1
|
Pettersson E, Thilander-Klang A, Bäck A. Prediction of proton stopping power ratios using dual-energy CT basis material decomposition. Med Phys 2024; 51:881-897. [PMID: 38194501 DOI: 10.1002/mp.16929] [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: 05/21/2023] [Revised: 12/04/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction. PURPOSE To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy. METHODS A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups. RESULTS The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR. CONCLUSIONS The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.
Collapse
Affiliation(s)
- Erik Pettersson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Therapeutic Radiation Physics, Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anne Thilander-Klang
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Diagnostic Radiation Physics, Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anna Bäck
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Therapeutic Radiation Physics, Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| |
Collapse
|
2
|
Peters N, Trier Taasti V, Ackermann B, Bolsi A, Vallhagen Dahlgren C, Ellerbrock M, Fracchiolla F, Gomà C, Góra J, Cambraia Lopes P, Rinaldi I, Salvo K, Sojat Tarp I, Vai A, Bortfeld T, Lomax A, Richter C, Wohlfahrt P. Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy. Radiother Oncol 2023; 184:109675. [PMID: 37084884 PMCID: PMC10351362 DOI: 10.1016/j.radonc.2023.109675] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/08/2023] [Accepted: 04/10/2023] [Indexed: 04/23/2023]
Abstract
BACKGROUND AND PURPOSE Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here. MATERIALS AND METHODS The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed. RESULTS The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility. CONCLUSION The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.
Collapse
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, Germany; Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, MA, USA.
| | - Vicki Trier Taasti
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Benjamin Ackermann
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alessandra Bolsi
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | | | - Malte Ellerbrock
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Francesco Fracchiolla
- Azienda Provinciale per i Servizi Sanitari (APSS) Protontherapy Department, Trento, Italy
| | - Carles Gomà
- Department of Radiation Oncology, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Joanna Góra
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | | | - Ilaria Rinaldi
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Koen Salvo
- AZ Sint-Maarten, Department of Radiotherapy, Mechelen, Belgium
| | - Ivanka Sojat Tarp
- Aarhus University Hospital, Danish Center for Particle Therapy, Aarhus, Denmark
| | - Alessandro Vai
- Radiotherapy Department, Center for National Oncological Hadrontherapy (CNAO), 27100 Pavia, Italy
| | - Thomas Bortfeld
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, MA, USA
| | - Antony Lomax
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - 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, 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, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patrick Wohlfahrt
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Boston, MA, USA
| |
Collapse
|
3
|
Dumas JL, Dal R, Zefkili S, Robilliard M, Losa S, Birba I, Vu-Bezin J, Beddok A, Calugaru V, Dutertre G, De Marzi L. Addressing the dosimetric impact of bone cement and vertebroplasty in stereotactic body radiation therapy. Phys Med 2021; 85:42-49. [PMID: 33965740 DOI: 10.1016/j.ejmp.2021.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/08/2021] [Accepted: 04/23/2021] [Indexed: 10/21/2022] Open
Abstract
PURPOSE Bone cement used for vertebroplasty can affect the accuracy on the dose calculation of the radiation therapy treatment. In addition the CT values of high density objects themselves can be misrepresented in kVCT images. The aim of our study is then to propose a streamlined approach for estimating the real density of cement implants used in stereotactic body radiation therapy. METHODS Several samples of cement were manufactured and irradiated in order to investigate the impact of their composition on the radiation dose. The validity of the CT conversion method for a range of photon energies was investigated, for the studied samples and on six patients. Calculations and measurements were carried out with various overridden densities and dose prediction algorithms (AXB with dose-to-medium reporting or AAA) in order to find the effective density override. RESULTS Relative dose differences of several percent were found between the dose measured and calculated downstream of the implant using an ion chamber and TPS or EPID dosimetry. If the correct density is assigned to the implant, calculations can provide clinically acceptable accuracy (gamma criteria of 3%/2 mm). The use of MV imaging significantly favors the attribution of a correct equivalent density to the implants compared to the use of kVCT images. CONCLUSION The porosity and relative density of the various studied implants vary significantly. Bone cement density estimations can be characterized using MV imaging or planar in vivo dosimetry, which could help determining whether errors in dose calculations are due to incorrect densities.
Collapse
Affiliation(s)
- Jean-Luc Dumas
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France.
| | - Romaric Dal
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Sofia Zefkili
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Magalie Robilliard
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Sandra Losa
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Imène Birba
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Jérémi Vu-Bezin
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Arnaud Beddok
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | - Valentin Calugaru
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France
| | | | - Ludovic De Marzi
- Institut Curie, PSL Research University, Radiation Oncology Department, Paris, France; Institut Curie, University Paris Saclay, PSL Research University, Inserm LITO, Orsay, France.
| |
Collapse
|