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Li B, Inscoe CR, Xu S, Capo T, Tyndall DA, Lee YZ, Lu J, Zhou O. A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner. Phys Med Biol 2024; 69:075028. [PMID: 38471174 DOI: 10.1088/1361-6560/ad3323] [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: 11/25/2023] [Accepted: 03/12/2024] [Indexed: 03/14/2024]
Abstract
Cone beam computed tomography (CBCT) is known to suffer from strong scatter and cone beam artifacts. The purpose of this study is to develop and characterize a rapidly scanning carbon nanotube (CNT) field emission x-ray source array to enable a multisource CBCT (ms-CBCT) image acquisition scheme which has been demonstrated to overcome these limitations. A CNT x-ray source array with eight evenly spaced focal spots was designed and fabricated for a medium field of view ms-CBCT for maxillofacial imaging. An external multisource collimator was used to confine the radiation from each focal spot to a narrow cone angle. For ms-CBCT imaging, the array was placed in the axial direction and rapidly scanned while rotating continuously around the object with a flat panel detector. The x-ray beam profile, temporal and spatial resolutions, energy and dose rate were characterized and evaluated for maxillofacial imaging. The CNT x-ray source array achieved a consistent focal spot size of 1.10 ± 0.04 mm × 0.84 ± 0.03 mm and individual beam cone angle of 2.4°±0.08 after collimation. The x-ray beams were rapidly switched with a rising and damping times of 0.21 ms and 0.19 ms, respectively. Under the designed operating condition of 110 kVp and 15 mA, a dose rate of 8245μGy s-1was obtained at the detector surface with the inherent Al filtration and 2312μGy s-1with an additional 0.3 mm Cu filter. There was negligible change of the x-ray dose rate over many operating cycles. A ms-CBCT scan of an adult head phantom was completed in 14.4 s total exposure time for the imaging dose in the range of that of a clinical CBCT scanner. A spatially distributed CNT x-ray source array was designed and fabricated. It has enabled a new multisource CBCT to overcome some of the main inherent limitations of the conventional CBCT.
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Affiliation(s)
- Boyuan Li
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Christina R Inscoe
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Shuang Xu
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Timothy Capo
- Independent Consultant, United States of America
| | - Donald A Tyndall
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Yueh Z Lee
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Jianping Lu
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Otto Zhou
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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Moglioni M, Carra P, Arezzini S, Belcari N, Bersani D, Berti A, Bisogni MG, Calderisi M, Ceppa I, Cerello P, Ciocca M, Ferrero V, Fiorina E, Kraan AC, Mazzoni E, Morrocchi M, Pennazio F, Retico A, Rosso V, Sbolgi F, Vitolo V, Sportelli G. Synthetic CT imaging for PET monitoring in proton therapy: a simulation study. Phys Med Biol 2024; 69:065011. [PMID: 38373343 DOI: 10.1088/1361-6560/ad2a99] [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: 09/26/2023] [Accepted: 02/19/2024] [Indexed: 02/21/2024]
Abstract
Objective.This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.Approach.We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a visual transformer (ViT) neural network, we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images-serving as the benchmark.Main results.The structural similarity index was computed at a mean value across all patients of 0.91, while the mean absolute error measured 22 Hounsfield Units (HU). Root mean squared error and peak signal-to-noise ratio values were 56 HU and 30 dB, respectively. The Dice similarity coefficient exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan.Significance.Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model's performance stands on par with existing models relying on input from cone beam CT or magnetic resonance imaging, which contain more anatomical information than activity maps.
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Affiliation(s)
- Martina Moglioni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Pietro Carra
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Silvia Arezzini
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Nicola Belcari
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Davide Bersani
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Andrea Berti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Maria Giuseppina Bisogni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | | | | | - Piergiorgio Cerello
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, I-27100 Pavia, Italy
| | - Veronica Ferrero
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Elisa Fiorina
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | | | - Enrico Mazzoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Matteo Morrocchi
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Francesco Pennazio
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Alessandra Retico
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Valeria Rosso
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | | | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, I-27100 Pavia, Italy
| | - Giancarlo Sportelli
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
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Li X, Bellotti R, Meier G, Bachtiary B, Weber D, Lomax A, Buhmann J, Zhang Y. Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour. Radiother Oncol 2024; 191:110056. [PMID: 38104781 DOI: 10.1016/j.radonc.2023.110056] [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: 05/01/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND AND PURPOSE Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning. MATERIALS AND METHODS A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ± 3 %) and non-robust plans. RESULTS In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84 ± 9.84HU (body), 35.78 ± 6.07HU (soft tissues) and 221.88 ± 31.69HU (bones), with Dice scores of 90.33 ± 2.43 %, 95.13 ± 0.80 %, and 85.53 ± 4.16 %, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62 ± 0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10 ± 1.24 % compared to conventional (1.64 ± 2.71 %) and non-robust (2.08 ± 2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes. CONCLUSION The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
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Affiliation(s)
- Xia Li
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Computer Science, ETH Zurich, Switzerland
| | - Renato Bellotti
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Gabriel Meier
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland
| | | | - Damien Weber
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Radiation Oncology, University Hospital of Zurich, Switzerland; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Antony Lomax
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | | | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland.
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Tian L, Lühr A. Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging. Acta Oncol 2023; 62:1461-1469. [PMID: 37703314 DOI: 10.1080/0284186x.2023.2256967] [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: 05/25/2023] [Accepted: 09/04/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND In proton therapy, it is disputed whether synthetic computed tomography (sCT), derived from magnetic resonance imaging (MRI), permits accurate dose calculations. On the one hand, an MRI-only workflow could eliminate errors caused by, e.g., MRI-CT registration. On the other hand, the extra error would be induced due to an sCT generation model. This work investigated the systematic and random model error induced by sCT generation of a widely discussed deep learning model, pix2pix. MATERIAL AND METHODS An open-source image dataset of 19 patients with cancer in the pelvis was employed and split into 10, 5, and 4 for training, testing, and validation of the model, respectively. Proton pencil beams (200 MeV) were simulated on the real CT and generated sCT using the tool for particle simulation (TOPAS). Monte Carlo (MC) dropout was used for error estimation (50 random sCT samples). Systematic and random model errors were investigated for sCT generation and dose calculation on sCT. RESULTS For sCT generation, random model error near the edge of the body (∼200 HU) was higher than that within the body (∼100 HU near the bone edge and <10 HU in soft tissue). The mean absolute error (MAE) was 49 ± 5, 191 ± 23, and 503 ± 70 HU for the whole body, bone, and air in the patient, respectively. Random model errors of the proton range were small (<0.2 mm) for all spots and evenly distributed throughout the proton fields. Systematic errors of the proton range were -1.0(±2.2) mm and 0.4(±0.9)%, respectively, and were unevenly distributed within the proton fields. For 4.5% of the spots, large errors (>5 mm) were found, which may relate to MRI-CT mismatch due to, e.g., registration, MRI distortion anatomical changes, etc. CONCLUSION The sCT model was shown to be robust, i.e., had a low random model error. However, further investigation to reduce and even predict and manage systematic error is still needed for future MRI-only proton therapy.
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Affiliation(s)
- Liheng Tian
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - Armin Lühr
- Department of Physics, TU Dortmund University, Dortmund, Germany
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Herrick M, Penfold S, Santos A, Hickson K. Correction to: A systematic review of volumetric image guidance in proton therapy. Phys Eng Sci Med 2023; 46:977-979. [PMID: 37470931 PMCID: PMC10480235 DOI: 10.1007/s13246-023-01301-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Affiliation(s)
- Mitchell Herrick
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, Australia.
- Department of Physics, University of Adelaide, Adelaide, Australia.
| | - Scott Penfold
- Department of Physics, University of Adelaide, Adelaide, Australia
- Australian Bragg Centre for Proton Therapy and Research, University of Adelaide, Adelaide, Australia
| | - Alexandre Santos
- Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, Australia
- Department of Physics, University of Adelaide, Adelaide, Australia
- Australian Bragg Centre for Proton Therapy and Research, University of Adelaide, Adelaide, Australia
| | - Kevin Hickson
- SA Medical Imaging, Adelaide, Australia
- University of South Australia, Allied Health & Human Performance, Adelaide, Australia
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La Greca Saint-Esteven A, Dal Bello R, Lapaeva M, Fankhauser L, Pouymayou B, Konukoglu E, Andratschke N, Balermpas P, Guckenberger M, Tanadini-Lang S. Synthetic computed tomography for low-field magnetic resonance-only radiotherapy in head-and-neck cancer using residual vision transformers. Phys Imaging Radiat Oncol 2023; 27:100471. [PMID: 37497191 PMCID: PMC10366636 DOI: 10.1016/j.phro.2023.100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 07/06/2023] [Accepted: 07/06/2023] [Indexed: 07/28/2023] Open
Abstract
Background and purpose Synthetic computed tomography (sCT) scans are necessary for dose calculation in magnetic resonance (MR)-only radiotherapy. While deep learning (DL) has shown remarkable performance in generating sCT scans from MR images, research has predominantly focused on high-field MR images. This study presents the first implementation of a DL model for sCT generation in head-and-neck (HN) cancer using low-field MR images. Specifically, the use of vision transformers (ViTs) was explored. Materials and methods The dataset consisted of 31 patients, resulting in 196 pairs of deformably-registered computed tomography (dCT) and MR scans. The latter were obtained using a balanced steady-state precession sequence on a 0.35T scanner. Residual ViTs were trained on 2D axial, sagittal, and coronal slices, respectively, and the final sCTs were generated by averaging the models' outputs. Different image similarity metrics, dose volume histogram (DVH) deviations, and gamma analyses were computed on the test set (n = 6). The overlap between auto-contours on sCT scans and manual contours on MR images was evaluated for different organs-at-risk using the Dice score. Results The median [range] value of the test mean absolute error was 57 [37-74] HU. DVH deviations were below 1% for all structures. The median gamma passing rates exceeded 94% in the 2%/2mm analysis (threshold = 90%). The median Dice scores were above 0.7 for all organs-at-risk. Conclusions The clinical applicability of DL-based sCT generation from low-field MR images in HN cancer was proved. High sCT-dCT similarity and dose metric accuracy were achieved, and sCT suitability for organs-at-risk auto-delineation was shown.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zurich 8092, Switzerland
| | - Ricardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Mariia Lapaeva
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Lisa Fankhauser
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Ender Konukoglu
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zurich 8092, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Panagiotis Balermpas
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Rämistrasse 100, Zurich 8091, Switzerland
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Trnkova P, Zhang Y, Toshito T, Heijmen B, Richter C, Aznar MC, Albertini F, Bolsi A, Daartz J, Knopf AC, Bertholet J. A survey of practice patterns for adaptive particle therapy for interfractional changes. Phys Imaging Radiat Oncol 2023; 26:100442. [PMID: 37197154 PMCID: PMC10183663 DOI: 10.1016/j.phro.2023.100442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
Background and purpose Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation. Materials and methods An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision. Results Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT. Conclusion Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.
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Affiliation(s)
- Petra Trnkova
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Corresponding author.
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Toshiyuki Toshito
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, Nagoya, Japan
| | - Ben Heijmen
- Department of Radiotherapy, Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
| | - Christian Richter
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden – Rossendorf, Dresden, Germany
| | - Marianne C. Aznar
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | | | - Alessandra Bolsi
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Juliane Daartz
- Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA 02114, United States of America
| | - Antje C. Knopf
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
- Institute for Medical Engineering and Medical Informatics, School of Life Science FHNW, Muttenz, Switzerland
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern, Switzerland
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Chang Y, Liang Y, Yang B, Qiu J, Pei X, Xu XG. Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy. Radiat Oncol 2023; 18:3. [PMID: 36604687 PMCID: PMC9817400 DOI: 10.1186/s13014-022-02191-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/27/2022] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy. METHODS In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain DoseCBCT, DosepCT, DosedpCT and DosesCT. Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually. RESULTS The global gamma pass rate of DosesCT and DosedpCT was 99.66% ± 0.34%, while that of DoseCBCT and DosedpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on DosedpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT. CONCLUSIONS For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.
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Affiliation(s)
- Yankui Chang
- grid.59053.3a0000000121679639School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Yongguang Liang
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Bo Yang
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Jie Qiu
- grid.506261.60000 0001 0706 7839Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Xi Pei
- grid.59053.3a0000000121679639School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China ,Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China
| | - Xie George Xu
- grid.59053.3a0000000121679639School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China ,grid.411395.b0000 0004 1757 0085Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, China
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10
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A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture. Phys Med 2022; 103:108-118. [DOI: 10.1016/j.ejmp.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 07/25/2022] [Accepted: 10/07/2022] [Indexed: 11/20/2022] Open
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11
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Rusanov B, Hassan GM, Reynolds M, Sabet M, Kendrick J, Farzad PR, Ebert M. Deep learning methods for enhancing cone-beam CT image quality towards adaptive radiation therapy: A systematic review. Med Phys 2022; 49:6019-6054. [PMID: 35789489 PMCID: PMC9543319 DOI: 10.1002/mp.15840] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 05/21/2022] [Accepted: 06/16/2022] [Indexed: 11/11/2022] Open
Abstract
The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings - with emphasis on study design and deep learning techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarised, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state of the art methods utilized in radiation oncology. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Branimir Rusanov
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mark Reynolds
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mahsheed Sabet
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Jake Kendrick
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Pejman Rowshan Farzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
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12
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Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow. Z Med Phys 2022:S0939-3889(22)00064-2. [PMID: 35764469 DOI: 10.1016/j.zemedi.2022.05.003] [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: 03/16/2022] [Revised: 05/29/2022] [Accepted: 05/29/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND PURPOSE Anatomical surveillance during ion-beam therapy is the basis for an effective tumor treatment and optimal organ at risk (OAR) sparing. Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) can replace the X-ray based planning CT (X-rayCT) in photon radiotherapy and improve the workflow efficiency without additional imaging dose. The extension to carbon-ion radiotherapy is highly challenging; complex patient positioning, unique anatomical situations, distinct horizontal and vertical beam incidence directions, and limited training data are only few problems. This study gives insight into the possibilities and challenges of using sCTs in carbon-ion therapy. MATERIALS AND METHODS For head and neck patients immobilised with thermoplastic masks 30 clinically applied actively scanned carbon-ion treatment plans on 15 CTs comprising 60 beams were analyzed. Those treatment plans were re-calculated on MRI based sCTs which were created employing a 3D U-Net. Dose differences and carbon-ion spot displacements between sCT and X-rayCT were evaluated on a patient specific basis. RESULTS Spot displacement analysis showed a peak displacement by 0.2 cm caused by the immobilisation mask not measurable with the MRI. 95.7% of all spot displacements were located within 1 cm. For the clinical target volume (CTV) the median D50% agreed within -0.2% (-1.3 to 1.4%), while the median D0.01cc differed up to 4.2% (-1.3 to 25.3%) comparing the dose distribution on the X-rayCT and the sCT. OAR deviations depended strongly on the position and the dose gradient. For three patients no deterioration of the OAR parameters was observed. Other patients showed large deteriorations, e.g. for one patient D2% of the chiasm differed by 28.1%. CONCLUSION The usage of sCTs opens several new questions, concluding that we are not ready yet for an MR-only workflow in carbon-ion therapy, as envisaged in photon therapy. Although omitting the X-rayCT seems unfavourable in the case of carbon-ion therapy, an sCT could be advantageous for monitoring, re-planning, and adaptation.
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13
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Choi MG, Law M, Djeng SK, Kim MS, Shin HB, Choe BY, Yoon DK, Suh TS. Daily adaptive proton therapy: Feasibility study of detection of tumor variations based on tomographic imaging of prompt gamma emission from proton–boron fusion reaction. NUCLEAR ENGINEERING AND TECHNOLOGY 2022. [DOI: 10.1016/j.net.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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14
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Qi M, Li Y, Wu A, Lu X, Zhou L, Song T. Multi-sequence MR generated sCT is promising for HNC MR-only RT: a comprehensive evaluation of previously developed sCT generation networks. Med Phys 2022; 49:2150-2158. [PMID: 35218040 DOI: 10.1002/mp.15572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/01/2022] [Accepted: 02/20/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To verify the feasibility of our in-house developed multi-sequence magnetic resonance (MR)-generated synthetic computed tomography (sCT) for the accurate dose calculation and fractional positioning for head and neck MR-only radiation therapy (RT). MATERIALS AND METHODS Forty-five patients with nasopharyngeal carcinoma were retrospectively studied. By applying our previously in-house developed network, a patient's sCT can rapidly be generated with respect to feeding the sole T1 image, T1C image, T1DixonC image, T2 image, and their combination respectively (five pipelines in total). A k(5)-fold strategy was implemented during model establishment. Dose recalculation was performed for each pipeline generation to attain a dosimetric feasibility evaluation. Fractional positioning evaluation was performed by calculating the digitally reconstructed radiograph (DRR) of the sCT and planning CT and their offset to the portal image. RESULTS The dose mean absolute error values are (0.47±0.16)%, (0.48±0.15)% (p<0.05), (0.50±0.16)% (p<0.05), (0.50±0.15)% (p<0.05), and (0.45±0.16)% (p<0.05) for the T1, T1C, T1Dixon C, T2, and 4-channel generated sCT to the prescription dose, respectively. The 4-channel-generated sCT outperforms any other single-sequence pipelines. Among the single-sequence MR imaging-generated sCTs, the T1-generated shows the most accurate HU image quality and provide a reliable dose result. Quantified positioning errors with calculation of the difference to the planning CT offsets are (-0.26±0.50)mm, (-0.58±0.52)mm (p<0.05), (-0.27±0.57)mm (p>0.05), (-0.31±0.44)mm (p>0.05), and (-0.19±0.37)mm (p>0.05) at LNG and (0.34±0.53)mm, (0.48±0.56)mm (p>0.05), (0.55±0.56)mm (p>0.05), (0.37±0.61)mm (p>0.05), and (0.24±0.43)mm (p>0.05) at LAT of the anterior-posterior direction for the five pipelines. CONCLUSION Multi-sequence MR-generated sCT allows for accurate dose calculation and fractional positioning for head and neck MR-only RT. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mengke Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Yongbao Li
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, 510060, China
| | - Aiqian Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Department of Radiation Oncology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, China
| | - Xingyu Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Ting Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
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15
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Scandurra D, Meijer T, Free J, van den Hoek J, Kelder L, Oldehinkel E, Steenbakkers R, Both S, Langendijk J. Evaluation of robustly optimised intensity modulated proton therapy for nasopharyngeal carcinoma. Radiother Oncol 2022; 168:221-228. [DOI: 10.1016/j.radonc.2022.01.043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 02/08/2023]
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16
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Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021; 11:772663. [PMID: 34869010 PMCID: PMC8637856 DOI: 10.3389/fonc.2021.772663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. RESULTS Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). DISCUSSION AND CONCLUSION The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Riccardo Santamaria
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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17
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Paganetti H, Botas P, Sharp GC, Winey B. Adaptive proton therapy. Phys Med Biol 2021; 66:10.1088/1361-6560/ac344f. [PMID: 34710858 PMCID: PMC8628198 DOI: 10.1088/1361-6560/ac344f] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 10/28/2021] [Indexed: 12/25/2022]
Abstract
Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.
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Affiliation(s)
- Harald Paganetti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pablo Botas
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Foundation 29 of February, Pozuelo de Alarcón, Madrid, Spain
| | - Gregory C Sharp
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brian Winey
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
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18
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Thummerer A, Seller Oria C, Zaffino P, Meijers A, Guterres Marmitt G, Wijsman R, Seco J, Langendijk JA, Knopf AC, Spadea MF, Both S. Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer. Med Phys 2021; 48:7673-7684. [PMID: 34725829 PMCID: PMC9299115 DOI: 10.1002/mp.15333] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/22/2021] [Accepted: 10/27/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. Methods A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors. Results On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). Conclusion CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.
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Affiliation(s)
- Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Gabriel Guterres Marmitt
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joao Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsfoschungszentrum (DKFZ), Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Johannes Albertus Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje-Christin Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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19
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Schmitz H, Rabe M, Janssens G, Bondesson D, Rit S, Parodi K, Belka C, Dinkel J, Kurz C, Kamp F, Landry G. Validation of proton dose calculation on scatter corrected 4D cone beam computed tomography using a porcine lung phantom. Phys Med Biol 2021; 66. [PMID: 34293737 DOI: 10.1088/1361-6560/ac16e9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/22/2021] [Indexed: 12/25/2022]
Abstract
Proton therapy treatment for lungs remains challenging as images enabling the detection of inter- and intra-fractional motion, which could be used for proton dose adaptation, are not readily available. 4D computed tomography (4DCT) provides high image quality but is rarely available in-room, while in-room 4D cone beam computed tomography (4DCBCT) suffers from image quality limitations stemming mostly from scatter detection. This study investigated the feasibility of using virtual 4D computed tomography (4DvCT) as a prior for a phase-per-phase scatter correction algorithm yielding a 4D scatter corrected cone beam computed tomography image (4DCBCTcor), which can be used for proton dose calculation. 4DCT and 4DCBCT scans of a porcine lung phantom, which generated reproducible ventilation, were acquired with matching breathing patterns. Diffeomorphic Morphons, a deformable image registration algorithm, was used to register the mid-position 4DCT to the mid-position 4DCBCT and yield a 4DvCT. The 4DCBCT was reconstructed using motion-aware reconstruction based on spatial and temporal regularization (MA-ROOSTER). Successively for each phase, digitally reconstructed radiographs of the 4DvCT, simulated without scatter, were exploited to correct scatter in the corresponding CBCT projections. The 4DCBCTcorwas then reconstructed with MA-ROOSTER using the corrected CBCT projections and the same settings and deformation vector fields as those already used for reconstructing the 4DCBCT. The 4DCBCTcorand the 4DvCT were evaluated phase-by-phase, performing proton dose calculations and comparison to those of a ground truth 4DCT by means of dose-volume-histograms (DVH) and gamma pass-rates (PR). For accumulated doses, DVH parameters deviated by at most 1.7% in the 4DvCT and 2.0% in the 4DCBCTcorcase. The gamma PR for a (2%, 2 mm) criterion with 10% threshold were at least 93.2% (4DvCT) and 94.2% (4DCBCTcor), respectively. The 4DCBCTcortechnique enabled accurate proton dose calculation, which indicates the potential for applicability to clinical 4DCBCT scans.
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Affiliation(s)
- Henning Schmitz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | | | - David Bondesson
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69373, LYON, France
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Radiation Oncology, University Hospital Cologne, Cologne, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
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20
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Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys 2021; 48:6537-6566. [PMID: 34407209 DOI: 10.1002/mp.15150] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 01/22/2023] Open
Abstract
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
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Affiliation(s)
- Maria Francesca Spadea
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Matteo Maspero
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands
| | - Paolo Zaffino
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Joao Seco
- Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
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21
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Seller Oria C, Thummerer A, Free J, Langendijk JA, Both S, Knopf AC, Meijers A. Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients. Med Phys 2021; 48:4498-4505. [PMID: 34077554 PMCID: PMC8456797 DOI: 10.1002/mp.15020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/28/2021] [Accepted: 05/28/2021] [Indexed: 01/12/2023] Open
Abstract
Purpose Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs. Methods A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. Results Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs. Conclusions The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.
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Affiliation(s)
- Carmen Seller Oria
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jeffrey Free
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje C Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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22
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Bobić M, Lalonde A, Sharp GC, Grassberger C, Verburg JM, Winey BA, Lomax AJ, Paganetti H. Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy. Phys Med Biol 2021; 66. [PMID: 33503592 DOI: 10.1088/1361-6560/abe050] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/27/2021] [Indexed: 12/11/2022]
Abstract
The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OAW), and daily online adaptation (OAD). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo (MC) dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OADmitigated random delivery errors more effectively than OAWon a fraction per fraction basis, both OAWand OADachieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D98%ranged between 97.15% and 99.73% of the prescription dose for OAD, with a median of 98.07%. For OAW, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OAWcould be used as an alternative approach to OADfor most patients in order to reduce the clinical workload.
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Affiliation(s)
- Mislav Bobić
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts, UNITED STATES
| | - Arthur Lalonde
- Radiation-Oncology, Massachusetts General Hospital, Boston, Massachusetts, 02114-2696, UNITED STATES
| | - Gregory C Sharp
- Dept of Radiation Oncology, Massachusetts General Hospital, 100 Blossom Street, Cox Building, 302, Boston, MA 02114, USA, Boston, UNITED STATES
| | | | - Joost M Verburg
- Department of Radiation Oncology, Harvard Medical School, Massachussets General Hospital, Francis H Burr Proton Therapy Center, 30 Fruit Street, Boston, 02114, UNITED STATES
| | - Brian A Winey
- Department of Radiation Oncology, Harvard Medical School, FH Burr Proton Therapy Center, 55 Fruit St, Boston, Massachusetts, 02114, UNITED STATES
| | - Antony John Lomax
- Department of Radiation Medicine, Paul Scherrer Institute, CH-5232 Villigen PSI, Villigen, SWITZERLAND
| | - Harald Paganetti
- Northeast Proton Therapy Centre, Massachusetts General Hospital, 30 Fruit Street, Boston, MA 02114, USA, Boston, Massachusetts, 02114, UNITED STATES
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