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Two experimental methods to integrate intra-oral scans into 3D stereophotogrammetric facial images.
Clin Oral Investig
January 2025
Department of Dentistry Section Orthodontics and Craniofacial Biology, Radboud University Medical Center, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
Objectives: For this research two different ways for integrating intra-oral scans into three-dimensional (3D) stereophotogrammetric images are analyzed and compared to the gold standard method.
Materials And Methods: A cross-sectional study was performed. For each patient a complete dataset was collected, which was used to generate 3D fusion models by three different methods: method A using cheek retractors, method B using a tracer and method C using full-skull CBCT.
Risk Estimation of Carcinogenic and Noncarcinogenic Diseases from Radiation for Medical X-ray Workers.
Health Phys
January 2025
Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
The objective of this paper is to construct a follow-up cohort of medical x-ray workers and analyze the risk estimates of radiation-induced carcinogenic and noncarcinogenic diseases induced by chronic low-dose ionizing radiation exposure in the follow-up cohort. A fixed cohort study was used. A total of 159 medical x-ray workers working in radiology departments of hospitals in Gansu Province from 1950 to 1980 were selected as the radiology group, and 149 medical workers in internal medicine, surgery, and other departments who had not engaged in radiology work at the same hospital were selected as the control group.
View Article and Find Full Text PDFRadiation-induced cardiac disease: Modern techniques to reduce cardiac toxicity.
Pract Radiat Oncol
January 2025
The Legacy Heritage Oncology Center & Dr Larry Norton Institute, Soroka University Medical Center, Ben Gurion University of the Negev, Faculty of Medicine, Be'er Sheva, Israel.
Purpose: Continuous advancements in cancer management have resulted in increased long-term survival rates amongst cancer survivors and in turn have exposed the full extent of radiotherapy-associated morbidities. Radiation-induced coronary heart disease (RICHD) is one of the leading causes of morbidity and mortality in cancer survivors, particularly in those having undergone mediastinal radiation. While mediastinal radiation has been shown to substantially reduce both recurrence and mortality rates in multiple thoracic malignancies, the risk for the development of RICHD is of significant concern.
View Article and Find Full Text PDFUncovering molecular mechanisms of soybean response to C heavy ion irradiation through integrated transcriptomic and metabolomic profiling.
Ecotoxicol Environ Saf
January 2025
Plant Production Department, Faculty of Agriculture Saba Basha, Alexandria University, Alexandria 21531, Egypt; Work Station of Science and Technique for Post-doctoral in Sugar Beet Institute Afliated to Heilongjiang University, 74 Xuefu Road, Harbin 150000, Heilongjiang, China.
Ion beam mutagenesis is an advanced technique capable of inducing substantial changes in plants, resulting in noticeable alterations in their growth. However, the precise molecular mechanisms underlying the effects of radiation on soybeans remain unclear. This study investigates the impact of ionizing radiation on soybean development through a comprehensive approach that integrates transcriptomics and metabolomics.
View Article and Find Full Text PDFGeneration of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy.
PLoS One
January 2025
Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application.
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