Faculty Bibliography

OBJECTIVE: The fractal dimension of retinal arteries and veins is a measure of the complexity of the vascular tree. We hypothesized that retinal fractal dimension would be associated with brain volume and white matter integrity in HIV-infected women. DESIGN: Nested case-control within longitudinal cohort study. METHODS: Women were recruited from the Brooklyn site of the Women's Interagency HIV study (WIHS); 34 HIV-infected and 21 HIV-uninfected women with analyzable MRIs and retinal photographs were included. Fractal dimension was determined using the SIVA software program on skeletonized retinal images. The relationship between predictors (retinal vascular measures) and outcomes (quantitative MRI measures) were analyzed with linear regression models. All models included age, intracranial volume, and both arterial and venous fractal dimension. Some models were adjusted for blood pressure, race/ethnicity, and HIV-infection. RESULTS: The women were 45.6 +/- 7.3 years of age. Higher arterial dimension was associated with larger cortical volumes, but higher venous dimension was associated with smaller cortical volumes. In fully adjusted models, venous dimension was significantly associated with fractional anisotropy (standardized beta = -0.41, p = 0.009) and total gray matter volume (beta = -0.24, p = 0.03), and arterial dimension with mean diffusivity (beta = -0.33,.p = 0.04) and fractional anisotropy (beta = 0.34, p = 0.03). HIV-infection was not associated with any retinal or MRI measure. CONCLUSIONS: Higher venous fractal dimension was associated with smaller cortical volumes and lower fractional anisotropy, whereas higher arterial fractal dimension was associated with the opposite patterns. Longitudinal studies are needed to validate this finding.

Fungal sinusitis is characterized into invasive and noninvasive forms. The invasive variety is further classified into acute, chronic and granulomatous forms; and the noninvasive variety into fungus ball and allergic fungal sinusitis. Each of these different forms has a unique radiologic appearance. The clinicopathologic and corresponding radiologic spectrum and differences in treatment strategies of fungal sinusitis make it an important diagnosis for clinicians and radiologists to always consider. This is particularly true of invasive fungal sinusitis, which typically affects immuno compromised patients and is associated with significant morbidity and mortality. Early diagnosis allows initiation of appropriate treatment strategies resulting in favorable outcome.

BACKGROUND: Orbital decompression is frequently performed in the management of patients with sight-threatening and disfiguring Graves' ophthalmopathy. The quantitative measurements of the change in orbital volume after orbital decompression procedures are not definitively known. Furthermore, the quantitative effect of septal deviation on volume change has not been previously analyzed. OBJECTIVES: To provide quantitative measurement of orbital volume change after medial and inferior endoscopic decompression and describe a straightforward method of measuring this change using open-source technologies. A secondary objective was to assess the effect of septal deviation on orbital volume change. METHODS: A retrospective review was performed on all patients undergoing medial and inferior endoscopic orbital decompression for Graves' ophthalmopathy at a tertiary care academic medical center. Pre-operative and post-operative orbital volumes were calculated from computed tomography (CT) data using a semi-automated segmenting technique and Osirix, an open-source DICOM reader. Data were collected for pre-operative and post-operative orbital volumes, degree of septal deviation, time to follow-up scan, and individual patient Hertel scores. RESULTS: Nine patients (12 orbits) were imaged before and after decompression. Mean pre-operative orbital volume was 26.99cm(3) (SD=2.86cm(3)). Mean post-operative volume was 33.07cm(3) (SD=3.96cm(3)). The mean change in volume was 6.08cm(3) (SD=2.31cm(3)). The mean change in Hertel score was 4.83 (SD=0.75). Regression analysis of change in volume versus follow-up time to imaging indicates that follow-up time to imaging has little effect on change in volume (R=-0.2), and overall mean maximal septal deviation toward the operative side was -0.5mm. Negative values were attributed to deviation away form the operative site. A significant correlation was demonstrated between change in orbital volume and septal deviation distance site (R=0.66), as well as between change in orbital volume and septal deviation angle (R=0.67). Greater volume changes were associated with greater degree of septal deviation away from the surgical site, whereas smaller volume changes were associated with greater degree of septal deviation toward the surgical site. CONCLUSION: A straightforward, semi-automated segmenting technique for measuring change in volume following endoscopic orbital decompression is described. This method proved useful in determining that a mean increase of approximately 6cm in volume was achieved in this group of patients undergoing medial and inferior orbital decompression. Septal deviation appears to have an effect on the surgical outcome and should be considered during operative planning.

Diffusion MRI combined with biophysical modeling allows for the description of a white matter (WM) fiber bundle in terms of compartment specific white matter tract integrity (WMTI) metrics, which include intra-axonal diffusivity (Daxon), extra-axonal axial diffusivity (De||), extra-axonal radial diffusivity (De upper left and right quadrants), axonal water fraction (AWF), and tortuosity (alpha) of extra-axonal space. Here we derive these parameters from diffusion kurtosis imaging to examine their relationship to concentrations of global WM N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho) and myo-Inositol (mI), as measured with proton MR spectroscopy (1H-MRS), in a cohort of 25 patients with mild traumatic brain injury (MTBI). We found statistically significant (p<0.05) positive correlations between NAA and Daxon, AWF, alpha, and fractional anisotropy; negative correlations between NAA and De, upper left and right quadrants and the overall radial diffusivity (D upper left and right quadrants). These correlations were supported by similar findings in regional analysis of the genu and splenium of the corpus callosum. Furthermore, a positive correlation in global WM was noted between Daxon and Cr, as well as a positive correlation between De|| and Cho, and a positive trend between De|| and mI. The specific correlations between NAA, an endogenous probe of the neuronal intracellular space, and WMTI metrics related to the intra-axonal space, combined with the specific correlations of De|| with mI and Cho, both predominantly present extra-axonally, corroborate the overarching assumption of many advanced modeling approaches that diffusion imaging can disentangle between the intra- and extra-axonal compartments in WM fiber bundles. Our findings are also generally consistent with what is known about the pathophysiology of MTBI, which appears to involve both intra-axonal injury (as reflected by a positive trend between NAA and Daxon) as well as axonal shrinkage, demyelination, degeneration, and/or loss (as reflected by correlations between NAA and De upper left and right quadrants, AWF, and alpha).

The past decade has seen impressive advances in the types of neuroimaging information that can be acquired in patients with traumatic brain injury. However, despite this increase in information, understanding of the contribution of this information to prognostic accuracy and treatment pathways for patients is limited. Available techniques often allow us to infer the presence of microscopic changes indicative of alterations in physiology and function in brain tissue. However, because histologic confirmation is typically lacking, conclusions reached by using these techniques remain solely inferential in almost all cases. Hence, a need exists for validation of these techniques by using data from large population samples that are obtained in a uniform manner, analyzed according to well-accepted procedures, and correlated with closely monitored clinical outcomes. At present, many of these approaches remain confined to population-based research rather than diagnosis at an individual level, particularly with regard to traumatic brain injury that is mild or moderate in degree. A need and a priority exist for patient-centered tools that will allow advanced neuroimaging tools to be brought into clinical settings. One barrier to developing these tools is a lack of an age-, sex-, and comorbidities-stratified, sequence-specific, reference imaging data base that could provide a clear understanding of normal variations across populations. Such a data base would provide researchers and clinicians with the information necessary to develop computational tools for the patient-based interpretation of advanced neuroimaging studies in the clinical setting. The recent "Joint ASNR-ACR HII-ASFNR TBI Workshop: Bringing Advanced Neuroimaging for Traumatic Brain Injury into the Clinic" on May 23, 2014, in Montreal, Quebec, Canada, brought together neuroradiologists, neurologists, psychiatrists, neuropsychologists, neuroimaging scientists, members of the National Institute of Neurologic Disorders and Stroke, industry representatives, and other traumatic brain injury stakeholders to attempt to reach consensus on issues related to and develop consensus recommendations in terms of creating both a well-characterized normative data base of comprehensive imaging and ancillary data to serve as a reference for tools that will allow interpretation of advanced neuroimaging tests at an individual level of a patient with traumatic brain injury. The workshop involved discussions concerning the following: 1) designation of the policies and infrastructure needed for a normative data base, 2) principles for characterizing normal control subjects, and 3) standardizing research neuroimaging protocols for traumatic brain injury. The present article summarizes these recommendations and examines practical steps to achieve them.

Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton (1 H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head 1 H-MRS from 18 healthy young adults were examined. Non-localized, whole-head 1 H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1 -weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 +/- 2.5 mm for integration, 12.8 +/- 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (+/-1.3 mm) for integration versus 7.0% (+/-0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer-Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head 1 H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders

OBJECTIVE: The purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls. METHODS: This was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population. We acquired data on a 3.0T Siemens Trio magnet including conventional brain imaging, resting-state fMRI, diffusion-weighted imaging, and magnetic field correlation (MFC), and performed multifeature analysis using the following MRI metrics: mean kurtosis (MK) of thalamus, MFC of thalamus and frontal white matter, thalamocortical resting-state networks, and 5 regional gray matter and white matter volumes including the anterior cingulum and left frontal and temporal poles. Feature selection was performed using minimal-redundancy maximal-relevance. We used classifiers including support vector machine, naive Bayesian, Bayesian network, radial basis network, and multilayer perceptron to test maximal accuracy. RESULTS: We studied 24 patients with mTBI and 26 controls. Best single-feature classification uses thalamic MK yielding 74% accuracy. Multifeature analysis yields 80% accuracy using the full feature set, and up to 86% accuracy using minimal-redundancy maximal-relevance feature selection (MK thalamus, right anterior cingulate volume, thalamic thickness, thalamocortical resting-state network, thalamic microscopic MFC, and sex). CONCLUSION: Multifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection and classification methods. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that classification algorithms using multiple MRI features accurately identifies patients with mTBI as defined by American Congress of Rehabilitation Medicine criteria compared with healthy controls.

PURPOSE: To examine thalamic and cortical injuries using fractional amplitude of low-frequency fluctuations (fALFFs) and functional connectivity MRI (fcMRI) based on resting state (RS) and task-related fMRI in patients with mild traumatic brain injury (MTBI). MATERIALS AND METHODS: Twenty-seven patients and 27 age-matched controls were recruited. The 3 Tesla fMRI at RS and finger tapping task were used to assess fALFF and fcMRI patterns. fALFFs were computed with filtering (0.01-0.08 Hz) and scaling after preprocessing. fcMRI was performed using a standard seed-based correlation method, and delayed fcMRI (coherence) in frequency domain were also performed between thalamus and cortex. RESULTS: In comparison with controls, MTBI patients exhibited significantly decreased fALFFs in the thalamus (and frontal/temporal subsegments) and cortical frontal and temporal lobes; as well as decreased thalamo-thalamo and thalamo-frontal/ thalamo-temporal fcMRI at rest based on RS-fMRI (corrected P < 0.05). This thalamic and cortical disruption also existed at task-related condition in patients. CONCLUSION: The decreased fALFFs (i.e., lower neuronal activity) in the thalamus and its segments provide additional evidence of thalamic injury in patients with MTBI. Our findings of fALFFs and fcMRI changes during motor task and resting state may offer insights into the underlying cause and primary location of disrupted thalamo-cortical networks after MTBI. J. Magn. Reson. Imaging 2013. (c) 2013 Wiley Periodicals, Inc.