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Traditional energy-integration X-ray imaging systems rely on total X-ray intensity for image contrast, ignoring energy-specific information. Recently developed multilayer stacked scintillators have enabled multispectral, large-area flat-panel X-ray imaging (FPXI), enhancing material discrimination capabilities. However, increased layering can lead to mutual excitation, which may affect the accurate discrimination of X-ray energy. This issue is tackled by proposing a novel design strategy utilizing rare earth ions doped quantum-cutting scintillators as the top layer. These scintillators create new luminescence centers via energy transfer, resulting in a significantly larger absorption-emission shift, as well as the potential to double the photoluminescence quantum yield (PLQY) and enhance light output. To verify this concept, a three-layer stacked scintillator detector is developed using ytterbium ions (Yb)-doped CsPbCl perovskite nanocrystals (PeNCs) as the top layer, which offers a high PLQY of over 100% and a significant absorption-emission shift of 570 nm. This configuration, CsAgCl and CsCuI as the middle and bottom layers, respectively, ensures non-overlapping optical absorption and radioluminescence (RL) emission spectra. By calculating the optimal thickness for each layer to absorb specific X-ray energies, the detector demonstrates distinct absorption differences across various energy bands, enhancing the identification of materials with similar densities.
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http://dx.doi.org/10.1002/adma.202416360 | DOI Listing |
J Appl Clin Med Phys
September 2025
Clinical Imaging Physics Group, Duke University Health System, Durham, North Carolina, USA.
Introduction: Medical physicists play a critical role in ensuring image quality and patient safety, but their routine evaluations are limited in scope and frequency compared to the breadth of clinical imaging practices. An electronic radiologist feedback system can augment medical physics oversight for quality improvement. This work presents a novel quality feedback system integrated into the Epic electronic medical record (EMR) at a university hospital system, designed to facilitate feedback from radiologists to medical physicists and technologist leaders.
View Article and Find Full Text PDFJ Appl Clin Med Phys
September 2025
Department of Radiation Oncology, University of Utah, Salt Lake City, Utah, USA.
Purpose: The development of on-board cone-beam computed tomography (CBCT) has led to improved target localization and evaluation of patient anatomical change throughout the course of radiation therapy. HyperSight, a newly developed on-board CBCT platform by Varian, has been shown to improve image quality and HU fidelity relative to conventional CBCT. The purpose of this study is to benchmark the dose calculation accuracy of Varian's HyperSight cone-beam computed tomography (CBCT) on the Halcyon platform relative to fan-beam CT-based dose calculations and to perform end-to-end testing of HyperSight CBCT-only based treatment planning.
View Article and Find Full Text PDFBMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
BMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
Zhonghua Jie He He Hu Xi Za Zhi
September 2025
Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Tracheobronchial Dieulafoy's disease (TBDD) is a rare bronchial artery vascular malformation, characterized clinically by sudden, recurrent, and life-threatening massive hemoptysis. This article reports the case of a 9-year-old female patient who presented with massive hemoptysis lasting two weeks. Following ineffective treatment at a local hospital, she was transferred to our institution.
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