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Auer B, Könik A, Fromme TJ, De Beenhouwer J, Kalluri KS, Lindsay C, Furenlid LR, Kuo PH, King MA. Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction. Phys Med Biol 2023; 68:10.1088/1361-6560/acbde2. [PMID: 36808915 PMCID: PMC10073298 DOI: 10.1088/1361-6560/acbde2] [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: 06/07/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective.Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes.Approach.In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system.Main results.We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like123I-IMP brain perfusion source distributions.Significance.This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.
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Affiliation(s)
- Benjamin Auer
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, 02215, United States of America
| | - Arda Könik
- Dana-Farber Cancer Institute, Department of Imaging, Boston, MA, 02215, United States of America
| | - Timothy J Fromme
- Worcester Polytechnic Institute, Worcester, MA, 01609, United States of America
| | | | - Kesava S Kalluri
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
| | - Clifford Lindsay
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
| | - Lars R Furenlid
- James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, , United States of America
| | - Philip H Kuo
- Department of Medical Imaging, University of Arizona, Tucson, AZ, 85724, United States of America
| | - Michael A King
- University of Massachusetts Chan Medical School, Department of Radiology, Worcester, MA, 01655, United States of America
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Park H, Paganetti H, Schuemann J, Jia X, Min CH. Monte Carlo methods for device simulations in radiation therapy. Phys Med Biol 2021. [PMID: 34384063 DOI: 10.1088/1361-6560/ac1d1f.10.1088/1361-6560/ac1d1f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Monte Carlo (MC) simulations play an important role in radiotherapy, especially as a method to evaluate physical properties that are either impossible or difficult to measure. For example, MC simulations (MCSs) are used to aid in the design of radiotherapy devices or to understand their properties. The aim of this article is to review the MC method for device simulations in radiation therapy. After a brief history of the MC method and popular codes in medical physics, we review applications of the MC method to model treatment heads for neutral and charged particle radiation therapy as well as specific in-room devices for imaging and therapy purposes. We conclude by discussing the impact that MCSs had in this field and the role of MC in future device design.
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Affiliation(s)
- Hyojun Park
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Jan Schuemann
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Xun Jia
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75235, United States of America
| | - Chul Hee Min
- Department of Radiation Convergence Engineering, Yonsei University, Wonju, Republic of Korea
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Ross Schmidtlein C, Lin Y, Li S, Krol A, Beattie BJ, Humm JL, Xu Y. Relaxed ordered subset preconditioned alternating projection algorithm for PET reconstruction with automated penalty weight selection. Med Phys 2017; 44:4083-4097. [PMID: 28437565 DOI: 10.1002/mp.12292] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Revised: 04/11/2017] [Accepted: 04/12/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Performance of the preconditioned alternating projection algorithm (PAPA) using relaxed ordered subsets (ROS) with a non-smooth penalty function was investigated in positron emission tomography (PET). A higher order total variation (HOTV) regularizer was applied and a method for unsupervised selection of penalty weights based on the measured data is introduced. METHODS A ROS version of PAPA with HOTV penalty (ROS-HOTV-PAPA) for PET image reconstruction was developed and implemented. Two-dimensional PET data were simulated using two synthetic phantoms (geometric and brain) in geometry similar to GE D690/710 PET/CT with uniform attenuation, and realistic scatter (25%) and randoms (25%). Three count levels (high/medium/low) corresponding to mean information densities (ID¯s) of 125, 25, and 5 noise equivalent counts (NEC) per support voxel were reconstructed using ROS-HOTV-PAPA. The patients' brain and whole body PET data were acquired at similar ID¯s on GE D690 PET/CT with time-of-fight and were reconstructed using ROS-HOTV-PAPA and available clinical ordered-subset expectation-maximization (OSEM) algorithms. A power-law model of the penalty weights' dependence on ID¯ was semi-empirically derived. Its parameters were elucidated from the data and used for unsupervised selection of the penalty weights within a reduced search space. The resulting image quality was evaluated qualitatively, including reduction of staircase artifacts, image noise, spatial resolution and contrast, and quantitatively using root mean squared error (RMSE) as a global metric. The convergence rates were also investigated. RESULTS ROS-HOTV-PAPA converged rapidly, in comparison to non-ROS-HOTV-PAPA, with no evidence of limit cycle behavior. The reconstructed image quality was superior to optimally post-filtered OSEM reconstruction in terms of noise, spatial resolution, and contrast. Staircase artifacts were not observed. Images of the measured phantom reconstructed using ROS-HOTV-PAPA showed reductions in RMSE of 5%-44% as compared with optimized OSEM. The greatest improvement occurred in the lowest count images. Further, ROS-HOTV-PAPA reconstructions produced images with RMSE similar to images reconstructed using optimally post-filtered OSEM but at one-quarter the NEC. CONCLUSION Acceleration of HOTV-PAPA was achieved using ROS. This was accompanied by an improved RMSE metric and perceptual image quality that were both superior to that obtained with either clinical or optimized OSEM. This may allow up to a four-fold reduction of the radiation dose to the patients in a PET study, as compared with current clinical practice. The proposed unsupervised parameter selection method provided useful estimates of the penalty weights for the selected phantoms' and patients' PET studies. In sum, the outcomes of this research indicate that ROS-HOTV-PAPA is an appropriate candidate for clinical applications and warrants further research.
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Affiliation(s)
- C Ross Schmidtlein
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY, 10065, USA
| | - Yizun Lin
- School of Mathematics, and Guangdong Provincial Key Lab of Computational Science, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, P R China
| | - Si Li
- School of Data and Computer Science, and Guangdong Provincial Key Lab of Computational Science, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou, 510275, P R China
| | - Andrzej Krol
- Department of Radiology, Department of Pharmacology, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, 13210, USA
| | - Bradley J Beattie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY, 10065, USA
| | - John L Humm
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY, 10065, USA
| | - Yuesheng Xu
- School of Data and Computer Science, and Guangdong Provincial Key Lab of Computational Science, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou, 510275, P R China.,Professor Emeritus of Mathematics, Syracuse University, Syracuse, NY, USA
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Tsai YJ, Huang HM, Fang YHD, Chang SI, Hsiao IT. Acceleration of MAP-EM algorithm via over-relaxation. Comput Med Imaging Graph 2014; 40:100-7. [PMID: 25465068 DOI: 10.1016/j.compmedimag.2014.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Revised: 10/10/2014] [Accepted: 11/03/2014] [Indexed: 11/27/2022]
Abstract
To improve the convergence rate of the effective maximum a posteriori expectation-maximization (MAP-EM) algorithm in tomographic reconstructions, this study proposes a modified MAP-EM which uses an over-relaxation factor to accelerate image reconstruction. The proposed method, called MAP-AEM, is evaluated and compared with the results for MAP-EM and for an ordered-subset algorithm, in terms of the convergence rate and noise properties. The results show that the proposed method converges numerically much faster than MAP-EM and with a speed that is comparable to that for an ordered-subset type method. The proposed method is effective in accelerating MAP-EM tomographic reconstruction.
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Affiliation(s)
- Yu-Jung Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Hsuan-Ming Huang
- Medical Physics Research Center, Institute of Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.
| | - Yu-Hua Dean Fang
- Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan.
| | - Shi-Ing Chang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Ing-Tsung Hsiao
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Physics Research Center, Institute of Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan; Molecular Imaging Center and Department of Nuclear Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
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Convergence optimization of parametric MLEM reconstruction for estimation of Patlak plot parameters. Comput Med Imaging Graph 2011; 35:407-16. [DOI: 10.1016/j.compmedimag.2011.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2010] [Revised: 10/14/2010] [Accepted: 01/10/2011] [Indexed: 11/23/2022]
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He X, Cheng L, Fessler JA, Frey EC. Regularized image reconstruction algorithms for dual-isotope myocardial perfusion SPECT (MPS) imaging using a cross-tracer prior. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:1169-83. [PMID: 20952334 PMCID: PMC3138082 DOI: 10.1109/tmi.2010.2087031] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
In simultaneous dual-isotope myocardial perfusion SPECT (MPS) imaging, data are simultaneously acquired to determine the distributions of two radioactive isotopes. The goal of this work was to develop penalized maximum likelihood (PML) algorithms for a novel cross-tracer prior that exploits the fact that the two images reconstructed from simultaneous dual-isotope MPS projection data are perfectly registered in space. We first formulated the simultaneous dual-isotope MPS reconstruction problem as a joint estimation problem. A cross-tracer prior that couples voxel values on both images was then proposed. We developed an iterative algorithm to reconstruct the MPS images that converges to the maximum a posteriori solution for this prior based on separable surrogate functions. To accelerate the convergence, we developed a fast algorithm for the cross-tracer prior based on the complete data OS-EM (COSEM) framework. The proposed algorithm was compared qualitatively and quantitatively to a single-tracer version of the prior that did not include the cross-tracer term. Quantitative evaluations included comparisons of mean and standard deviation images as well as assessment of image fidelity using the mean square error. We also evaluated the cross tracer prior using a three-class observer study with respect to the three-class MPS diagnostic task, i.e., classifying patients as having either no defect, reversible defect, or fixed defects. For this study, a comparison with conventional ordered subsets-expectation maximization (OS-EM) reconstruction with postfiltering was performed. The comparisons to the single-tracer prior demonstrated similar resolution for areas of the image with large intensity changes and reduced noise in uniform regions. The cross-tracer prior was also superior to the single-tracer version in terms of restoring image fidelity. Results of the three-class observer study showed that the proposed cross-tracer prior and the convergent algorithms improved the image quality of dual-isotope MPS images compared to OS-EM.
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Affiliation(s)
- Xin He
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287 USA ()
| | - Lishui Cheng
- Department of Biomedical Engineering and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287 USA ()
| | - Jeffrey A. Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA
| | - Eric C. Frey
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21287 USA ()
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Helvie MA. Digital mammography imaging: breast tomosynthesis and advanced applications. Radiol Clin North Am 2010; 48:917-29. [PMID: 20868894 DOI: 10.1016/j.rcl.2010.06.009] [Citation(s) in RCA: 149] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This article discusses recent developments in advanced derivative technologies associated with digital mammography. Digital breast tomosynthesis, its principles, development, and early clinical trials, are reviewed. Contrast-enhanced digital mammography and combined imaging systems with digital mammography and ultrasound are also discussed. Although all these methods are currently research programs, they hold promise for improving cancer detection and characterization if early results are confirmed by clinical trials.
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Affiliation(s)
- Mark A Helvie
- Department of Radiology, University of Michigan Health System, 1500 East Medical Center Drive, SPC 5326, Ann Arbor, MI 48109, USA.
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