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Bridger CA, Caraça Santos AM, Reich PD, Douglass MJJ. An evaluation of consumer smartphones for generating bolus and surface mould applicators for radiation oncology. Med Phys 2024; 51:4447-4457. [PMID: 38709978 DOI: 10.1002/mp.17103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/21/2023] [Accepted: 03/30/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND The use of Computed Tomography (CT) imaging data to create 3D printable patient-specific devices for radiation oncology purposes is already well established in the literature and has shown to have superior conformity than conventional methods. Using non-ionizing radiation imaging techniques such as photogrammetry or laser scanners in-lieu of a CT scanner presents many desirable benefits including reduced imaging dose and fabrication of the device can be completed prior to simulation. With recent advancements in smartphone-based technology, photographic and LiDAR-based technologies are more readily available than ever before and to a high level of quality. As a result, these non-ionizing radiation imaging methods are now able to generate patient-specific devices that can be acceptable for clinical use. PURPOSE In this work, we aim to determine if smartphones can be used by radiation oncologists or other radiation oncology staff to generate bolus or brachytherapy surface moulds instead of conventional CT with equivalent or comparable accuracy. METHODS This work involved two separate studies: a phantom and participant study. For the phantom study, a RANDO anthropomorphic phantom (limited to the nose region) was used to generate 3D models based on three different imaging techniques: conventional CT, photogrammetry & LiDAR which were both acquired on a smartphone. Virtual boli were designed in Blender and 3D printed from PLA plastic material. The conformity of each printed boli was assessed by measuring the air gap volume and approximate thickness between the phantom & bolus acquired together on a CT. For the participant study, photographs, and a LiDAR scan of four volunteers were captured using an iPhone 13 Pro™ to assess their feasibility for generating human models. Each virtual 3D model was visually assessed to identify any issues in their reconstruction. The LiDAR models were registered to the photogrammetry models where a distance to agreement analysis was performed to assess their level of similarity. Additionally, a 3D virtual bolus was designed and printed using ABS material from all models to assess their conformity onto the participants skin surface using a verbal feedback method. RESULTS The photogrammetry derived bolus showed comparable conformity to the CT derived bolus while the LiDAR derived bolus showed poorer conformity as shown by their respective air gap volume and thickness measurements. The reconstruction quality of both the photogrammetry and LiDAR models of the volunteers was inadequate in regions of facial hair and occlusion, which may lead to clinically unacceptable patient-specific device that are created from these areas. All participants found the photogrammetry 3D printed bolus to conform to their nose region with minimal room to move while three of the four participants found the LiDAR was acceptable and could be positioned comfortably over their entire nose. CONCLUSIONS Smartphone-based photogrammetry and LiDAR software show great potential for future use in generating 3D reference models for radiation oncology purposes. Further investigations into whether they can be used to fabricate clinically acceptable patient-specific devices on a larger and more diverse cohort of participants and anatomical locations is required for a thorough validation of their clinical usefulness.
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Affiliation(s)
- Corey A Bridger
- School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Alexandre M Caraça Santos
- School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Paul D Reich
- School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Michael J J Douglass
- School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
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Sourvanos D, Sun H, Zhu TC, Dimofte A, Byrd B, Busch TM, Cengel KA, Neiva R, Fiorellini JP. Three-dimensional printing of the human lung pleural cavity model for PDT malignant mesothelioma. Photodiagnosis Photodyn Ther 2024; 46:104014. [PMID: 38346466 DOI: 10.1016/j.pdpdt.2024.104014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVE The primary aim was to investigate emerging 3D printing and optical acquisition technologies to refine and enhance photodynamic therapy (PDT) dosimetry in the management of malignant pleural mesothelioma (MPM). MATERIALS AND METHODS A rigorous digital reconstruction of the pleural lung cavity was conducted utilizing 3D printing and optical scanning methodologies. These reconstructions were systematically assessed against CT-derived data to ascertain their accuracy in representing critical anatomic features and post-resection topographical variations. RESULTS The resulting reconstructions excelled in their anatomical precision, proving instrumental translation for precise dosimetry calculations for PDT. Validation against CT data confirmed the utility of these models not only for enhancing therapeutic planning but also as critical tools for educational and calibration purposes. CONCLUSION The research outlined a successful protocol for the precise calculation of light distribution within the complex environment of the pleural cavity, marking a substantive advance in the application of PDT for MPM. This work holds significant promise for individualizing patient care, minimizing collateral radiation exposure, and improving the overall efficiency of MPM treatments.
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Affiliation(s)
- Dennis Sourvanos
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, PA, USA; Center for Innovation and Precision Dentistry (CiPD), School of Dental Medicine, School of Engineering, University of Pennsylvania, PA, USA.
| | - Hongjing Sun
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Timothy C Zhu
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Andreea Dimofte
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Brook Byrd
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Theresa M Busch
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Keith A Cengel
- Department of Radiation Oncology, Perelman Center for Advanced Medicine, University of Pennsylvania, PA, USA
| | - Rodrigo Neiva
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, PA, USA
| | - Joseph P Fiorellini
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, PA, USA
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Douglass M, Gorayski P, Patel S, Santos A. Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning. Phys Eng Sci Med 2023; 46:367-375. [PMID: 36752996 PMCID: PMC10030422 DOI: 10.1007/s13246-023-01229-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/29/2023] [Indexed: 02/09/2023]
Abstract
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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Affiliation(s)
- Michael Douglass
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.
- Australian Bragg Centre for Proton Therapy and Research, SAHMRI, Adelaide, SA, 5000, Australia.
- School of Physical Sciences, University of Adelaide, Adelaide, SA, 5005, Australia.
| | - Peter Gorayski
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
- Australian Bragg Centre for Proton Therapy and Research, SAHMRI, Adelaide, SA, 5000, Australia
- University of South Australia, Allied Health & Human Performance, Adelaide, SA, 5000, Australia
| | - Sandy Patel
- Department of Radiology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
| | - Alexandre Santos
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
- Australian Bragg Centre for Proton Therapy and Research, SAHMRI, Adelaide, SA, 5000, Australia
- School of Physical Sciences, University of Adelaide, Adelaide, SA, 5005, Australia
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Douglass MJJ. Can optical scanning technologies replace CT for 3D printed medical devices in radiation oncology? J Med Radiat Sci 2022; 69:139-142. [PMID: 35366049 PMCID: PMC9163457 DOI: 10.1002/jmrs.579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/16/2022] [Accepted: 03/16/2022] [Indexed: 12/21/2022] Open
Affiliation(s)
- Michael John James Douglass
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia, Australia.,Australian Bragg Centre for Proton Therapy and Research, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.,School of Physical Sciences - University of Adelaide, Adelaide, South Australia, Australia
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Zahia S, Garcia-Zapirain B, Anakabe J, Ander J, Jossa Bastidas O, Loizate Totoricagüena A. A Comparative Study between Scanning Devices for 3D Printing of Personalized Ostomy Patches. SENSORS (BASEL, SWITZERLAND) 2022; 22:560. [PMID: 35062521 PMCID: PMC8780182 DOI: 10.3390/s22020560] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
This papers presents a comparative study of three different 3D scanning modalities to acquire 3D meshes of stoma barrier rings from ostomized patients. Computerized Tomography and Structured light scanning methods were the digitization technologies studied in this research. Among the Structured Light systems, the Go!Scan 20 and the Structure Sensor were chosen as the handheld 3D scanners. Nineteen ostomized patients took part in this study, starting from the 3D scans acquisition until the printed ostomy patches validation. 3D mesh processing, mesh generation and 3D mesh comparison was carried out using commercial softwares. The results of the presented study show that the Structure Sensor, which is the low cost structured light 3D sensor, has a great potential for such applications. This study also discusses the benefits and reliability of low-cost structured light systems.
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Affiliation(s)
- Sofia Zahia
- eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain; (B.G.-Z.); (O.J.B.)
| | | | - Jon Anakabe
- LEARTIKER S.COOP, 48270 Markina, Spain; (J.A.); (J.A.)
| | - Joan Ander
- LEARTIKER S.COOP, 48270 Markina, Spain; (J.A.); (J.A.)
| | - Oscar Jossa Bastidas
- eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain; (B.G.-Z.); (O.J.B.)
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