Automated patient positioning in CT using a 3D camera for body contour detection: accuracy in pediatric patients

Abstract

Objective To assess the accuracy of a 3D camera for body contour detection in pediatric patient positioning in CT compared with routine manual positioning by radiographers. Methods and materials One hundred and ninety-one patients, with and without fixation aid, which underwent CT of the head, thorax, and/or abdomen on a scanner with manual table height selection and with table height suggestion by a 3D camera were retrospectively included. The ideal table height was defined as the position at which the scanner isocenter coincides with the patient's isocenter. Table heights suggested by the camera and selected by the radiographer were compared with the ideal height. Results For pediatric patients without fixation aid like a baby cradle or vacuum cushion and positioned by radiographers, the median (interquartile range) absolute table height deviation in mm was 10.2 (16.8) for abdomen, 16.4 (16.6) for head, 4.1 (5.1) for thorax-abdomen, and 9.7 (9.7) for thorax CT scans. The deviation was less for the 3D camera: 3.1 (4.7) for abdomen, 3.9 (6.3) for head, 2.2 (4.3) for thorax-abdomen, and 4.8 (6.7) for thorax CT scans (p < 0.05 for all body parts combined). Conclusion A 3D camera for body contour detection allows for automated and more accurate pediatric patient positioning than manual positioning done by radiographers, resulting in overall significantly smaller deviations from the ideal table height. The 3D camera may be also useful in the positioning of patients with fixation aid; however, evaluation of possible improvements in positioning accuracy was limited by the small sample size. Key Points • A 3D camera for body contour detection allows for automated and accurate pediatric patient positioning in CT. • A 3D camera outperformed radiographers in positioning pediatric patients without a fixation aid in CT. • Positioning of pediatric patients with fixation aid was feasible using the 3D camera, but no definite conclusions were drawn regarding the positioning accuracy due to the small sample size.

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

“…Our results showed very similar radiation exposure reduction findings. Booij et al [7] and Saltybaeva et al [8] also reported the patient centering accuracy in CT using 3D cameras that relies on deep neural network for image contouring. Our results also agreed with their conclusions that the AI-based technique improved patient centering accuracy, and in turn improved image quality.…”

“…Yang Wang et al (2020) reported that U-HAPPY (United imaging Human Automatic Planbox for PulmonarY) CT has a function with automatic positioning and scanning, which helps to reduce the radiation dose [6]. Booij et al [7] and Saltybaeva et al [8] also reported the patient centering accuracy in CT using 3D cameras that relies on deep neural network for image contouring. Recently, GE Healthcare introduced a Revolution Maxima CT, which relies on deep learning algorithms and realtime depth-sensing technology to center patients, locate desired anatomies, and perform scan automatically.…”

Impact of additional mattresses in emergency CT on the automated patient centering proposed by a 3D camera: a phantom study

“…In clinical practice, the use of 3D camera leads to real benefits to radiographers in terms of patient care. Under most circumstances and within the field of use defined by the manufacturer, the 3D camera developed by Siemens Healthineers allows the patient to be centered at the isocentre precisely, which improves examination practices and quality from a dosimetric and image quality point of view 7 , 8 . However, the manufacturer warns in the user manual explicitly that the 3D workflow is not optimized for examinations that use non-original and non-released equipment, such as the mattresses, because it can impact the detection of body landmarks or the patient’s isocentre.…”

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients

“…Our results showed very similar radiation exposure reduction ndings. Booij et al [7] and Saltybaeva et al [8] also reported the patient centering accuracy in CT using 3D cameras that relies on deep neural network for image contouring. Our results also agreed with their conclusions that the AI-based technique improved patient centering accuracy, and in turn improved image quality.…”

“…Yang Wang et al (2020) reported that U-HAPPY (United imaging Human Automatic Planbox for PulmonarY) CT has a function with automatic positioning and scanning, which helps to reduce the radiation dose [6]. Booij et al [7] and Saltybaeva et al [8] also reported the patient centering accuracy in CT using 3D cameras that relies on deep neural network for image contouring. Recently, GE Healthcare introduced a Revolution Maxima CT, which relies on deep learning algorithms and real-time depth sensing technology to center patients, locate desired anatomies and perform scan automatically.…”