Mohammad Reza Nazem-Zadeh

Regional Variation in Brain White Matter Diffusion Index Changes following Chemoradiotherapy: A Prospective Study Using Tract-Based Spatial Statistics

Purpose There is little known about how brain white matter structures differ in their response to radiation, which may have implications for radiation-induced neurocognitive impairment. We used diffusion tensor imaging (DTI) to examine regional variation in white matter changes following chemoradiotherapy. Methods Fourteen patients receiving two or three weeks of whole-brain radiation therapy (RT) ± chemotherapy underwent DTI pre-RT, at end-RT, and one month post-RT. Three diffusion indices were measured: fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). We determined significant individual voxel changes of diffusion indices using tract-based spatial statistics, and mean changes of the indices within fourteen white matter structures of interest. Results Voxels of significant FA decreases and RD increases were seen in all structures (p<0.05), with the largest changes (20–50%) in the fornix, cingula, and corpus callosum. There were highly significant between-structure differences in pre-RT to end-RT mean FA changes (p<0.001). The inferior cingula had a mean FA decrease from pre-RT to end-RT significantly greater than 11 of the 13 other structures (p<0.00385). Conclusions Brain white matter structures varied greatly in their response to chemoradiotherapy as measured by DTI changes. Changes in FA and RD related to white matter demyelination were prominent in the cingula and fornix, structures relevant to radiation-induced neurocognitive impairment. Future research should evaluate DTI as a predictive biomarker of brain chemoradiotherapy adverse effects.

Radiation therapy effects on white matter fiber tracts of the limbic circuit

PURPOSE To segment fiber tracts in the limbic circuit and to assess their sensitivity to radiation therapy (RT). METHODS Twelve patients with brain metastases who had received fractionated whole brain radiation therapy to 30 Gy or 37.5 Gy were included in the study. Diffusion weighted images were acquired pre-RT, at the end of RT, and 1-month post-RT. The fornix, corpus callosum, and cingulum were extracted from diffusion weighted images by combining fiber tracking and segmentation methods based upon characteristics of the fiber bundles. Cingulum was segmented by a seed-based tractography, fornix by a region of interests (ROI)-based tractography, and corpus callosum by a level-set segmentation algorithm. The radiation-induced longitudinal changes of diffusion indices of the structures were evaluated. RESULTS Significant decreases were observed in the fractional anisotropy of the posterior part of the cingulum, fornix, and corpus callosum from pre-RT to end of RT by -14.0%, -12.5%, and -5.2%, respectively (p < 0.001), and from pre-RT to 1-month post-RT by -11.9%, -12.8%, and -6.4%, respectively (p < 0.001). Moreover, significant increases were observed in the mean diffusivity of the corpus callosum and the posterior part of the cingulum from pre-RT to end of RT by 6.8% and 6.5%, respectively, and from pre-RT to 1-month post-RT by 8.5% and 6.3%, respectively. The increase in the radial diffusivity primarily contributed to the significant decrease in the fractional anisotropy, indicating that demyelination is the predominant radiation effect on the white matter structures. CONCLUSIONS Our findings indicate that the fornix and the posterior part of the cingulum are significantly susceptible to radiation damage. We have developed robust computer-aided semiautomatic segmentation and fiber tracking tools to facilitate the ROI delineation of critical structures, which is important for assessment of radiation damage in a longitudinal fashion.

Atlas-based fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients.

PURPOSE To develop an automatic atlas-based method for segmentation of fiber bundles using High Angular Resolution Diffusion Imaging (HARDI) data. HYPOTHESIS Quantitative evaluation of diffusion characteristics inside specific fiber bundles provides new insights into disease developments, evolutions, therapy effects, and surgical interventions. BACKGROUND Most of previous segmentation methods use similarity measures and strategies that do not lead to accurate segmentation results. They also suffer from subjectivity of initial seeds and regions of interest (ROI) defined by operator. MATERIALS AND METHODS We propose a novel method that uses Spherical Harmonic Coefficients (SHC) of HARDI diffusion signals to compute Orientation Distribution Function (ODF) and to extract Principal Diffusion Directions (PDDs). The proposed method selects most collinear PDD of neighbors of each voxel. Then, based on SHC and selected PDD, a similarity measure is proposed and used as a speed function in the level set framework that segments fiber bundles. To automate the process, an atlas-based method is used to select initial seeds for fiber bundles. To generate data for evaluation of the proposed method, an artificial pattern consisting of three crossing bundles intersected by a circular bundle is created. Also, two normal controls are imaged by two different HARDI protocols. RESULTS Segmentation results for different fiber bundles in simulated data and normal control data are presented. Influence of threshold selection on the proposed segmentation method is evaluated using Dice coefficient. Also, effect of increasing the number of gradient directions on accuracy of extracted PDDs is evaluated. CONCLUSION The proposed segmentation method has advantages over previous methods especially those that use similarity measures based on diffusion tensor imaging (DTI) data. These advantages are achieved by proper propagation of a hyper-surface in fiber crossing areas without making assumptions about diffusivity profile and selection of initial seeds or ROI.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

PURPOSE Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. METHODS A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. RESULTS Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. CONCLUSIONS The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.

Lateralization of temporal lobe epilepsy using a novel uncertainty analysis of MR diffusion in hippocampus, cingulum, and fornix, and hippocampal volume and FLAIR intensity

PURPOSE To analyze the utility of a quantitative uncertainty analysis approach for evaluation and comparison of various MRI findings for the lateralization of epileptogenicity in mesial temporal lobe epilepsy (mTLE), including novel diffusion-based analyses. METHODS We estimated the hemispheric variation uncertainty (HVU) of hippocampal T1 volumetry and FLAIR (Fluid Attenuated Inversion Recovery) intensity. Using diffusion tensor images of 23 nonepileptic subjects, we estimated the HVU levels of mean diffusivity (MD) in the hippocampus, and fractional anisotropy (FA) in the posteroinferior cingulum and crus of fornix. Imaging from a retrospective cohort of 20 TLE patients who had undergone surgical resection with Engel class I outcomes was analyzed to determine whether asymmetry of preoperative volumetrics, FLAIR intensities, and MD values in hippocampi, as well as FA values in posteroinferior cingula and fornix crura correctly predicted laterality of seizure onset. Ten of the cohort had pathologically proven mesial temporal sclerosis (MTS). Seven of these patients had undergone extraoperative electrocorticography (ECoG) for lateralization or to rule out extra-temporal foci. RESULTS HVU was estimated to be 3.1×10(-5) for hippocampal MD, 0.027 for FA in posteroinferior cingulum, 0.018 for FA in crus of fornix, 0.069 for hippocampal normalized volume, and 0.099 for hippocampal normalized FLAIR intensity. Using HVU analysis, a higher hippocampal MD value, lower FA within the posteroinferior cingulum and crus of fornix, shrinkage in hippocampal volume, and higher hippocampal FLAIR intensity were observed beyond uncertainty on the side ipsilateral to seizure onset for 10, 10, 9, 9, and 10 out of 10 pathology-proven MTS patients, respectively. Considering all 20 TLE patients, these numbers were 18, 15, 14, 13, and 16, respectively. However, consolidating the lateralization results of HVU analysis on these quantities by majority voting has detected the epileptogenic side for 19 out of 20 cases with no wrong lateralization. CONCLUSION The presence of MTS in TLE patients is associated with an elevated MD value in the ipsilateral hippocampus and a reduced FA value in the posteroinferior subregion of the ipsilateral cingulum and crus of ipsilateral fornix. When considering all TLE patients, among the mentioned biomarkers the hippocampal MD had the best performance with true detection rate of 90% without any wrong lateralization. The proposed uncertainty based analyses hold promise for improving decision-making for surgical resection.

Segmentation of corpus callosum using diffusion tensor imaging: Validation in patients with glioblastoma

BackgroundThis paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma.MethodsNineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases.ResultsDice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results.ConclusionsThe proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity).

Comparison of neurite density measured by MRI and histology after TBI.

Background Functional recovery after brain injury in animals is improved by marrow stromal cells (MSC) which stimulate neurite reorganization. However, MRI measurement of neurite density changes after injury has not been performed. In this study, we investigate the feasibility of MRI measurement of neurite density in an animal model of traumatic brain injury (TBI) with and without MSC treatment. Methods Fifteen male Wistar rats, were treated with saline (n = 6) or MSCs (n = 9) and were sacrificed at 6 weeks after controlled cortical impact (CCI). Healthy non-CCI rats (n = 5), were also employed. Ex-vivo MRI scans were performed two days after the rats were sacrificed. Multiple-shell hybrid diffusion imaging encoding scheme and spherical harmonic expansion of a two-compartment water diffusion displacement model were used to extract neurite related parameters. Bielshowski and Luxol Fast blue was used for staining axons and myelin, respectively. Modified Morris water maze and neurological severity score (mNSS) test were performed for functional evaluation. The treatment effects, the correlations between neurite densities measured by MRI and histology, and the correlations between MRI and functional variables were calculated by repeated measures analysis of variance, the regression correlation analysis tests, and spearman correlation coefficients. Results Neurite densities exhibited a significant correlation (R2>0.80, p<1E−20) between MRI and immuno-histochemistry measurements with 95% lower bound of the intra-correlation coefficient (ICC) as 0.86. The conventional fractional anisotropy (FA) correlated moderately with histological neurite density (R2 = 0.59, P<1E−5) with 95% lower bound of ICC as 0.76. MRI data revealed increased neurite reorganization with MSC treatment compared with saline treatment, confirmed by histological data from the same animals. mNSS were significantly correlated with MRI neurite density in the hippocampus region. Conclusions The present studies demonstrated that neurite density can be estimated by MRI after TBI and MRI measurement of neurite density is a sensitive marker to MSC treatment response.

Uncertainty in assessment of radiation-induced diffusion index changes in individual patients

The purpose of this study is to evaluate repeatability coefficients of diffusion tensor indices to assess whether longitudinal changes in diffusion indices were true changes beyond the uncertainty for individual patients undergoing radiation therapy (RT). Twenty-two patients who had low-grade or benign tumors and were treated by partial brain radiation therapy (PBRT) participated in an IRB-approved MRI protocol. The diffusion tensor images in the patients were acquired pre-RT, week 3 during RT, at the end of RT, and 1, 6, and 18 months after RT. As a measure of uncertainty, repeatability coefficients (RC) of diffusion indices in the segmented cingulum, corpus callosum, and fornix were estimated by using test-retest diffusion tensor datasets from the National Biomedical Imaging Archive (NBIA) database. The upper and lower limits of the 95% confidence interval of the estimated RC from the test and retest data were used to evaluate whether the longitudinal percentage changes in diffusion indices in the segmented structures in the individual patients were beyond the uncertainty and thus could be considered as true radiation-induced changes. Diffusion indices in different white matter structures showed different uncertainty ranges. The estimated RC for fractional anisotropy (FA) ranged from 5.3% to 9.6%, for mean diffusivity (MD) from 2.2% to 6.8%, for axial diffusivity (AD) from 2.4% to 5.5%, and for radial diffusivity (RD) from 2.9% to 9.7%. Overall, 23% of the patients treated by RT had FA changes, 44% had MD changes, 50% had AD changes, and 50% had RD changes beyond the uncertainty ranges. In the fornix, 85.7% and 100% of the patients showed changes beyond the uncertainty range at 6 and 18 months after RT, demonstrating that radiation has a pronounced late effect on the fornix compared to other segmented structures. It is critical to determine reliability of a change observed in an individual patient for clinical decision making. Assessments of the repeatability and confidence interval of diffusion tensor measurements in white matter structures allow us to determine the true longitudinal change in individual patients.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models

PURPOSE Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. METHODS AND MATERIALS The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. RESULTS A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. CONCLUSION The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment.

The corpus callosum Wallerian degeneration in the unilateral brain tumors: Evaluation with diffusion tensor imaging (DTI)

PURPOSE The purpose of this study was to evaluate whether DTI could demonstrate the water diffusivity changes in the corpus callosum (CC), which were not visible on the morphologic imaging in patients with glioblastoma multiforme (GBM) and brain metastases with no midline CC infiltration. MATERIALS AND METHODS Twenty-seven patients with treatment naïve unilateral GBM and eleven patients with a solitary brain metastasis with no midline CC infiltration underwent DTI. Ten controls with normal brain MRI were also included. Based on the tensors, the principal diffusion directions, the anisotropy values, and the prior information about the diffusivity pattern in CC, a similarity measure was proposed. Subsequently, the CC was automatically divided into the Witelson subdivisions. RESULTS We observed significantly decreased fractional anisotropy values in all the regions of CC in the patients with GBM and metastases as compared to those in the controls. The mean diffusivity values showed a significant increase in all the regions of CC, except the splenium in patients with GBM and the isthmus in the patients with metastases, as compared to that in the controls respectively. CONCLUSION In conclusion, DTI is more sensitive than the morphologic MR imaging in the evaluation of changes within the CC, in brain tumours which do not infiltrate the CC. However, these changes of the DTI metrics in the CC are due to a Wallerian degeneration rather than a tumour infiltration, as was shown by our results, as similar changes were seen in the GBM as well as the non-infiltrating metastases patients.