Faculty Bibliography

Deep and periventricular white matter hyperintensities (dWMH/pvWMH) are bright appearing white matter tissue lesions in T2-weighted fluid attenuated inversion recovery magnetic resonance images and are frequent observations in the aging human brain. While early stages of these white matter lesions are only weakly associated with cognitive impairment, their progressive growth is a strong indicator for long-term functional decline. DWMHs are typically associated with vascular degeneration in diffuse white matter locations; for pvWMHs, however, no unifying theory exists to explain their consistent onset around the horns of the lateral ventricles. We use patient imaging data to create anatomically accurate finite element models of the lateral ventricles, white and gray matter, and cerebrospinal fluid, as well as to reconstruct their WMH volumes. We simulated the mechanical loading of the ependymal cells forming the primary brain-fluid interface, the ventricular wall, and its surrounding tissues at peak ventricular pressure during the hemodynamic cycle. We observe that both the maximum principal tissue strain and the largest ependymal cell stretch consistently localize in the anterior and posterior horns. Our simulations show that ependymal cells experience a loading state that causes the ventricular wall to be stretched thin. Moreover, we show that maximum wall loading coincides with the pvWMH locations observed in our patient scans. These results warrant further analysis of white matter pathology in the periventricular zone that includes a mechanics-driven deterioration model for the ventricular wall.

PURPOSE/OBJECTIVE:To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics. METHOD/METHODS:In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences. RESULTS:ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively. CONCLUSION/CONCLUSIONS:A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.

Background: Multiple commercial and open-source software applications are available for texture analysis. Nonstandard techniques can cause undesirable variability that impedes result reproducibility and limits clinical utility. Objective: The purpose of this study is to measure agreement of texture metrics extracted by 6 software packages. Methods: This retrospective study included 40 renal cell carcinomas with contrast-enhanced CT from The Cancer Genome Atlas and Imaging Archive. Images were analyzed by 7 readers at 6 sites. Each reader used 1 of 6 software packages to extract commonly studied texture features. Inter and intra-reader agreement for segmentation was assessed with intra-class correlation coefficients. First-order (available in 6 packages) and second-order (available in 3 packages) texture features were compared between software pairs using Pearson correlation. Results: Inter- and intra-reader agreement was excellent (ICC 0.93-1). First-order feature correlations were strong (r>0.8, p<0.001) between 75% (21/28) of software pairs for mean and standard deviation, 48% (10/21) for entropy, 29% (8/28) for skewness, and 25% (7/28) for kurtosis. Of 15 second-order features, only co-occurrence matrix correlation, grey-level non-uniformity, and run-length non-uniformity showed strong correlation between software packages (0.90-1, p<0.001). Conclusion: Variability in first and second order texture features was common across software configurations and produced inconsistent results. Standardized algorithms and reporting methods are needed before texture data can be reliably used for clinical applications. Clinical Impact: It is important to be aware of variability related to texture software processing and configuration when reporting and comparing outputs.

PURPOSE/OBJECTIVE:To assess visceral (VAT), subcutaneous (SAT), and total adipose tissue (TAT) estimates at abdominopelvic CT in COVID-19 patients with different severity, and analyze Body Mass Index (BMI) and CT estimates of fat content in patients requiring hospitalization. METHODS:to discriminate hospitalized patients from outpatients. RESULTS:in hospitalized patients compared to the outpatients (all p < 0.05). Area under the curve (AUC) of the clinical + CT model was higher compared to the clinical model (AUC 0.847 versus 0.750) for identifying patients requiring hospitalization. CONCLUSION/CONCLUSIONS:to the clinical model improved AUC in discriminating hospitalized from outpatients in this preliminary study.

BACKGROUND AND PURPOSE/OBJECTIVE:Previous hippocampal proton MR spectroscopic imaging distinguished patients with schizophrenia from controls by elevated Cr levels and significantly more variable NAA and Cho concentrations. This goal of this study was to ascertain whether this metabolic variability is associated with clinical features of the syndrome, possibly reflecting heterogeneous hippocampal pathologies and perhaps variability in its "positive" (psychotic) and "negative" (social and emotional deficits) symptoms. MATERIALS AND METHODS/METHODS:, we examined the association of NAA and Cho levels with research diagnostic interviews and clinical symptom ratings of the patients. Metabolite concentrations were previously obtained with 3D proton MR spectroscopic imaging at 3T, a technique that facilitates complete coverage of this small, irregularly shaped, bilateral, temporal lobe structure. RESULTS: ≥  .055). CONCLUSIONS:These preliminary findings suggest that NAA and Cho variations reflect different pathophysiologic processes, consistent with microgliosis/astrogliosis and/or lower vitality (reduced NAA) and demyelination (elevated Cho). In particular, the active state-related symptoms, including psychosis and mania, were associated with demyelination. Consequently, their deviations from the means of healthy controls may be a marker that may benefit precision medicine in selection and monitoring of schizophrenia treatment.

Background: Despite studies suggesting that blacks may be at greater risk of developing AD, there have been few studies investigating health disparities, and blacks have been underrepresented in many prominent AD biomarker studies and clinical trials. The current ATN biomarker classification system may not fully account for health disparities and can't explain the increased prevalence among blacks for both AD and AD vascular risks of diabetes and hypertension when compared to whites. Research on cognitive aging has traditionally focused on how decline in various cortical and hippocampal (Hip) regions influences cognition. However, tau pathology emerges decades before amyloid pathology, appearing first in the brainstem (BS); particularly in the locus coeruleus (LC), the source of brain's norepinephrine (NE). Our decade-long studies in humans using a norepinephrine transporter (NET)-selective radiotracer ([11C]MRB) have demonstrated a special vulnerability of LC to aging and stress.
Method(s): Co-registration of PET (dynamic [11C]MRB), MRI and the FreeSurfer (FS) atlas images of each individual was used to generate regional time-activity curves using Firevoxel. Binding potential (BPND) values were determined using MRTM2 with occipital as the reference region. Annual percent change (APC) of BPND was calculated based on linear regression (APC = 100 x (em-1), m: slope) and effects of age, gender and ethnicity on tracer binding were evaluated.
Result(s): For all HC (N=31), with both genders and all races included, age-sensitive decline of NET availability was observed; e.g., 0.3-0.5%/yr for Hip, BS and olfactory. However, our data reveals that the decline rate of NET is much faster among blacks starting in the mid-30s, particularly in black males; e.g., 2-3%/yr vs. 0.14-0.23%/yr in thalamus and brainstem for black males vs. white males (p < 0.00001).
Conclusion(s): In addition to our previously determined age effect on MRB-NET binding, this report further reveals the role of ethnicity effects on NET availability. Our study showed that a faster decline of LC-NE function occurs in blacks, possibly caused by cumulative stress to socioeconomic disadvantage and racial discrimination and may be responsible for the different disease expression among blacks. Thus, NET availability imaging represents a novel biomarker approach to racial-dependent strategies for diagnosis and assessment of therapeutic interventions

The human brain functions at the center of a network of systems aimed at providing a structural and immunological layer of protection. The cerebrospinal fluid (CSF) maintains a physiological homeostasis that is of paramount importance to proper neurological activity. CSF is largely produced in the choroid plexus where it is continuous with the brain extracellular fluid and circulates through the ventricles. CSF movement through the central nervous system has been extensively explored. Across numerous animal species, the involvement of various drainage pathways in CSF, including arachnoid granulations, cranial nerves, perivascular pathways, and meningeal lymphatics, has been studied. Among these, there is a proposed CSF clearance route spanning the olfactory nerve and exiting the brain at the cribriform plate and entering lymphatics. While this pathway has been demonstrated in multiple animal species, evidence of a similar CSF egress mechanism involving the nasal cavity in humans remains poorly consolidated. This review will synthesize contemporary evidence surrounding CSF clearance at the nose-brain interface, examining across species this anatomical pathway, and its possible significance to human neurodegenerative disease. Our discussion of a bidirectional nasal pathway includes examination of the immune surveillance in the olfactory region protecting the brain. Overall, we expect that an expanded discussion of the brain-nose pathway and interactions with the environment will contribute to an improved understanding of neurodegenerative and infectious diseases, and potentially to novel prevention and treatment considerations.

In this study, we investigate the feasibility of using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and dynamic positron emission tomography (PET) for detection of metastatic lymph nodes in head and neck squamous cell carcinoma (HNSCC) cases. Twenty HNSCC patients scheduled for lymph node dissection underwent DCE-MRI, dynamic PET, and DWI using a PET-MR scanner within one week prior to their planned surgery. During surgery, resected nodes were labeled to identify their nodal levels and sent for routine clinical pathology evaluation. Quantitative parameters of metastatic and normal nodes were calculated from DCE-MRI (v_e, v_p, PS, F_p, K^trans), DWI (ADC) and PET (K_i, K₁, k₂, k₃) to assess if an individual or a combination of parameters can classify normal and metastatic lymph nodes accurately. There were 38 normal and 11 metastatic nodes covered by all three imaging methods and confirmed by pathology. 34% of all normal nodes had volumes greater than or equal to the smallest metastatic node while 4 normal nodes had SUV > 4.5. Among the MRI parameters, the median v_p, F_p, PS, and K^trans values of the metastatic lymph nodes were significantly lower (p = <0.05) than those of normal nodes. v_e and ADC did not show any statistical significance. For the dynamic PET parameters, the metastatic nodes had significantly higher k₃ (p value = 8.8 × 10^-8) and K_i (p value = 5.3 × 10^-8) than normal nodes. K₁ and k₂ did not show any statistically significant difference. K_i had the best separation with accuracy = 0.96 (sensitivity = 1, specificity = 0.95) using a cutoff of K_i = 5.3 × 10^-3 mL/cm³/min, while k₃ and volume had accuracy of 0.94 (sensitivity = 0.82, specificity = 0.97) and 0.90 (sensitivity = 0.64, specificity = 0.97) respectively. 100% accuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresholding the data with K_i < 5.3 × 10^-3 mL/cm³/min. The results of this preliminary study suggest that quantitative MRI may provide additional value in distinguishing metastatic nodes, particularly among small nodes, when used together with FDG-PET.