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Kaser S, Bergauer T, Biguri A, Birkfellner W, Hatamikia S, Hirtl A, Irmler C, Kirchmayer B, Ulrich-Pur F. Extension of the open-source TIGRE toolbox for proton imaging. Z Med Phys 2023; 33:552-566. [PMID: 36195519 PMCID: PMC10751710 DOI: 10.1016/j.zemedi.2022.08.005] [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: 04/29/2022] [Revised: 08/08/2022] [Accepted: 08/31/2022] [Indexed: 10/07/2022]
Abstract
Proton irradiation is a well-established method to treat deep-seated tumors in radio oncology. Usually, an X-ray computed tomography (CT) scan is used for treatment planning. Since proton therapy is based on the precise knowledge of the stopping power describing the energy loss of protons in the patient tissues, the Hounsfield units of the planning CT have to be converted. This conversion introduces range errors in the treatment plan, which could be reduced, if the stopping power values were extracted directly from an image obtained using protons instead of X-rays. Since protons are affected by multiple Coulomb scattering, reconstruction of the 3D stopping power map results in limited image quality if the curved proton path is not considered. This work presents a substantial code extension of the open-source toolbox TIGRE for proton CT (pCT) image reconstruction based on proton radiographs including a curved proton path estimate. The code extension and the reconstruction algorithms are GPU-based, allowing to achieve reconstruction results within minutes. The performance of the pCT code extension was tested with Monte Carlo simulated data using three phantoms (Catphan® high resolution and sensitometry modules and a CIRS patient phantom). In the simulations, ideal and non-ideal conditions for a pCT setup were assumed. The obtained mean absolute percentage error was found to be below 1% and up to 8 lp/cm could be resolved using an idealized setup. These findings demonstrate that the presented code extension to the TIGRE toolbox offers the possibility for other research groups to use a fast and accurate open-source pCT reconstruction.
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Affiliation(s)
- Stefanie Kaser
- Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria.
| | - Thomas Bergauer
- Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria
| | - Ander Biguri
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, United Kingdom
| | - Wolfgang Birkfellner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sepideh Hatamikia
- Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria; Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Christian Irmler
- Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria
| | | | - Felix Ulrich-Pur
- GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany; Institute of High Energy Physics, Austrian Academy of Sciences, Vienna, Austria
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2
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Zhao F, Liu M, Gao Z, Jiang X, Wang R, Zhang L. Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising. Comput Biol Med 2023; 161:107029. [PMID: 37230021 DOI: 10.1016/j.compbiomed.2023.107029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/10/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023]
Abstract
Removing the noise in low-dose CT (LDCT) is crucial to improving the diagnostic quality. Previously, many supervised or unsupervised deep learning-based LDCT denoising algorithms have been proposed. Unsupervised LDCT denoising algorithms are more practical than supervised ones since they do not need paired samples. However, unsupervised LDCT denoising algorithms are rarely used clinically due to their unsatisfactory denoising ability. In unsupervised LDCT denoising, the lack of paired samples makes the direction of gradient descent full of uncertainty. On the contrary, paired samples used in supervised denoising allow the parameters of networks to have a clear direction of gradient descent. To bridge the gap in performance between unsupervised and supervised LDCT denoising, we propose dual-scale similarity-guided cycle generative adversarial network (DSC-GAN). DSC-GAN uses similarity-based pseudo-pairing to better accomplish unsupervised LDCT denoising. We design a Vision Transformer-based global similarity descriptor and a residual neural network-based local similarity descriptor for DSC-GAN to effectively describe the similarity between two samples. During training, pseudo-pairs, i.e., similar LDCT samples and normal-dose CT (NDCT) samples, dominate parameter updates. Thus, the training can achieve equivalent effect as training with paired samples. Experiments on two datasets demonstrate that DSC-GAN beats the state-of-the-art unsupervised algorithms and reaches a level close to supervised LDCT denoising algorithms.
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Affiliation(s)
- Feixiang Zhao
- College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610000, China.
| | - Mingzhe Liu
- College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610000, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
| | - Zhihong Gao
- Department of Big Data in Health Science, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xin Jiang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, China.
| | - Ruili Wang
- School of Mathematical and Computational Science, Massey University, Auckland, 0632, New Zealand.
| | - Lejun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China; College of Information Engineering, Yangzhou University, Yangzhou, 225127, China.
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Fullarton R, Volz L, Dikaios N, Schulte R, Royle G, Evans PM, Seco J, Collins‐Fekete C. A likelihood-based particle imaging filter using prior information. Med Phys 2023; 50:2336-2353. [PMID: 36727634 PMCID: PMC10947404 DOI: 10.1002/mp.16258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 01/13/2023] [Accepted: 01/13/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. PURPOSE This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. METHODS The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. RESULTS The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. CONCLUSIONS The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.
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Affiliation(s)
- Ryan Fullarton
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Lennart Volz
- Department of Biomedical Physics in Radiation OncologyDeutsches Krebsforschungszentrum (DKFZ)HeidelbergGermany
- Department of Physics and AstronomyHeidelberg UniversityHeidelbergGermany
- GSI Helmholtz Centre for Heavy Ion Research GmbHDarmstadtGermany
| | - Nikolaos Dikaios
- Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildfordUK
- Mathematics Research CenterAcademy of AthensAthensGreece
| | - Reinhard Schulte
- Department of Basic SciencesDivision of Biomedical Engineering SciencesLoma Linda UniversityLoma LindaCaliforniaUSA
| | - Gary Royle
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Philip M. Evans
- Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildfordUK
- Chemical, Medical and Environmental ScienceNational Physical LaboratoryTeddingtonUK
| | - Joao Seco
- Department of Biomedical Physics in Radiation OncologyDeutsches Krebsforschungszentrum (DKFZ)HeidelbergGermany
- Department of Physics and AstronomyHeidelberg UniversityHeidelbergGermany
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Krah N, Dauvergne D, Létang JM, Rit S, Testa É. Relative stopping power resolution in time-of-flight proton CT. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/19/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Proton computed tomography (CT) is similar to x-ray CT but relies on protons rather than photons to form an image. In its most common operation mode, the measured quantity is the amount of energy that a proton has lost while traversing the imaged object from which a relative stopping power map can be obtained via tomographic reconstruction. To this end, a calorimeter which measures the energy deposited by protons downstream of the scanned object has been studied or implemented as energy detector in several proton CT prototypes. An alternative method is to measure the proton’s residual velocity and thus its kinetic energy via the time of flight (TOF) between at least two sensor planes. In this work, we study the RSP resolution, seen as image noise, which can be expected from TOF proton CT systems.
Approach. We rely on physics models on the one hand and statistical models of the relevant uncertainties on the other to derive closed form expressions for the noise in projection images. The TOF measurement error scales with the distance between the TOF sensor planes and is reported as velocity error in ps/m. We use variance reconstruction to obtain noise maps of a water cylinder phantom given the scanner characteristics and additionally reconstruct noise maps for a calorimeter-based proton CT system as reference. We use Monte Carlo simulations to verify our model and to estimate the noise due to multiple Coulomb scattering inside the object. We also provide a comparison of TOF helium and proton CT.
Main results. We find that TOF proton CT with 30 ps m−1 velocity error reaches similar image noise as a calorimeter-based proton CT system with 1% energy error (1 sigma error). A TOF proton CT system with a 50 ps m−1 velocity error produces slightly less noise than a 2% calorimeter system. Noise in a reconstructed TOF proton CT image is spatially inhomogeneous with a marked increase towards the object periphery. Our modelled noise was consistent with Monte Carlo simulated images. TOF helium CT offers lower RSP noise at equal fluence, but is less advantageous at equal imaging dose.
Significance. This systematic study of image noise in TOF proton CT can serve as a guide for future developments of this alternative solution for estimating the residual energy of protons and helium ions after the scanned object.
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Götz S, Dickmann J, Rit S, Krah N, Khellaf F, Schulte RW, Parodi K, Dedes G, Landry G. Evaluation of the impact of a scanner prototype on proton CT and helium CT image quality and dose efficiency with Monte Carlo simulation. Phys Med Biol 2022; 67. [PMID: 35086073 DOI: 10.1088/1361-6560/ac4fa4] [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: 11/08/2021] [Accepted: 01/27/2022] [Indexed: 11/12/2022]
Abstract
Objective.The use of ion computed tomography (CT) promises to yield improved relative stopping power (RSP) estimation as input to particle therapy treatment planning. Recently, proton CT (pCT) has been shown to yield RSP accuracy on par with state-of-the-art x-ray dual energy CT. There are however concerns that the lower spatial resolution of pCT compared to x-ray CT may limit its potential, which has spurred interest in the use of helium ion CT (HeCT). The goal of this study was to investigate image quality of pCT and HeCT in terms of noise, spatial resolution, RSP accuracy and imaging dose using a detailed Monte Carlo (MC) model of an existing ion CT prototype.Approach.Three phantoms were used in simulated pCT and HeCT scans allowing estimation of noise, spatial resolution and the scoring of dose. An additional phantom was used to evaluate RSP accuracy. The imaging dose required to achieve the same image noise in a water and a head phantom was estimated at both native spatial resolution, and in a scenario where the HeCT spatial resolution was reduced and matched to that of pCT using Hann windowing of the reconstruction filter. A variance reconstruction formalism was adapted to account for Hann windowing.Main results.We confirmed that the scanner prototype would produce higher spatial resolution for HeCT than pCT by a factor 1.8 (0.86 lp mm-1versus 0.48 lp mm-1at the center of a 20 cm water phantom). At native resolution, HeCT required a factor 2.9 more dose than pCT to achieve the same noise, while at matched resolution, HeCT required only 38% of the pCT dose. Finally, RSP mean absolute percent error (MAPE) was found to be 0.59% for pCT and 0.67% for HeCT.Significance.This work compared the imaging performance of pCT and HeCT when using an existing scanner prototype, with the spatial resolution advantage of HeCT coming at the cost of increased dose. When matching spatial resolution via Hann windowing, HeCT had a substantial dose advantage. Both modalities provided state-of-the-art RSP MAPE. HeCT might therefore help reduce the dose exposure of patients with comparable image noise to pCT, enhanced spatial resolution and acceptable RSP accuracy at the same time.
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Affiliation(s)
- S Götz
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany
| | - J Dickmann
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany
| | - S Rit
- University of Lyon, INSA-Lyon, Unversité Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS, UMR 5220, U1294 F-69373, Lyon, France
| | - N Krah
- University of Lyon, INSA-Lyon, Unversité Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS, UMR 5220, U1294 F-69373, Lyon, France.,IP2I, UMR 5822 F-69622, Villeurbanne, France
| | - F Khellaf
- University of Lyon, INSA-Lyon, Unversité Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS, UMR 5220, U1294 F-69373, Lyon, France
| | - R W Schulte
- Division of Biomedical Engineering Sciences, Loma Linda University, Loma Linda, CA 92354, United States of America
| | - K Parodi
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany
| | - G Dedes
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany
| | - G Landry
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany.,Department of Radiation Oncology, University Hospital, LMU Munich, D-81377 Munich, Germany.,German Cancer Consortium (DKTK), D-81377 Munich, Germany
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6
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Bär E, Volz L, Collins-Fekete CA, Brons S, Runz A, Schulte RW, Seco J. Experimental comparison of photon versus particle computed tomography to predict tissue relative stopping powers. Med Phys 2022; 49:474-487. [PMID: 34709667 DOI: 10.1002/mp.15283] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 09/18/2021] [Accepted: 09/20/2021] [Indexed: 01/21/2023] Open
Abstract
PURPOSE Measurements comparing relative stopping power (RSP) accuracy of state-of-the-art systems representing single-energy and dual-energy computed tomography (SECT/DECT) with proton CT (pCT) and helium CT (HeCT) in biological tissue samples. METHODS We used 16 porcine and bovine samples of various tissue types and water, covering an RSP range from 0.90 ± 0.06 to 1.78 ± 0.05. Samples were packed and sealed into 3D-printed cylinders ( d = 2 cm, h = 5 cm) and inserted into an in-house designed cylindrical polymethyl methacrylate (PMMA) phantom ( d = 10 cm, h = 10 cm). We scanned the phantom in a commercial SECT and DECT (120 kV; 100 and 140 kV/Sn (tin-filtered)); and acquired pCT and HeCT ( E ∼ 200 MeV/u, 2 ∘ steps, ∼ 6.2 × 10 6 (p)/ ∼ 2.3 × 10 6 (He) particles/projection) with a particle imaging prototype. RSP maps were calculated from SECT/DECT using stoichiometric methods and from pCT/HeCT using the DROP-TVS algorithm. We estimated the average RSP of each tissue per modality in cylindrical volumes of interest and compared it to ground truth RSP taken from peak-detection measurements. RESULTS Throughout all samples, we observe the following root-mean-squared RSP prediction errors ± combined uncertainty from reference measurement and imaging: SECT 3.10 ± 2.88%, DECT 0.75 ± 2.80%, pCT 1.19 ± 2.81%, and HeCT 0.78 ± 2.81%. The largest mean errors ± combined uncertainty per modality are SECT 8.22 ± 2.79% in cortical bone, DECT 1.74 ± 2.00% in back fat, pCT 1.80 ± 4.27% in bone marrow, and HeCT 1.37 ± 4.25% in bone marrow. Ring artifacts were observed in both pCT and HeCT reconstructions, imposing a systematic shift to predicted RSPs. CONCLUSION Comparing state-of-the-art SECT/DECT technology and a pCT/HeCT prototype, DECT provided the most accurate RSP prediction, closely followed by particle imaging. The novel modalities pCT and HeCT have the potential to further improve on RSP accuracies with work focusing on the origin and correction of ring artifacts. Future work will study accuracy of proton treatment plans using RSP maps from investigated imaging modalities.
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Affiliation(s)
- Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Department of Radiotherapy Physics, University College London Hospitals NHS Foundation Trust, Radiotherapy Physics, London, UK
| | - Lennart Volz
- Biomedical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany
| | | | - Stephan Brons
- Heidelberg Ion Beam Therapy Center, Im Neuenheimer Feld, Heidelberg, Germany
| | - Armin Runz
- Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany
| | | | - Joao Seco
- Biomedical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld, Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Germany
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Zhang S, Qiang Y. Fast parallel implementation for total variation constrained algebraic reconstruction technique. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:737-750. [PMID: 35527622 DOI: 10.3233/xst-221163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.
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Affiliation(s)
- Shunli Zhang
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Yu Qiang
- School of Information Science and Technology, Northwest University, Xi'an, China
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Collins-Fekete CA, Dikaios N, Bär E, Evans PM. Statistical limitations in ion imaging. Phys Med Biol 2021; 66. [PMID: 33711829 PMCID: PMC8112161 DOI: 10.1088/1361-6560/abee57] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/12/2021] [Indexed: 11/12/2022]
Abstract
In this study, we investigated the capacity of various ion beams available for radiotherapy to produce high quality relative stopping power map acquired from energy-loss measurements. The image quality metrics chosen to compare the different ions were signal-to-noise ratio (SNR) as a function of dose and spatial resolution. Geant4 Monte Carlo simulations were performed for: hydrogen, helium, lithium, boron and carbon ion beams crossing a 20 cm diameter water phantom to determine SNR and spatial resolution. It has been found that protons possess a significantly larger SNR when compared with other ions at a fixed range (up to 36% higher than helium) due to the proton nuclear stability and low dose per primary. However, it also yields the lowest spatial resolution against all other ions, with a resolution lowered by a factor 4 compared to that of carbon imaging, for a beam with the same initial range. When comparing for a fixed spatial resolution of 10 lp cm−1, carbon ions produce the highest image quality metrics with proton ions producing the lowest. In conclusion, it has been found that no ion can maximize all image quality metrics simultaneously and that a choice must be made between spatial resolution, SNR, and dose.
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Affiliation(s)
- Charles-Antoine Collins-Fekete
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, United Kingdom.,Chemical, Medical and Environmental Science, National Physical Laboratory, Hampton Road, Teddington, United Kingdom
| | - Nikolaos Dikaios
- Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, United Kingdom
| | - Esther Bär
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, United Kingdom
| | - Philip M Evans
- Chemical, Medical and Environmental Science, National Physical Laboratory, Hampton Road, Teddington, United Kingdom.,Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, United Kingdom
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Dickmann J, Kamp F, Hillbrand M, Corradini S, Belka C, Schulte RW, Parodi K, Dedes G, Landry G. Fluence-modulated proton CT optimized with patient-specific dose and variance objectives for proton dose calculation. Phys Med Biol 2021; 66:064001. [PMID: 33545701 DOI: 10.1088/1361-6560/abe3d2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Particle therapy treatment planning requires accurate volumetric maps of the relative stopping power, which can directly be acquired using proton computed tomography (pCT). With fluence-modulated pCT (FMpCT) imaging fluence is concentrated in a region-of-interest (ROI), which can be the vicinity of the treatment beam path, and imaging dose is reduced elsewhere. In this work we present a novel optimization algorithm for FMpCT which, for the first time, calculates modulated imaging fluences for joint imaging dose and image variance objectives. Thereby, image quality is maintained in the ROI to ensure accurate calculations of the treatment dose, and imaging dose is minimized outside the ROI with stronger minimization penalties given to imaging organs-at-risk. The optimization requires an initial scan at uniform fluence or a previous x-ray CT scan. We simulated and optimized FMpCT images for three pediatric patients with tumors in the head region. We verified that the target image variance inside the ROI was achieved and demonstrated imaging dose reductions outside of the ROI of 74% on average, reducing the imaging dose from 1.2 to 0.3 mGy. Such dose savings are expected to be relevant compared to the therapeutic dose outside of the treatment field. Treatment doses were re-calculated on the FMpCT images and compared to treatment doses re-recalculated on uniform fluence pCT scans using a 1% criterion. Passing rates were above 98.3% for all patients. Passing rates comparing FMpCT treatment doses to the ground truth treatment dose were above 88.5% for all patients. Evaluation of the proton range with a 1 mm criterion resulted in passing rates above 97.5% (FMpCT/pCT) and 95.3% (FMpCT/ground truth). Jointly optimized fluence-modulated pCT images can be used for proton dose calculation maintaining the full dosimetric accuracy of pCT but reducing the required imaging dose considerably by three quarters. This may allow for daily imaging during particle therapy ensuring a safe and accurate delivery of the therapeutic dose and avoiding excess dose from imaging.
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Affiliation(s)
- J Dickmann
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), D-85748 Garching bei München, Germany
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10
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Dickmann J, Sarosiek C, Rykalin V, Pankuch M, Coutrakon G, Johnson RP, Bashkirov V, Schulte RW, Parodi K, Landry G, Dedes G. Proof of concept image artifact reduction by energy-modulated proton computed tomography (EMpCT). Phys Med 2021; 81:237-244. [DOI: 10.1016/j.ejmp.2020.12.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 11/29/2022] Open
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12
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Dickmann J, Sarosiek C, Rykalin V, Pankuch M, Rit S, Detrich N, Coutrakon G, Johnson RP, Schulte RW, Parodi K, Landry G, Dedes G. Experimental realization of dynamic fluence field optimization for proton computed tomography. ACTA ACUST UNITED AC 2020; 65:195001. [DOI: 10.1088/1361-6560/ab9f5f] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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13
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Lazos D, Collins-Fekete CA, Evans P, Dikaios N. Molière maximum likelihood proton path estimation approximated by cubic Bézier curve for scatter corrected proton CT reconstruction. ACTA ACUST UNITED AC 2020; 65:175003. [DOI: 10.1088/1361-6560/ab9413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Dedes G, Dickmann J, Giacometti V, Rit S, Krah N, Meyer S, Bashkirov V, Schulte R, Johnson RP, Parodi K, Landry G. The role of Monte Carlo simulation in understanding the performance of proton computed tomography. Z Med Phys 2020; 32:23-38. [PMID: 32798033 PMCID: PMC9948882 DOI: 10.1016/j.zemedi.2020.06.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 05/18/2020] [Accepted: 06/16/2020] [Indexed: 01/28/2023]
Abstract
Proton computed tomography (pCT) is a promising tomographic imaging modality allowing direct reconstruction of proton relative stopping power (RSP) required for proton therapy dose calculation. In this review article, we aim at highlighting the role of Monte Carlo (MC) simulation in pCT studies. After describing the requirements for performing proton computed tomography and the various pCT scanners actively used in recent research projects, we present an overview of available MC simulation platforms. The use of MC simulations in the scope of investigations of image reconstruction, and for the evaluation of optimal RSP accuracy, precision and spatial resolution omitting detector effects is then described. In the final sections of the review article, we present specific applications of realistic MC simulations of an existing pCT scanner prototype, which we describe in detail.
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Affiliation(s)
- George Dedes
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching b. München, Germany.
| | - Jannis Dickmann
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching b. München, Germany
| | - Valentina Giacometti
- The Patrick G Johnston Centre for Cancer Research, Queen's University of Belfast, Northern Ireland Cancer Centre, Belfast, Northern Ireland, United Kingdom
| | - Simon Rit
- University of Lyon, CREATIS, CNRS UMR5220; Inserm U1044, INSA-Lyon, Université Lyon 1, Centre Léon Bérard, Lyon, France
| | - Nils Krah
- University of Lyon, CREATIS, CNRS UMR5220; Inserm U1044, INSA-Lyon, Université Lyon 1, Centre Léon Bérard, Lyon, France,University of Lyon, Institute of Nuclear Physics Lyon (IPNL), CNRS UMR 5822, Villeurbanne, France
| | - Sebastian Meyer
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching b. München, Germany,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Vladimir Bashkirov
- Division of Biomedical Engineering Sciences, Loma Linda University, Loma Linda, CA, United States of America
| | - Reinhard Schulte
- Division of Biomedical Engineering Sciences, Loma Linda University, Loma Linda, CA, United States of America
| | - Robert P. Johnson
- Department of Physics, U. C. Santa Cruz, Santa Cruz, CA, United States of America
| | - Katia Parodi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching b. München, Germany
| | - Guillaume Landry
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching b. München, Germany,Department of Radiation Oncology, Department of Medical Physics, University Hospital, LMU Munich, Munich, Germany,German Cancer Consortium, (DKTK), Munich, Germany
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Khellaf F, Krah N, Létang JM, Collins-Fekete CA, Rit S. A comparison of direct reconstruction algorithms in proton computed tomography. ACTA ACUST UNITED AC 2020; 65:105010. [DOI: 10.1088/1361-6560/ab7d53] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
Proton imaging is a promising technology for proton radiotherapy as it can be used for: (1) direct sampling of the tissue stopping power, (2) input information for multi-modality RSP reconstruction, (3) gold-standard calibration against concurrent techniques, (4) tracking motion and (5) pre-treatment positioning. However, no end-to-end characterization of the image quality (signal-to-noise ratio and spatial resolution, blurring uncertainty) against the dose has been done. This work aims to establish a model relating these characteristics and to describe their relationship with proton energy and object size. The imaging noise originates from two processes: the Coulomb scattering with the nucleus, producing a path deviation, and the energy loss straggling with electrons. The noise is found to increases with thickness crossed and, independently, decreases with decreasing energy. The scattering noise is dominant around high-gradient edge whereas the straggling noise is maximal in homogeneous regions. Image quality metrics are found to behave oppositely against energy: lower energy minimizes both the noise and the spatial resolution, with the optimal energy choice depending on the application and location in the imaged object. In conclusion, the model presented will help define an optimal usage of proton imaging to reach the promised application of this technology and establish a fair comparison with other imaging techniques.
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Affiliation(s)
- Charles-Antoine Collins-Fekete
- Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, United Kingdom. Chemical,Medical and Environmental Science, National Physical Laboratory, Hampton Road, Teddington, United Kingdom
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Dickmann J, Rit S, Pankuch M, Johnson RP, Schulte RW, Parodi K, Dedes G, Landry G. An optimization algorithm for dose reduction with fluence‐modulated proton CT. Med Phys 2020; 47:1895-1906. [DOI: 10.1002/mp.14084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/30/2020] [Accepted: 02/05/2020] [Indexed: 01/12/2023] Open
Affiliation(s)
- J. Dickmann
- Department of Medical Physics Faculty of Physics Ludwig‐Maximilians‐Universität München Am Coulombwall 1 85748 Garching b. München Germany
| | - S. Rit
- Univ Lyon INSA‐Lyon Université Claude Bernard Lyon 1 UJM‐Saint Étienne CNRS, Inserm CREATIS UMR 5220 U1206 F‐69373 Lyon France
| | - M. Pankuch
- Northwestern Medicine Chicago Proton Center Warrenville IL 60555 USA
| | - R. P. Johnson
- Department of Physics University of California Santa Cruz Santa Cruz CA 95064 USA
| | - R. W. Schulte
- Division of Biomedical Engineering Sciences Loma Linda University Loma Linda CA 92354 USA
| | - K. Parodi
- Department of Medical Physics Faculty of Physics Ludwig‐Maximilians‐Universität München Am Coulombwall 1 85748 Garching b. München Germany
| | - G. Dedes
- Department of Medical Physics Faculty of Physics Ludwig‐Maximilians‐Universität München Am Coulombwall 1 85748 Garching b. München Germany
| | - G. Landry
- Department of Medical Physics Faculty of Physics Ludwig‐Maximilians‐Universität München Am Coulombwall 1 85748 Garching b. München Germany
- Department of Radiation Oncology University Hospital, LMU Munich 81377 Munich Germany
- German Cancer Consortium (DKTK) 81377 Munich Germany
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Volz L, Piersimoni P, Johnson RP, Bashkirov VA, Schulte RW, Seco J. Improving single-event proton CT by removing nuclear interaction events within the energy/range detector. ACTA ACUST UNITED AC 2019; 64:15NT01. [DOI: 10.1088/1361-6560/ab2671] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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19
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Dickmann J, Wesp P, Rädler M, Rit S, Pankuch M, Johnson RP, Bashkirov V, Schulte RW, Parodi K, Landry G, Dedes G. Prediction of image noise contributions in proton computed tomography and comparison to measurements. ACTA ACUST UNITED AC 2019; 64:145016. [DOI: 10.1088/1361-6560/ab2474] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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