1
|
Niepel K, Tattenberg S, Marants R, Hu G, Bortfeld T, Verburg J, Sudhyadhom A, Landry G, Parodi K. Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples. Phys Med Biol 2024; 69:165033. [PMID: 39074494 PMCID: PMC11334240 DOI: 10.1088/1361-6560/ad68bc] [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: 09/26/2022] [Revised: 06/11/2024] [Accepted: 07/29/2024] [Indexed: 07/31/2024]
Abstract
Objective.Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton rangein-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.Approach.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.Results.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Significance.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute toin-vivodose verification measurements.
Collapse
Affiliation(s)
- Katharina Niepel
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany
| | - Sebastian Tattenberg
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
| | - Raanan Marants
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, United States of America
| | - Guyue Hu
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany
| | - Thomas Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
| | - Joost Verburg
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, United States of America
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Germany
| |
Collapse
|
2
|
Taylor PA, Mirandola A, Ciocca M, Hartzell S, Vai A, Alvarez P, Howell RM, Koay EJ, Peeler CR, Peterson CB, Kry SF. Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy. Med Phys 2024; 51:5154-5158. [PMID: 38598230 PMCID: PMC11233228 DOI: 10.1002/mp.17056] [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: 09/15/2023] [Revised: 03/05/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
PURPOSE As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. METHODS Eight materials were tested for radiological material-equivalence in a carbon ion beam. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship. RESULTS Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). CONCLUSIONS Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.
Collapse
Affiliation(s)
- Paige A Taylor
- Department of Radiation Physics, The University of MD Anderson Cancer Center, Houston, Texas, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alfredo Mirandola
- Department of Medical Physics, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Mario Ciocca
- Department of Medical Physics, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Shannon Hartzell
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, Florida, USA
| | - Alessandro Vai
- Department of Medical Physics, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Paola Alvarez
- Department of Radiation Physics, The University of MD Anderson Cancer Center, Houston, Texas, USA
| | - Rebecca M Howell
- Department of Radiation Physics, The University of MD Anderson Cancer Center, Houston, Texas, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Eugene J Koay
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Gastrointestinal Radiation Oncology, The University of MD Anderson Cancer Center, Houston, Texas, USA
| | - Christopher R Peeler
- Department of Radiation Physics, The University of MD Anderson Cancer Center, Houston, Texas, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Christine B Peterson
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Biostatistics, The University of MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen F Kry
- Department of Radiation Physics, The University of MD Anderson Cancer Center, Houston, Texas, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| |
Collapse
|
3
|
Fogazzi E, Bruzzi M, D'Amato E, Farace P, Righetto R, Scaringella M, Scarpa M, Tommasino F, Civinini C. Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning. Phys Med Biol 2024; 69:135009. [PMID: 38862001 DOI: 10.1088/1361-6560/ad56f5] [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: 12/28/2023] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms.Approach.A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves-i.e. tissue substitutes and stoichiometric-were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures.Main results.Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (-2.5 0.5) HU °C-1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure.Significance.Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.
Collapse
Affiliation(s)
- Elena Fogazzi
- Physics department, University of Trento, via Sommarive 14, Povo, TN, Italy
- Trento Institute for Fundamental Physics and Applications (TIFPA), Italian National Institute of Nuclear Physics (INFN), via Sommarive, 14, Povo, TN, Italy
| | - Mara Bruzzi
- Physics and Astronomy Department, University of Florence, via G. Sansone 1, Sesto Fiorentino, FI, Italy
- Italian National Institute of Nuclear Physics (INFN), Florence section, Via G. Sansone 1, Sesto Fiorentino, FI, Italy
| | - Elvira D'Amato
- Physics department, University of Trento, via Sommarive 14, Povo, TN, Italy
| | - Paolo Farace
- Trento Institute for Fundamental Physics and Applications (TIFPA), Italian National Institute of Nuclear Physics (INFN), via Sommarive, 14, Povo, TN, Italy
- Medical Physics Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Via Paolo Orsi 1, Trento, Italy
| | - Roberto Righetto
- Trento Institute for Fundamental Physics and Applications (TIFPA), Italian National Institute of Nuclear Physics (INFN), via Sommarive, 14, Povo, TN, Italy
- Medical Physics Unit, Hospital of Trento, Azienda Provinciale per i Servizi Sanitari (APSS), Via Paolo Orsi 1, Trento, Italy
| | - Monica Scaringella
- Italian National Institute of Nuclear Physics (INFN), Florence section, Via G. Sansone 1, Sesto Fiorentino, FI, Italy
| | - Marina Scarpa
- Physics department, University of Trento, via Sommarive 14, Povo, TN, Italy
- Trento Institute for Fundamental Physics and Applications (TIFPA), Italian National Institute of Nuclear Physics (INFN), via Sommarive, 14, Povo, TN, Italy
| | - Francesco Tommasino
- Physics department, University of Trento, via Sommarive 14, Povo, TN, Italy
- Trento Institute for Fundamental Physics and Applications (TIFPA), Italian National Institute of Nuclear Physics (INFN), via Sommarive, 14, Povo, TN, Italy
| | - Carlo Civinini
- Italian National Institute of Nuclear Physics (INFN), Florence section, Via G. Sansone 1, Sesto Fiorentino, FI, Italy
| |
Collapse
|
4
|
Gao Y, Chang CW, Mandava S, Marants R, Scholey JE, Goette M, Lei Y, Mao H, Bradley JD, Liu T, Zhou J, Sudhyadhom A, Yang X. MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study. Sci Rep 2024; 14:11166. [PMID: 38750148 PMCID: PMC11096170 DOI: 10.1038/s41598-024-61869-8] [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: 06/13/2023] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.
Collapse
Affiliation(s)
- Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | | | - Raanan Marants
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Jessica E Scholey
- Department of Radiation Oncology, The University of California, San Francisco, CA, 94143, USA
| | - Matthew Goette
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jeffrey D Bradley
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Tian Liu
- Radiation Oncology, Mount Sinai Medical Center, New York, NY, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30308, USA.
| |
Collapse
|
5
|
Ye P, Zhao W, Shimomura T, Li KW, Haga A, Geng LS. Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT. Phys Med Biol 2024; 69:105020. [PMID: 38640915 DOI: 10.1088/1361-6560/ad40fa] [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: 01/04/2024] [Accepted: 04/19/2024] [Indexed: 04/21/2024]
Abstract
Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.
Collapse
Affiliation(s)
- Ping Ye
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, People's Republic of China
| | - Taisei Shimomura
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Kai-Wen Li
- Research and Development Department, CAS Ion Medical Technology Co., Ltd, Beijing 100190, People's Republic of China
| | - Akihiro Haga
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Li-Sheng Geng
- School of Physics, Beihang University, Beijing 102206, People's Republic of China
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing 102206, People's Republic of China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing 100191, People's Republic of China
- Southern Center for Nuclear-Science Theory (SCNT), Institute of Modern Physics, Chinese Academy of Sciences, Huizhou 516000, People's Republic of China
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Nakao M, Ozawa S, Miura H, Yamada K, Hayata M, Hayashi K, Kawahara D, Nakashima T, Ochi Y, Okumura T, Kunimoto H, Kawakubo A, Kusaba H, Nozaki H, Habara K, Tohyama N, Nishio T, Nakamura M, Minemura T, Okamoto H, Ishikawa M, Kurooka M, Shimizu H, Hotta K, Saito M, Nakano M, Tsuneda M, Nagata Y. CT number calibration audit in photon radiation therapy. Med Phys 2024; 51:1571-1582. [PMID: 38112216 DOI: 10.1002/mp.16887] [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: 01/03/2023] [Revised: 06/29/2023] [Accepted: 11/26/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs. PURPOSE The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe ). METHODS A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute-specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute-specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute-specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m , respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT-ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT-ρ calibration curves because the available tissue-equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM , respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT-ρ or CT-ρe calibration curve. RESULTS The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were -1.1 ± 1.2 g/cm3 and -0.2 ± 1.1, -0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and -0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN-SR2) was -1.8 ± 0.4, implying that the tissue equivalency of the BRN-SR2 plug was slightly inferior to that of other plugs. CONCLUSIONS Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes.
Collapse
Affiliation(s)
- Minoru Nakao
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
| | - Shuichi Ozawa
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
| | - Hideharu Miura
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| | - Kiyoshi Yamada
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| | - Masahiro Hayata
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| | - Kosuke Hayashi
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
| | - Takeo Nakashima
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Radiation Therapy Section, Department of Clinical Support, Hiroshima University Hospital, Hiroshima, Japan
| | - Yusuke Ochi
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Radiation Therapy Section, Department of Clinical Support, Hiroshima University Hospital, Hiroshima, Japan
| | - Takuro Okumura
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Radiation Therapy Section, Department of Clinical Support, Hiroshima University Hospital, Hiroshima, Japan
| | - Haruhide Kunimoto
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Radiation Therapy Department, Hiroshima Prefectural Hospital, Hiroshima, Japan
| | - Atsushi Kawakubo
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Radiation Therapy Department, Hiroshima City Hiroshima Citizens Hospital, Hiroshima, Japan
| | - Hayate Kusaba
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Radiation Therapy Department, Hiroshima City Hiroshima Citizens Hospital, Hiroshima, Japan
| | - Hiroshige Nozaki
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Division of Radiology, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, Hiroshima, Japan
| | - Kosaku Habara
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Division of Radiology, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, Hiroshima, Japan
| | - Naoki Tohyama
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Division of Medical Physics, Tokyo Bay Makuhari Clinic for Advanced Imaging, Cancer Screening, and High-Precision Radiotherapy, Chiba, Japan
| | - Teiji Nishio
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Mitsuhiro Nakamura
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiyuki Minemura
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Division of Medical Support and Partnership, Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Hiroyuki Okamoto
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, Tokyo, Japan
| | - Masayori Ishikawa
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Faculty of Health Sciences, Hokkaido University, Hokkaido, Japan
| | - Masahiko Kurooka
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Department of Radiation Therapy, Tokyo Medical University Hospital, Tokyo, Japan
| | - Hidetoshi Shimizu
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Department of Radiation Oncology, Aichi Cancer Center Hospital, Aichi, Japan
| | - Kenji Hotta
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Radiation Safety and Quality Assurance division, National Cancer Center Hospital East, Chiba, Japan
- Particle Therapy Division, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Chiba, Japan
| | - Masahide Saito
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Masahiro Nakano
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Department of Radiation Oncology, Kitasato University School of Medicine, Kanagawa, Japan
| | - Masato Tsuneda
- Medical Physics Working Group in Japan Clinical Oncology Group - Radiation Therapy Study Group, Tokyo, Japan
- Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yasushi Nagata
- Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
- Department of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
- Technical Support Working Group in Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan
| |
Collapse
|
8
|
Peters N, Taasti VT, Ackermann B, Bolsi A, Dahlgren CV, Ellerbrock M, Fracchiolla F, Gomà C, Góra J, Lopes PC, Rinaldi I, Salvo K, Tarp IS, Vai A, Bortfeld T, Lomax A, Richter C, Wohlfahrt P. Response to "Letter regarding Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy". Radiother Oncol 2024; 190:109961. [PMID: 37871749 DOI: 10.1016/j.radonc.2023.109961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 10/25/2023]
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
|
9
|
Poludniowski G, Zimmerman J. Letter regarding "Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy". Radiother Oncol 2024; 190:109962. [PMID: 37871750 DOI: 10.1016/j.radonc.2023.109962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/09/2023] [Accepted: 09/26/2023] [Indexed: 10/25/2023]
Affiliation(s)
- 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.
| | - Jens Zimmerman
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
10
|
Gao Y, Chang CW, Roper J, Axente M, Lei Y, Pan S, Bradley JD, Zhou J, Liu T, Yang X. Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method. Front Oncol 2023; 13:1278180. [PMID: 38074686 PMCID: PMC10702508 DOI: 10.3389/fonc.2023.1278180] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/06/2023] [Indexed: 02/09/2024] Open
Abstract
Background The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT. Objectives The purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment. Methods Artificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models. Results For M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%. Conclusion The results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation. Advances in knowledge Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.
Collapse
Affiliation(s)
- Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Marian Axente
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Shaoyan Pan
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Jeffrey D. Bradley
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| |
Collapse
|
11
|
Kneepkens E, Wolfs C, Wanders RG, Traneus E, Eekers D, Verhaegen F. Shoot-through proton FLASH irradiation lowers linear energy transfer in organs at risk for neurological tumors and is robust against density variations. Phys Med Biol 2023; 68:215020. [PMID: 37820687 DOI: 10.1088/1361-6560/ad0280] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Objective. The goal of the study was to test the hypothesis that shoot-through FLASH proton beams would lead to lower dose-averaged LET (LETD) values in critical organs, while providing at least equal normal tissue sparing as clinical proton therapy plans.Approach. For five neurological tumor patients, pencil beam scanning (PBS) shoot-through plans were made, using the maximum energy of 227 MeV and assuming a hypothetical FLASH protective factor (FPF) of 1.5. The effect of different FPF ranging from 1.2 to 1.8 on the clinical goals were also considered. LETDwas calculated for the clinical plan and the shoot-through plan, applying a 2 Gy total dose threshold (RayStation 8 A/9B and 9A-IonRPG). Robust evaluation was performed considering density uncertainty (±3% throughout entire volume).Main results.Clinical plans showed large LETDvariations compared to shoot-through plans and the maximum LETDin OAR is 1.2-8 times lower for the latter. Although less conformal, shoot-through plans met the same clinical goals as the clinical plans, for FLASH protection factors above 1.4. The FLASH shoot-through plans were more robust to density uncertainties with a maximum OAR D2%increase of 0.6 Gy versus 5.7 Gy in the clinical plans.Significance.Shoot-through proton FLASH beams avoid uncertainties in LETDdistributions and proton range, provide adequate target coverage, meet planning constraints and are robust to density variations.
Collapse
Affiliation(s)
- Esther Kneepkens
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cecile Wolfs
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Roel-Germ Wanders
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Erik Traneus
- RaySearch Laboratories AB, SE-103 65, Stockholm, Sweden
| | - Danielle Eekers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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
|
14
|
Decoene C, Crop F. Using density computed tomography images for photon dose calculations in radiation oncology: A patient study. Phys Imaging Radiat Oncol 2023; 27:100463. [PMID: 37497189 PMCID: PMC10366581 DOI: 10.1016/j.phro.2023.100463] [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: 03/21/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/28/2023] Open
Abstract
Background and purpose Conventional workflows for dose calculations require conversions between Hounsfield Units (HU) and the mass or electron density for Computed Tomography (CT) images in the Treatment Planning System (TPS). These conversions are scanner- and mostly kVp-dependent. A density representation or reconstruction at the CT level can potentially simplify the workflow. This study aimed to investigate the agreement between these two methods for patients and different calculation algorithms. Materials and methods Density conversions for conventional HU-density conversions were first established using two phantoms with appropriate inserts. Next, the differences in density and dose calculations between both methods were assessed using 95% Limits of Agreement (LOA) Bland-Altman analysis for 44 consecutive clinical patient cases. These cases represented a mix of indications, algorithms (collapsed cone, convolution superposition, ray tracing, finite-size pencil beam, and Monte Carlo), and scan kVp (80 to 140) in two different commercial TPS. Results No statistically significant bias in density or dose calculations was found between the two methods. Furthermore, 95% LOAs between both methods were ±0.05 g/cm3 and ±0.1 Gy for density and dose, respectively. Small but clinically irrelevant dose differences were found in high-density gradient regions for convolution superposition calculations or CT scans with non-delayed contrast agent injections with targets nearby vessels. Conclusions The in vivo density-reconstructed images at the CT level were assessed to be equivalent. Therefore, they can simplify and improve clinical workflows, allowing patient-specific acquisitions for contouring and density-reconstructed images for dose calculations.
Collapse
Affiliation(s)
- Camille Decoene
- Corresponding author at: Service of Medical physics, Centre Oscar Lambret, 3, Rue Frédéric Combemale, Lille 59000.
| | | |
Collapse
|
15
|
Higuchi T, Haga A. X-ray energy spectrum estimation based on a virtual computed tomography system. Biomed Phys Eng Express 2023; 9. [PMID: 36623292 DOI: 10.1088/2057-1976/acb158] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT images for the Gammex phantom labeled by the corresponding energy spectra, were generated. Using these datasets, an artificial neural network (ANN) model was trained to reproduce the energy spectrum from the CT values in the Gammex inserts. In the actual application, an aluminum-based bow-tie filter was used in the virtual CT system, and an ANN model with a bow-tie filter was also developed. Both ANN models without/with a bow-tie filter can estimate the x-ray spectrum within the agreement, which is defined as one minus the absolute error, of more than 80% on average. The agreement increases as the tube voltage increases. The estimation was occasionally inaccurate when the amount of noise on the CT image was considerable. Image quality with a signal-to-noise ratio of more than 10 for the basis material of the Gammex phantom was required to predict the spectrum accurately. Based on the experimental data acquired from Activion16 (Canon Medical System, Japan), the ANN model with a bow-tie filter produced a reasonable energy spectrum by simultaneous optimization of the shape of the bow-tie filter. The present method requires a CT image for the Gammex phantom only, and no special setup, thus it is expected to be readily applied in clinical applications, such as beam hardening reduction, CT dose management, and material decomposition, all of which require exact information on the x-ray energy spectrum.
Collapse
Affiliation(s)
- Takayuki Higuchi
- Department of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Akihiro Haga
- Department of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| |
Collapse
|
16
|
Anam C, Amilia R, Naufal A, Budi WS, Maya AT, Dougherty G. The automated measurement of CT number linearity using an ACR accreditation phantom. Biomed Phys Eng Express 2022; 9. [PMID: 36541467 DOI: 10.1088/2057-1976/aca9d5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R2, were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R2> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR2= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR2< 0.99.
Collapse
Affiliation(s)
- Choirul Anam
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof Soedarto, SH Tembalang, Semarang 50275, Central Java, Indonesia
| | - Riska Amilia
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof Soedarto, SH Tembalang, Semarang 50275, Central Java, Indonesia
| | - Ariij Naufal
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof Soedarto, SH Tembalang, Semarang 50275, Central Java, Indonesia
| | - Wahyu S Budi
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof Soedarto, SH Tembalang, Semarang 50275, Central Java, Indonesia
| | - Anisa T Maya
- Loka Pengamanan Fasilitas Kesehatan (LPFK) Surakarta, Mojosongo, Jebres, Surakarta City 57127, Central Java, Indonesia
| | - Geoff Dougherty
- Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, United States of America
| |
Collapse
|
17
|
Stoichiometric CT number calibration using three-parameter fit model for ion therapy. Phys Med 2022; 99:22-30. [DOI: 10.1016/j.ejmp.2022.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/06/2022] [Accepted: 05/15/2022] [Indexed: 11/19/2022] Open
|
18
|
Longarino FK, Kowalewski A, Tessonnier T, Mein S, Ackermann B, Debus J, Mairani A, Stiller W. Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning. Front Oncol 2022; 12:853495. [PMID: 35530308 PMCID: PMC9069208 DOI: 10.3389/fonc.2022.853495] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/14/2022] [Indexed: 11/20/2022] Open
Abstract
In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.
Collapse
Affiliation(s)
- Friderike K Longarino
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Antonia Kowalewski
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.,Translational Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Physics, Simon Fraser University, Burnaby, BC, Canada
| | | | - Stewart Mein
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.,Translational Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Heidelberg Ion Beam Therapy Center (HIT), Heidelberg, Germany.,Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | | | - Jürgen Debus
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.,Heidelberg Ion Beam Therapy Center (HIT), Heidelberg, Germany.,Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Heidelberg, Germany.,German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Andrea Mairani
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.,Heidelberg Ion Beam Therapy Center (HIT), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Heidelberg, Germany.,Medical Physics, National Center of Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Wolfram Stiller
- Diagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
19
|
Sheikh K, Liu D, Li H, Acharya S, Ladra MM, Hrinivich WT. Dosimetric evaluation of cone-beam CT-based synthetic CTs in pediatric patients undergoing intensity-modulated proton therapy. J Appl Clin Med Phys 2022; 23:e13604. [PMID: 35413144 PMCID: PMC9194971 DOI: 10.1002/acm2.13604] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone-beam CTs (CBCT) in pediatric patients treated using intensity-modulated proton therapy (IMPT). METHODS Ten pediatric patients undergoing IMPT and aligned daily using proton gantry-mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re-computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT. RESULTS The correlation coefficients (p-values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (< .001), 0.84 (.002) and 0.91 (< .001), respectively. Mean ± SD gamma pass rates of the sCT-based dose compared to the qCT-based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung. CONCLUSION While insufficient for re-planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically-standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs.
Collapse
Affiliation(s)
- Khadija Sheikh
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dezhi Liu
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heng Li
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sahaja Acharya
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Matthew M Ladra
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - William T Hrinivich
- Department of Radiation Oncology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
20
|
Yasui K, Muramatsu R, Kamomae T, Toshito T, Kawabata F, Hayashi N. Evaluating the usefulness of the direct density reconstruction algorithm for intensity modulated and passively scattered proton therapy: Validation using an anthropomorphic phantom. Phys Med 2021; 92:95-101. [PMID: 34891108 DOI: 10.1016/j.ejmp.2021.11.008] [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: 06/11/2021] [Revised: 11/14/2021] [Accepted: 11/20/2021] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Accurate calculation of the proton beam range inside a patient is an important topic in proton therapy. In recent times, a computed tomography (CT) image reconstruction algorithm was developed for treatment planning to reduce the impact of the variation of the CT number with changes in imaging conditions. In this study, we investigated the usefulness of this new reconstruction algorithm (DirectDensity™: DD) in proton therapy based on its comparison with filtered back projection (FBP). METHODS We evaluated the effects of variations in the X-ray tube potential and target size on the FBP- and DD-image values and investigated the usefulness of the DD algorithm based on the range variations and dosimetric quantity variations. RESULTS For X-ray tube potential variations, the range variation in the case of FBP was up to 12.5 mm (20.8%), whereas that of DD was up to 3.3 mm (5.6%). Meanwhile, for target size variations, the range variation in the case of FBP was up to 2.2 mm (2.5%), whereas that of DD was up to 0.9 mm (1.4%). Moreover, the variations observed in the case of DD were smaller than those of FBP for all dosimetric quantities. CONCLUSION The dose distributions obtained using DD were more robust against variations in the CT imaging conditions (X-ray tube potential and target size) than those obtained using FBP, and the range variations were often less than the dose calculation grid (2 mm). Therefore, the DD algorithm is effective in a robust workflow and reduces uncertainty in range calculations.
Collapse
Affiliation(s)
- Keisuke Yasui
- Fujita Health University, Faculty of Radiological Technology, School of Health Sciences, 1-98 Dengakugakubo Kutsukake-cho, Toyoake, Aichi 470-1192, Japan.
| | - Rie Muramatsu
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, 1-1-1 Hirate-cho Kita-ku, Nagoya, Aichi 462-8508, Japan
| | - Takeshi Kamomae
- Nagoya University Hospital, 65 Tsuruma-cho Shouwa-ku, Nagoya, Aichi 466-8560, Japan
| | - Toshiyuki Toshito
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, 1-1-1 Hirate-cho Kita-ku, Nagoya, Aichi 462-8508, Japan
| | - Fumitaka Kawabata
- Nagoya University Hospital, 65 Tsuruma-cho Shouwa-ku, Nagoya, Aichi 466-8560, Japan
| | - Naoki Hayashi
- Fujita Health University, Faculty of Radiological Technology, School of Health Sciences, 1-98 Dengakugakubo Kutsukake-cho, Toyoake, Aichi 470-1192, Japan
| |
Collapse
|
21
|
Volz L, Collins-Fekete CA, Bär E, Brons S, Graeff C, Johnson RP, Runz A, Sarosiek C, Schulte RW, Seco J. The accuracy of helium ion CT based particle therapy range prediction: an experimental study comparing different particle and x-ray CT modalities. Phys Med Biol 2021; 66:10.1088/1361-6560/ac33ec. [PMID: 34706355 PMCID: PMC8792995 DOI: 10.1088/1361-6560/ac33ec] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/27/2021] [Indexed: 11/12/2022]
Abstract
This work provides a quantitative assessment of helium ion CT (HeCT) for particle therapy treatment planning. For the first time, HeCT based range prediction accuracy in a heterogeneous tissue phantom is presented and compared to single-energy x-ray CT (SECT), dual-energy x-ray CT (DECT) and proton CT (pCT). HeCT and pCT scans were acquired using the US pCT collaboration prototype particle CT scanner at the Heidelberg Ion-Beam Therapy Center. SECT and DECT scans were done with a Siemens Somatom Definition Flash and converted to RSP. A Catphan CTP404 module was used to study the RSP accuracy of HeCT. A custom phantom of 20 cm diameter containing several tissue equivalent plastic cubes was used to assess the spatial resolution of HeCT and compare it to DECT. A clinically realistic heterogeneous tissue phantom was constructed using cranial slices from a pig head placed inside a cylindrical phantom (ø150 mm). A proton beam (84.67 mm range) depth-dose measurement was acquired using a stack of GafchromicTM EBT-XD films in a central dosimetry insert in the phantom. CT scans of the phantom were acquired with each modality, and proton depth-dose estimates were simulated based on the reconstructions. The RSP accuracy of HeCT for the plastic phantom was found to be 0.3 ± 0.1%. The spatial resolution for HeCT of the cube phantom was 5.9 ± 0.4 lp cm-1for central, and 7.6 ± 0.8 lp cm-1for peripheral cubes, comparable to DECT spatial resolution (7.7 ± 0.3 lp cm-1and 7.4 ± 0.2 lp cm-1, respectively). For the pig head, HeCT, SECT, DECT and pCT predicted range accuracy was 0.25%, -1.40%, -0.45% and 0.39%, respectively. In this study, HeCT acquired with a prototype system showed potential for particle therapy treatment planning, offering RSP accuracy, spatial resolution, and range prediction accuracy comparable to that achieved with a commercial DECT scanner. Still, technical improvements of HeCT are needed to enable clinical implementation.
Collapse
Affiliation(s)
- L Volz
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - C-A Collins-Fekete
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - E Bär
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Department of Radiotherapy Physics, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - S Brons
- Heidelberg Ion-Beam Therapy Center, Universitäts Klinikum Heidelberg, Heidelberg, Germany
| | - C Graeff
- Biophysics, GSI Helmholtz Center for Heavy Ion Research GmbH, Darmstadt, Germany
| | - R P Johnson
- Department of Physics, University of California at Santa Cruz, Santa Cruz, United States of America
| | - A Runz
- Department of Medical Physics in Radiation Therapy, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - C Sarosiek
- Department of Physics, Northern Illinois University, DeKalb, United States of America
| | - R W Schulte
- Department of Basic Sciences, Division of Biomedical Engineering Sciences, Loma Linda University, Loma Linda, United States of America
| | - J Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| |
Collapse
|
22
|
Bäumer C, Bäcker CM, Conti M, Fragoso Costa P, Herrmann K, Kazek SL, Jentzen W, Panin V, Siegel S, Teimoorisichani M, Wulff J, Timmermann B. Can a ToF-PET photon attenuation reconstruction test stopping-power estimations in proton therapy? A phantom study. Phys Med Biol 2021; 66. [PMID: 34534971 DOI: 10.1088/1361-6560/ac27b5] [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: 06/02/2021] [Accepted: 09/13/2021] [Indexed: 01/19/2023]
Abstract
Objective. The aim of the phantom study was to validate and to improve the computed tomography (CT) images used for the dose computation in proton therapy. It was tested, if the joint reconstruction of activity and attenuation images of time-of-flight PET (ToF-PET) scans could improve the estimation of the proton stopping-power.Approach. The attenuation images, i.e. CT images with 511 keV gamma-rays (γCTs), were jointly reconstructed with activity maps from ToF-PET scans. Theβ+activity was produced with FDG and in a separate experiment with proton-induced radioactivation. The phantoms contained slabs of tissue substitutes. The use of theγCTs for the prediction of the beam stopping in proton therapy was based on a linear relationship between theγ-ray attenuation, the electron density, and the stopping-power of fast protons.Main results. The FDG based experiment showed sufficient linearity to detect a bias of bony tissue in the heuristic look-up table, which maps between x-ray CT images and proton stopping-power.γCTs can be used for dose computation, if the electron density of one type of tissue is provided as a scaling factor. A possible limitation is imposed by the spatial resolution, which is inferior by a factor of 2.5 compared to the one of the x-ray CT.γCTs can also be derived from off-line, ToF-PET scans subsequent to the application of a proton field with a hypofractionated dose level.Significance. γCTs are a viable tool to support the estimation of proton stopping with radiotracer-based ToF-PET data from diagnosis or staging. This could be of higher potential relevance in MRI-guided proton therapy.γCTs could form an alternative approach to make use of in-beam or off-line PET scans of proton-inducedβ+activity with possible clinical limitations due to the low number of coincidence counts.
Collapse
Affiliation(s)
- C Bäumer
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,TU Dortmund University, Department of Physics, Otto-Hahn-Str. 4a, Dortmund, Germany
| | - C M Bäcker
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,TU Dortmund University, Department of Physics, Otto-Hahn-Str. 4a, Dortmund, Germany
| | - M Conti
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - P Fragoso Costa
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - K Herrmann
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - S L Kazek
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - W Jentzen
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - V Panin
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - S Siegel
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - M Teimoorisichani
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - J Wulff
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany
| | - B Timmermann
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,University Hospital Essen, Department of Particle Therapy, Hufelandstr. 55, Essen, Germany
| |
Collapse
|
23
|
Li KW, Fujiwara D, Haga A, Liu H, Geng LS. Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study. Br J Radiol 2021; 94:20201236. [PMID: 34541866 DOI: 10.1259/bjr.20201236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA). METHODS We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms. RESULTS The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV-kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom. CONCLUSION Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations. ADVANCES IN KNOWLEDGE The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV-kV DECT, and even kV-MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms.
Collapse
Affiliation(s)
- Kai-Wen Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China.,School of Physics, Beihang University, Beijing, China
| | - Daiyu Fujiwara
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Akihiro Haga
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Huisheng Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China
| | - Li-Sheng Geng
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China.,School of Physics, Beihang University, Beijing, China.,Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing, China.,School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| |
Collapse
|
24
|
Spadea MF, Maspero M, Zaffino P, Seco J. Deep learning based synthetic-CT generation in radiotherapy and PET: A review. Med Phys 2021; 48:6537-6566. [PMID: 34407209 DOI: 10.1002/mp.15150] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/06/2021] [Accepted: 07/13/2021] [Indexed: 01/22/2023] Open
Abstract
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
Collapse
Affiliation(s)
- Maria Francesca Spadea
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Matteo Maspero
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands
| | - Paolo Zaffino
- Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy
| | - Joao Seco
- Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| |
Collapse
|
25
|
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: 18] [Impact Index Per Article: 6.0] [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.
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, 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
| |
Collapse
|
26
|
Chacko MS, Grewal HS, Wu D, Sonnad JR. Accuracy of proton stopping power estimation of silicone breast implants with single and dual-energy CT calibration techniques. J Appl Clin Med Phys 2021; 22:159-170. [PMID: 34275175 PMCID: PMC8425908 DOI: 10.1002/acm2.13358] [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: 02/28/2021] [Revised: 06/17/2021] [Accepted: 07/01/2021] [Indexed: 11/05/2022] 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 elevated with implanted devices, such as silicone breast implants when computed with single energy CT (SECT). In recent years, manufacturers have introduced implants with variations in gel cohesivity. Deriving the RSP for these implants from dual-energy CT (DECT) can result in a marked reduction of the error associated with SECT. In this study, we investigate the validity of DECT calibration of HU to RSP on silicone breast implants of varying cohesivity levels. A DECT capable scanner was calibrated using the stoichiometric method of Bourque et al for SECT and DECT using a tissue substitute phantom. Three silicone breast implants of increasing gel cohesivity were measured in a proton beam of clinical energy to determine ground-truth RSP and water equivalent thickness (WET). These were compared to SECT-derived RSP at three CT spectrum energies and DECT with two energy pairs (80/140 kVp and 100/140 kVp) as obtained from scans with and without an anthropomorphic phantom. The RSP derived from parameters estimates from CT vendor-specific software (syngo.via) was compared. The WET estimates from SECT deviated from MLIC ground truth approximately +11%-19%, which would result in overpenetration if used clinically. Both the Bourque calibration and syngo.via WET estimates from DECT yielded error ≤0.5% from ground truth; no significant difference was found between models of varying gel cohesivity levels. WET estimates without the anthropomorphic phantom were significantly different than ground truth for the Bourque calibration. From these results, gel cohesivity had no effect on proton RSP. User-generated DECT calibration can yield comparably accurate RSP estimates for silicone breast implants to vendor software methods. However, care must be taken to account for beam hardening effects.
Collapse
Affiliation(s)
- Michael S Chacko
- Oklahoma Proton Center, Oklahoma City, OK, USA.,Department of Radiological Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hardev S Grewal
- Oklahoma Proton Center, Oklahoma City, OK, USA.,Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Dee Wu
- Department of Radiological Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Jagadeesh R Sonnad
- Department of Radiological Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| |
Collapse
|
27
|
Kang DJ, Shin YJ, Jeong S, Jung JY, Lee H, Lee B. Development of clinical application program for radiotherapy induced cancer risk calculation using Monte Carlo engine in volumetric-modulated arc therapy. Radiat Oncol 2021; 16:108. [PMID: 34118968 PMCID: PMC8199704 DOI: 10.1186/s13014-020-01722-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 12/06/2020] [Indexed: 11/10/2022] Open
Abstract
Background The purpose of this study is to develop a clinical application program that automatically calculates the effect for secondary cancer risk (SCR) of individual patient. The program was designed based on accurate dose calculations using patient computed tomography (CT) data and Monte Carlo engine. Automated patient-specific evaluation program was configured to calculate SCR. Methods The application program is designed to re-calculate the beam sequence of treatment plan using the Monte Carlo engine and patient CT data, so it is possible to accurately calculate and evaluate scatter and leakage radiation, difficult to calculate in TPS. The Monte Carlo dose calculation system was performed through stoichiometric calibration using patient CT data. The automatic SCR evaluation program in application program created with a MATLAB was set to analyze the results to calculate SCR. The SCR for organ of patient was calculated based on Biological Effects of Ionizing Radiation (BEIR) VII models. The program is designed to sequentially calculate organ equivalent dose (OED), excess absolute risk (EAR), excess relative risk (ERR), and the lifetime attributable risk (LAR) in consideration of 3D dose distribution analysis. In order to confirm the usefulness of the developed clinical application program, the result values from clinical application program were compared with the manual calculation method used in the previous study. Results The OED values calculated in program were calculated to be at most approximately 13.3% higher than results in TPS. The SCR result calculated by the developed clinical application program showed a maximum difference of 1.24% compared to the result of the conventional manual calculation method. And it was confirmed that EAR, ERR and LAR values can be easily calculated by changing the biological parameters. Conclusions We have developed a patient-specific SCR evaluation program that can be used conveniently in the clinic. The program consists of a Monte Carlo dose calculation system for accurate calculation of scatter and leakage radiation and a patient-specific automatic SCR evaluation program using 3D dose distribution. The clinical application program that improved the disadvantages of the existing process can be used as an index for evaluating a patient treatment plan.
Collapse
Affiliation(s)
- Dong-Jin Kang
- Department of Radiation Oncology, Inje University Sanggye Paik Hospital, 1342, Dongil-ro, Nowon-gu, Seoul, Korea
| | - Young-Joo Shin
- Department of Radiation Oncology, Inje University Sanggye Paik Hospital, 1342, Dongil-ro, Nowon-gu, Seoul, Korea.
| | - Seonghoon Jeong
- Proton Therapy Center, National Cancer Center, Goyang, Korea
| | - Jae-Yong Jung
- Department of Radiation Oncology, Inje University Sanggye Paik Hospital, 1342, Dongil-ro, Nowon-gu, Seoul, Korea
| | | | - Boram Lee
- Department of Radiation Oncology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, 81, Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea.
| |
Collapse
|
28
|
Saito M. Quadratic relation for mass density calibration in human body using dual-energy CT data. Med Phys 2021; 48:3065-3073. [PMID: 33905548 DOI: 10.1002/mp.14899] [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: 12/08/2020] [Revised: 02/25/2021] [Accepted: 04/13/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To derive the mass density (ρ) from dual-energy computed tomography (DECT) data by calibrating electron density (ρe ) and effective atomic numbers (Zeff ) of human tissues. METHODS We propose the DEEDZ-MD method, in which a single polynomial parameterization covers the entire human-tissue range to establish an empirical quadratic relation between the atomic number-to-mass ratio and Zeff . Then, we numerically evaluate the DEEDZ-MD method in reference human tissues listed in the ICRP Publication 110 and ICRU Report 46. The tissues are considered to have unknown ρ values. The attenuation coefficients of these tissues are calculated using the XCOM Photon Cross Sections Database. The DEEDZ-MD method is also applied to experimental DECT data acquired from a tissue characterization phantom and an anthropomorphic phantom at 90 kV and 150 kV/Sn. RESULTS The numerical analysis of the DEEDZ-MD method reveals a single quadratic relation between the atomic number-to-mass ratio and Zeff in a wide range of human tissues. The simulated ρ values are in excellent agreement with the reference values over ρ values from 0.260 (lung) to 3.225 (hydroxyapatite). The relative deviations from the reference ρ remain within ±0.6% for all the reference human tissues, except for the eye lens (approximate deviation of -1.0%). The overall root-mean-square error is 0.24%. The application of the DEEDZ-MD method to experimental dual-energy CT data confirms this agreement within experimental accuracy, indicating the practical feasibility of the method. The DEEDZ-MD method enables the generation of ρ images with less image noise than the existing DECT-based conversion of ρ from ρe and with fewer beam-hardening artifacts than conventional single-energy CT images. CONCLUSIONS The DEEDZ-MD method can facilitate the generation of ρ images from dual-energy CT data without relying on the nontrivial segmentation of different tissues.
Collapse
Affiliation(s)
- Masatoshi Saito
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, Niigata, 951-8518, Japan
| |
Collapse
|
29
|
Verhaegen F, Wanders RG, Wolfs C, Eekers D. Considerations for shoot-through FLASH proton therapy. Phys Med Biol 2021; 66:06NT01. [PMID: 33571981 DOI: 10.1088/1361-6560/abe55a] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE To discuss several pertinent issues related to shoot-through FLASH proton therapy based on an illustrative case. METHODS We argue that with the advent of FLASH proton radiotherapy and due to the issues associated with conventional proton radiotherapy regarding the uncertainties of positioning of the Bragg peaks, the difficulties of in vivo verification of the dose distribution, the use of treatment margins and the uncertainties surrounding linear energy transfer (LET) and relative biological effectiveness (RBE), a special mode of shoot-through FLASH proton radiotherapy should be investigated. In shoot-through FLASH, the proton beams have sufficient energy to reach the distal exit side of the patient. Due to the FLASH sparing effect of normal tissues at both the proximal and distal side of tumors, radiotherapy plans can be developed that meet current planning constraints and issues regarding RBE can be avoided. RESULTS A preliminary proton plan for a neurological tumor in close proximity to various organs at risk (OAR) with strict dose constraints was studied. A plan with four beams mostly met the constraints for the OAR, using a treatment planning system that was not optimized for this novel treatment modality. When new treatment planning algorithms would be developed for shoot-through FLASH, constraints would be easier to meet. The shoot-through FLASH plan led to a significant effective dose reduction in large parts of the healthy tissue. The plan had no uncertainties associated to Bragg peak positioning, needed in principle no large proximal or distal margins and LET increases near the Bragg peak became irrelevant. CONCLUSION Shoot-through FLASH proton radiotherapy may be an interesting treatment modality to explore further. It would remove some of the current sources of uncertainty in proton radiotherapy. An additional advantage could be that portal dosimetry may be possible with beams penetrating the patient and impinging on a distally placed imaging detector, potentially leading to a practical treatment verification method. With current proton accelerator technology, trials could be conducted for neurological, head&neck and thoracic cancers. For abdominal and pelvic cancer a higher proton energy would be required.
Collapse
Affiliation(s)
- Frank Verhaegen
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | | | | |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Civinini C, Scaringella M, Brianzi M, Intravaia M, Randazzo N, Sipala V, Rovituso M, Tommasino F, Schwarz M, Bruzzi M. Relative stopping power measurements and prosthesis artifacts reduction in proton CT. ACTA ACUST UNITED AC 2020; 65:225012. [DOI: 10.1088/1361-6560/abb0c8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
32
|
Faller FK, Mein S, Ackermann B, Debus J, Stiller W, Mairani A. Pre-clinical evaluation of dual-layer spectral computed tomography-based stopping power prediction for particle therapy planning at the Heidelberg Ion Beam Therapy Center. ACTA ACUST UNITED AC 2020; 65:095007. [DOI: 10.1088/1361-6560/ab735e] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
33
|
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: 38] [Impact Index Per Article: 9.5] [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.
Collapse
Affiliation(s)
- Patrick Wohlfahrt
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | |
Collapse
|
34
|
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]
|
35
|
van der Heyden B, Almeida IP, Vilches-Freixas G, Van Beveren C, Vaniqui A, Ares C, Terhaag K, Fonseca GP, Eekers DBP, Verhaegen F. A comparison study between single- and dual-energy CT density extraction methods for neurological proton monte carlo treatment planning. Acta Oncol 2020; 59:171-179. [PMID: 31646923 DOI: 10.1080/0284186x.2019.1679879] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Monte Carlo proton dose calculations requires mass densities calculated from the patient CT image. This work investigates the impact of different single-energy CT (SECT) and dual-energy CT (DECT) to density conversion methods in proton dose distributions for brain tumours.Material and methods: Head CT scans for four patients were acquired in SECT and DECT acquisition modes. Commercial software was used to reconstruct DirectDensity™ images in Relative Electron Densities (RED, [Formula: see text]) and to obtain DECT-based pseudo-monoenergetic images (PMI). PMI and SECT images were converted to RED using piecewise linear interpolations calibrated on a head-sized phantom, these fits were referred to as "PMI2RED" and "CT2RED". Two DECT-based calibration methods ("Hünemohr-15it" and "Saito-15it") were also investigated. [Formula: see text] images were converted to mass-densities ([Formula: see text]) to investigate [Formula: see text]differences and one representative patient case was used to make a proton treatment plan. Using CT2RED as reference method, dose distribution differences in the target and in five organs-at-risk (OARs) were quantified.Results: In the phantom study, Saito-15it and Hünemohr-15it produced the lowest [Formula: see text]root-mean-square error (0.7%) and DirectDensity™ the highest error (2.7%). The proton plan evaluated in the Saito-15it and Hünemohr-15it datasets showed the largest relative differences compared to initial CT2RED plan down to -6% of the prescribed dose. Compared to CT2RED, average range differences were calculated: -0.1 ± 0.3 mm for PMI2RED; -0.8 ± 0.4 mm for Hünemohr-15it, and -1.2 ± 0.4 mm for Saito-15it.Conclusion: Given the wide choice of available conversion methods, studies investigating the density accuracy for proton dose calculations are necessary. However, there is still a gap between performing accuracy studies in reference [Formula: see text]phantoms and applying these methods in human CT images. For this treatment case, the PMI2RED method was equivalent to the conventional CT2RED method in terms of dose distribution, CTV coverage and OAR sparing, whereas Hünemohr-15it and Saito-15it presented the largest differences.
Collapse
Affiliation(s)
- B. van der Heyden
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - I. P. Almeida
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
- Maastro Protonentherapie, Maastricht, Netherlands
| | | | - C. Van Beveren
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - A. Vaniqui
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - C. Ares
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - K. Terhaag
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - G. P. Fonseca
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - D. B. P. Eekers
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
- Maastro Protonentherapie, Maastricht, Netherlands
| | - F. Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| |
Collapse
|