CNN application for automated determination of the patient's size to obtain the size-specific dose estimated in CT.
Erik R Hernández-DávilaEugenio Torres-GarcíaLiliana Aranda-LaraErnesto Roldan-ValadezKeila Isaac-OlivéMario Flores-ReyesPublished in: Biomedical physics & engineering express (2024)
Introduction . The currently available dosimetry techniques in computed tomography can be inaccurate which overestimate the absorbed dose. Therefore, we aimed to provide an automated and fast methodology to more accurately calculate the SSDE usingDwobtained by using CNN from thorax and abdominal CT study images. Methods . The SSDE was determined from the 200 records files. For that purpose, patients' size was measured in two ways: (a) by developing an algorithm following the AAPM Report No. 204 methodology; and (b) using a CNN according to AAPM Report No. 220. Results . The patient's size measured by the in-house software in the region of thorax and abdomen was 27.63 ± 3.23 cm and 28.66 ± 3.37 cm, while CNN was 18.90 ± 2.6 cm and 21.77 ± 2.45 cm. The SSDE in thorax according to 204 and 220 reports were 17.26 ± 2.81 mGy and 23.70 ± 2.96 mGy for women and 17.08 ± 2.09 mGy and 23.47 ± 2.34 mGy for men. In abdomen was 18.54 ± 2.25 mGy and 23.40 ± 1.88 mGy in women and 18.37 ± 2.31 mGy and 23.84 ± 2.36 mGy in men. Conclusions . Implementing CNN-based automated methodologies can contribute to fast and accurate dose calculations, thereby improving patient-specific radiation safety in clinical practice.
Keyphrases
- convolutional neural network
- deep learning
- computed tomography
- machine learning
- positron emission tomography
- dual energy
- polycystic ovary syndrome
- image quality
- clinical practice
- end stage renal disease
- contrast enhanced
- case report
- magnetic resonance imaging
- chronic kidney disease
- ejection fraction
- high throughput
- newly diagnosed
- emergency department
- prognostic factors
- type diabetes
- molecular dynamics
- high resolution
- adipose tissue
- metabolic syndrome
- pregnancy outcomes
- insulin resistance
- quality improvement
- magnetic resonance
- molecular dynamics simulations
- optical coherence tomography
- pregnant women
- single cell
- electronic health record
- cervical cancer screening