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Koh CWY, Lew KS, Wibawa A, Master Z, Yeap PL, Chua CGA, Lee JCL, Tan HQ, Park SY. First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre. Phys Med 2024; 120:103341. [PMID: 38554639 DOI: 10.1016/j.ejmp.2024.103341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 03/07/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND AND PURPOSE This work introduces the first assessment of CT calibration following the ESTRO's consensus guidelines and validating the HLUT through the irradiation of biological material. METHODS Two electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider's and ESTRO's methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored. RESULTS The results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider's and ESTRO's methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%. CONCLUSION This is the first report on calibrating an HLUT following the ESTRO's guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO's guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.
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Affiliation(s)
| | - Kah Seng Lew
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore; Nanyang Technological University Singapore, Singapore
| | - Andrew Wibawa
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Zubin Master
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Ping Lin Yeap
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore; Department of Oncology, University of Cambridge, United Kingdom
| | | | - James Cheow Lei Lee
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore; Nanyang Technological University Singapore, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore; Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore.
| | - Sung Yong Park
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore; Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
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Isabelle Choi J, Wojcieszynski A, Amos RA, Giap H, Apisarnthanarax S, Ashman JB, Anand A, Perles LA, Williamson T, Ramkumar S, Molitoris J, Simone CB, Chuong MD. PTCOG Gastrointestinal Subcommittee Lower Gastrointestinal Tract Malignancies Consensus Statement. Int J Part Ther 2024; 11:100019. [PMID: 38757077 PMCID: PMC11095104 DOI: 10.1016/j.ijpt.2024.100019] [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: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 05/18/2024] Open
Abstract
Purpose Radiotherapy delivery in the definitive management of lower gastrointestinal (LGI) tract malignancies is associated with substantial risk of acute and late gastrointestinal (GI), genitourinary, dermatologic, and hematologic toxicities. Advanced radiation therapy techniques such as proton beam therapy (PBT) offer optimal dosimetric sparing of critical organs at risk, achieving a more favorable therapeutic ratio compared with photon therapy. Materials and Methods The international Particle Therapy Cooperative Group GI Subcommittee conducted a systematic literature review, from which consensus recommendations were developed on the application of PBT for LGI malignancies. Results Eleven recommendations on clinical indications for which PBT should be considered are presented with supporting literature, and each recommendation was assessed for level of evidence and strength of recommendation. Detailed technical guidelines pertaining to simulation, treatment planning and delivery, and image guidance are also provided. Conclusion PBT may be of significant value in select patients with LGI malignancies. Additional clinical data are needed to further elucidate the potential benefits of PBT for patients with anal cancer and rectal cancer.
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Affiliation(s)
- J. Isabelle Choi
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- New York Proton Center, New York, New York, USA
| | | | - Richard A. Amos
- Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Huan Giap
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Smith Apisarnthanarax
- Department of Radiation Oncology, University of Washington, Seattle, Washington, USA
| | | | - Aman Anand
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Luis A. Perles
- Department of Radiation Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
| | - Tyler Williamson
- Department of Radiation Physics, UT MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Jason Molitoris
- Department of Radiation Oncology, University of Maryland Medical Center, Baltimore, Maryland, USA
| | - Charles B. Simone
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- New York Proton Center, New York, New York, USA
| | - Michael D. Chuong
- Department of Radiation Oncology, Miami Cancer Institute, Miami, Florida, USA
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Winter A, Vorselaars B, Esposito M, Badiee A, Price T, Allport P, Allinson N. OPTIma: simplifying calorimetry for proton computed tomography in high proton flux environments. Phys Med Biol 2024; 69:055034. [PMID: 38346338 DOI: 10.1088/1361-6560/ad2883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 03/01/2024]
Abstract
Objective.Proton computed tomography (pCT) offers a potential route to reducing range uncertainties for proton therapy treatment planning, however the current trend towards high current spot scanning treatment systems leads to high proton fluxes which are challenging for existing systems. Here we demonstrate a novel approach to energy reconstruction, referred to as 'de-averaging', which allows individual proton energies to be recovered using only a measurement of their integrated energy without the need for spatial information from the calorimeter.Approach.The method is evaluated in the context of the Optimising Proton Therapy through Imaging (OPTIma) system which uses a simple, relatively inexpensive, scintillator-based calorimeter that reports only the integrated energy deposited by all protons within a cyclotron period, alongside a silicon strip based tracking system capable of reconstructing individual protons in a high flux environment. GEANT4 simulations have been performed to examine the performance of such a system at a modern commercial cyclotron facility using aσ≈ 10 mm beam for currents in the range 10-50 pA at the nozzle.Main results.Apart from low-density lung tissue, a discrepancy of less than 1% on the Relative Stopping Power is found for all other considered tissues when embedded within a 150 mm spherical Perspex phantom in the 10-30 pA current range, and for some tissues even up to 50 pA.Significance.By removing the need for the calorimeter system to provide spatial information, it is hoped that the de-averaging approach can facilitate clinically relevant, cost effective and less complex calorimeter systems for performing high current pCTs.
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Affiliation(s)
- A Winter
- University of Birmingham, Birmingham, United Kingdom
- University of Lincoln, Lincoln, United Kingdom
| | | | - M Esposito
- University of Lincoln, Lincoln, United Kingdom
| | - A Badiee
- University of Lincoln, Lincoln, United Kingdom
| | - T Price
- University of Birmingham, Birmingham, United Kingdom
| | - P Allport
- University of Birmingham, Birmingham, United Kingdom
| | - N Allinson
- University of Lincoln, Lincoln, United Kingdom
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Han D, Zhang S, Chen S, Hooshangnejad H, Yu F, Ding K, Lin H. Enhancement of Stopping Power Ratio (SPR) Estimation Accuracy through Image-Domain Dual-Energy Computer Tomography for Pencil Beam Scanning System: A Simulation Study. Cancers (Basel) 2024; 16:467. [PMID: 38275907 PMCID: PMC10814952 DOI: 10.3390/cancers16020467] [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: 11/06/2023] [Revised: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 01/27/2024] Open
Abstract
Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.
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Affiliation(s)
- Dong Han
- New York Proton Center, 225 E 126th St., New York, NY 10035, USA; (F.Y.); (H.L.)
| | | | - Sixia Chen
- Department of Mathematics and Computer Science, College of Arts and Sciences, Adelphi University, One South Avenue, Garden City, NY 11530, USA;
| | - Hamed Hooshangnejad
- Department of Radiation Oncology, Johns Hopkins University, 401 North Broadway, Suite 1440, Baltimore, MD 21231, USA; (H.H.); (K.D.)
| | - Francis Yu
- New York Proton Center, 225 E 126th St., New York, NY 10035, USA; (F.Y.); (H.L.)
| | - Kai Ding
- Department of Radiation Oncology, Johns Hopkins University, 401 North Broadway, Suite 1440, Baltimore, MD 21231, USA; (H.H.); (K.D.)
| | - Haibo Lin
- New York Proton Center, 225 E 126th St., New York, NY 10035, USA; (F.Y.); (H.L.)
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Cohilis M, Souris K, Buti G, Chang CW, Lin L, Lee JA, Sterpin E. A spot-specific range uncertainty framework for robust optimization of proton therapy treatments. Med Phys 2023; 50:6554-6568. [PMID: 37676906 DOI: 10.1002/mp.16706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 09/09/2023] Open
Abstract
PURPOSE An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk. METHODS The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible. RESULTS For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness. CONCLUSIONS Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specific treatments.
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Affiliation(s)
- Marie Cohilis
- Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium
| | - Kevin Souris
- Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium
| | - Gregory Buti
- Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium
| | - Chih-Wei Chang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Liyong Lin
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - John A Lee
- Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium
| | - Edmond Sterpin
- Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium
- Department of Oncology, KU Leuven, Laboratory of Experimental Radiotherapy, Leuven, Belgium
- Particle Therapy Interuniversity Center Leuven-PARTICLE, Leuven, Belgium
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6
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Taasti VT, Decabooter E, Eekers D, Compter I, Rinaldi I, Bogowicz M, van der Maas T, Kneepkens E, Schiffelers J, Stultiens C, Hendrix N, Pijls M, Emmah R, Fonseca GP, Unipan M, van Elmpt W. Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients. Br J Radiol 2023; 96:20230110. [PMID: 37493227 PMCID: PMC10461272 DOI: 10.1259/bjr.20230110] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 07/27/2023] Open
Abstract
OBJECTIVE Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients. METHODS DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% (as used clinically; denoted 3%-plan); the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose-volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed. RESULTS For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient. CONCLUSION Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%. ADVANCES IN KNOWLEDGE This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease.
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Affiliation(s)
- Vicki Trier Taasti
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Esther Decabooter
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ilaria Rinaldi
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marta Bogowicz
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tim van der Maas
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Esther Kneepkens
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jacqueline Schiffelers
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cissy Stultiens
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicole Hendrix
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Mirthe Pijls
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Rik Emmah
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Gabriel Paiva Fonseca
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Mirko Unipan
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Marants R, Tattenberg S, Scholey J, Kaza E, Miao X, Benkert T, Magneson O, Fischer J, Vinas L, Niepel K, Bortfeld T, Landry G, Parodi K, Verburg J, Sudhyadhom A. Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms. Phys Med Biol 2023; 68:10.1088/1361-6560/ace876. [PMID: 37463589 PMCID: PMC10645122 DOI: 10.1088/1361-6560/ace876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/18/2023] [Indexed: 07/20/2023]
Abstract
Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.
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Affiliation(s)
- Raanan Marants
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sebastian Tattenberg
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jessica Scholey
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, California, United States of America
| | - Evangelia Kaza
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Xin Miao
- Siemens Medical Solutions USA Inc., Boston, Massachusetts, United States of America
| | | | - Olivia Magneson
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jade Fischer
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medical Physics, University of Calgary, Calgary, Alberta, Canada
| | - Luciano Vinas
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Statistics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Katharina Niepel
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
| | - Thomas Bortfeld
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Joost Verburg
- Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, Massachusetts, United States of America
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8
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Vestergaard CD, Muren LP, Elstrøm UV, Johansen JG, Taasti VT. Tissue-specific range uncertainty estimation in proton therapy. Phys Imaging Radiat Oncol 2023; 26:100441. [PMID: 37182194 PMCID: PMC10173296 DOI: 10.1016/j.phro.2023.100441] [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: 12/21/2022] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023] Open
Abstract
Background and Purpose Proton therapy is sensitive to range uncertainties, which typically are accounted for by margins or robust optimization, based on tissue-independent uncertainties. However, range uncertainties have been shown to depend on the specific tissues traversed. The aim of this study was to investigate the differences between range margins based on stopping power ratio (SPR) uncertainties which were tissue-specific (applied voxel-wise) or fixed (tissue-independent or composite). Materials and Methods Uncertainties originating from imaging, computed tomography (CT) number estimation, and SPR estimation were calculated for low-, medium-, and high-density tissues to quantify the tissue-specific SPR uncertainties. Four clinical treatment plans (four different tumor sites) were created and recomputed after applying either tissue-specific or fixed SPR uncertainties. Plans with tissue-specific and fixed uncertainties were compared, based on dose-volume-histogram parameters for both targets and organs-at-risk. Results The total SPR uncertainties were 7.0% for low-, 1.0% for medium-, and 1.3% for high-density tissues. Differences between the proton plans with tissue-specific and fixed uncertainties were mainly found in the vicinity of the target. Composite uncertainties were found to capture the tissue-specific uncertainties more accurately than the tissue-independent uncertainties. Conclusion Different SPR uncertainties were found for low-, medium-, and high-density tissues indicating that range margins based on tissue-specific uncertainties may be more exact than the standard approach of using tissue-independent uncertainties. Differences between applying tissue-specific and fixed uncertainties were found, however, a fixed uncertainty might still be sufficient, but with a magnitude that depends on the body region.
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Affiliation(s)
- Casper Dueholm Vestergaard
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Corresponding author at: Danish Centre for Particle Therapy, Palle Juul Jensens Boulevard 25, 8200 Aarhus N, Denmark.
| | - Ludvig Paul Muren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | | | - Vicki Trier Taasti
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
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9
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Chen Z, Dominello MM, Joiner MC, Burmeister JW. Proton versus photon radiation therapy: A clinical review. Front Oncol 2023; 13:1133909. [PMID: 37064131 PMCID: PMC10091462 DOI: 10.3389/fonc.2023.1133909] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
While proton radiation therapy offers substantially better dose distribution characteristics than photon radiation therapy in certain clinical applications, data demonstrating a quantifiable clinical advantage is still needed for many treatment sites. Unfortunately, the number of patients treated with proton radiation therapy is still comparatively small, in some part due to the lack of evidence of clear benefits over lower-cost photon-based treatments. This review is designed to present the comparative clinical outcomes between proton and photon therapies, and to provide an overview of the current state of knowledge regarding the effectiveness of proton radiation therapy.
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Affiliation(s)
- Zhe Chen
- School of Medicine, Wayne State University, Detroit, MI, United States
- *Correspondence: Zhe Chen,
| | - Michael M. Dominello
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Michael C. Joiner
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Jay W. Burmeister
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
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10
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Zimmerman J, Thor D, Poludniowski G. Stopping-power ratio estimation for proton radiotherapy using dual-energy computed tomography and prior-image constrained denoising. Med Phys 2023; 50:1481-1495. [PMID: 36322128 DOI: 10.1002/mp.16063] [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: 04/02/2022] [Revised: 09/12/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Dual-energy computed tomography (DECT) is a promising technique for estimating stopping-power ratio (SPR) for proton therapy planning. It is known, however, that deriving electron density (ED) and effective atomic number (EAN) from DECT data can cause noise amplification in the resulting SPR images. This can negate the benefits of DECT. PURPOSE This work introduces a new algorithm for estimating SPR from DECT with noise suppression, using a pair of CT scans with spectral separation. The method is demonstrated using phantom measurements. MATERIALS AND METHODS An iterative algorithm is presented, reconstructing ED and EAN with noise suppression, based on Prior Image Constrained Denoising (PIC-D). The algorithm is tested using a Siemens Definition AS+ CT scanner (Siemens Healthcare, Forchheim, Germany). Three phantoms are investigated: a calibration phantom (CIRS 062M), a QA phantom (CATPHAN 700), and an anthropomorphic head phantom (CIRS 731-HN). A task-transfer function (TTF) and the noise power spectrum are derived from SPR images of the QA phantom for the evaluation of image quality. Comparisons of accuracy and noise for ED, EAN, and SPR are made for various versions of the algorithm in comparison to a solution based on Siemens syngo.via Rho/Z software and the current clinical standard of a single-energy CT stoichiometric calibration. A gamma analysis is also applied to the SPR images of the head phantom and water-equivalent distance (WED) is evaluated in a treatment planning system for a proton treatment field. RESULTS The algorithm is effective at suppressing noise in both ED and EAN and hence also SPR. The noise is tunable to a level equivalent to or lower than that of the syngo.via Rho/Z software. The spatial resolution (10% and 50% frequencies in the TTF) does not degrade even for the highest noise suppression investigated, although the average spatial frequency of noise does decrease. The PIC-D algorithm showed better accuracy than syngo.via Rho/Z for low density materials. In the calibration phantom, it was superior even when excluding lung substitutes, with root-mean-square deviations for ED and EAN less than 0.3% and 2%, respectively, compared to 0.5% and 3%. In the head phantom, however, the SPR accuracy of the PIC-D algorithm was comparable (excluding sinus tissue) to that derived from syngo.via Rho/Z: less than 1% error for soft tissue, brain, and trabecular bone substitutes and 5-7% for cortical bone, with the larger error for the latter likely related to the phantom geometry. Gamma evaluation showed that PIC-D can suppress noise in a patient-like geometry without introducing substantial errors in SPR. The absolute pass rates were almost identical for PIC-D and syngo.via Rho/Z. In the WED evaluations no general differences were shown. CONCLUSIONS The PIC-D DECT algorithm provides scanner-specific calibration and tunable noise suppression. It is vendor agnostic and applicable to any pair of CT scans with spectral separation. Improved accuracy to current methods was not clearly demonstrated for the complex geometry of a head phantom, but the suppression of noise without spatial resolution degradation and the possibility of incorporating constraints on image properties, suggests the usefulness of the approach.
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Affiliation(s)
- Jens Zimmerman
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Thor
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Gavin Poludniowski
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
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11
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Yang M, Wohlfahrt P, Shen C, Bouchard H. Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential. Phys Med Biol 2023; 68. [PMID: 36595276 DOI: 10.1088/1361-6560/acabfa] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρs) within patients. Currently, theρsdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρsusing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρsshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρsprediction has been shown to be effective in reducing the uncertainty inρsestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρsestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρsestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρsestimation.
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Affiliation(s)
- Ming Yang
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, 1515 Holcombe Blvd Houston, TX 77030, United States of America
| | - Patrick Wohlfahrt
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, MA 02115, United States of America
| | - Chenyang Shen
- University of Texas Southwestern Medical Center, Department of Radiation Oncology, 2280 Inwood Rd Dallas, TX 75235, United States of America
| | - Hugo Bouchard
- Département de physique, Université de Montréal, Complexe des sciences, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec H2V0B3, Canada.,Centre de recherche du Centre hospitalier de l'Université de Montréal, 900 Rue Saint-Denis, Montréal, Québec, H2X 0A9, Canada.,Département de radio-oncologie, Centre hospitalier de l'Université de Montréal, 1051 Rue Sanguinet, Montréal, Québec H2X 3E4, Canada
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12
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Zhu XR, Li Y, Yang M, Whitaker TJ, Taylor PA, Zhang X, Poenisch F, Sahoo N, Liao Z, Chang JY. Stereotactic body proton therapy for early stage non-small cell lung cancer - Technical challenges and solutions: The MD Anderson experience. JOURNAL OF RADIOSURGERY AND SBRT 2023; 9:75-82. [PMID: 38029015 PMCID: PMC10681148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 08/10/2023] [Indexed: 12/01/2023]
Abstract
Our randomized clinical study comparing stereotactic body radiotherapy (SBRT) and stereotactic body proton therapy (SBPT) for early stage non-small cell lung cancer (NSCLC) was closed prematurely owing to poor enrollment, largely because of lack of volumetric imaging and difficulty in obtaining insurance coverage for the SBPT group. In this article, we describe technology improvements in our new proton therapy center, particularly in image guidance with cone beam CT (CBCT) and CT on rail (CTOR), as well as motion management with real-time gated proton therapy (RGPT) and optical surface imaging. In addition, we have a treatment planning system that provides better treatment plan optimization and more accurate dose calculation. We expect to re-start the SBPT program, including for early stage NSCLC as well as for other disease sites soon after starting patient treatment at our new proton therapy center.
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Affiliation(s)
- X Ronald Zhu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yuting Li
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Thomas J Whitaker
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paige A Taylor
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiaodong Zhang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Falk Poenisch
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Narayan Sahoo
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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13
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Lei Y, Fu Y, Tian Z, Wang T, Dai X, Roper J, Yu DS, McDonald M, Bradley JD, Liu T, Zhou J, Yang X. Deformable CT image registration via a dual feasible neural network. Med Phys 2022; 49:7545-7554. [PMID: 35869866 PMCID: PMC9792435 DOI: 10.1002/mp.15875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 05/23/2022] [Accepted: 07/15/2022] [Indexed: 12/30/2022] Open
Abstract
PURPOSE A quality assurance (QA) CT scans are usually acquired during cancer radiotherapy to assess for any anatomical changes, which may cause an unacceptable dose deviation and therefore warrant a replan. Accurate and rapid deformable image registration (DIR) is needed to support contour propagation from the planning CT (pCT) to the QA CT to facilitate dose volume histogram (DVH) review. Further, the generated deformation maps are used to track the anatomical variations throughout the treatment course and calculate the corresponding accumulated dose from one or more treatment plans. METHODS In this study, we aim to develop a deep learning (DL)-based method for automatic deformable registration to align the pCT and the QA CT. Our proposed method, named dual-feasible framework, was implemented by a mutual network that functions as both a forward module and a backward module. The mutual network was trained to predict two deformation vector fields (DVFs) simultaneously, which were then used to register the pCT and QA CT in both directions. A novel dual feasible loss was proposed to train the mutual network. The dual-feasible framework was able to provide additional DVF regularization during network training, which preserves the topology and reduces folding problems. We conducted experiments on 65 head-and-neck cancer patients (228 CTs in total), each with 1 pCT and 2-6 QA CTs. For evaluations, we calculated the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), target registration error (TRE) between the deformed and target images and the Jacobian determinant of the predicted DVFs. RESULTS Within the body contour, the mean MAE, PSNR, SSIM, and TRE are 122.7 HU, 21.8 dB, 0.62 and 4.1 mm before registration and are 40.6 HU, 30.8 dB, 0.94, and 2.0 mm after registration using the proposed method. These results demonstrate the feasibility and efficacy of our proposed method for pCT and QA CT DIR. CONCLUSION In summary, we proposed a DL-based method for automatic DIR to match the pCT to the QA CT. Such DIR method would not only benefit current workflow of evaluating DVHs on QA CTs but may also facilitate studies of treatment response assessment and radiomics that depend heavily on the accurate localization of tissues across longitudinal images.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Zhen Tian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xianjin Dai
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - David S Yu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
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14
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Investigation on Accuracy of Stopping Power Ratio Prediction Based on Spectral CT. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00761-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Chacko MS, Wu D, Grewal HS, Sonnad JR. Impact of beam-hardening corrections on proton relative stopping power estimates from single- and dual-energy CT. J Appl Clin Med Phys 2022; 23:e13711. [PMID: 35816460 PMCID: PMC9512361 DOI: 10.1002/acm2.13711] [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: 03/21/2022] [Revised: 05/19/2022] [Accepted: 06/14/2022] [Indexed: 12/30/2022] Open
Abstract
A major contributing factor to proton range uncertainty is the conversion of computed tomography (CT) Hounsfield units (HU) to proton relative stopping power (RSP). This uncertainty is heightened in the presence of X-ray beam-hardening artifact (BHA), which has two manifestations: cupping and streaking, especially in and near bone tissue. This uncertainty can affect the accuracy of proton RSP calculation for treatment planning in proton radiotherapy. Dual-energy CT (DECT) and iterative beam-hardening correction (iBHC) both show promise in mitigating CT BHA. This present work attempts to analyze the relative robustness of iBHC and DECT techniques on both manifestations of BHA. The stoichiometric method for HU to RSP conversion was used for single-energy CT (SECT) and DECT-based monochromatic techniques using a tissue substitute phantom. Cupping BHA was simulated by measuring the HU of a bone substitute plug in wax/3D-printed phantoms of increasing size. Streaking BHA was simulated by placing a solid water plug between two bone plugs in a wax phantom. Finally, the effect of varying calibration phantom size on RSP was calculated in an anthropomorphic head phantom. The RSP decreased -0.002 cm-1 as phantom size increased for SECT but remained largely constant when iBHC applied or with DECT techniques. The RSP varied a maximum of 2.60% in the presence of streaking BHA in SECT but was reduced to 1.40% with iBHC. For DECT techniques, the maximum difference was 2.40%, reduced to 0.6% with iBHC. Comparing calibration phantoms of 20- and 33-cm diameter, maximum voxel differences of 5 mm in the water-equivalent thickness were observed in the skull but reduced to 1.3 mm with iBHC. The DECT techniques excelled in mitigating cupping BHA, but streaking BHA still could be observed. The use of iBHC reduced RSP variation with BHA in both SECT and DECT techniques.
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Affiliation(s)
- Michael S. Chacko
- Department of Medical Physics and DosimetryOklahoma Proton CenterOklahoma CityOklahomaUSA,Department of Radiological SciencesUniversity of Oklahoma Health Sciences CenterOklahoma CityOklahomaUSA
| | - Dee Wu
- Department of Radiological SciencesUniversity of Oklahoma Health Sciences CenterOklahoma CityOklahomaUSA
| | - Hardev S. Grewal
- Department of Radiation OncologyUniversity of Florida College of MedicineGainesvilleFloridaUSA,Department of Radiation OncologyUniversity of Florida Health Proton Therapy InstituteJacksonvilleFloridaUSA
| | - Jagadeesh R. Sonnad
- Department of Radiological SciencesUniversity of Oklahoma Health Sciences CenterOklahoma CityOklahomaUSA
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16
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Kobeissi JM, Simone CB, Lin H, Hilal L, Hajj C. Proton Therapy in the Management of Pancreatic Cancer. Cancers (Basel) 2022; 14:cancers14112789. [PMID: 35681769 PMCID: PMC9179382 DOI: 10.3390/cancers14112789] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Radiation treatment plays a pivotal a role in the management of pancreatic cancer, mainly in the neoadjuvant setting for borderline resectable tumors and in the definitive setting for unresectable localized disease. Most of the studies on pancreatic cancer use photon-based radiation therapy modalities. However, there is a growing interest in the application of protons therapy for gastrointestinal cancers. This review summarizes the literature on the use of proton therapy in the management of pancreatic cancer. Abstract Radiation therapy plays a central role in the treatment of pancreatic cancer. While generally shown to be feasible, proton irradiation, particularly when an ablative dose is planned, remains a challenge, especially due to tumor motion and the proximity to organs at risk, like the stomach, duodenum, and bowel. Clinically, standard doses of proton radiation treatment have not been shown to be statistically different from photon radiation treatment in terms of oncologic outcomes and toxicity rates as per non-randomized comparative studies. Fractionation schedules and concurrent chemotherapy combinations are yet to be optimized for proton therapy and are the subject of ongoing trials.
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Affiliation(s)
- Jana M. Kobeissi
- Department of Radiation Oncology, School of Medicine, American University of Beirut Medical Center, Beirut 1107, Lebanon; (J.M.K.); (L.H.)
| | - Charles B. Simone
- Department of Radiation Oncology, New York Proton Center, New York, NY 10035, USA; (C.B.S.II); (H.L.)
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
| | - Haibo Lin
- Department of Radiation Oncology, New York Proton Center, New York, NY 10035, USA; (C.B.S.II); (H.L.)
| | - Lara Hilal
- Department of Radiation Oncology, School of Medicine, American University of Beirut Medical Center, Beirut 1107, Lebanon; (J.M.K.); (L.H.)
| | - Carla Hajj
- Department of Radiation Oncology, New York Proton Center, New York, NY 10035, USA; (C.B.S.II); (H.L.)
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10027, USA
- Correspondence:
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17
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Scholey J, Vinas L, Kearney V, Yom S, Larson PEZ, Descovich M, Sudhyadhom A. Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning. Phys Med Biol 2022; 67. [PMID: 35417903 PMCID: PMC9121765 DOI: 10.1088/1361-6560/ac6725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio. For the latter, kVCT may have greater uncertainty in determining relative electron density (
ρ
e
) and proton stopping power ratio (SPR). Conversely, megavoltage CT (MVCT) may result in superior dose calculation accuracy. The purpose of this work was to convert kVCT HU to MVCT HU using deep learning to obtain higher accuracy
ρ
e
and SPR. Approach. Tissue-mimicking phantoms were created to compare kVCT- and MVCT-determined
ρ
e
and SPR to physical measurements. Using 100 head-and-neck datasets, an unpaired deep learning model was trained to learn the relationship between kVCTs and MVCTs, creating synthetic MVCTs (sMVCTs). Similarity metrics were calculated between kVCTs, sMVCTs, and MVCTs in 20 test datasets. An anthropomorphic head phantom containing bone-mimicking material with known composition was scanned to provide an independent determination of
ρ
e
and SPR accuracy by sMVCT. Main results. In tissue-mimicking bone,
ρ
e
errors were 2.20% versus 0.19% and SPR errors were 4.38% versus 0.22%, for kVCT versus MVCT, respectively. Compared to MVCT, in vivo mean difference (MD) values were 11 and 327 HU for kVCT and 2 and 3 HU for sMVCT in soft tissue and bone, respectively.
ρ
e
MD decreased from 1.3% to 0.35% in soft tissue and 2.9% to 0.13% in bone, for kVCT and sMVCT, respectively. SPR MD decreased from 1.8% to 0.24% in soft tissue and 6.8% to 0.16% in bone, for kVCT and sMVCT, respectively. Relative to physical measurements,
ρ
e
and SPR error in anthropomorphic bone decreased from 7.50% and 7.48% for kVCT to <1% for both MVCT and sMVCT. Significance. Deep learning can be used to map kVCT to sMVCT, suggesting higher accuracy
ρ
e
and SPR is achievable with sMVCT versus kVCT.
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18
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Moskvin VP, Pirlepesov F, Yan Y, Ates O, Myers WJ, Uh J, Zhao L, Shapira N, Yagil Y, Merchant TE, Hua CH. Accuracy of stopping power ratio calculation and experimental validation of proton range with dual-layer computed tomography. Acta Oncol 2022; 61:864-868. [PMID: 35502150 DOI: 10.1080/0284186x.2022.2069477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Vadim P. Moskvin
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Fakhriddin Pirlepesov
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Yue Yan
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Ozgur Ates
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - William J. Myers
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Jinsoo Uh
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Li Zhao
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Nadav Shapira
- Global Advanced Technology, Philips Medical Systems, Haifa, Israel
| | - Yoad Yagil
- Global Advanced Technology, Philips Medical Systems, Haifa, Israel
| | - Thomas E. Merchant
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Chia-ho Hua
- Department of Radiation Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
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19
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Medrano M, Liu R, Zhao T, Webb T, Politte DG, Whiting BR, Liao R, Ge T, Porras-Chaverri MA, O'Sullivan JA, Williamson JF. Towards sub-percentage uncertainty proton stopping-power mapping via dual-energy CT: direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method. Med Phys 2022; 49:1599-1618. [PMID: 35029302 DOI: 10.1002/mp.15457] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 10/28/2021] [Accepted: 12/22/2021] [Indexed: 11/06/2022] Open
Abstract
PURPOSE To assess the potential of a joint dual-energy CT reconstruction process (Statistical Image Reconstruction method built on a Basis Vector Model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial-resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. METHODS JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for ten high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 kVp and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and for our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for 5 different scenarios (comparison of JSIR-BVM SPR/SP to International Commission on Radiation Measurements and Units (ICRU) benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology (JCGM) and the Guide to expression of Uncertainty in Measurement (GUM), including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross-section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. RESULTS Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (Head Phantom) and 1.1% and -1.1% (Body Phantom). The overall RMS deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and two-fold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6%-0.9%. CONCLUSIONS Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial-resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving <1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with sub-percentage accuracy and estimated uncertainty in the clinical setting. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Maria Medrano
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Ruirui Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tianyu Zhao
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Tyler Webb
- Department of Physics, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - David G Politte
- Mallinckrodt Institute of Radiology, St. Louis, MO, 63110, USA
| | - Bruce R Whiting
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Rui Liao
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Tao Ge
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Mariela A Porras-Chaverri
- Atomic, Nuclear and Molecular Sciences Research Center (CICANUM), University of Costa Rica, San Jose, Costa Rica
| | - Joseph A O'Sullivan
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Jeffrey F Williamson
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA
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20
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Charyyev S, Wang T, Lei Y, Ghavidel B, Beitler JJ, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy. Br J Radiol 2022; 95:20210644. [PMID: 34709948 PMCID: PMC8722254 DOI: 10.1259/bjr.20210644] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based method to synthesize DECT images from SECT image for proton radiotherapy. METHODS The proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model to focus on the difference between DECT images and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 70 head-and-neck cancer patients whose DECT and SECT scans were acquired simultaneously. The model was trained to generate both a high and low energy DECT image based on a SECT image. The generated synthetic low and high DECT images were evaluated against the true DECT images using leave-one-out cross-validation. To evaluate our method in the context of a practical application, we generated SPR maps from synthetic DECT (sDECT) using a dual-energy based stoichiometric method and compared the SPR maps to those generated from DECT. A dosimetric comparison for dose obtained from DECT was performed against that derived from sDECT. RESULTS The mean of mean absolute error, peak signal-to-noise ratio and normalized cross-correlation for the synthetic high and low energy CT images was 36.9 HU, 29.3 dB, 0.96 and 35.8 HU, 29.2 dB, and 0.96, respectively. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square deviation of about 1% with reduced noise level and artifacts than those from original DECT. Dose-volume histogram (DVH) metrics for the clinical target volume agree within 1% between the DECT and sDECT calculated dose. CONCLUSION Our method synthesized accurate DECT images and showed a potential feasibility for proton SPR map generation. ADVANCES IN KNOWLEDGE This study investigated a learning-based method to synthesize DECT images from SECT image for proton radiotherapy.
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Affiliation(s)
- Serdar Charyyev
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Beth Ghavidel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jonathan J Beitler
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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21
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Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction. Radiother Oncol 2021; 166:71-78. [PMID: 34774653 DOI: 10.1016/j.radonc.2021.11.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE To quantifiy the range uncertainty in proton treatment planning using dual-energy computed tomography (DECT) for a direct stopping-power prediction (DirectSPR) algorithm and its clinical implementation. METHODS AND MATERIALS To assess the overall uncertainty in stopping-power ratio (SPR) prediction of a DirectSPR implementation calibrated for different patient geometries, the influencing factors were categorized in imaging, modeling as well as others. The respective SPR uncertainty was quantified for lung, soft tissue and bone and translated into range uncertainty for several tumor types. The amount of healthy tissue spared was quantified for 250 patients treated with DirectSPR and the dosimetric impact was evaluated exemplarily for a representative brain-tumor patient. RESULTS For bone, soft tissue and lung, an SPR uncertainty (1σ) of 1.6%, 1.3% and 1.3% was determined for DirectSPR, respectively. This allowed for a reduction of the clinically applied range uncertainty from currently (3.5% + 2 mm) to (1.7% + 2 mm) for brain-tumor and (2.0% + 2 mm) for prostate-cancer patients. The 150 brain-tumor and 100 prostate-cancer patients treated using DirectSPR benefitted from sparing on average 2.6 mm and 4.4 mm of healthy tissue in beam direction, respectively. In the representative patient case, dose reduction in organs at risk close to the target volume was achieved, with a mean dose reduction of up to 16% in the brainstem. Patient-specific DECT-based treatment planning with reduced safety margins was successfully introduced into clinical routine. CONCLUSIONS A substantial increase in range prediction accuracy in clinical proton treatment planning was achieved by patient-specific DECT-based SPR prediction. For the first time, a relevant imaging-based reduction of range prediction uncertainty on a 2% level has been achieved.
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22
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Kruis MF. Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT. J Appl Clin Med Phys 2021; 23:e13468. [PMID: 34743405 PMCID: PMC8803285 DOI: 10.1002/acm2.13468] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/13/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022] Open
Abstract
Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT. In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.
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23
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Li B, Luo N, Zhong A, Li Y, Chen A, Zhou L, Xu Y. A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography. Quant Imaging Med Surg 2021; 11:4097-4114. [PMID: 34476191 DOI: 10.21037/qims-20-844] [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: 07/08/2020] [Accepted: 04/16/2021] [Indexed: 11/06/2022]
Abstract
Background Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice. Methods We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT. For the data acquisition, the X-ray source is controlled to maintain an energy within a segmental arc, and then switch alternately to another energy level. This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction. To solve this problem, we propose a prior image constraint robust principal component analysis (PIC-RPCA) reconstruction method, which introduces structural similarity and spectral correlation into the reconstruction. Results A numerical simulation and a real phantom experiment were conducted to demonstrate the efficacy and robustness of the scan scheme and reconstruction method. The results showed that our proposed reconstruction method could have achieved better multi-energy images than other competing methods both quantitatively and qualitatively. Conclusions Our proposed SSMECT scan with PIC-RPCA reconstruction method could lower kVp switching frequency while achieving satisfactory reconstruction accuracy and image quality.
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Affiliation(s)
- Bin Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ning Luo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.,The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Anni Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yongbao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Along Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuan Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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24
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Peters N, Wohlfahrt P, Dahlgren CV, de Marzi L, Ellerbrock M, Fracchiolla F, Free J, Gomà C, Góra J, Jensen MF, Kajdrowicz T, Mackay R, Molinelli S, Rinaldi I, Rompokos V, Siewert D, van der Tol P, Vermeren X, Nyström H, Lomax A, Richter C. Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy. Radiother Oncol 2021; 163:7-13. [PMID: 34329653 DOI: 10.1016/j.radonc.2021.07.019] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 07/01/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Experimental assessment of inter-centre variation and absolute accuracy of stopping-power-ratio (SPR) prediction within 17 particle therapy centres of the European Particle Therapy Network. MATERIAL AND METHODS A head and body phantom with seventeen tissue-equivalent materials were scanned consecutively at the participating centres using their individual clinical CT scan protocol and translated into SPR with their in-house CT-number-to-SPR conversion. Inter-centre variation and absolute accuracy in SPR prediction were quantified for three tissue groups: lung, soft tissues and bones. The integral effect on range prediction for typical clinical beams traversing different tissues was determined for representative beam paths for the treatment of primary brain tumours as well as lung and prostate cancer. RESULTS An inter-centre variation in SPR prediction (2σ) of 8.7%, 6.3% and 1.5% relative to water was determined for bone, lung and soft-tissue surrogates in the head setup, respectively. Slightly smaller variations were observed in the body phantom (6.2%, 3.1%, 1.3%). This translated into inter-centre variation of integral range prediction (2σ) of 2.9%, 2.6% and 1.3% for typical beam paths of prostate-, lung- and primary brain-tumour treatments, respectively. The absolute error in range exceeded 2% in every fourth participating centre. The consideration of beam hardening and the execution of an independent HLUT validation had a positive effect, on average. CONCLUSION The large inter-centre variations in SPR and range prediction justify the currently clinically used margins accounting for range uncertainty, which are of the same magnitude as the inter-centre variation. This study underlines the necessity of higher standardisation in CT-number-to-SPR conversion.
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Affiliation(s)
- Nils Peters
- 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.
| | - Patrick Wohlfahrt
- 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
| | | | - Ludovic de Marzi
- Institut Curie, PSL Research University, University Paris Saclay, LITO, Orsay, France; Institut Curie, PSL Research University, Radiation Oncology Department, Proton Therapy Centre, Centre Universitaire, Orsay, France
| | - Malte Ellerbrock
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Jeffrey Free
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Carles Gomà
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - Joanna Góra
- EBG Medaustron GmbH, Wiener Neustadt, Austria
| | | | - Tomasz Kajdrowicz
- Institute of Nuclear Physics - Polish Academy of Sciences, Krakow, Poland
| | - Ranald Mackay
- University of Manchester - Faculty of Life Sciences, Manchester, United Kingdom
| | | | - Ilaria Rinaldi
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | | | - Xavier Vermeren
- Westdeutsches Protonentherapiezentrum Essen, Universitätsklinkum Essen, Germany
| | | | | | - 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; 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, Germany; German Cancer Consortium (DKTK), partner site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
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25
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van der Heyden B, Cohilis M, Souris K, de Freitas Nascimento L, Sterpin E. Artificial intelligence supported single detector multi-energy proton radiography system. Phys Med Biol 2021; 66. [PMID: 33621962 DOI: 10.1088/1361-6560/abe918] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (ITotal), the primary proton dose (IPrimary), and the ProtonDSE corrected detector dose (ICorrected). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% forWET(ITotal),2.2% ± 1.4% forWET(IPrimary),and 2.5% ± 2.0% forWET(ICorrected).
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Affiliation(s)
- Brent van der Heyden
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - Marie Cohilis
- UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
| | - Kevin Souris
- UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
| | | | - Edmond Sterpin
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium.,UCLouvain, Institut de recherche expérimentale et clinique, Molecular Imaging Radiotherapy and Oncology Lab, Brussels, Belgium
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26
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Wang T, Lei Y, Harms J, Ghavidel B, Lin L, Beitler JJ, McDonald M, Curran WJ, Liu T, Zhou J, Yang X. Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy. Int J Part Ther 2021; 7:46-60. [PMID: 33604415 PMCID: PMC7886267 DOI: 10.14338/ijpt-d-20-00020.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 12/04/2020] [Indexed: 12/30/2022] Open
Abstract
Purpose Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy. Materials and Methods The proposed method uses a residual attention cycle-consistent generative adversarial network to bring DECT-to-RSP mapping close to a 1-to-1 mapping by introducing an inverse RSP-to-DECT mapping. To evaluate the proposed method, we retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions and acted as learning targets in the training process for DECT datasets; they were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. Results The predicted RSP maps showed an average normalized mean square error of 2.83% across the whole body volume and an average mean error less than 3% in all volumes of interest. With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences from ground truth in dose volume histogram metrics for clinical target volumes were less than 0.2 Gy for D95% and Dmax with no statistical significance. Maximum difference in dose volume histogram metrics of organs at risk was around 1 Gy on average. Conclusion These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning–based method and show its potential feasibility for proton treatment planning and dose calculation.
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Affiliation(s)
- Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Joseph Harms
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Beth Ghavidel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jonathan J Beitler
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Mark McDonald
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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27
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Meijers A, Seller Oria C, Free J, Langendijk JA, Knopf AC, Both S. Technical Note: First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients. Med Phys 2021; 48:1372-1380. [PMID: 33428795 DOI: 10.1002/mp.14713] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/23/2020] [Accepted: 01/05/2021] [Indexed: 01/07/2023] Open
Abstract
PURPOSE The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting. METHODS AND MATERIALS This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocenter were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented. RESULTS Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation). CONCLUSIONS The results of an RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy.
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Affiliation(s)
- Arturs Meijers
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Jeffrey Free
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Antje C Knopf
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands.,Division for Medical Radiation Physics, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
| | - Stefan Both
- Department of Radiation Oncology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
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28
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Ates O, Hua CH, Zhao L, Shapira N, Yagil Y, Merchant TE, Krasin M. Feasibility of using post-contrast dual-energy CT for pediatric radiation treatment planning and dose calculation. Br J Radiol 2021; 94:20200170. [PMID: 33201728 DOI: 10.1259/bjr.20200170] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES When iodinated contrast is administered during CT simulation, standard practice requires a separate non-contrast CT for dose calculation. The objective of this study is to validate our hypothesis that since iodine affects Hounsfield units (HUs) more than electron density (ED), the information from post-contrast dual-layer CT (DLCT) would be sufficient for accurate dose calculation for both photon and proton therapy. METHODS AND MATERIALS 10 pediatric patients with abdominal tumors underwent DLCT scans before and after iodinated contrast administration for radiotherapy planning. Dose distributions with these DLCT-based methods were compared to those with conventional calibration-curve methods that map HU images to ED and stopping-power ratio (SPR) images. RESULTS For photon plans, conventional and DLCT approaches based on post-contrast scans underestimated the PTV D99 by 0.87 ± 0.70% (p = 0.18) and 0.36 ± 0.31% (p = 0.34), respectively, comparing to their non-contrast optimization plans. Renal iodine concentration was weakly associated with D99 deviation for both conventional (R2 = 0.10) and DLCT (R2 = 0.02) approaches. For proton plans, the clinical target volume D99 errors were 3.67 ± 2.43% (p = 0.0001) and 0.30 ± 0.25% (p = 0.40) for conventional and DLCT approaches, respectively. The proton beam range changed noticeably with the conventional approach. Renal iodine concentration was highly associated with D99 deviation for the conventional approach (R2 = 0.83) but not for DLCT (R2 = 0.007). CONCLUSION Conventional CT with iodine contrast resulted in a large dosimetric error for proton therapy, compared to true non-contrast plans, but the error was less for photon therapy. These errors can be greatly reduced in the case of the proton plans if DLCT is used, raising the possibility of using only a single post-contrast CT for radiotherapy dose calculation, thus reducing the time and imaging dose required. ADVANCES IN KNOWLEDGE This study is the first to compare directly the differences in the calculated dose distributions between pre- and post-contrast CT images generated by single-energy CT and dual-energy CT methods for photon and proton therapy.
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Affiliation(s)
- Ozgur Ates
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Li Zhao
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nadav Shapira
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Global Advanced Technology, Philips Medical Systems, Haifa, Israel
| | - Yoad Yagil
- Global Advanced Technology, Philips Medical Systems, Haifa, Israel
| | - Thomas E Merchant
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Matthew Krasin
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
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29
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Wang T, Lei Y, Fu Y, Wynne JF, Curran WJ, Liu T, Yang X. A review on medical imaging synthesis using deep learning and its clinical applications. J Appl Clin Med Phys 2021; 22:11-36. [PMID: 33305538 PMCID: PMC7856512 DOI: 10.1002/acm2.13121] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 11/12/2020] [Accepted: 11/21/2020] [Indexed: 02/06/2023] Open
Abstract
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.
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Affiliation(s)
- Tonghe Wang
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
- Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
| | - Jacob F. Wynne
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
- Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
- Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation OncologyEmory UniversityAtlantaGAUSA
- Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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Sudhyadhom A. On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT). PLoS One 2020; 15:e0244861. [PMID: 33382794 PMCID: PMC7775093 DOI: 10.1371/journal.pone.0244861] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 12/17/2020] [Indexed: 11/19/2022] Open
Abstract
Accurate determination of physical/mass and electron densities are critical to accurate spatial and dosimetric delivery of radiotherapy for photon and charged particles. In this manuscript, the biology, chemistry, and physics that underly the relationship between computed tomography (CT) Hounsfield Unit (HU), mass density, and electron density was explored. In standard radiation physics practice, quantities such as mass and electron density are typically calculated based off a single kilovoltage CT (kVCT) scan assuming a one-to-one relationship between HU and density. It is shown that, in absence of mass density assumptions on tissues, the relationship between HU and density is not one-to-one with uncertainties as large as 7%. To mitigate this uncertainty, a novel multi-dimensional theoretical approach is defined between molecular (water, lipid, protein, and mineral) composition, HU, mass density, and electron density. Empirical parameters defining this relationship are x-ray beam energy/spectrum dependent and, in this study, two methods are proposed to solve for them including through a tissue mimicking phantom calibration process. As a proof of concept, this methodology was implemented in a separate in-house created tissue mimicking phantom and it is shown that sub 1% accuracy is possible for both mass and electron density. As molecular composition is not always known, the sensitivity of this model to uncertainties in molecular composition was investigated and it was found that, for soft tissue, sub 1% accuracy is achievable assuming nominal organ/tissue compositions. For boney tissues, the uncertainty in mineral content may lead to larger errors in mass and electron density compared with soft tissue. In this manuscript, a novel methodology to directly determine mass and electron density based off CT HU and knowledge of molecular compositions is presented. If used in conjunction with a methodology to determine molecular compositions, mass and electron density can be accurately calculated from CT HU.
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Affiliation(s)
- Atchar Sudhyadhom
- Brigham & Women’s Hospital, Boston, MA, United States of America
- Dana Farber Cancer Institute, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- University of California, San Francisco, San Francisco, CA, United States of America
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Scholey JE, Chandramohan D, Naren T, Liu W, Larson PEZ, Sudhyadhom A. Technical Note: A methodology for improved accuracy in stopping power estimation using MRI and CT. Med Phys 2020; 48:342-353. [PMID: 33107997 DOI: 10.1002/mp.14555] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 09/21/2020] [Accepted: 10/19/2020] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Proton therapy is becoming an increasingly popular cancer treatment modality due to the proton's physical advantage in that it deposits the majority of its energy at the distal end of its track where the tumor is located. The proton range in a material is determined from the stopping power ratio (SPR) of the material. However, SPR is typically estimated based on a computed tomography (CT) scan which can lead to range estimation errors due to the difference in x-ray and proton interactions in matter, which can preclude the ability to utilize protons to their full potential. Applications of magnetic resonance imaging (MRI) in radiotherapy have increased over the past decade and using MRI to calculate SPR directly could provide numerous advantages. The purpose of this study was to develop a practical implementation of a novel multimodal imaging method for estimating SPR and compare the results of this method to physical measurements in which values were computed directly using tissue substitute materials fabricated to mimic skin, muscle, adipose, and spongiosa bone. METHODS For both the multimodal imaging method and physical measurements, SPR was calculated using the Bethe-Bloch equation from values of relative electron density and mean ionization potential determined for each tissue. Parameters used to estimate SPR using the multimodal imaging method were extracted from Dixon water-only and (1 H) proton density-weighted zero echo time MRI sequences and CT, with both kVCT and MVCT used separately to evaluate the performance of each. For comparison, SPR was also computed from kVCT using the stoichiometric method, the current clinical standard. RESULTS Results showed that our multimodal imaging approach using MRI with either kVCT or MVCT was in close agreement to SPR calculated from physical measurements for the four tissue substitutes evaluated. Using MRI and MVCT, SPR values estimated using our method were within 1% of physical measurements and were more accurate than the stoichiometric method for the tissue types studied. CONCLUSIONS We have demonstrated the methodology for improved estimation of SPR using the proposed multimodal imaging framework.
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Affiliation(s)
- Jessica E Scholey
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Dharshan Chandramohan
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Tarun Naren
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - William Liu
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Peder Eric Zufall Larson
- Department of Radiology and Biomedical Imaging, The University of California, San Francisco, CA, USA
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
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Lee HH, Park YK, Duan X, Jia X, Jiang S, Yang M. Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study. Phys Med Biol 2020; 65:215016. [PMID: 32736368 DOI: 10.1088/1361-6560/abab57] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limitations in resolving the influence from the beam-hardening artifact, i.e. CT number variation of the same object scanned under different imaging conditions, such as different patient size and location in the field-of-view (FOV). We present a convolutional neural network (CNN)-based framework to estimate proton SPR that accounts for patient geometry variation and addresses CT number variation. The proposed framework was tested on both prostate and head-and-neck (HN) patient datasets. Simulated CT images were used in order to have a well-defined ground-truth SPR for evaluation. Two training scenarios were evaluated: training with patient CT images (ideal scenario) and training with computational phantoms (realistic scenario). For the training in ideal scenario, computational phantoms were created based on 120 kVp patient CT images using a custom-defined density and material translation curve. Then, 80 kVp and 150 kVp Sn DECT image pairs were obtained using ray-tracing simulation, and their corresponding SPR was calculated from the known density and elemental compositions. For the training in realistic scenario, computational phantoms were created based on the geometry of calibration phantoms. For both scenarios, evaluation was performed on the phantoms created from patient CT images. Compared to a conventional parametric model, U-net trained with computational phantoms (realistic scenario) reduced the SPR estimation uncertainty (95th percentile) of the prostate patient from 1.10% to 0.71%, and HN patient from 2.11% to 1.20%. With the U-net trained with patient images (ideal scenario) uncertainty values were 0.32% and 0.42% for prostate and HN patients, respectively. These results suggest that CNN has great potential to improve the accuracy of SPR estimation in proton therapy by incorporating individual patient geometry information.
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Affiliation(s)
- H Hc Lee
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
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Head and neck IMPT probabilistic dose accumulation: Feasibility of a 2 mm setup uncertainty setting. Radiother Oncol 2020; 154:45-52. [PMID: 32898561 DOI: 10.1016/j.radonc.2020.09.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 08/14/2020] [Accepted: 09/02/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy. METHODS We analyzed ten consecutive HNC patients treated with 70 and 54.25 GyRBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy. RESULTS Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V95 ≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 GyRBE/mm setup uncertainty reduction and 0.5 GyRBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction. CONCLUSION The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction.
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McCollough CH, Boedeker K, Cody D, Duan X, Flohr T, Halliburton SS, Hsieh J, Layman RR, Pelc NJ. Principles and applications of multienergy CT: Report of AAPM Task Group 291. Med Phys 2020; 47:e881-e912. [DOI: 10.1002/mp.14157] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 02/11/2020] [Accepted: 03/10/2020] [Indexed: 12/14/2022] Open
Affiliation(s)
| | - Kirsten Boedeker
- Canon (formerly Toshiba) Medical Systems Corporation 1440 Warnall Ave Los Angeles CA 90024 USA
| | - Dianna Cody
- University of Texas, M.D. Anderson Cancer Center 7163 Spanish Grant Galveston TX 77554‐7756 USA
| | - Xinhui Duan
- Southwestern Medical Center University of Texas 5323 Harry Hines Blvd Dallas TX 75390‐9071 USA
| | - Thomas Flohr
- Siemens Healthcare GmbH Siemensstr. 3 Forchheim BY 91031 Germany
| | | | - Jiang Hsieh
- GE Healthcare Technologies 3000 N. Grandview Blvd. W-1190 Waukesha WI 53188 USA
| | - Rick R. Layman
- University of Texas, M.D. Anderson Cancer Center 7163 Spanish Grant Galveston TX 77554‐7756 USA
| | - Norbert J. Pelc
- Stanford University 443 Via Ortega, Room 203 Stanford CA 94305‐4125 USA
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Wohlfahrt P, Richter C. Status and innovations in pre-treatment CT imaging for proton therapy. Br J Radiol 2020; 93:20190590. [PMID: 31642709 PMCID: PMC7066941 DOI: 10.1259/bjr.20190590] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/04/2019] [Accepted: 10/21/2019] [Indexed: 12/19/2022] Open
Abstract
Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.
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Affiliation(s)
- Patrick Wohlfahrt
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Meijers A, Free J, Wagenaar D, Deffet S, Knopf AC, Langendijk JA, Both S. Validation of the proton range accuracy and optimization of CT calibration curves utilizing range probing. ACTA ACUST UNITED AC 2020; 65:03NT02. [DOI: 10.1088/1361-6560/ab66e1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Sajja S, Lee Y, Eriksson M, Nordström H, Sahgal A, Hashemi M, Mainprize JG, Ruschin M. Technical Principles of Dual-Energy Cone Beam Computed Tomography and Clinical Applications for Radiation Therapy. Adv Radiat Oncol 2020; 5:1-16. [PMID: 32051885 PMCID: PMC7004939 DOI: 10.1016/j.adro.2019.07.013] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 05/21/2019] [Accepted: 07/20/2019] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Medical imaging is an indispensable tool in radiotherapy for dose planning, image guidance and treatment monitoring. Cone beam CT (CBCT) is a low dose imaging technique with high spatial resolution capability as a direct by-product of using flat-panel detectors. However, certain issues such as x-ray scatter, beam hardening and other artifacts limit its utility to the verification of patient positioning using image-guided radiotherapy. METHODS AND MATERIALS Dual-energy (DE)-CBCT has recently demonstrated promise as an improved tool for tumor visualization in benchtop applications. It has the potential to improve soft-tissue contrast and reduce artifacts caused by beam hardening and metal. In this review, the practical aspects of developing a DE-CBCT based clinical and technical workflow are presented based on existing DE-CBCT literature and concepts adapted from the well-established library of work in DE-CT. Furthermore, the potential applications of DE-CBCT on its future role in radiotherapy are discussed. RESULTS AND CONCLUSIONS Based on current literature and an investigation of future applications, there is a clear potential for DE-CBCT technologies to be incorporated into radiotherapy. The applications of DE-CBCT include (but are not limited to): adaptive radiotherapy, brachytherapy, proton therapy, radiomics and theranostics.
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Affiliation(s)
- Shailaja Sajja
- Sunnybrook Research Institute, Toronto, Ontario, Canada
- QIPCM Imaging Core Lab, Techna Institute, Toronto, Ontario, Canada
| | - Young Lee
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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Je E, Lee HHC, Duan X, Li B, Jia X, Yang M. Optimal energy selection for proton stopping-power-ratio estimation using dual-energy CT-based monoenergetic imaging. ACTA ACUST UNITED AC 2019; 64:195015. [DOI: 10.1088/1361-6560/ab3dec] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Lee HHC, Li B, Duan X, Zhou L, Jia X, Yang M. Systematic analysis of the impact of imaging noise on dual-energy CT-based proton stopping power ratio estimation. Med Phys 2019; 46:2251-2263. [PMID: 30883827 DOI: 10.1002/mp.13493] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 02/25/2019] [Accepted: 03/06/2019] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Dual-energy CT (DECT) has been shown to have a great potential in reducing the uncertainty in proton stopping power ratio (SPR) estimation, when compared to current standard method - the stoichiometric method based on single-energy CT (SECT). However, a few recent studies indicated that imaging noise may have a substantial impact on the performance of the DECT-based approach, especially at a high noise level. The goal of this study is to quantify the uncertainty in SPR and range estimation caused by noise in the DECT-based approach under various conditions. METHODS Two widely referred parametric DECT methods were studied: the Hünemohr-Saito (HS) method and the Bourque method. Both methods were calibrated using Gammex tissue substitute inserts scanned on the Siemens Force DECT scanner. An energy pair of 80 and 150 kVp with a tin filter was chosen to maximize the spectral separation. After calibrating the model with the Gammex phantom, CT numbers were synthesized using the density and elemental composition from ICRU 44 human tissues to be used as a reference, in order to evaluate the impact of noise alone while putting aside other sources of uncertainty. Gaussian noise was introduced to the reference CT numbers and its impact was measured with the difference between estimated SPR and its noiseless reference SPR. The uncertainty caused by noise was divided into two independent categories: shift of the mean SPR and variation of SPR. Their overall impact on range uncertainty was evaluated on homogeneous and heterogeneous tissue samples of various water equivalent path lengths (WEPL). RESULTS Due to the algorithms being nonlinear and/or having hard thresholds in the CT number to SPR mapping, noise in the CT numbers induced a shift in the mean SPR from its noiseless reference SPR. The degree of the mean shift was dependent on the algorithm and tissue type, but its impact on the SPR uncertainty was mostly small compared to the variation. All mean shifts observed in this study were within 0.5% at a noise level of 2%. The ratio of the influence of variation to mean shift was mostly greater than 1, indicating that variation more likely determined the uncertainty caused by noise. Overall, the range uncertainty (95th percentile) caused by noise was within 1.2% and 1.0% for soft and bone tissues, respectively, at 2% noise with 50 voxels. This value can be considered an upper limit as more voxels and lower noise level rapidly decreased the uncertainty. CONCLUSIONS We have systematically evaluated the impact of noise to the DECT-based SPR estimation and identified under various conditions that the variation caused by noise is the dominant uncertainty-contributing component. We conclude that, based on the noise level and tumor depth, it is important to estimate and include the uncertainty due to noise in estimating the overall range uncertainty before implementing a small margin in the range of 1%.
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Affiliation(s)
- Hugh H C Lee
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Bin Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Xinhui Duan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ming Yang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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Zhang S, Han D, Williamson JF, Zhao T, Politte DG, Whiting BR, O'Sullivan JA. Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data. Med Phys 2018; 46:273-285. [PMID: 30421790 DOI: 10.1002/mp.13287] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 09/24/2018] [Accepted: 11/02/2018] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation. METHODS The JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equation for SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140 kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared. RESULTS The overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively. CONCLUSION By taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.
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Affiliation(s)
- Shuangyue Zhang
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Dong Han
- Medical Physics Graduate Program, Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Jeffrey F Williamson
- Department of Radiation Oncology, Washington University, St. Louis, MO, 63110, USA
| | - Tianyu Zhao
- Department of Radiation Oncology, Washington University, St. Louis, MO, 63110, USA
| | - David G Politte
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Bruce R Whiting
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Joseph A O'Sullivan
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
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Ohira S, Washio H, Yagi M, Karino T, Nakamura K, Ueda Y, Miyazaki M, Koizumi M, Teshima T. Estimation of electron density, effective atomic number and stopping power ratio using dual-layer computed tomography for radiotherapy treatment planning. Phys Med 2018; 56:34-40. [DOI: 10.1016/j.ejmp.2018.11.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 09/25/2018] [Accepted: 11/15/2018] [Indexed: 01/17/2023] Open
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Shen C, Li B, Lou Y, Yang M, Zhou L, Jia X. Multienergy element-resolved cone beam CT (MEER-CBCT) realized on a conventional CBCT platform. Med Phys 2018; 45:4461-4470. [PMID: 30179261 DOI: 10.1002/mp.13169] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 07/29/2018] [Accepted: 08/20/2018] [Indexed: 01/10/2023] Open
Abstract
PURPOSE Cone beam CT (CBCT) has been widely used in radiation therapy. However, its main application is still to acquire anatomical information for patient positioning. This study proposes a multienergy element-resolved (MEER) CBCT framework that employs energy-resolved data acquisition on a conventional CBCT platform and then simultaneously reconstructs images of x-ray attenuation coefficients, electron density relative to water (rED), and elemental composition (EC) to support advanced applications. METHODS The MEER-CBCT framework is realized on a Varian TrueBeam CBCT platform using a kVp-switching scanning scheme. A simultaneous image reconstruction and elemental decomposition model is formulated as an optimization problem. The objective function uses a least square term to enforce fidelity between x-ray attenuation coefficients and projection measurements. Spatial regularization is introduced via sparsity under a tight wavelet-frame transform. Consistency is imposed among rED, EC, and attenuation coefficients and inherently serves as a regularization term along the energy direction. The EC is further constrained by a sparse combination of ECs in a dictionary containing tissues commonly existing in humans. The optimization problem is solved by a novel alternating-direction minimization scheme. The MEER-CBCT framework was tested in a simulation study using an NCAT phantom and an experimental study using a Gammex phantom. RESULTS MEER-CBCT framework was successfully realized on a clinical Varian TrueBeam onboard CBCT platform with three energy channels of 80, 100, and 120 kVp. In the simulation study, the attenuation coefficient image achieved a structural similarity index of 0.98, compared to 0.61 for the image reconstructed by the conventional conjugate gradient least square (CGLS) algorithm, primarily because of reduction in artifacts. In the experimental study, the attenuation image obtained a contrast-to-noise ratio ≥60, much higher than that of CGLS results (~16) because of noise reduction. The median errors in rED and EC were 0.5% and 1.4% in the simulation study and 1.4% and 2.3% in the experimental study. CONCLUSION We proposed a novel MEER-CBCT framework realized on a clinical CBCT platform. Simulation and experimental studies demonstrated its capability to simultaneously reconstruct x-ray attenuation coefficient, rED, and EC images accurately.
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Affiliation(s)
- Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Bin Li
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, China
| | - Yifei Lou
- Department of Mathematical Science, University of Texas at Dallas, Dallas, TX, 75080, USA
| | - Ming Yang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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Li B, Shen C, Chi Y, Yang M, Lou Y, Zhou L, Jia X. MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM. SIAM JOURNAL ON IMAGING SCIENCES 2018; 11:1205-1229. [PMID: 30298098 PMCID: PMC6173488 DOI: 10.1137/17m1123237] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.
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Affiliation(s)
- Bin Li
- Department of Biomedical Engineering, Southern Medical University, GuangZhou, Guangdong 510515, China
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Yujie Chi
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Ming Yang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Yifei Lou
- Department of Mathematical Science, University of Texas at Dallas, Dallas, TX 75080, USA
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, GuangZhou, Guangdong 510515, China
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
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Taasti VT, Bäumer C, Dahlgren CV, Deisher AJ, Ellerbrock M, Free J, Gora J, Kozera A, Lomax AJ, De Marzi L, Molinelli S, Kevin Teo BK, Wohlfahrt P, Petersen JBB, Muren LP, Hansen DC, Richter C. Inter-centre variability of CT-based stopping-power prediction in particle therapy: Survey-based evaluation. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 6:25-30. [PMID: 33458385 PMCID: PMC7807627 DOI: 10.1016/j.phro.2018.04.006] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/20/2018] [Accepted: 04/23/2018] [Indexed: 01/31/2023]
Abstract
Background and purpose Stopping-power ratios (SPRs) are used in particle therapy to calculate particle range in patients. The heuristic CT-to-SPR conversion (Hounsfield Look-Up-Table, HLUT), needed for treatment planning, depends on CT-scan and reconstruction parameters as well as the specific HLUT definition. To assess inter-centre differences in these parameters, we performed a survey-based qualitative evaluation, as a first step towards better standardisation of CT-based SPR derivation. Materials and methods A questionnaire was sent to twelve particle therapy centres (ten from Europe and two from USA). It asked for details on CT scanners, image acquisition and reconstruction, definition of the HLUT, body-region specific HLUT selection, investigations of beam-hardening and experimental validations of the HLUT. Technological improvements were rated regarding their potential to improve SPR accuracy. Results Scan parameters and HLUT definition varied widely. Either the stoichiometric method (eight centres) or a tissue-substitute-only HLUT definition (three centres) was used. One centre combined both methods. The number of HLUT line segments varied widely between two and eleven. Nine centres had investigated influence of beam-hardening, often including patient-size dependence. Ten centres had validated their HLUT experimentally, with very different validation schemes. Most centres deemed dual-energy CT promising for improving SPR accuracy. Conclusions Large inter-centre variability was found in implementation of CT scans, image reconstruction and especially in specification of the CT-to-SPR conversion. A future standardisation would reduce time-intensive institution-specific efforts and variations in treatment quality. Due to the interdependency of multiple parameters, no conclusion can be drawn on the derived SPR accuracy and its inter-centre variability.
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Affiliation(s)
- Vicki T Taasti
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Christian Bäumer
- Westdeutsches Protonentherapiezentrum, Essen gGmbH, Essen, Germany
| | | | - Amanda J Deisher
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
| | - Malte Ellerbrock
- Heidelberg Ion-Beam Therapy Center (HIT), Medical Physics, Heidelberg, Germany
| | - Jeffrey Free
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joanna Gora
- EBG MedAustron GmbH, Wiener Neustadt, Austria
| | - Anna Kozera
- The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Kraków, Poland
| | - Antony J Lomax
- Center for Proton Therapy, Paul Scherrer Institut, Villigen, Switzerland
| | - Ludovic De Marzi
- Institut Curie, Centre de Protonthérapie, d'Orsay, Orsay, France
| | | | - Boon-Keng Kevin Teo
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Patrick Wohlfahrt
- 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
| | | | - Ludvig P Muren
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - David C Hansen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - 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.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
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45
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Zhang S, Han D, Politte DG, Williamson JF, O'Sullivan JA. Impact of joint statistical dual-energy CT reconstruction of proton stopping power images: Comparison to image- and sinogram-domain material decomposition approaches. Med Phys 2018; 45:2129-2142. [PMID: 29570809 DOI: 10.1002/mp.12875] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 02/23/2018] [Accepted: 03/07/2018] [Indexed: 01/30/2023] Open
Abstract
PURPOSE The purpose of this study was to assess the performance of a novel dual-energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR-BVM method and previously described DECT image- and sinogram-domain decomposition approaches is also carried out on synthetic data. METHODS The JSIR-BVM method was implemented to estimate the electron densities and mean excitation energies (I-values) required by the Bethe equation for SPR mapping. In addition, image- and sinogram-domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated. RESULTS All three selected DECT-SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR-BVM method achieves the best performance with mean and RMS-average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image- and sinogram-domain decomposition methods show increasing mean and RMS-average errors with increasing noise level. The JSIR-BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image-domain decomposition approach, while the JSIR-BVM method and sinogram-domain decomposition methods are insensitive to size change. CONCLUSION Among all the investigated methods, the JSIR-BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam-hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image- and sinogram-domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes.
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Affiliation(s)
- Shuangyue Zhang
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Dong Han
- Medical Physics Graduate Program, Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - David G Politte
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Jeffrey F Williamson
- Department of Radiation Oncology, Washington University, St. Louis, MO, 63110, USA
| | - Joseph A O'Sullivan
- Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA
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46
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Shen C, Li B, Chen L, Yang M, Lou Y, Jia X. Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation. Med Phys 2018; 45:1491-1503. [PMID: 29405340 DOI: 10.1002/mp.12796] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 12/11/2017] [Accepted: 01/20/2018] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation. METHOD A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies. RESULTS Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method. CONCLUSION The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.
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Affiliation(s)
- Chenyang Shen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA
| | - Bin Li
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA.,Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA
| | - Ming Yang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA
| | - Yifei Lou
- Department of Mathematical Science, University of Texas at Dallas, Dallas, TX, 75080, USA
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA
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Wohlfahrt P, Möhler C, Stützer K, Greilich S, Richter C. Dual-energy CT based proton range prediction in head and pelvic tumor patients. Radiother Oncol 2017; 125:526-533. [PMID: 29050953 DOI: 10.1016/j.radonc.2017.09.042] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 09/28/2017] [Accepted: 09/28/2017] [Indexed: 10/18/2022]
Abstract
BACKGROUND AND PURPOSE To reduce range uncertainty in particle therapy, an accurate computation of stopping-power ratios (SPRs) based on computed tomography (CT) is crucial. Here, we assess range differences between the state-of-the-art CT-number-to-SPR conversion using a generic Hounsfield look-up table (HLUT) and a direct patient-specific SPR prediction (RhoSigma) based on dual-energy CT (DECT) in 100 proton treatment fields. MATERIAL AND METHODS For 25 head-tumor and 25 prostate-cancer patients, the clinically applied treatment plan, optimized using a HLUT, was recalculated with RhoSigma as CT-number-to-SPR conversion. Depth-dose curves in beam direction were extracted for both dose distributions in a regular grid and range deviations were determined and correlated to SPR differences within the irradiated volume. RESULTS Absolute (relative) mean water-equivalent range shifts of 1.1mm (1.2%) and 4.1mm (1.7%) were observed in the head-tumor and prostate-cancer cohort, respectively. Due to the case dependency of a generic HLUT, range deviations within treatment fields strongly depend on the tissues traversed leading to a larger variation within one patient than between patients. CONCLUSIONS The magnitude of patient-specific range deviations between HLUT and the more accurate DECT-based SPR prediction is clinically relevant. A clinical application of the latter seems feasible as demonstrated in this study using medically approved systems from CT acquisition to treatment planning.
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Affiliation(s)
- Patrick Wohlfahrt
- 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.
| | - Christian Möhler
- German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - Kristin Stützer
- 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
| | - Steffen Greilich
- German Cancer Research Center (DKFZ), Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - 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; 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; German Cancer Consortium (DKTK), partner site Dresden, Germany
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