The U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) and the U.S. Army Research Laboratory have developed a terahertz (THz) -band imaging system performance model for detection and identification of concealed weaponry. The MATLAB-based model accounts for the effects of all critical sensor and display components and for the effects of atmospheric attenuation, concealment material attenuation, and active illumination. The model is based on recent U.S. Army NVESD sensor performance modeling technology that couples system design parameters to observer-sensor field performance by using the acquire methodology for weapon identification performance predictions. This THz model has been developed in support of the Defense Advanced Research Project Agencies' Terahertz Imaging Focal-Plane Technology (TIFT) program and is currently being used to guide the design and development of a 0.650 THz active-passive imaging system. This paper will describe the THz model in detail, provide and discuss initial modeling results for a prototype THz imaging system, and outline plans to calibrate and validate the model through human perception testing.
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http://dx.doi.org/10.1364/ao.47.001286 | DOI Listing |
Transl Psychiatry
January 2025
Genetic Epidemiology Group, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Experiencing a traumatic event may lead to Posttraumatic Stress Disorder (PTSD), including symptoms such as flashbacks and hyperarousal. Individuals suffering from PTSD are at increased risk of cardiovascular disease (CVD), but it is unclear why. This study assesses shared genetic liability and potential causal pathways between PTSD and CVD.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFAcad Radiol
January 2025
University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network-Sinai Health System -Women's College Hospital, University of Toronto, Toronto, ON, Canada (S.A.M., P.V.H., U.M., A.B.D.). Electronic address:
Rationale And Objectives: Recently, the Response Evaluation Using PSMA PET/CT in Patients with Metastatic Castration-Resistant Prostate Cancer (RECIP 1.0) was proposed to better evaluate treatment response in prostate cancer patients using PET/CT with prostate-specific membrane antigen (PSMA) than more traditional approaches like metabolic PET evaluation response criteria in solid tumor (PERCIST 1.0).
View Article and Find Full Text PDFClin Breast Cancer
December 2024
Hospital Universitario de Bellvitge, Gynecology, Hospitalet de Llobregat, Barcelona, Spain.
Purpose: To validate the Axillary Reverse Mapping (ARM) technique with indocyanine green (ICG), focusing on the detection rate and the procedure's feasibility. The predictive factors for metastatic involvement of ARM nodes are also analyzed to define the target population for ARM indication.
Methods: This prospective, observational, non-randomized study of patients with breast cancer included patients with an indication for axillary lymph node dissection (ALND) performed between June 2021 and June 2023.
Cancer Lett
January 2025
Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, P.R. China, 210029; The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu Province, China. Electronic address:
Preoperative detection of muscle-invasive bladder cancer (MIBC) remains a great challenge in practice. We aimed to develop and validate a deep Vesical Imaging Network (ViNet) model for the detection of MIBC using high-resolution Tweighted MR imaging (hrTWI) in a multicenter cohort. ViNet was designed using a modified 3D ResNet, in which, the encoder layers were pretrained using a self-supervised foundation model on over 40,000 cross-modal imaging datasets for transfer learning, and the classification modules were weakly supervised by an experiential knowledge-domain mask indicated by a nnUNet segmentation model.
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