Introduction Huntington’s disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). Methods As part of the PREDICT-HD study, N =191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1–5 year follow-up) DWI, with n =649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. Results Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. Conclusions This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the pre-motor area and the putamen, which may be closely tied to the onset of motor symptoms in HD.

Purpose Molecular imaging has provided unparalleled opportunities to monitor disease processes, although tools for evaluating infection remain limited. Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mediated by lung injury that we sought to model. Activated macrophages/phagocytes have an important role in lung injury, which is responsible for subsequent respiratory failure and death. We performed pulmonary PET/CT with 124 I-iodo-DPA-713, a low-molecular-weight pyrazolopyrimidine ligand selectively trapped by activated macrophages cells, to evaluate the local immune response in a hamster model of SARS-CoV-2 infection. Procedures Pulmonary 124 I-iodo-DPA-713 PET/CT was performed in SARS-CoV-2-infected golden Syrian hamsters. CT images were quantified using a custom-built lung segmentation tool. Studies with DPA-713-IRDye680LT and a fluorescent analog of DPA-713 as well as histopathology and flow cytometry were performed on post-mortem tissues. Results Infected hamsters were imaged at the peak of inflammatory lung disease (7 days post-infection). Quantitative CT analysis was successful for all scans and demonstrated worse pulmonary disease in male versus female animals ( P < 0.01). Increased 124 I-iodo-DPA-713 PET activity co-localized with the pneumonic lesions. Additionally, higher pulmonary 124 I-iodo-DPA-713 PET activity was noted in male versus female hamsters ( P = 0.02). DPA-713-IRDye680LT also localized to the pneumonic lesions. Flow cytometry demonstrated a higher percentage of myeloid and CD11b + cells (macrophages, phagocytes) in male versus female lung tissues ( P = 0.02). Conclusion 124 I-Iodo-DPA-713 accumulates within pneumonic lesions in a hamster model of SARS-CoV-2 infection. As a novel molecular imaging tool, 124 I-Iodo-DPA-713 PET could serve as a noninvasive, clinically translatable approach to monitor SARS-CoV-2-associated pulmonary inflammation and expedite the development of novel therapeutics for COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s11307-021-01638-5.

Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points. This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the proposed method is accurate as the average error between the automatically and manually labeled landmark points is less than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds of images with various orientation, spacing, and origin.

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.