Evaluation of camera settings for photogrammetric reconstruction of humanoid phantoms for EBRT bolus and HDR surface brachytherapy applications

Bridger, Corey; Douglass, Michael; Reich, P; Santos, Alexandre

doi:10.1007/s13246-021-00994-4

Cited by 9 publications

(15 citation statements)

References 35 publications

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“…The Artec Leo was commissioned by scanning objects of known dimensions and different colours, specifically white and brown interchangeable rods of the Gammex Model 467 tissue characterisation phantom (Sun Nuclear, Melbourne, USA), a brown head of a RANDO phantom (Radiology Support Devices, Long Beach, USA) and a white plaster positive of a breast with a marked treatment area. The RANDO phantom has previously been used for commissioning of scanners and photogrammetry systems for radiotherapy 1,2,10 and in some cases has required the application of a powder spray for improved detection, due to the dark tone. Scans were also performed of five leather swatches approximating different skin tones, with both red marker ink and wire markings, on a curved surface.…”

Section: Scanner Commissioningmentioning

confidence: 99%

“…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%

“…7,11 The technologies used for 3D scanning vary in cost, complexity and precision. The use of inexpensive smartphones has been described by LeCompte et al 5 and Kang et al 7 Douglass and Santos, 4 Maxwell et al 8 and Bridger et al 10 have described the use of consumer-grade camera-based photogrammetry for brachytherapy surface mould and bolus design. Yoshida et al 9 and Skinner et al 11 have described the use of consumer-grade 3D camera systems like the Microsoft Kinect and Intel RealSense for device production.…”

Section: Introductionmentioning

confidence: 99%

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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: Scanner Commissioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 same year, LeCompte et al 11 used an Apple® iPhone X to produce a 3D model and super cial bolus of a nose. In 2021, Bridger et al investigated the effect of camera type and settings on the reconstruction accuracy of photogrammetry for radiation therapy applications 14 . A paper published in 2022 by the same group demonstrated that the dosimetry of a 3D printed super cial brachytherapy applicator achievable using photogrammetry was almost equivalent to that of a conventional CT-derived applicator 9 .…”

Section: Introductionmentioning

confidence: 99%

Predicting cranial MRI anatomy from 3D optical surface scans using deep learning for radiation therapy treatment planning

Douglass

Gorayski

Patel

et al. 2022

Preprint

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Background Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging modality is not feasible. Deep learning is useful for automating various manual tasks in radiation oncology, most notably, organ segmentation and treatment planning. Deep learning models have also been used to transform MRI datasets into synthetic CT datasets, facilitating the development of MRI-only radiation therapy planning. Aims To train a pix2pix generative adversarial network was trained to transform 3D optical scan data into estimated MRI datasets for a given patient to provide additional anatomical data for a select few radiation therapy treatment sites. The proposed network may provide useful anatomical information for treatment planning of surface mould brachytherapy, total body irradiation, and total skin electron therapy, for example, without delivering any imaging dose. Methods A 2D pix2pix GAN was trained on 15,000 axial MRI slices of healthy adult brains paired with corresponding external mask slices. The model was validated on a further 5000 previously unseen external mask slices. The predictions were compared with the “ground-truth” MRI slices using the multi-scale structural similarity index (MSSI) metric. A certified neuro-radiologist was subsequently consulted to provide an independent review of the model’s performance in terms of anatomical accuracy and consistency. The network was then applied to a 3D photogrammetry scan of a test subject to demonstrate the feasibility of this novel technique. Results The trained pix2pix network predicted MRI slices with a mean MSSI of 0.831 ± 0.057 for the 5000 validation images indicating that it is possible to estimate a significant proportion of a patient’s gross cranial anatomy from a patient’s exterior contour. When independently reviewed by a certified neuro-radiologist, the model’s performance was described as “quite amazing, but there are limitations in the regions where there is wide variation within the normal population.” When the trained network was applied to a 3D model of a human subject acquired using optical photogrammetry, the network could estimate the corresponding MRI volume for that subject with good qualitative accuracy. However, a ground-truth MRI baseline was not available for quantitative comparison. Conclusions A deep learning model was developed, to transform 3D optical scan data of a patient into an estimated MRI volume, potentially increasing the usefulness of optical scanning in radiation therapy planning. This work has demonstrated that much of the human cranial anatomy can be predicted from the external shape of the head and may provide an additional source of valuable imaging data. Further research is required to investigate the feasibility of this approach for use in a clinical setting and further improve the model’s accuracy.

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Evaluation of camera settings for photogrammetric reconstruction of humanoid phantoms for EBRT bolus and HDR surface brachytherapy applications

Cited by 9 publications

References 35 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

Predicting cranial MRI anatomy from 3D optical surface scans using deep learning for radiation therapy treatment planning

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