Use of artificial intelligence in computed tomography dose optimisation

Abstract

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.

Evaluation of Organ Dose and Image Quality Metrics of Pediatric CT Chest-Abdomen-Pelvis (CAP) Examination: An Anthropomorphic Phantom Study

“…Our aim in this work is to correlate between dose and image quality metrics in pediatric while implementing CT optimization protocols. Among radiation dose metrics, organ equivalent dose is considered one of the best indicators for characterizing patient radiation burden [10,32]. Several researchers have been inspired to reduce the risk by altering the variable of scan parameters, including growing the pitch factor, increasing slice collimation, choosing tube voltage, and utilizing automatic tube current modulation (ATCM) feature [33][34][35].…”

“…However, lowering the capacity of the X-ray tube is an acceptable operation to evaluate iodine structures as a consequence of the rise in iodine attenuation energy due to the proximity of the K-edge of iodine and the photoelectric influence. The optimum kilovoltage for a CT analysis should be selected depending on the imaging task and patient habitus [32]. Furthermore, the practice of low tube potential technique with a proper selection of mAs value will improve contrast resolution in a patient with a smaller body thickness.…”

The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics

“…Artificial intelligence is expected to soon play a key role in diagnostic imaging [1,2], radiation therapy [3,4] and medical physics in general. It has already been demonstrated to successfully improve image quality [1], decrease radiation dosage [5,6], assign label types and identify pathology locations [7][8][9][10][11][12][13], create and optimize protocols [14], accurately segment pathology areas and organs [15,16], and optimize technology utilization [17].…”

Analysis of Shielding Performance of Radiation-Shielding Materials According to Particle Size and Clustering Effects

“…In the medical field, radiation is the most important tool for the diagnosis and treatment of diseases, and it is developing into a specialization. As the medical and industrial radiation fields are expanding, exposure to radiation from medical [1,2] and industrial [3,4] diagnostic devices is also increasing. Exposure to medical radiation occurs at low doses and only in some parts of the body.…”