Simple and Rapid Creation of Customized 3-dimensional Printed Bolus Using iPhone X True Depth Camera

LeCompte, M.C.; Chung, Scotty A.; McKee, M; Marshall, Travis; Frizzell, Bart; Parker, Mandy; Blackstock, A. William; Farris, Michael

doi:10.1016/j.prro.2019.03.005

Cited by 16 publications

(17 citation statements)

References 7 publications

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“…Optical 3D scanning is a safe, cost-effective, easy-to-use solution to acquire topographical 3D surface data. In addition to the use of optical surface reconstruction methods for the production of treatment devices such as bolus, moulds and shields, [1][2][3][4][5][6][7][8][9][10][11] optical scanning can also be used for patient position verification, 12 as well as to overcome limited field of view in simulation imaging. 13 Use of 3D optical scanning can reduce the need for radiographic imaging and thereby reduce radiation exposure, facilitate the production of treatment devices prior to CT simulation and is particularly suited to treatment of superficial disease, where the treatment volume is defined on the patient surface.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of optical 3D scanning system for radiotherapy use

Crowe

Luscombe

Maxwell

et al. 2021

J of Medical Radiation Sci

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Introduction Optical three‐dimensional scanning devices can produce geometrically accurate, high‐resolution models of patients suitable for clinical use. This article describes the use of a metrology‐grade structured light scanner for the design and production of radiotherapy medical devices and synthetic water‐equivalent computer tomography images. Methods Following commissioning of the device by scanning objects of known properties, 173 scans were performed on 26 volunteers, with observations of subjects and operators collected. Results The fit of devices produced using these scans was assessed, and a workflow for the design of complex devices using a treatment planning system was identified. Conclusions Recommendations are provided on the use of the device within a radiation oncology department.

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Section: Introductionmentioning

confidence: 99%

Evaluation of optical 3D scanning system for radiotherapy use

Crowe

Luscombe

Maxwell

et al. 2021

J of Medical Radiation Sci

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“…These require little modification to train a similar model presented in the current work and thus mitigates much of the required expertise. Whilst 3D modelling may be considered a niche skill, it is becoming more common with the increasing utilisation of 3D printing technologies [65][66][67][68] in radiation oncology for applications such as printing of phantoms, bolus, and brachytherapy surface applicators. This suggests that the difficulty in generating the proposed linac model using the method of the current work compared with a traditional Monte Carlo approach is significantly less given that there is very little or no coding expertise required and accurate geometric modelling of the linac components is unnecessary.…”

Section: Discussionmentioning

confidence: 99%

DeepWL: Robust EPID based Winston-Lutz Analysis using Deep Learning and Synthetic Image Generation

Douglass¹,

Keal²

2021

Preprint

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Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localizes the radiation isocentre of the linac. In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data.In addition, we propose a novel method of generating the synthetic WL images and associated 'ground-truth' masks using an optical ray-tracing engine to 'fake' mega-voltage EPID images. The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 hours. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm. DeepWL was demonstrated to analyse Winston-Lutz images with accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.

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“…The resulting scans were used to print custom masks for Halloween within 30 to 60 s. Although the Kinect has been discontinued, other devices such as Intel RealSense and the iPhone X can be used (Table 2). LeCompte et al used an iPhone X with a third-party application, Scandy, to access the depth camera for 3D modeling purposes [11]. However, with third-party applications, there may be potential unresolved privacy, security, and HIPAA compliance issues regarding the data collection and usage of detailed facial information that permits facial recognition.…”

Section: Discussionmentioning

confidence: 99%

Capturing 3D patient features for rapid prototyping in radiotherapy prior to simulation

et al. 2020

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Objective When superficial tumors are treated with radiotherapy, bolus may be needed to ensure adequate dose delivery to target tissue. The bolus is typically fabricated at the time of computed tomography (CT) simulation, but in many cases, creating a bolus prior to simulation can be more efficient. Therefore, we devised a workflow that would allow for 3D patient scanning at the initial consultation so that bolus can be fabricated by rapid prototyping before CT simulation. Methods A 3D-printed bolus was fabricated using surface scans of a head phantom that were obtained from a Microsoft Kinect. After processing the scans, a commercially printed 3D bolus was made. To test the feasibility and conformity of the process, masks were made for patients using the above method. Results 3D-printed bolus created using the rapid scanning technique proved to be the most conformal when compared with bolus made of VPS and SuperFlab. The printed masks also showed that they were formed fitting and comfortable to wear. Conclusion We present a new workflow that combines current known methods of both rapidly scanning a patient's surface and the subsequent rapid creation of bolus that then increases bolus conformity, improves comfort and the patient experience, reduces simulation time, and may decrease exposure to radiation.

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Simple and Rapid Creation of Customized 3-dimensional Printed Bolus Using iPhone X True Depth Camera

Cited by 16 publications

References 7 publications

Evaluation of optical 3D scanning system for radiotherapy use

Evaluation of optical 3D scanning system for radiotherapy use

DeepWL: Robust EPID based Winston-Lutz Analysis using Deep Learning and Synthetic Image Generation

Capturing 3D patient features for rapid prototyping in radiotherapy prior to simulation

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