Surface guided radiation therapy: An international survey on current clinical practice

Batista, V.; Gober, M.; Moura, Filipe; Webster, A.; Oellers, M.; Ramtohul, Mark; Kügele, M.; Freislederer, P.; Buschmann, Martin; Anastasi, Gail; Steiner, Elisabeth; Al‐Hallaq, Hania; Lehmann, Jöerg

doi:10.1016/j.tipsro.2022.03.003

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…The application of SGRT in a C-arm linac setting is the most common [7,21], mainly due to the prevalence of these treatment systems, but also because of their multifaceted uses, ranging from patient setup, monitoring, motion management (free-breathing (FB)-gating, breath-hold to non-coplanar techniques). However, this diversity results in an extensive combination of parameters, tolerances and tests, which users need to adapt to their clinical practices.…”

Section: C-arm Linacsmentioning

confidence: 99%

“…The European Society for Radiotherapy and Oncology (ESTRO) SGRT working group was initiated at the 3rd ESTRO physics workshop 2019 in Budapest. As confirmed by the survey carried out by this working group in 2020 [7], one major concern shared by many prospective users is the lack of consensus-based clinical guidelines including specific quality assurance (QA) recommendations. This led to the need for a comprehensive up-to-date European consensus on the clinical use and QA of SGRT and prompted the development of these recommendations.…”

mentioning

confidence: 99%

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ESTRO-ACROP guideline on surface guided radiation therapy

Freislederer

Batista

Öllers

et al. 2022

Radiotherapy and Oncology

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Section: C-arm Linacsmentioning

confidence: 99%

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confidence: 99%

ESTRO-ACROP guideline on surface guided radiation therapy

Freislederer

Batista

Öllers

et al. 2022

Radiotherapy and Oncology

Self Cite

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“…The advent of surface guided radiation therapy (SGRT) has offered the potential to remove tattoos entirely, using the patient’s surface as the primary set up point with encouraging results [9] , [10] . However, these systems can be cost prohibitive, and it may not be feasible for institutions to implement [11] . A recent UK study made recommendations regarding tattoo practice that included the use of specialised equipment needles and tattoo ink and semi-permanent inks that have the potential to fade after the completion of treatment [12] .…”

Section: Introductionmentioning

confidence: 99%

Clinical experience of a tattoo application device

Brown,

Barry,

Mai

et al. 2024

Technical Innovations & Patient Support in Radiation Oncolo

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“…Three-dimensional (3D) optical and infrared surface scanning in radiation oncology is increasingly being investigated for determining the 3D exterior surface of a patient without the need for a computed tomography (CT) scan. This technology is often utilised for surface-guided radiation therapy (SGRT) to assist with patient positioning and realtime motion management [1][2][3]. While this approach most often utilises structured light scanning (SLS) to determine the patient's external contour, other types of optical surface scanning such as photogrammetry or Lidar (light detection and ranging) have been investigated for applications in radiation oncology [4].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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 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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Surface guided radiation therapy: An international survey on current clinical practice

Cited by 21 publications

References 22 publications

ESTRO-ACROP guideline on surface guided radiation therapy

ESTRO-ACROP guideline on surface guided radiation therapy

Clinical experience of a tattoo application device

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

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