qualitatively and quantitatively using in-house developed code. Fractional anisotropy (FA) and mean diffusivity (MD) indices were calculated and tested for statistical significance pre- and post- motion correction. Images corrected using rigid methods showed improvements in image quality, while affine methods frequently showed residual distortions in corrected images. Blinded evaluation of pre and post correction images showed significant improvement in cord homogeneity and edge conspicuity in corrected images (p < 0.0001). The average FA changes were statistically significant (p < 0.0001) in the spinal cord injury group, while healthy subjects showed less FA change and were not significant. In both healthy subjects and subjects with spinal cord injury, quantitative and qualitative analysis showed the rigid scaled-least-squares registration technique to be the most reliable and effective in improving image quality.     

Intravoxel incoherent motion diffusion-weighted imaging in head and neck squamous cell carcinoma: Assessment of perfusion-related parameters compared to dynamic contrast-enhanced MRI

Purpose

To investigate the correlation between perfusion-related parameters obtained with intravoxel incoherent motion (IVIM) and classical perfusion parameters obtained with dynamic contrast-enhanced (DCE) magnetic resonance imaging in patients with head and neck squamous cell carcinoma (HNSCC), and to compare direct and asymptotic fitting, the pixel-by-pixel approach, and a region of interest (ROI)-based approach respectively for IVIM parameter calculation.

Materials and methods

Seventeen patients with HNSCC were included in this retrospective study. All magnetic resonance (MR) scanning was performed using a 3 T MR unit. Acquisition of IVIM was performed using single-shot spin-echo echo-planar imaging with three orthogonal gradients with 12 b-values (0, 10, 20, 30, 50, 80, 100, 200, 400, 800, 1000, and 2000). Perfusion-related parameters of perfusion fraction ‘f’ and the pseudo-diffusion coefficient ‘D*’ were calculated from IVIM data by using least square fitting with the two fitting methods of direct and asymptotic fitting, respectively. DCE perfusion was performed in a total of 64 dynamic phases with a 3.2-s phase interval. The two-compartment exchange model was used for the quantification of tumor blood volume (TBV) and tumor blood flow (TBF). Each tumor was delineated with a polygonal ROI for the calculation of f, f ? D* performed using both the pixel-by-pixel approach and the ROI-based approach. In the pixel-by-pixel approach, after fitting each pixel to obtain f, f ? D* maps, the mean value in the delineated ROI on these maps was calculated. In the ROI-based approach, the mean value of signal intensity was calculated within the ROI for each b-value in IVIM images, and then fitting was performed using these values. Correlations between f in a total of four combinations (direct or asymptotic fitting and pixel-by-pixel or ROI-based approach) and TBV were respectively analyzed using Pearson's correlation coefficients. Correlations between f ? D* and TBF were also similarly analyzed.

Results

In all combinations of f and TBV, f ? D* and TBF, there was a significant correlation. In the comparison of f and TBV, a moderate correlation was observed only between f obtained by direct fitting with the pixel-by-pixel approach, whereas a good correlation was observed in the comparisons using the other three combinations. In the comparison of f ? D* and TBF, a good correlation was observed only with f ? D* obtained by asymptotic fitting with the ROI-based approach. In contrast, moderate correlations were observed in the comparisons using the other three combinations.

Conclusion

IVIM was found to be feasible for the analysis of perfusion-related parameters in patients with HNSCC. Especially, the combination of asymptotic fitting with the ROI-based approach was better correlated with DCE perfusion.     

Mapping the human brain white matter tracts relative to cortical and deep gray matter using diffusion tensor imaging at high spatial resolution

The mapping of the human brain white matter fiber networks relative to deep subcortical and cortical gray matter requires high spatial resolution which is challenged by the low signal-to-noise ratio. The purpose of this short report was to introduce a whole brain high spatial resolution diffusion tensor imaging (DTI) protocol that enabled for the first time the mapping of corticopontocerebellar, frontostriatal and thalamofrontal fiber pathways in addition to other limbic, commissural, association and projection white matter pathways relative to the segmented deep gray (e.g., caudate nuclei) and the cortical lobes. Our DTI acquisition protocol and analysis strategy provide important template for brain-behavior research and for teaching brain mapping and are clinically affordable for patient comfort.     

Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Purposes

To evaluate the diagnostic value of diffusion-weighted MRI (DWI) and combination of conventional MRI and DWI to predict metastatic axillary lymph nodes in breast cancer.

Materials and methods

Two hundred fifty-two breast cancer patients with 253 axillae were included. The morphological parameters on axial T2-weighted images without fat saturation and apparent diffusion coefficient (ADC) values were retrospectively analyzed. An independent t-test/chi-square test and receiver operating characteristics (ROC) curve analysis were used.

Results

On conventional MRI, short and long axis length, maximal cortical thickness, relative T2 value, loss of fatty hilum (p < 0.001 for each), and eccentric cortical thickening (p < 0.003) were statistically significantly different between the metastatic and nonmetastatic groups. The short axis to long axis ratio was not a statistically significant parameter. The ADC value was significantly different between the 2 groups, with an AUC that was higher than that of conventional MR parameters (AUC, 0.815; threshold, ≤ 0.986 × 10–3 mm²/sec; sensitivity, 75.8%; specificity, 83.9%). Using the adopted thresholds for each parameter, a total number of findings suggesting malignancy of 4 or higher was determined as the threshold, with high specificity (90.1%).

Conclusion

Using conventional MRI and DWI, we can evaluate the axilla in breast cancer with high specificity.     

Complimentary aspects of diffusion imaging and fMRI: II. Elucidating contributions to the fMRI signal with diffusion sensitization

Tissue water molecules reside in different biophysical compartments. For example, water molecules in the vasculature reside for variable periods of time within arteries, arterioles, capillaries, venuoles and veins, and may be within blood cells or blood plasma. Water molecules outside of the vasculature, in the extravascular space, reside, for a time, either within cells or within the interstitial space between cells. Within these different compartments, different types of microscopic motion that water molecules may experience have been identified and discussed. These range from Brownian diffusion to more coherent flow over the time scales relevant to functional magnetic resonance imaging (fMRI) experiments, on the order of several 10s of milliseconds. How these different types of motion are reflected in magnetic resonance imaging (MRI) methods developed for "diffusion" imaging studies has been an ongoing and active area of research. Here we briefly review the ideas that have developed regarding these motions within the context of modern "diffusion" imaging techniques and, in particular, how they have been accessed in attempts to further our understanding of the various contributions to the fMRI signal changes sought in studies of human brain activation.