Potential of a machine-learning model for dose optimization in CT quality assurance

Meineke, A.; Rubbert, Christian; Sawicki, L; Thomas, Christoph; Klosterkemper, Yan; Appel, Elisabeth; Caspers, Julian; Bethge, O; Kröpil, Patric; Antoch, Gerald; Boos, Johannes

doi:10.1007/s00330-019-6013-6

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…In addition, artificial intelligence has lately been commonly used to optimize radiationbased processes and has several benefits over conventional approaches. The usage of artificial intelligence contributes to refining photon radiation-based applications in both the medical and manufacturing industries [47][48][49]. Likewise, Machine Learning and Deep Learning a subset of artificial intelligence have been used in a number of applications to evaluate complicated data sets and to identify similarities and associations within those data without being directly configured [50,51].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p > 0.05) observed between the protocols and image metrics.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Recent literature underlines the importance of PDMS on optimization [40,84,89] and mentions that they will play a major role in ensuring best practice ensuring appropriate image quality at the required radiation dose [88]. Recently the use of artificial intelligence (AI) is also proposed as a tool for dose optimization [90]. In 2019, ESR published a white paper on what should the radiologist know about AI [91].…”

Section: Discussionmentioning

confidence: 99%

Radiation dose optimization in diagnostic and interventional radiology: Current issues and future perspectives

Tsapaki

2020

Physica Medica

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The medical radiological imaging technological evolution and wide availability has resulted in the exponential increase in utilization. Evidence for a risk of cancer arising from radiation doses of lower than 100 mSv is still limited. There is a need for patient dose optimization. A literature review was conducted to identify the reasons why optimization is important and the steps to be followed for a successful optimization process in digital radiology, Computed Tomography, interventional radiology and mammography.Based on recent literature, 5 steps for a dose optimization process are proposed. These steps are: 1) establishment of a quality assurance programme; a mistake, misuse or malfunction of an X-ray machine can potentially affect the health or life of thousands of people, 2) establishment of a dose optimization team consisting of a radiologist, medical physicist and radiation technologist, 3) determination of baseline dose levels and image quality as well as comparisons with benchmarks to decide which exam protocols should be optimized, 4) modification of protocols by the medical physicist and 5) evaluation of the optimization process and its effect on patient dose and image quality.The optimization process should include joint efforts focused on 1) equipment performance 2) exam protocol customization and 3) staff behaviour. Manufacturers should provide detailed descriptions of exam protocols and training on the use of dose reduction features. The diagnostic radiology medical physicist should emerge and take a proactive lead in the everyday clinical routine in order to promote the value of optimization process.

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“…In interventional radiology, DL has been proposed for skin dose estimation [52]. In chest CT, ML could be used to predict the volumetric computed tomography dose index (CTDIvol) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (DW)) and identify exams, which hold potential for dose reduction by tuning the acquisition parameters [53].…”

Section: Imagingmentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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Potential of a machine-learning model for dose optimization in CT quality assurance

Cited by 15 publications

References 29 publications

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

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

Radiation dose optimization in diagnostic and interventional radiology: Current issues and future perspectives

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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