John H. Woo

High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis

Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.

Cognitive decline and reduced survival in C9orf72 expansion frontotemporal degeneration and amyotrophic lateral sclerosis

Background Significant heterogeneity in clinical features of frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS) cases with the pathogenic C9orf72 expansion (C9P) have been described. To clarify this issue, we compared a large C9P cohort with carefully matched non-expansion (C9N) cases with a known or highly-suspected underlying TAR DNA-binding protein 43 (TDP-43) proteinopathy. Methods A retrospective case-control study was carried out using available cross-sectional and longitudinal clinical and neuropsychological data, MRI voxel-based morphometry (VBM) and neuropathological assessment from 64 C9P cases (ALS=31, FTLD=33) and 79 C9N cases (ALS=36, FTLD=43). Results C9P cases had an earlier age of onset (p=0.047) and, in the subset of patients who were deceased, an earlier age of death (p=0.014) than C9N. C9P had more rapid progression than C9N: C9P ALS cases had a shortened survival (2.6±0.3 years) compared to C9N ALS (3.8±0.4 years; log-rank λ2=4.183, p=0.041), and C9P FTLD showed a significantly greater annualised rate of decline in letter fluency (4.5±1.3 words/year) than C9N FTLD (1.4±0.8 words/year, p=0.023). VBM revealed greater atrophy in the right frontoinsular, thalamus, cerebellum and bilateral parietal regions for C9P FTLD relative to C9N FTLD, and regression analysis related verbal fluency scores to atrophy in frontal and parietal regions. Neuropathological analysis found greater neuronal loss in the mid-frontal cortex in C9P FTLD, and mid-frontal cortex TDP-43 inclusion severity correlated with poor letter fluency performance. Conclusions C9P cases may have a shorter survival in ALS and more rapid rate of cognitive decline related to frontal and parietal disease in FTLD. C9orf72 genotyping may provide useful prognostic and diagnostic clinical information for patients with ALS and FTLD.

Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas.

BACKGROUND AND PURPOSE: Noninvasive grading of gliomas remains a challenge despite its important role in the prognosis and management of patients with intracranial neoplasms. In this study, we evaluated the ability of cerebral blood flow (CBF)-guided voxel-by-voxel analysis of multivoxel proton MR spectroscopic imaging (1H-MRSI) to differentiate low-grade from high-grade gliomas. MATERIALS AND METHODS: A total of 35 patients with primary gliomas (22 high grade and 13 low grade) underwent continuous arterial spin-labeling perfusion-weighted imaging (PWI) and 1H-MRSI. Different regions of the gliomas were categorized as “hypoperfused,” “isoperfused,” and “hyperperfused” on the basis of the average CBF obtained from contralateral healthy white matter. 1H-MRSI indices were computed from these regions and compared between low- and high-grade gliomas. Using a similar approach, we applied a subgroup analysis to differentiate low- from high-grade oligodendrogliomas because they show different physiologic and genetic characteristics. RESULTS: Choglioma (G)/white matter (WM), GlxG/WM, and Lip+LacG/CrWM were significantly higher in the “hyperperfused” regions of high-grade gliomas compared with low-grade gliomas. ChoG/WM and Lip+LacG/CrWM were also significantly higher in the “hyperperfused” regions of high-grade oligodendrogliomas. However, metabolite ratios from the “hypoperfused” or “isoperfused” regions did not exhibit any significant differences between high-grade and low-grade gliomas. CONCLUSION: The results suggest that 1H-MRSI indices from the “hyperperfused” regions of gliomas, on the basis of PWI, may be helpful in distinguishing high-grade from low-grade gliomas including oligodendrogliomas.

Posttreatment recurrence of malignant brain neoplasm: Accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction

PURPOSE To determine the accuracy of relative cerebral blood volume (rCBV) fraction for distinguishing high-grade recurrent neoplasm from treatment-related necrosis (TRN) in enhancing masses identified on surveillance magnetic resonance (MR) images following treatment for primary or secondary brain neoplasm. MATERIALS AND METHODS This institutional review board approved and HIPAA-compliant retrospective study included 30 patients undergoing resection of recurrent enhancing mass appearing after treatment with surgery and radiation, with or without chemotherapy. The enhancing mass volume was manually segmented on three-dimensional T1-weighted images. The rCBV maps were created by using T2-weighted dynamic susceptibility contrast perfusion MR imaging and registered to T1-weighted images, and the fraction of enhancing mass with rCBV above a range of thresholds was calculated. A receiver operating characteristic (ROC) curve was created by calculating sensitivity-specificity pairs at each threshold for rCBV fraction (< or = 20% or > 20%) by using percentage of malignant features at histologic evaluation as the reference criterion. Relationships between rCBV and probability of recurrence were estimated by using logistic regression analysis. RESULTS ROC analysis showed excellent discriminating accuracy of rCBV fraction (area under the ROC curve, 0.97 +/- 0.03 [standard error]) and high efficiency (93%) with an rCBV threshold of 1.8 times that of normal-appearing white matter. Logistic regression analysis showed that a unit increase of rCBV is associated with a 254-fold increase (95% confidence interval: 43, 1504, P < .001) of the odds that enhanced tissue is recurrence, adjusting for age, treatment, volume of enhancing tissue, and time to suspected recurrence. CONCLUSION The fraction of malignant histologic features in enhancing masses recurring after treatment for brain neoplasms can be predicted by using the rCBV fraction, with improved differentiation between recurrent neoplasm and TRN.

Prediction of oligodendroglial tumor subtype and grade using perfusion weighted magnetic resonance imaging

OBJECT Treatment of patients with oligodendrogliomas relies on histopathological grade and characteristic cytogenetic deletions of 1p and 19q, shown to predict radio- and chemosensitivity and prolonged survival. Perfusion weighted magnetic resonance (MR) imaging allows for noninvasive determination of relative tumor blood volume (rTBV) and has been used to predict the grade of astrocytic neoplasms. The aim of this study was to use perfusion weighted MR imaging to predict tumor grade and cytogenetic profile in oligodendroglial neoplasms. METHODS Thirty patients with oligodendroglial neoplasms who underwent preoperative perfusion MR imaging were retrospectively identified. Tumors were classified by histopathological grade and stratified into two cytogenetic groups: 1p or 1p and 19q loss of heterozygosity (LOH) (Group 1), and 19q LOH only on intact alleles (Group 2). Tumor blood volume was calculated in relation to contralateral white matter. Multivariate logistic regression analysis was used to develop predictive models of cytogenetic profile and tumor grade. RESULTS In World Health Organization Grade II neoplasms, the rTBV was significantly greater (p < 0.05) in Group 1 (mean 2.44, range 0.96-3.28; seven patients) compared with Group 2 (mean 1.69, range 1.27-2.08; seven patients). In Grade III neoplasms, the differences between Group 1 (mean 3.38, range 1.59-6.26; four patients) and Group 2 (mean 2.83, range 1.81-3.76; 12 patients) were not significant. The rTBV was significantly greater (p < 0.05) in Grade III neoplasms (mean 2.97, range 1.59-6.26; 16 patients) compared with Grade II neoplasms (mean 2.07, range 0.96-3.28; 14 patients). The models integrating rTBV with cytogenetic profile and grade showed prediction accuracies of 68 and 73%, respectively. CONCLUSIONS Oligodendroglial classification models derived from advanced imaging will improve the accuracy of tumor grading, provide prognostic information, and have potential to influence treatment decisions.

A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features

Diffusion tensor imaging plays a key role in our understanding of white matter both in normal populations and in populations with brain disorders. Existing techniques focus primarily on using diffusivity-based quantities derived from diffusion tensor as surrogate measures of microstructural tissue properties of white matter. In this paper, we describe a novel tract-specific framework that enables the examination of white matter morphometry at both the macroscopic and microscopic scales. The framework leverages the skeleton-based modeling of sheet-like white matter fasciculi using the continuous medial representation, which gives a natural definition of thickness and supports its comparison across subjects. The thickness measure provides a macroscopic characterization of white matter fasciculi that complements existing analysis of microstructural features. The utility of the framework is demonstrated in quantifying white matter atrophy in Amyotrophic Lateral Sclerosis, a severe neurodegenerative disease of motor neurons. We show that, compared to using microscopic features alone, combining the macroscopic and microscopic features gives a more complete characterization of the disease.

Mineral Volume and Morphology in Carotid Plaque Specimens Using High-Resolution MRI and CT

Objective—High-resolution MRI methods have been used to evaluate carotid artery atherosclerotic plaque content. The purpose of this study was to assess the performance of high-resolution MRI in evaluation of the quantity and pattern of mineral deposition in carotid endarterectomy (CEA) specimens, with quantitative micro-CT as the gold standard. Methods and Results—High-resolution MRI and CT were compared in 20 CEA specimens. Linear regression comparing mineral volumes generated from CT (VCT) and MRI (VMRI) data demonstrated good correlation using simple thresholding (VMRI=−0.01+0.98VCT; R2=0.90; threshold=4×noise) and k-means clustering methods (VMRI=−0.005+1.38VCT; R2=0.93). Bone mineral density (BMD) and bone mineral content (BMC [mineral mass]) were calculated for CT data and BMC verified with ash weight. Patterns of mineralization like particles, granules, and sheets were more clearly depicted on CT. Conclusions—Mineral volumes generated from MRI or CT data were highly correlated. CT provided a more detailed depiction of mineralization patterns and provided BMD and BMC in addition to mineral volume. The extent of mineralization as well as the morphology may ultimately be useful in assessing plaque stability.

ALS-Plus syndrome: non-pyramidal features in a large ALS cohort.

OBJECTIVE Autopsy studies show widespread pathology in amyotrophic lateral sclerosis (ALS), but clinical surveys of multisystem disease in ALS are rare. We investigated ALS-Plus syndrome, an understudied group of patients with clinical features extending beyond pyramidal and neuromuscular systems with or without cognitive/behavioral deficits. METHODS In a large, consecutively-ascertained cohort of 550 patients with ALS, we documented atypical clinical manifestations. Genetic screening for C9orf72 hexanucleotide expansions was performed in 343 patients, and SOD1, TARDBP, and VCP were tested in the subgroup of patients with a family history of ALS. Gray matter and white matter imaging was available in a subgroup of 30 patients. RESULTS Seventy-five (13.6%) patients were identified with ALS-Plus syndrome. We found disorders of ocular motility, cerebellar, extrapyramidal and autonomic functioning. Relative to those without ALS-Plus, cognitive impairment (8.0% vs 2.9%, p=0.029), bulbar-onset (49.3% vs 23.2%, p<0.001), and pathogenic mutations (20.0% vs 8.4%, p=0.015) were more than twice as common in ALS-Plus. Survival was significantly shorter in ALS-Plus (29.66 months vs 42.50 months, p=0.02), regardless of bulbar-onset or mutation status. Imaging revealed significantly greater cerebellar and cerebral disease in ALS-Plus compared to those without ALS-Plus. CONCLUSIONS ALS-Plus syndrome is not uncommon, and the presence of these atypical features is consistent with neuropathological observations that ALS is a multisystem disorder. ALS-Plus syndrome is associated with increased risk for poor survival and the presence of a pathogenic mutation.

Neuroimaging in amyotrophic lateral sclerosis.

SummaryAmyotrophic lateral sclerosis (ALS) is a motor neuron disease characterized by progressive degeneration of upper motor neurons (UMN) and lower motor neurons (LMN). While LMN dysfunction can be confirmed by electromyography (EMG) and muscle biopsy, UMN involvement is more difficult to detect, particularly in the early phase. Objective and sensitive measures of UMN dysfunction are needed for early diagnosis and monitoring of disease progression and therapeutic efficacy. Advanced magnetic resonance imaging (MRI) techniques, such as diffusion, perfusion, magnetization transfer imaging, functional MRI, and MR spectroscopy, provide insight into the pathophysiological processes of ALS and may have a role in the identification and monitoring of UMN pathology. This article provides an overview of these neuroimaging techniques and their potential roles in ALS.

Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases.

BACKGROUND AND PURPOSE: Differentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases. MATERIALS AND METHODS: One hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined. RESULTS: The fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model. CONCLUSIONS: Classification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.