Patient positioning using artificial intelligence neural networks, trained magnetic field sensors and magnetic implants.
The purpose of this study was to evaluate the precision of a sensor and to ascertain the maximum distance between the sensor and the magnet, in a magnetic positioning system for external beam radiotherapy using a trained artificial intelligence neural network for position determination. Magnetic positioning for radiotherapy, previously described by Lennernäs and Nilsson, is a functional technique, but it is time consuming. The sensors are large and the distance between the sensor and the magnetic implant is limited to short distances. This paper presents a new technique for positioning, using an artificial intelligence neural network, which was trained to position the magnetic implant with at least 0.5 mm resolution in X and Y dimensions. The possibility of using the system for determination in the Z dimension, that is the distance between the magnet and the sensor, was also investigated. After training, this system positioned the magnet with a mean error of maximum 0.15 mm in all dimensions and up to 13 mm from the sensor. Of 400 test positions, 8 determinations had an error larger than 0.5 mm, maximum 0.55 mm. A position was determined in approximately 0.01 s.
Lennernäs
B
B
Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden. bo.lennernas@oncology.gu.se
Edgren
M
M
Nilsson
S
S
D006801:Humans; D008280:Magnetics; D009369:Neoplasms / Q000532:radiotherapy*; D016571:Neural Networks, Computer; D011187:Posture; D011878:Radiotherapy / Q000295:instrumentation / Q000379:methods; D013679:Technology, Radiologic