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Fridström KML, Winter RM, Hornik N, Almberg SS, Danielsen S, Redalen KR. Evaluation of magnetic resonance imaging derived synthetic computed tomography for proton therapy planning in prostate cancer. Phys Imaging Radiat Oncol 2024; 31:100625. [PMID: 39253731 PMCID: PMC11381754 DOI: 10.1016/j.phro.2024.100625] [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: 03/21/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 09/11/2024] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI)-only workflow is used in photon radiotherapy (RT) today, but not yet for protons. To bring MRI-only proton RT into clinical use, proton dose calculation on MRI-derived synthetic CT (sCT) must be validated. We evaluated proton dose calculation accuracy of prostate cancer proton plans using a commercially available sCT generator already validated for photon planning. Materials and methods The retrospective planning study included 10 prostate cancer patients who underwent MRI and planning CT (pCT) before RT. sCT were generated from the MRI with MRI Planner v2.3, and compared to pCT using structural mean absolute error (MAE). The pCT was used to create one-arc volumetric modulated arc therapy (VMAT) photon plan and two-field intensity modulated proton therapy (IMPT) proton plan. Each plan was recalculated on the sCT and compared to pCT doses. Dose volume histogram parameters, gamma analyses and range differences were evaluated. Results Median MAE for the body contour was 71 HU. Dose differences between pCT and sCT were small and similar for VMAT and IMPT plans. Median (range) gamma pass rates were lower for IMPT plans with 95.8 (89.3-98.7) % compared to VMAT plans with 99.4 (91.2-99.6) %. The proton range difference was 1.0 (interquartile range -0.1 - 0.2) mm deeper for sCT compared to the reference. Conclusion MRI-only IMPT planning for prostate cancer seems feasible in a clinical setting for the evaluated beam arrangement and sCT generator. More patients and evaluation of other beam arrangements are needed for a more general conclusion.
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Affiliation(s)
- Kajsa M L Fridström
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Cancer Clinic, St. Olav Hospital, Trondheim, Norway
| | - René M Winter
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Natalie Hornik
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Eberhard Karls University of Tübingen, Tübingen, Germany
| | | | - Signe Danielsen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
- Cancer Clinic, St. Olav Hospital, Trondheim, Norway
| | - Kathrine R Redalen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
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Chourak H, Barateau A, Tahri S, Cadin C, Lafond C, Nunes JC, Boue-Rafle A, Perazzi M, Greer PB, Dowling J, de Crevoisier R, Acosta O. Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods. Front Oncol 2022; 12:968689. [PMID: 36300084 PMCID: PMC9589295 DOI: 10.3389/fonc.2022.968689] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.
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Affiliation(s)
- Hilda Chourak
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Anaïs Barateau
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Safaa Tahri
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Capucine Cadin
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Caroline Lafond
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Jean-Claude Nunes
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Adrien Boue-Rafle
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Mathias Perazzi
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Peter B. Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia
- Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, NSW, Australia
| | - Jason Dowling
- The Australian eHealth Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Health and Biosecurity, Brisbane, QLD, Australia
- *Correspondence: Hilda Chourak, ; Jason Dowling,
| | - Renaud de Crevoisier
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
| | - Oscar Acosta
- University of Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, France
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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: 3] [Impact Index Per Article: 1.5] [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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Anto GJ, Sekaran S, Perumal B, Ramar N, Vaitheeswaran R, Karthikeyan SK. A study to determine the impact of IMPT optimization techniques on prostate synthetic CT image sets dose comparison against CT image sets. Rep Pract Oncol Radiother 2022; 27:161-169. [PMID: 35402035 PMCID: PMC8989438 DOI: 10.5603/rpor.a2022.0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/20/2022] [Indexed: 11/25/2022] Open
Abstract
Background The objective of this study is to determine the impact of intensity modulated proton therapty (IMPT) optimization techniques on the proton dose comparison of commercially available magnetic resonance for calculating attenuation (MRCA T) images, a synthetic computed tomography CT (sCT) based on magnetic resonance imaging (MRI) scan against the CT images and find out the optimization technique which creates plans with the least dose differences against the regular CT image sets. Material and methods Regular CT data sets and sCT image sets were obtained for 10 prostate patients for the study. Six plans were created using six distinct IMPT optimization techniques including multi-field optimization (MFO), single field uniform dose (SFUD) optimization, and robust optimization (RO) in CT image sets. These plans were copied to MRCA T, sCT datasets and doses were computed. Doses from CT and MRCA T data sets were compared for each patient using 2D dose distribution display, dose volume histograms (DVH), homogeneity index (HI), conformation number (CN) and 3D gamma analysis. A two tailed t-test was conducted on HI and CN with 5% significance level with a null hypothesis for CT and sCT image sets. Results Analysis of ten CT and sCT image sets with different IMPT optimization techniques shows that a few of the techniques show significant differences between plans for a few evaluation parameters. Isodose lines, DVH, HI, CN and t-test analysis shows that robust optimizations with 2% range error incorporated results in plans, when re-computed in sCT image sets results in the least dose differences against CT plans compared to other optimization techniques. The second best optimization technique with the least dose differences was robust optimization with 5% range error. Conclusion This study affirmatively demonstrates the impact of IMPT optimization techniques on synthetic CT image sets dose comparison against CT images and determines the robust optimization with 2% range error as the optimization technique which gives the least dose difference when compared to CT plans.
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Affiliation(s)
- Gipson Joe Anto
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Sureka Sekaran
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Bojarajan Perumal
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - Natarajan Ramar
- Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India
| | - R Vaitheeswaran
- Department of Medical Physics, Bharathiar University, Coimbatore, India
| | - S K Karthikeyan
- Department of Medical Physics, Bharathiar University, Coimbatore, India
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6
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Poon DMC, Wu S, Ho L, Cheung KY, Yu B. Proton Therapy for Prostate Cancer: Challenges and Opportunities. Cancers (Basel) 2022; 14:cancers14040925. [PMID: 35205673 PMCID: PMC8870339 DOI: 10.3390/cancers14040925] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 01/02/2023] Open
Abstract
Simple Summary Reported clinical outcomes of proton therapy (PT) for localized prostate cancer are similar to photon-based external beam radiotherapy. Apparently, the dosimetric advantages of PT have yet to be translated to clinical benefits. The suboptimal clinical outcomes of PT might be attributable to inadequate dose prescription, as indicated by the ASCENDE-RT trial. Moreover, uncertainties involved in the treatment planning and delivery processes, as well as technological limitations in PT treatment systems, may lead to discrepancies between planned doses and actual doses delivered to patients. In this article, we reviewed the current status of PT for prostate cancer and discussed different clinical implementations that could potentially improve the clinical outcome of PT for prostate cancer. Various technological advancements under which uncertainties in dose calculations can be minimized, including MRI-guided PT, dual-energy photon-counting CT and high-resolution Monte Carlo-based treatment planning systems, are highlighted. Abstract The dosimetric advantages of proton therapy (PT) treatment plans are demonstrably superior to photon-based external beam radiotherapy (EBRT) for localized prostate cancer, but the reported clinical outcomes are similar. This may be due to inadequate dose prescription, especially in high-risk disease, as indicated by the ASCENDE-RT trial. Alternatively, the lack of clinical benefits with PT may be attributable to improper dose delivery, mainly due to geometric and dosimetric uncertainties during treatment planning, as well as delivery procedures that compromise the dose conformity of treatments. Advanced high-precision PT technologies, and treatment planning and beam delivery techniques are being developed to address these uncertainties. For instance, external magnetic resonance imaging (MRI)-guided patient setup rooms are being developed to improve the accuracy of patient positioning for treatment. In-room MRI-guided patient positioning systems are also being investigated to improve the geometric accuracy of PT. Soon, high-dose rate beam delivery systems will shorten beam delivery time to within one breath hold, minimizing the effects of organ motion and patient movements. Dual-energy photon-counting computed tomography and high-resolution Monte Carlo-based treatment planning systems are available to minimize uncertainties in dose planning calculations. Advanced in-room treatment verification tools such as prompt gamma detector systems will be used to verify the depth of PT. Clinical implementation of these new technologies is expected to improve the accuracy and dose conformity of PT in the treatment of localized prostate cancers, and lead to better clinical outcomes. Improvement in dose conformity may also facilitate dose escalation, improving local control and implementation of hypofractionation treatment schemes to improve patient throughput and make PT more cost effective.
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Affiliation(s)
- Darren M. C. Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China;
| | - Stephen Wu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China; (L.H.); (K.Y.C.); (B.Y.)
- Correspondence: ; Tel.: +852-29171413
| | - Leon Ho
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China; (L.H.); (K.Y.C.); (B.Y.)
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China; (L.H.); (K.Y.C.); (B.Y.)
| | - Ben Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China; (L.H.); (K.Y.C.); (B.Y.)
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Florkow MC, Guerreiro F, Zijlstra F, Seravalli E, Janssens GO, Maduro JH, Knopf AC, Castelein RM, van Stralen M, Raaymakers BW, Seevinck PR. Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours. Radiother Oncol 2020; 153:220-227. [DOI: 10.1016/j.radonc.2020.09.056] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/24/2020] [Accepted: 09/28/2020] [Indexed: 01/24/2023]
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Dumlu HS, Meschini G, Kurz C, Kamp F, Baroni G, Belka C, Paganelli C, Riboldi M. Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas. Z Med Phys 2020; 32:85-97. [PMID: 33168274 PMCID: PMC9948883 DOI: 10.1016/j.zemedi.2020.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 09/02/2020] [Accepted: 10/01/2020] [Indexed: 12/25/2022]
Abstract
In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD2%, ΔD98%, and ΔDmean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD2%≥0.3%, ΔD98%≥15.9%, ΔDmean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD2%≤0.4%, ΔD98%≤5.6%, ΔDmean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.
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Affiliation(s)
- Hatice Selcen Dumlu
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany
| | - Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy; Centro Nazionale di Adroterapia Oncologica, Strada Campeggi 53, 27100 Pavia, Italy
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 München, Germany; German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching bei München, Germany.
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Hoffmann A, Oborn B, Moteabbed M, Yan S, Bortfeld T, Knopf A, Fuchs H, Georg D, Seco J, Spadea MF, Jäkel O, Kurz C, Parodi K. MR-guided proton therapy: a review and a preview. Radiat Oncol 2020; 15:129. [PMID: 32471500 PMCID: PMC7260752 DOI: 10.1186/s13014-020-01571-x] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 05/17/2020] [Indexed: 02/14/2023] Open
Abstract
Background The targeting accuracy of proton therapy (PT) for moving soft-tissue tumours is expected to greatly improve by real-time magnetic resonance imaging (MRI) guidance. The integration of MRI and PT at the treatment isocenter would offer the opportunity of combining the unparalleled soft-tissue contrast and real-time imaging capabilities of MRI with the most conformal dose distribution and best dose steering capability provided by modern PT. However, hybrid systems for MR-integrated PT (MRiPT) have not been realized so far due to a number of hitherto open technological challenges. In recent years, various research groups have started addressing these challenges and exploring the technical feasibility and clinical potential of MRiPT. The aim of this contribution is to review the different aspects of MRiPT, to report on the status quo and to identify important future research topics. Methods Four aspects currently under study and their future directions are discussed: modelling and experimental investigations of electromagnetic interactions between the MRI and PT systems, integration of MRiPT workflows in clinical facilities, proton dose calculation algorithms in magnetic fields, and MRI-only based proton treatment planning approaches. Conclusions Although MRiPT is still in its infancy, significant progress on all four aspects has been made, showing promising results that justify further efforts for research and development to be undertaken. First non-clinical research solutions have recently been realized and are being thoroughly characterized. The prospect that first prototype MRiPT systems for clinical use will likely exist within the next 5 to 10 years seems realistic, but requires significant work to be performed by collaborative efforts of research groups and industrial partners.
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Affiliation(s)
- Aswin Hoffmann
- 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.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Bradley Oborn
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.,Illawarra Cancer Care Centre, Wollongong Hospital, Wollongong, Australia
| | - Maryam Moteabbed
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Susu Yan
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Herman Fuchs
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna/AKH, Vienna, Austria.,Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joao Seco
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Maria Francesca Spadea
- Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ, Heidelberg, Germany.,Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Oliver Jäkel
- Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum DKFZ and Heidelberg Ion-Beam Therapy Center at the University Medical Center, Heidelberg, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany.
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