Purpose: The purpose of this study was to identify the main pathologies for which CT is applied on pediatric patients and the related radiation doses as reported in the literature in order to facilitate justification and CT optimization.
Methods: A critical analysis of a literature review was performed. Different search engines were used such as PubMed, Google Scholar and Science Direct. Various terms and keywords were used to locate pertinent articles such as Pediatric, Computed tomography, Radiation Dose, Organ dose, Effective dose.
Results: The results showed that the main pathologies for which CT is applied are: Crohn's disease, hydrocephalus, cystic fibrosis and pediatric malignancies-mainly lymphoma. The related radiation dose data are extremely scarce and are in the range of 3.48-17.56, 0.2-15.3mSv, 0.14-6.20mSv, and 2.8-518.0mSv, respectively. The radiation doses reported are high especially in pediatric oncology.
Conclusions: Pediatric patients with malignancies are those exposed to the higher levels of radiation during CT imaging. Literature is lacking reporting of dose in Pediatric CT imaging. More studies need to be realized for the determination of radiation dose in those patients. Special protocols need to be recommended in order to reduce the exposure of children in radiation.
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http://dx.doi.org/10.1016/j.ejmp.2017.03.014 | DOI Listing |
Acad Radiol
January 2025
Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany (R.D., J.M.B., B.S., J.M., S.G., P.K., S.W., J.H., K.N., S.A., A.B.).
Rationale And Objectives: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at lower doses. This study aims to evaluate the effectiveness of a deep learning (DL)-based denoising algorithm in maintaining diagnostic image quality in whole-body PCCT imaging at reduced radiation levels, using real intraindividual cadaveric scans.
Materials And Methods: Twenty-four cadaveric human bodies underwent whole-body CT scans on a PCCT scanner (NAEOTOM Alpha, Siemens Healthineers) at four different dose levels (100%, 50%, 25%, and 10% mAs).
Med Dosim
January 2025
Department of Radiation Oncology, Peking University First Hospital, Beijing, China. Electronic address:
This study presents a patient with a PET-CT detected residual lacrimal sac tumor who was treated with intensity modulated proton therapy (IMPT) and concurrent chemotherapy. The patient a 49-year-old male diagnosed with squamous cell carcinoma of the left lacrimal sac had under-went endoscopic surgery. Postoperative PET-CT implied tumor residual in the left lacrimal sac.
View Article and Find Full Text PDFMutat Res Rev Mutat Res
January 2025
Radiation Epidemiology Branch, National Cancer Institute, MD 20892-9778, USA; Faculty of Health, Science and Technology, Oxford Brookes University, Headington Campus, OX3 0BP, UK.
Biological effects of ionizing radiation vary with radiation quality, which is often expressed as the amount of energy deposited per unit length, i.e., linear energy transfer (LET).
View Article and Find Full Text PDFDiagn Interv Radiol
January 2025
Erzincan Binali Yıldırım University Faculty of Medicine, Department of Radiology, Erzincan, Türkiye.
Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in radiation dose is not applicable in US and MRI, advancements in technology have made it possible in CT, with ongoing studies aimed at further optimization.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
January 2025
Department of Radiation Oncology, Hokkaido University Faculty of Medicine and Graduate School of Medicine, North 15 West 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan.
Background And Purpose: Radiation-induced lymphopenia (RIL) may be associated with a worse prognosis in pancreatic cancer. This study aimed to develop a normal tissue complication probability (NTCP) model to predict severe RIL in patients with pancreatic cancer undergoing concurrent chemoradiotherapy (CCRT).
Materials And Methods: We reviewed pancreatic cancer patients treated at our facility for model training and internal validation.
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