Understanding, justifying, and optimizing radiation exposure for CT imaging in nephrourology

Abstract

An estimated 4–5 million CT scans are performed in the USA every year to investigate nephrourological diseases such as urinary stones and renal masses. Despite the clinical benefits of CT imaging, concerns remain regarding the potential risks associated with exposure to ionizing radiation. To assess the potential risk of harmful biological effects from exposure to ionizing radiation, understanding the mechanisms by which radiation damage and repair occur is essential. Although radiation level and cancer risk follow a linear association at high doses, no strong relationship is apparent below 100 mSv, the doses used in diagnostic imaging. Furthermore, the small theoretical increase in risk of cancer incidence must be considered in the context of the clinical benefit derived from a medically indicated CT and the likelihood of cancer occurrence in the general population. Elimination of unnecessary imaging is the most important method to reduce imaging-related radiation; however, technical aspects of medically justified imaging should also be optimized, such that the required diagnostic information is retained while minimizing the dose of radiation. Despite intensive study, evidence to prove an increased cancer risk associated with radiation doses below ~100 mSv is lacking; however, concerns about ionizing radiation in medical imaging remain and can affect patient care. Overall, the principles of justification and optimization must remain the basis of clinical decision-making regarding the use of ionizing radiation in medicine.

Assessment of uncertainties associated with Monte Carlo-based personalized dosimetry in clinical CT examinations

“…Computed tomography (CT) has become a key diagnostic imaging modality in clinical diagnosis of a wide range of diseases. The number of CT scans in the US had an average annual increase of 10% between 1995 and 2015 (IMV Publising 2018, Ferrero et al 2019. The sharp utilization trend of CT imaging in clinical setting has raised health concerns regarding potential risks of ionizing radiation on patients undergoing CT examinations.…”

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

“…As the use of CT in modern medicine continues to increase, people are beginning to pay attention to increasing the radiation dose from biomedical imaging to the community and the associated increase in the estimated risk of radiation-induced cancer [38]. The optimization of the scanning method is important, so the necessary clinical information can be collected or captured while minimizing the radiation dose [39]. The proposed general workflow for biomedical image denoising is presented in Figure 1.…”

Diffusion Tensor Imaging in early prediction of renal fibrosis in patients with renal disease: Functional and histopathological correlations

“…Ultrasound and computerised tomography (CT) provide good anatomic details but limited functional evaluation. CT additionally requires the use of iodinated contrast material and exposure to ionising radiation, which is injurious to the kidney 7 . Nuclear medicine assessments provide functional information but lack spatial resolution 8 .…”

“…CT additionally requires the use of iodinated contrast material and exposure to ionising radiation, which is injurious to the kidney. 7 Nuclear medicine assessments provide functional information but lack spatial resolution. 8 Magnetic resonance imaging (MRI) is the unique modality to provide both structural and functional information.…”