This study investigates the feasibility of using 3D-Printing to manufacture masks used to immobilize patients undergoing radiotherapy treatment for tumors affecting the head and neck.
The current types of masks that are used in radiotherapy treatment sessions for those patients who have head-and-neck cancer are 'made to measure' and the methods currently employed to make them are invasive and in some cases patients' have found this to be particularly unpleasant. Their experience motivates research into less invasive and potentially more accurate techniques for their manufacture. To effectively develop an automatic manufacturing pipeline, and to assess the accuracy of the approach we need access to CT data from a cohort of patients for which we also have an accurate (ground truth) 3D facial model. Such a model can be acquired in a few minutes using a hand-held laser scanner.
Study Type
OBSERVATIONAL
Enrollment
12
The laser scanning is non-intrusive and the scan can be completed in approximately 15 minutes. The scanning process involves the patient sitting in a chair while a researcher performs the scan. As a precautionary measure we require the patient to wear an eye mask as although the scanner is CE marked and completely safe . The process can be completed in stages (e.g. 3 x 5 minutes) with a break of 2-3 minutes between each session. The laser scanner will then be moved around the patient to obtain a set of 3D points forming the surface of the patient's head. We believe the laser scanning to be the most accurate way to acquire the surface of the patient's face / head and we wish to compare the models we obtain with surfaces rendered from the patient's CT data set.
School of Computing Sciences, University of East Anglia
Norwich, Norfolk, United Kingdom
Norfolk and Norwich Univeristy Hospital NHS foundation Trust
Norwich, Norfolk, United Kingdom
3D facial geometry of patient's head
The resultant 3D facial geometry of patient's head will be used to construct a 3D digital model which will be used to evaluate the accuracy of the model generated by CT images. In other words, the outcome will be used as a ground-truth for the CT-derived model.
Time frame: 15 minutes
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