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Verfaillie G, Rutten J, D'Asseler Y, Bacher K. Accuracy of patient-specific CT organ doses from Monte Carlo simulations: influence of CT-based voxel models. Phys Eng Sci Med 2024:10.1007/s13246-024-01422-z. [PMID: 38634980 DOI: 10.1007/s13246-024-01422-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/01/2024] [Indexed: 04/19/2024]
Abstract
Monte Carlo simulations using patient CT images as input are the gold standard to perform patient-specific dosimetry. However, in standard clinical practice patient's CT images are limited to the reconstructed CT scan range. In this study, organ dose calculations were performed with ImpactMC for chest and cardiac CT using whole-body and anatomy-specific voxel models to estimate the accuracy of CT organ doses based on the latter model. When the 3D patient model is limited to the CT scan range, CT organ doses from Monte Carlo simulations are the most accurate for organs entirely in the field of view. For these organs only the radiation dose related to scatter from the rest of the body is not incorporated. For organs lying partially outside the field of view organ doses are overestimated by not accounting for the non-irradiated tissue mass. This overestimation depends strongly on the amount of the organ volume located outside the field of view. To get a more accurate estimation of the radiation dose to these organs, the ICRP reference organ masses and densities could form a solution. Except for the breast, good agreement in dose was found for most organs. Voxel models generated from clinical CT examinations do not include the overscan in the z-direction. The availability of whole-body voxel models allowed to study this influence as well. As expected, overscan induces slightly higher organ doses.
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Affiliation(s)
- Gwenny Verfaillie
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium.
| | - Jeff Rutten
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Yves D'Asseler
- Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Klaus Bacher
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
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Carter LM, Ocampo Ramos JC, Olguin EA, Brown JL, Lafontaine D, Jokisch DW, Bolch WE, Kesner AL. MIRD Pamphlet No. 28, Part 2: Comparative Evaluation of MIRDcalc Dosimetry Software Across a Compendium of Diagnostic Radiopharmaceuticals. J Nucl Med 2023; 64:1295-1303. [PMID: 37268423 PMCID: PMC10394313 DOI: 10.2967/jnumed.122.264230] [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: 04/04/2022] [Revised: 03/21/2023] [Indexed: 06/04/2023] Open
Abstract
Radiopharmaceutical dosimetry is usually estimated via organ-level MIRD schema-style formalisms, which form the computational basis for commonly used clinical and research dosimetry software. Recently, MIRDcalc internal dosimetry software was developed to provide a freely available organ-level dosimetry solution that incorporates up-to-date models of human anatomy, addresses uncertainty in radiopharmaceutical biokinetics and patient organ masses, and offers a 1-screen user interface as well as quality assurance tools. The present work describes the validation of MIRDcalc and, secondarily, provides a compendium of radiopharmaceutical dose coefficients obtained with MIRDcalc. Biokinetic data for about 70 currently and historically used radiopharmaceuticals were obtained from the International Commission on Radiological Protection (ICRP) publication 128 radiopharmaceutical data compendium. Absorbed dose and effective dose coefficients were derived from the biokinetic datasets using MIRDcalc, IDAC-Dose, and OLINDA software. The dose coefficients obtained with MIRDcalc were systematically compared against the other software-derived dose coefficients and those originally presented in ICRP publication 128. Dose coefficients computed with MIRDcalc and IDAC-Dose showed excellent overall agreement. The dose coefficients derived from other software and the dose coefficients promulgated in ICRP publication 128 both were in reasonable agreement with the dose coefficients computed with MIRDcalc. Future work should expand the scope of the validation to include personalized dosimetry calculations.
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Affiliation(s)
- Lukas M Carter
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York;
| | - Juan C Ocampo Ramos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Edmond A Olguin
- Beth Israel Deaconess Medical Center, Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Justin L Brown
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Daniel Lafontaine
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Derek W Jokisch
- Department of Physics and Engineering, Francis Marion University, Florence, South Carolina; and
- Center for Radiation Protection Knowledge, Oak Ridge National Laboratory, Oak Ridge, Tennessee
| | - Wesley E Bolch
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Adam L Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
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Kesner AL, Carter LM, Ramos JCO, Lafontaine D, Olguin EA, Brown JL, President B, Jokisch DW, Fisher DR, Bolch WE. MIRD Pamphlet No. 28, Part 1: MIRDcalc-A Software Tool for Medical Internal Radiation Dosimetry. J Nucl Med 2023; 64:1117-1124. [PMID: 37268428 PMCID: PMC10315701 DOI: 10.2967/jnumed.122.264225] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 03/21/2023] [Indexed: 06/04/2023] Open
Abstract
Medical internal radiation dosimetry constitutes a fundamental aspect of diagnosis, treatment, optimization, and safety in nuclear medicine. The MIRD committee of the Society of Nuclear Medicine and Medical Imaging developed a new computational tool to support organ-level and suborgan tissue dosimetry (MIRDcalc, version 1). Based on a standard Excel spreadsheet platform, MIRDcalc provides enhanced capabilities to facilitate radiopharmaceutical internal dosimetry. This new computational tool implements the well-established MIRD schema for internal dosimetry. The spreadsheet incorporates a significantly enhanced database comprising details for 333 radionuclides, 12 phantom reference models (International Commission on Radiological Protection), 81 source regions, and 48 target regions, along with the ability to interpolate between models for patient-specific dosimetry. The software also includes sphere models of various composition for tumor dosimetry. MIRDcalc offers several noteworthy features for organ-level dosimetry, including modeling of blood source regions and dynamic source regions defined by user input, integration of tumor tissues, error propagation, quality control checks, batch processing, and report-preparation capabilities. MIRDcalc implements an immediate, easy-to-use single-screen interface. The MIRDcalc software is available for free download (www.mirdsoft.org) and has been approved by the Society of Nuclear Medicine and Molecular Imaging.
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Affiliation(s)
- Adam L Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York;
| | - Lukas M Carter
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Juan C Ocampo Ramos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daniel Lafontaine
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Edmond A Olguin
- Beth Israel Deaconess Medical Center, Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Justin L Brown
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Bonnie President
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
| | - Derek W Jokisch
- Department of Physics and Engineering, Francis Marion University, Florence, South Carolina
- Center for Radiation Protection Knowledge, Oak Ridge National Laboratory, Oak Ridge, Tennessee; and
| | - Darrell R Fisher
- University of Washington and Versant Medical Physics and Radiation Safety, Richland, Washington
| | - Wesley E Bolch
- J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, Florida
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Buoso S, Joyce T, Schulthess N, Kozerke S. MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation. J Cardiovasc Magn Reson 2023; 25:25. [PMID: 37076840 PMCID: PMC10116689 DOI: 10.1186/s12968-023-00934-z] [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: 09/16/2022] [Accepted: 03/15/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Standardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. To this end, we propose MRXCAT2.0 to generate synthetic data, covering healthy and pathological function, using a biophysical model. We exemplify the approach by generating cardiovascular magnetic resonance (CMR) images of healthy, infarcted, dilated and hypertrophic left-ventricular (LV) function. METHOD In MRXCAT2.0, the XCAT torso phantom is coupled with a statistical shape model, describing population (patho)physiological variability, and a biophysical model, providing known and detailed functional ground truth of LV morphology and function. CMR balanced steady-state free precession images are generated using MRXCAT2.0 while realistic image appearance is ensured by assigning texturized tissue properties to the phantom labels. FINDING Paired CMR image and ground truth data of LV function were generated with a range of LV masses (85-140 g), ejection fractions (34-51%) and peak radial and circumferential strains (0.45 to 0.95 and - 0.18 to - 0.13, respectively). These ranges cover healthy and pathological cases, including infarction, dilated and hypertrophic cardiomyopathy. The generation of the anatomy takes a few seconds and it improves on current state-of-the-art models where the pathological representation is not explicitly addressed. For the full simulation framework, the biophysical models require approximately two hours, while image generation requires a few minutes per slice. CONCLUSION MRXCAT2.0 offers synthesis of realistic images embedding population-based anatomical and functional variability and associated ground truth parameters to facilitate a standardized assessment of CMR acquisition, reconstruction and processing methods.
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Affiliation(s)
- Stefano Buoso
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland.
| | - Thomas Joyce
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| | - Nico Schulthess
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
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Caravaca J, Peter R, Yang J, Gunther C, Antonio Camara Serrano J, Nostrand C, Steri V, Seo Y. Comparison and calibration of dose delivered by 137Cs and x-ray irradiators in mice. Phys Med Biol 2022; 67:10.1088/1361-6560/ac9e88. [PMID: 36317316 PMCID: PMC9933773 DOI: 10.1088/1361-6560/ac9e88] [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: 07/15/2022] [Accepted: 10/28/2022] [Indexed: 11/07/2022]
Abstract
Objective.The Office of Radiological Security, U.S. Department of Energy's National Nuclear Security Administration, is implementing a radiological risk reduction program which seeks to minimize or eliminate the use of high activity radiological sources, including137Cs, by replacing them with non-radioisotopic technologies, such as x-ray irradiators. The main goal of this paper is to evaluate the equivalence of the dose delivered by gamma- and x-ray irradiators in mice using experimental measurements and Monte Carlo simulations. We also propose a novel biophantom as anin situdose calibration method.Approach.We irradiated mouse carcasses and 3D-printed mouse biophantoms in a137Cs irradiator (Mark I-68) and an x-ray irradiator (X-Rad320) at three voltages (160 kVp, 225 kVp and 320 kVp) and measured the delivered radiation dose. A Geant4-based Monte Carlo model was developed and validated to provide a comprehensive picture of gamma- and x-ray irradiation in mice.Main Results.Our Monte Carlo model predicts a uniform dose delivered in soft-tissue for all the explored irradiation programs and in agreement with the absolute dose measurements. Our Monte Carlo model shows an energy-dependent difference between dose in bone and in soft tissue that decreases as photon energy increases. Dose rate depends on irradiator and photon energy. We observed a deviation of the measured dose from the target value of up to -9% for the Mark I-68, and up to 35% for the X-Rad320. The dose measured in the 3D-printed phantoms are equivalent to that in the carcasses within 6% uncertainty.Significance.Our results suggest that 320 kVp irradiation is a good candidate to substitute137Cs irradiation barring a few caveats. There is a significant difference between measured and targeted doses for x-ray irradiation that suggests a strong need forin situcalibration, which can be achieved with 3D-printed mouse biophantoms. A dose correction is necessary for bone doses, which can be provided by a Monte Carlo calculation. Finally, the biological implications of the differences in dose rates and dose per photon for the different irradiation methods should be carefully assessed for each small-animal irradiation experiment.
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Affiliation(s)
- Javier Caravaca
- Physics Research Laboratory, University of California, San Francisco, United States of America
| | - Robin Peter
- Physics Research Laboratory, University of California, San Francisco, United States of America
- Department of Nuclear Engineering, University of California, Berkeley, United States of America
| | - Jaewon Yang
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Chad Gunther
- C&C Irradiator Service, LLC, Washington, DC. United States of America
| | - Juan Antonio Camara Serrano
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, United States of America
| | | | - Veronica Steri
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, United States of America
| | - Youngho Seo
- Physics Research Laboratory, University of California, San Francisco, United States of America
- Department of Nuclear Engineering, University of California, Berkeley, United States of America
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Martinez-Movilla A, Mix M, Torres-Espallardo I, Teijeiro E, Bello P, Baltas D, Martí-Bonmatí L, Carles M. Comparison of protocols with respiratory-gated (4D) motion compensation in PET/CT: open-source package for quantification of phantom image quality. EJNMMI Phys 2022; 9:80. [PMID: 36394640 PMCID: PMC9672236 DOI: 10.1186/s40658-022-00509-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 10/31/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Patient's breathing affects the quality of chest images acquired with positron emission tomography/computed tomography (PET/CT) studies. Movement correction is required to optimize PET quantification in clinical settings. We present a reproducible methodology to compare the impact of different movement compensation protocols on PET image quality. Static phantom images were set as reference values, and recovery coefficients (RCs) were calculated from motion compensated images for the phantoms in respiratory movement. Image quality was evaluated in terms of: (1) volume accuracy (VA) with the NEMA phantom; (2) concentration accuracy (CA) by six refillable inserts within the electron density CIRS phantom; and (3) spatial resolution (R) with the Jaszczak phantom. Three different respiratory patterns were applied to the phantoms. We developed an open-source package to automatically analyze VA, CA and R. We compared 10 different movement compensation protocols available in the Philips Gemini TF-64 PET/CT (4-, 6-, 8- and 10-time bins, 20%-, 30%-, 40%-window width in Inhale and Exhale). RESULTS The homemade package provided RC values for VA, CA and R of 102 PET images in less than 5 min. Results of the comparison of the 10 different protocols demonstrated the feasibility of the proposed method for quantifying the variations observed qualitatively. Overall, prospective protocols showed better motion compensation than retrospective. The best performance was obtained for the protocol Exhale 30% (0.3 s after maximum Exhale position and window width of 30%) with RC[Formula: see text], RC[Formula: see text] and RC[Formula: see text]. Among retrospective protocols, 8 Phase protocol showed the best performance. CONCLUSION We provided an open-source package able to automatically evaluate the impact of motion compensation methods on PET image quality. A setup based on commonly available experimental phantoms is recommended. Its application for the comparison of 10 time-based approaches showed that Exhale 30% protocol had the best performance. The proposed framework is not specific to the phantoms and protocols presented on this study.
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Affiliation(s)
- Andrea Martinez-Movilla
- Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB), Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain
| | - Michael Mix
- Department of Nuclear Medicine, University Medical Center Freiburg, Faculty of Medicine, 79106, Freiburg, Germany
| | - Irene Torres-Espallardo
- Department of Nuclear Medicine, Medical Imaging Clinical Area, La Fe University and Polytechnic Hospital, 46026, Valencia, Spain
| | - Elena Teijeiro
- Department of Nuclear Medicine, Medical Imaging Clinical Area, La Fe University and Polytechnic Hospital, 46026, Valencia, Spain
| | - Pilar Bello
- Department of Nuclear Medicine, Medical Imaging Clinical Area, La Fe University and Polytechnic Hospital, 46026, Valencia, Spain
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Luis Martí-Bonmatí
- Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB), Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain
| | - Montserrat Carles
- Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB), Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain.
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Caravaca J, Huh Y, Gullberg GT, Seo Y. Compton and proximity imaging of 225Ac in vivo with a CZT gamma camera: a proof of principle with simulations. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:904-915. [PMID: 36338821 PMCID: PMC9632644 DOI: 10.1109/trpms.2022.3166116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In vivo imaging of 225Ac is a major challenge in the development of targeted alpha therapy radiopharmaceuticals due to the extremely low injected doses. In this paper, we present the design of a multi-modality gamma camera that integrates both proximity and Compton imaging in order to achieve the demanding sensitivities required to image 225Ac with good image quality. We consider a dual-head camera, each of the heads consisting of two planar cadmium zinc telluride detectors acting as scatterer and absorber for Compton imaging, and with the scatterer practically in contact with the subject to allow for proximity imaging. We optimize the detector's design and characterize the detector's performance using Monte Carlo simulations. We show that Compton imaging can resolve features of up to 1.5 mm for hot rod phantoms with an activity of 1 μCi, and can reconstruct 3D images of a mouse injected with 0.5 μCi after a 15 minutes exposure and with a single bed position, for both 221Fr and 213Bi. Proximity imaging is able to resolve two 1 mm-radius sources of less than 0.1 μCi separated by 1 cm and at 1 mm from the detector, as well as it can provide planar images of 221Fr and 213Bi biodistributions of the mouse phantom in 5 minutes.
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Affiliation(s)
- Javier Caravaca
- Department of Radiology and Biomedical Imaging of the University of California San Francisco in San Francisco (CA) USA
| | - Yoonsuk Huh
- Department of Radiology and Biomedical Imaging of the University of California San Francisco in San Francisco (CA) USA
| | - Grant T Gullberg
- Department of Radiology and Biomedical Imaging of the University of California San Francisco in San Francisco (CA) USA
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging of the University of California San Francisco in San Francisco (CA) USA
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8
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A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Sci Rep 2022; 12:8682. [PMID: 35606398 PMCID: PMC9127105 DOI: 10.1038/s41598-022-10335-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 04/05/2022] [Indexed: 11/28/2022] Open
Abstract
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
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Sugiura T, Tanaka R, Samei E, Segars WP, Abadi E, Kasahara K, Ohkura N, Tamura M, Matsumoto I. Quantitative analysis of changes in lung density by dynamic chest radiography in association with CT values: a virtual imaging study and initial clinical corroboration. Radiol Phys Technol 2022; 15:45-53. [PMID: 35091991 PMCID: PMC9536504 DOI: 10.1007/s12194-021-00648-w] [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: 07/24/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 10/19/2022]
Abstract
Dynamic chest radiography (DCR) identifies pulmonary impairments as decreased changes in radiographic lung density during respiration (Δpixel values), but not as scaled/standardized computed tomography (CT) values. Quantitative analysis correlated with CT values is beneficial for a better understanding of Δpixel values in DCR-based assessment of pulmonary function. The present study aimed to correlate Δpixel values from DCR with changes in CT values during respiration (ΔCT values) through a computer-based phantom study. A total of 20 four-dimensional computational phantoms during forced breathing were created to simulate both CT and projection images of the same virtual patients. The Δpixel and ΔCT values of the lung fields were correlated on a regression line, and the inclination was statistically evaluated to determine whether there were significant differences among physical types, sex, and breathing methods. The resulting conversion expression was also assessed in the DCR images of 37 patients. The resulting Δpixel values for 30/37 (81%) real patients, 6/7 (86%) normal controls, and 24/30 (80%) chronic obstructive pulmonary disorder patients were within the range of ΔCT values ± standard deviation (SD) reported in a previous study. In addition, no significant differences were detected for each condition of thoracic breathing, suggesting that the same regression line inclination values measured across the entire lung can be used for the conversion of Δpixel values, providing a quantitative analysis that can be correlated with ΔCT values. The developed conversion expression may be helpful for improving the understanding of respiratory changes using radiographic lung densities from DCR-based assessments of pulmonary function.
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Affiliation(s)
- Teruyo Sugiura
- Clinical Radiology Service Unit, Kyoto University Hospital, 54 Kawaharacho, Syogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
- College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Rie Tanaka
- College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, Ishikawa, 920-0942, Japan.
| | - Ehsan Samei
- Carl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, USA
| | - William Paul Segars
- Carl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, USA
| | - Ehsan Abadi
- Carl E Ravin Advanced Imaging Labs, Department of Radiology, Duke University, Durham, NC, 27705, USA
| | - Kazuo Kasahara
- Department of Respiratory Medicine, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Noriyuki Ohkura
- Department of Respiratory Medicine, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Masaya Tamura
- Department of Thoracic Surgery, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Isao Matsumoto
- Department of Thoracic Surgery, Kanazawa University, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan
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10
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Pelt DM, Hendriksen AA, Batenburg KJ. Foam-like phantoms for comparing tomography algorithms. JOURNAL OF SYNCHROTRON RADIATION 2022; 29:254-265. [PMID: 34985443 PMCID: PMC8733984 DOI: 10.1107/s1600577521011322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/27/2021] [Indexed: 06/14/2023]
Abstract
Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms.
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Affiliation(s)
| | | | - Kees Joost Batenburg
- LIACS, Leiden University, Leiden, The Netherlands
- Computational Imaging Group, CWI, Amsterdam, The Netherlands
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11
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Elshami W, Tekin HO, Issa SAM, Abuzaid MM, Zakaly HMH, Issa B, Ene A. Impact of Eye and Breast Shielding on Organ Doses During Cervical Spine Radiography: Design and Validation of MIRD Computational Phantom. Front Public Health 2021; 9:751577. [PMID: 34746086 PMCID: PMC8569301 DOI: 10.3389/fpubh.2021.751577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose: The study aimed to design and validate computational phantoms (MIRD) using the MCNPX code to assess the impact of shielding on organ doses. Method: To validate the optimized phantom, the obtained results were compared with experimental results. The validation of the optimized MIRD phantom was provided by using the results of a previous anthropomorphic phantom study. MIRD phantom was designed by considering the parameters used in the anthropomorphic phantom study. A test simulation was performed to compare the dose reduction percentages (%) between the experimental anthropomorphic phantom study and the MCNPX-MIRD phantom. The simulation was performed twice, with and without shielding materials, using the same number and locations of the detector. Results: The absorbed dose amounts were directly extracted from the required organ and tissue cell parts of output files. Dose reduction percentages between the simulation with shielding and simulation without shielding were compared. The highest dose reduction was noted in the thymus (95%) and breasts (88%). The obtained dose reduction percentages between the anthropomorphic phantom study and the MCNPX-MIRD phantom were highly consistent and correlated values with experimental anthropomorphic data. Both methods showed Relative Difference (%) ranges between 0.88 and 2.22. Moreover, the MCNPX-MIRD optimized phantom provides detailed dose analysis for target and non-target organs and can be used to assess the efficiency of shielding in radiological examination. Conclusion: Shielding breasts and eyes during cervical radiography reduced the radiation dose to many organs. The decision to not shield patients should be based on research evidence as this approach does not apply to all cases.
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Affiliation(s)
- Wiam Elshami
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Huseyin Ozan Tekin
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Shams A. M. Issa
- Physics Department, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia
- Physics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
| | - Mohamed M. Abuzaid
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Hesham M. H. Zakaly
- Physics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
- Department of Experimental Physics, Institute of Physics and Technology, Ural Federal University, Yekaterinburg, Russia
| | - Bashar Issa
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Antoaneta Ene
- Department of Chemistry, Physics and Environment, Faculty of Sciences and Environment, INPOLDE Research Center, Dunarea de Jos University of Galati, Galati, Romania
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12
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Morales MA, van den Boomen M, Nguyen C, Kalpathy-Cramer J, Rosen BR, Stultz CM, Izquierdo-Garcia D, Catana C. DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics. Front Cardiovasc Med 2021; 8:730316. [PMID: 34540923 PMCID: PMC8446607 DOI: 10.3389/fcvm.2021.730316] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/10/2021] [Indexed: 12/04/2022] Open
Abstract
Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough ad hoc implementation that we carefully planned specific for strain quantification, tested, and compared to other potential alternatives. The optimal configuration was trained using healthy and cardiovascular disease (CVD) subjects (n = 150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD test subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 10 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was good to excellent (>0.75) for all global measures and most polar map segments. In conclusion, we developed and evaluated the first end-to-end learning-based workflow for automated strain analysis from cine-MRI data to quantitatively characterize cardiac mechanics of healthy and CVD subjects.
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Affiliation(s)
- Manuel A Morales
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Maaike van den Boomen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.,Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Christopher Nguyen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bruce R Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Collin M Stultz
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.,Division of Cardiology, Massachusetts General Hospital, Boston, MA, United States
| | - David Izquierdo-Garcia
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Ciprian Catana
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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13
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An in-silico method to predict and quantify the effect of gold nanoparticles in X-ray imaging. Phys Med 2021; 89:160-168. [PMID: 34380106 DOI: 10.1016/j.ejmp.2021.07.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 07/21/2021] [Accepted: 07/28/2021] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Over the last few years studies are conducted, highlighting the feasibility of Gold Nanoparticles (GNPs) to be used in clinical CT imaging and as an efficient contrast agent for cancer research. After ensuring that GNPs formulations are appropriate for in vivo or clinical use, the next step is to determine the parameters for an X-ray system's optimal contrast for applications and to extract quantitative information. There is currently a gap and need to exploit new X-ray imaging protocols and processing algorithms, through specific models avoiding trial-and-error procedures and provide an imaging prognosis tool. Such a model can be used to confirm the accumulation of GNPs in target organs before radiotherapy treatments with a system easily available in hospitals, as low energy X-rays. METHODS In this study a complete, easy-to-use, simulation platform is designed and built, where simple parameters, as the X-ray's specifications and experimentally defined biodistributions of specific GNPs are imported. The induced contrast and images can be exported, and accurate quantification can be performed. This platform is based on the GATE Monte Carlo simulation toolkit, based on the GEANT4 toolkit and the MOBY phantom, a realistic 4D digital mouse. RESULTS We have validated this simulation platform to predict the contrast induction and minimum detectable concentration of GNPs on any given X-ray system. The study was applied to preclinical studies but is also expandable to clinical studies. CONCLUSIONS According to our knowledge, no other such validated simulation model currently exists, and this model could help radiology imaging with GNPs to be truly deployed.
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14
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Lévêque L, Outtas M, Liu H, Zhang L. Comparative study of the methodologies used for subjective medical image quality assessment. Phys Med Biol 2021; 66. [PMID: 34225264 DOI: 10.1088/1361-6560/ac1157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 07/05/2021] [Indexed: 11/12/2022]
Abstract
Healthcare professionals have been increasingly viewing medical images and videos in their routine clinical practice, and this in a wide variety of environments. Both the perception and interpretation of medical visual information, across all branches of practice or medical specialties (e.g. diagnostic, therapeutic, or surgical medicine), career stages, and practice settings (e.g. emergency care), appear to be critical for patient care. However, medical images and videos are not self-explanatory and, therefore, need to be interpreted by humans, i.e. medical experts. In addition, various types of degradations and artifacts may appear during image acquisition or processing, and consequently affect medical imaging data. Such distortions tend to impact viewers' quality of experience, as well as their clinical practice. It is accordingly essential to better understand how medical experts perceive the quality of visual content. Thankfully, progress has been made in the recent literature towards such understanding. In this article, we present an up-to-date state-of the-art of relatively recent (i.e. not older than ten years old) existing studies on the subjective quality assessment of medical images and videos, as well as research works using task-based approaches. Furthermore, we discuss the merits and drawbacks of the methodologies used, and we provide recommendations about experimental designs and statistical processes to evaluate the perception of medical images and videos for future studies, which could then be used to optimise the visual experience of image readers in real clinical practice. Finally, we tackle the issue of the lack of available annotated medical image and video quality databases, which appear to be indispensable for the development of new dedicated objective metrics.
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Affiliation(s)
- Lucie Lévêque
- Nantes Laboratory of Digital Sciences (LS2N), University of Nantes, Nantes, France
| | - Meriem Outtas
- Department of Industrial Computer Science and Electronics, National Institute of Applied Sciences, Rennes, France
| | - Hantao Liu
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Lu Zhang
- Department of Industrial Computer Science and Electronics, National Institute of Applied Sciences, Rennes, France
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15
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Paredes-Pacheco J, López-González FJ, Silva-Rodríguez J, Efthimiou N, Niñerola-Baizán A, Ruibal Á, Roé-Vellvé N, Aguiar P. SimPET-An open online platform for the Monte Carlo simulation of realistic brain PET data. Validation for 18 F-FDG scans. Med Phys 2021; 48:2482-2493. [PMID: 33713354 PMCID: PMC8252452 DOI: 10.1002/mp.14838] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 12/11/2022] Open
Abstract
Purpose SimPET (www.sim‐pet.org) is a free cloud‐based platform for the generation of realistic brain positron emission tomography (PET) data. In this work, we introduce the key features of the platform. In addition, we validate the platform by performing a comparison between simulated healthy brain FDG‐PET images and real healthy subject data for three commercial scanners (GE Advance NXi, GE Discovery ST, and Siemens Biograph mCT). Methods The platform provides a graphical user interface to a set of automatic scripts taking care of the code execution for the phantom generation, simulation (SimSET), and tomographic image reconstruction (STIR). We characterize the performance using activity and attenuation maps derived from PET/CT and MRI data of 25 healthy subjects acquired with a GE Discovery ST. We then use the created maps to generate synthetic data for the GE Discovery ST, the GE Advance NXi, and the Siemens Biograph mCT. The validation was carried out by evaluating Bland‐Altman differences between real and simulated images for each scanner. In addition, SPM voxel‐wise comparison was performed to highlight regional differences. Examples for amyloid PET and for the generation of ground‐truth pathological patients are included. Results The platform can be efficiently used for generating realistic simulated FDG‐PET images in a reasonable amount of time. The validation showed small differences between SimPET and acquired FDG‐PET images, with errors below 10% for 98.09% (GE Discovery ST), 95.09% (GE Advance NXi), and 91.35% (Siemens Biograph mCT) of the voxels. Nevertheless, our SPM analysis showed significant regional differences between the simulated images and real healthy patients, and thus, the use of the platform for converting control subject databases between different scanners requires further investigation. Conclusions The presented platform can potentially allow scientists in clinical and research settings to perform MC simulation experiments without the need for high‐end hardware or advanced computing knowledge and in a reasonable amount of time.
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Affiliation(s)
- José Paredes-Pacheco
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Francisco Javier López-González
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, General Foundation of the University of Málaga, Málaga, Spain
| | - Jesús Silva-Rodríguez
- Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain.,R&D Department, Qubiotech Health Intelligence SL, A Coruña, Galicia, Spain
| | - Nikos Efthimiou
- Positron Emission Tomography Research Centre, University of Hull, Hull, HU6 7RX, UK
| | - Aida Niñerola-Baizán
- Nuclear Medicine Department, Hospital Clinic Barcelona, Universitat de Barcelona, Barcelona, Spain.,Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Álvaro Ruibal
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
| | - Núria Roé-Vellvé
- Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Pablo Aguiar
- Radiology and Psychiatry Department, Faculty of Medicine, Universidade de Santiago de Compostela, Galicia, Spain.,Nuclear Medicine Department & Molecular Imaging Research Group, University Hospital (SERGAS) & Health Research Institute of Santiago de Compostela (IDIS), Galicia, Spain
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16
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Carter LM, Camilo Ocampo Ramos J, Bolch WE, Lewis JS, Kesner AL. Technical Note: Patient-morphed mesh-type phantoms to support personalized nuclear medicine dosimetry - a proof of concept study. Med Phys 2021; 48:2018-2026. [PMID: 33595863 DOI: 10.1002/mp.14784] [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: 06/19/2020] [Revised: 01/03/2021] [Accepted: 02/12/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Current standard practice for clinical radionuclide dosimetry utilizes reference phantoms, where defined organ dimensions represent population averages for a given sex and age. Greater phantom personalization would support more accurate dose estimations and personalized dosimetry. Tailoring phantoms is traditionally accomplished using operator-intensive organ-level segmentation of anatomic images. Modern mesh phantoms provide enhanced anatomical realism, which has motivated their integration within Monte Carlo codes. Here, we present an automatable strategy for generating patient-specific phantoms/dosimetry using intensity-based deformable image registration between mesh reference phantoms and patient CT images. This work demonstrates a proof-of-concept personalized dosimetry workflow, presented in comparison to the manual segmentation approach. METHODS A linear attenuation coefficient phantom was generated by resampling the PSRK-Man reference phantom onto a voxel grid and defining organ regions with corresponding Hounsfield unit (HU) reference values. The HU phantom was co-registered with a patient CT scan using Plastimatch B-spline deformable registration. In parallel, major organs were manually contoured to generate a "ground truth" patient-specific phantom for comparisons. Monte Carlo derived S-values, which support nuclear medicine dosimetry, were calculated using both approaches and compared. RESULTS Application of the derived B-spline transform to the polygon vertices comprising the PSRK-Man yielded a deformed variant more closely matching the patient's body contour and most organ volumes as-evaluated by Hausdorff distance and Dice metrics. S-values computed for fluorine-18 for the deformed phantom using the Particle and Heavy Ion Transport code System showed improved agreement with those derived from the patient-specific analog. CONCLUSIONS Deformable registration techniques can be used to create a personalized phantom and better support patient-specific dosimetry. This method is shown to be easier and faster than manual segmentation. Our study is limited to a proof-of-concept scope, but demonstrates that integration of personalized phantoms into clinical dosimetry workflows can reasonably be achieved when anatomical images (CT) are available.
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Affiliation(s)
- Lukas M Carter
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Wesley E Bolch
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Jason S Lewis
- Department of Radiology, Program in Pharmacology and the Radiochemistry and Molecular Imaging Probes Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology and Department of Pharmacology, Weill Cornell Medical College, New York, NY, USA
| | - Adam L Kesner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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17
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Sung Y, Nelson B, Shanblatt ER, Gupta R, McCollough CH, Graves WS. Wave optics simulation of grating-based X-ray phase-contrast imaging using 4D Mouse Whole Body (MOBY) phantom. Med Phys 2020; 47:5761-5771. [PMID: 32969031 DOI: 10.1002/mp.14479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Demonstrate realistic simulation of grating-based x-ray phase-contrast imaging (GB-XPCI) using wave optics and the four-dimensional Mouse Whole Body (MOBY) phantom defined with non-uniform rational B-splines (NURBS). METHODS We use a full-wave approach, which uses wave optics for x-ray wave propagation from the source to the detector. This forward imaging model can be directly applied to NURBS-defined numerical phantoms such as MOBY. We assign the material properties (attenuation coefficient and electron density) of each model part using the data for adult human tissues. The Poisson noise is added to the simulated images based on the calculated photon flux at each pixel. RESULTS We simulate the intensity images of the MOBY phantom for eight different grating positions. From the simulated images, we calculate the absorption, differential phase, and normalized visibility contrast images. We also predict how the image quality is affected by different exposure times. CONCLUSIONS GB-XPCI can be simulated with the full-wave approach and a realistic numerical phantom defined with NURBS.
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Affiliation(s)
- Yongjin Sung
- College of Engineering & Applied Science, University of Wisconsin-Milwaukee, 3200 North Cramer Street, Milwaukee, WI, 53211, USA
| | - Brandon Nelson
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Elisabeth R Shanblatt
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Rajiv Gupta
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Cynthia H McCollough
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - William S Graves
- Department of Physics, Arizona State University, 550 East Tyler Drive, Tempe, AZ, 85287, USA
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18
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Harrison RL, Elston BF, Byrd DW, Alessio AM, Swanson KR, Kinahan PE. Technical Note: A digital reference object representing Hoffman's 3D brain phantom for PET scanner simulations. Med Phys 2020; 47:1174-1180. [PMID: 31913507 DOI: 10.1002/mp.14012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 11/06/2019] [Accepted: 11/12/2019] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom. METHODS The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance. RESULTS We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement. CONCLUSIONS This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.
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Affiliation(s)
- Robert L Harrison
- Department of Radiology, University of Washington Medical Center, Box 357987, Seattle, WA, 98195-7987, USA
| | | | - Darrin W Byrd
- Department of Radiology, University of Washington Medical Center, Box 357987, Seattle, WA, 98195-7987, USA
| | - Adam M Alessio
- Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, Bioengineering Building, East Lansing, MI, 48824, USA
| | - Kristin R Swanson
- Mayo Clinic Arizona, Support Services Bldg. (SSB) 2-700, Phoenix, AZ, 85054, USA
| | - Paul E Kinahan
- Department of Radiology, University of Washington Medical Center, Box 357987, Seattle, WA, 98195-7987, USA
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PET/CT-guided biopsy with respiratory motion correction. Int J Comput Assist Radiol Surg 2019; 14:2187-2198. [PMID: 31512193 DOI: 10.1007/s11548-019-02047-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 07/30/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE Given the ability of positron emission tomography (PET) imaging to localize malignancies in heterogeneous tumors and tumors that lack an X-ray computed tomography (CT) correlate, combined PET/CT-guided biopsy may improve the diagnostic yield of biopsies. However, PET and CT images are naturally susceptible to problems due to respiratory motion, leading to imprecise tumor localization and shape distortion. To facilitate PET/CT-guided needle biopsy, we developed and investigated the feasibility of a workflow that allows to bring PET image guidance into interventional CT suite while accounting for respiratory motion. METHODS The performance of PET/CT respiratory motion correction using registered and summed phases method was evaluated through computer simulations using the mathematical 4D extended cardiac-torso phantom, with motion simulated from real respiratory traces. The performance of PET/CT-guided biopsy procedure was evaluated through operation on a physical anthropomorphic phantom. Vials containing radiolabeled 18F-fluorodeoxyglucose were placed within the physical phantom thorax as biopsy targets. We measured the average distance between target center and the simulated biopsy location among multiple trials to evaluate the biopsy localization accuracy. RESULTS The computer simulation results showed that the RASP method generated PET images with a significantly reduced noise of 0.10 ± 0.01 standardized uptake value (SUV) as compared to an end-of-expiration image noise of 0.34 ± 0.04 SUV. The respiratory motion increased the apparent liver lesion size from 5.4 ± 1.1 to 35.3 ± 3.0 cc. The RASP algorithm reduced this to 15.7 ± 3.7 cc. The distances between the centroids for the static image lesion and two moving lesions in the liver and lung, when reconstructed with the RASP algorithm, were 0.83 ± 0.72 mm and 0.42 ± 0.72 mm. For the ungated imaging, these values increased to 3.48 ± 1.45 mm and 2.5 ± 0.12 mm, respectively. For the ungated imaging, this increased to 1.99 ± 1.72 mm. In addition, the lesion activity estimation (e.g., SUV) was accurate and constant for images reconstructed using the RASP algorithm, whereas large activity bias and variations (± 50%) were observed for lesions in the ungated images. The physical phantom studies demonstrated a biopsy needle localization error of 2.9 ± 0.9 mm from CT. Combined with the localization errors due to respiration for the PET images from simulations, the overall estimated lesion localization error would be 3.08 mm for PET-guided biopsies images using RASP and 3.64 mm when using ungated PET images. In other words, RASP reduced the localization error by approximately 0.6 mm. The combined error analysis showed that replacing the standard end-of-expiration images with the proposed RASP method in PET/CT-guided biopsy workflow yields comparable lesion localization accuracy and reduced image noise. CONCLUSION The RASP method can produce PET images with reduced noise, attenuation artifacts and respiratory motion, resulting in more accurate lesion localization. Testing the PET/CT-guided biopsy workflow using computer simulation and physical phantoms with respiratory motion, we demonstrated that guided biopsy procedure with the RASP method can benefit from improved PET image quality due to noise reduction, without compromising the accuracy of lesion localization.
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Heinrich MP, Stille M, Buzug TM. Residual U-Net Convolutional Neural Network Architecture for Low-Dose CT Denoising. CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 2018. [DOI: 10.1515/cdbme-2018-0072] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractLow-dose CT has received increasing attention in the recent years and is considered a promising method to reduce the risk of cancer in patients. However, the reduction of the dosage leads to quantum noise in the raw data, which is carried on in the reconstructed images. Two different multilayer convolutional neural network (CNN) architectures for the denoising of CT images are investigated. ResFCN is based on a fully-convolutional network that consists of three blocks of 5×5 convolutions filters and a ResUNet that is trained with 10 convolutional blocks that are arranged in a multi-scale fashion. Both architectures feature a residual connection of the input image to ease learning. Training images are based on realistic simulations by using the XCAT phantom. The ResUNet approach shows the most promising results with a peak signal to noise ratio of 44.00 compared to ResFCN with 41.79.
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Affiliation(s)
| | - Maik Stille
- 2Institute of Medical Engineering, University of Lübeck,Lübeck, Germany
| | - Thorsten M. Buzug
- 2Institute of Medical Engineering, University of Lübeck,Lübeck, Germany
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21
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Wang H, Sun X, Wu T, Li C, Chen Z, Liao M, Li M, Yan W, Huang H, Yang J, Tan Z, Hui L, Liu Y, Pan H, Qu Y, Chen Z, Tan L, Yu L, Shi H, Huo L, Zhang Y, Tang X, Zhang S, Liu C. Deformable torso phantoms of Chinese adults for personalized anatomy modelling. J Anat 2018; 233:121-134. [PMID: 29663370 PMCID: PMC5987821 DOI: 10.1111/joa.12815] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2018] [Indexed: 11/26/2022] Open
Abstract
In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.
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Affiliation(s)
- Hongkai Wang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Xiaobang Sun
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
- Department of Information TechnologyUniversity of JyväskyläJyväskyläFinland
| | - Tongning Wu
- China Academy of Industry and Communications TechnologyBeijingChina
| | - Congsheng Li
- China Academy of Industry and Communications TechnologyBeijingChina
| | - Zhonghua Chen
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Meiying Liao
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Mengci Li
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Wen Yan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Hui Huang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Jia Yang
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Ziyu Tan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Libo Hui
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Yue Liu
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Hang Pan
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Yue Qu
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Zhaofeng Chen
- Department of Biomedical EngineeringFaculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianLiaoningChina
| | - Liwen Tan
- Institute of Digital MedicineThird Military Medical UniversityChongqingChina
| | - Lijuan Yu
- The Affiliated Cancer Hospital of Hainan Medical CollegeHaikouHainanChina
| | - Hongcheng Shi
- Department of Nuclear MedicineZhongshan HospitalFudan UniversityShanghaiChina
| | - Li Huo
- Department of Nuclear MedicinePeking Union Medical College HospitalBeijingChina
| | - Yanjun Zhang
- Department of Nuclear Medicinethe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
| | - Xin Tang
- Trauma Department of Orthopaedicsthe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
| | - Shaoxiang Zhang
- Institute of Digital MedicineThird Military Medical UniversityChongqingChina
| | - Changjian Liu
- Trauma Department of Orthopaedicsthe First Affiliated Hospital of Dalian Medical UniversityDalianLiaoningChina
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Polycarpou I, Soultanidis G, Tsoumpas C. Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:703-711. [PMID: 29533892 DOI: 10.1109/tmi.2017.2768130] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, 18F-FDG and 68Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions.
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Abadi E, Segars WP, Sturgeon GM, Roos JE, Ravin CE, Samei E. Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:693-702. [PMID: 29533891 PMCID: PMC6434530 DOI: 10.1109/tmi.2017.2769640] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The purpose of this paper was to extend the extended cardiac-torso (XCAT) series of computational phantoms to include a detailed lung architecture including airways and pulmonary vasculature. Eleven XCAT phantoms of varying anatomy were used in this paper. The lung lobes and initial branches of the airways, pulmonary arteries, and veins were previously defined in each XCAT model. These models were extended from the initial branches of the airways and vessels to the level of terminal branches using an anatomically-based volume-filling branching algorithm. This algorithm grew the airway and vasculature branches separately and iteratively without intersecting each other using cylindrical models with diameters estimated by order-based anatomical measurements. Geometrical features of the extended branches were compared with the literature anatomy values to quantitatively evaluate the models. These features include branching angle, length to diameter ratio, daughter to parent diameter ratio, asymmetrical branching pattern, diameter, and length ratios. The XCAT phantoms were then used to simulate CT images to qualitatively compare them with the original phantom images. The proposed growth model produced 46369 ± 12521 airways, 44737 ± 11773 arteries, and 39819 ± 9988 veins to the XCAT phantoms. Furthermore, the growth model was shown to produce asymmetrical airway, artery, and vein networks with geometrical attributes close to morphometry and model based studies. The simulated CT images of the phantoms were judged to be more realistic, including more airways and pulmonary vessels compared with the original phantoms. Future work will seek to add a heterogeneous parenchymal background into the XCAT lungs to make the phantoms even more representative of human anatomy, paving the way towards the use of XCAT models as a tool to virtually evaluate the current and emerging medical imaging technologies.
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Optimal dose reduction algorithm using an attenuation-based tube current modulation method for cone-beam CT imaging. PLoS One 2018; 13:e0192933. [PMID: 29447260 PMCID: PMC5814001 DOI: 10.1371/journal.pone.0192933] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 01/25/2018] [Indexed: 11/19/2022] Open
Abstract
To reduce the radiation dose given to patients, a tube current modulation (TCM) method has been widely used in diagnostic CT systems. However, the TCM method has not yet been applied to a kV-CBCT system on a LINAC machine. The purpose of this study is to investigate if a TCM method would be desirable in a kV-CBCT system for image-guided radiation therapy (IGRT) or not. We have developed an attenuation–based TCM method using prior knowledge from planning CT images of patients. The TCM method can provide optimized dose reductions without degrading image quality for kV-CBCT imaging. Here, we investigate whether or not our suggested TCM method is desirable to use in kV-CBCT systems to confirm and revise the exact position of a patient for IGRT. Patients go through diagnostic CT scans for RT planning; therefore, using information from prior CT images can enable estimations of the total X-ray attenuation through a patient’s body in a CBCT setting for radiation treatment. Having this planning CT image allows to use the proposed TCM method in RT. The proposed TCM method provides a minimal amount of current for each projection, as well as total current, required to reconstruct the current modulated CBCT image with an image quality similar to that of CBCT. After applying a calculated TCM current for each projection, projection images were acquired and the current modulated CBCT image was reconstructed using a FDK algorithm. To validate the proposed approach, we used a numerical XCAT phantom and a real ATOM phantom and evaluated the performance of the proposed method via visual and quantitative image quality metrics. The organ dose due to imaging radiation was calculated in both cases and compared using the GATE simulation toolkit. As shown in the quantitative evaluation, normalized noise and SSIM values of the TCM were similar to those of conventional CBCT images. In addition, the proposed TCM method yielded comparable image quality to that of conventional CBCT images for both simulations and experimental studies as organ doses were decreased. We have successfully demonstrated the feasibility and dosimetric merit of a prototypical TCM method for kV-CBCT via simulations and experimental study. The results indicate that the proposed TCM method and overall framework can be a viable option for CBCT imaging that utilizes an optimal dose reduction without degrading image quality. Thus, this method reduces the probability for side effects due to radiation exposure.
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25
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M'hiri F, Duong L, Desrosiers C, Dahdah N, Miró J, Cheriet M. Automatic evaluation of vessel diameter variation from 2D X-ray angiography. Int J Comput Assist Radiol Surg 2017. [PMID: 28707212 DOI: 10.1007/s11548-017-1639-9/figures/9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
PURPOSE Early detection of blood vessel pathologies can be made through the evaluation of functional and structural abnormalities in the arteries, including the arterial distensibility measure. We propose a feasibility study on computing arterial distensibility automatically from monoplane 2D X-ray sequences for both small arteries (such as coronary arteries) and larger arteries (such as the aorta). METHODS To compute the distensibility measure, three steps were developed: First, the segment of an artery is extracted using our graph-based segmentation method. Then, the same segment is tracked in the moving sequence using our spatio-temporal segmentation method: the Temporal Vessel Walker. Finally, the diameter of the artery is measured automatically at each frame of the sequence based on the segmentation results. RESULTS The method was evaluated using one simulated sequence and 4 patients' angiograms depicting the coronary arteries and three depicting the ascending aorta. Results of the simulated sequence achieved a Dice index of 98%, with a mean squared error in diameter measurement of [Formula: see text] mm. Results obtained from patients' X-ray sequences are consistent with manual assessment of the diameter by experts. CONCLUSIONS The proposed method measures changes in diameter of a specific segment of a blood vessel during the cardiac sequence, automatically based on monoplane 2D X-ray sequence. Such information might become a key to help physicians in the detection of variations of arterial stiffness associated with early stages of various vasculopathies.
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Affiliation(s)
- Faten M'hiri
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada.
| | - Luc Duong
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada
| | - Christian Desrosiers
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada
| | - Nagib Dahdah
- Department of Cardiology, Sainte-Justine Hospital, Montreal, Canada
| | - Joaquim Miró
- Department of Cardiology, Sainte-Justine Hospital, Montreal, Canada
| | - Mohamed Cheriet
- Automated Production Engineering, École de technologie supérieure, Montreal, Canada
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26
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Wiyaporn K, Tocharoenchai C, Pusuwan P, Higuchi T, Fung GS, Feng T, Park MJ, Tsui BM. Optimization of imaging protocols for myocardial blood flow (MBF) quantification with 18 F-flurpiridaz PET. Phys Med 2017; 42:127-134. [DOI: 10.1016/j.ejmp.2017.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Revised: 07/27/2017] [Accepted: 08/03/2017] [Indexed: 01/24/2023] Open
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27
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Maitree R, Perez-Carrillo GJG, Shimony JS, Gach HM, Chundury A, Roach M, Li HH, Yang D. Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI. J Med Imaging (Bellingham) 2017; 4:034004. [PMID: 28894763 DOI: 10.1117/1.jmi.4.3.034004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 08/09/2017] [Indexed: 11/14/2022] Open
Abstract
Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.
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Affiliation(s)
- Rapeepan Maitree
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States
| | - Gloria J Guzman Perez-Carrillo
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States.,University of Arizona, Department of Radiology, Tucson, Arizona, United States
| | - Joshua S Shimony
- Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - H Michael Gach
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States.,Washington University School of Medicine, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States.,Washington University School of Medicine, Department of Biomedical Engineering, St. Louis, Missouri, United States
| | - Anupama Chundury
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States
| | - Michael Roach
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States
| | - H Harold Li
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States
| | - Deshan Yang
- Washington University School of Medicine, Department of Radiation Oncology, St. Louis, Missouri, United States.,Washington University School of Medicine, Department of Biomedical Engineering, St. Louis, Missouri, United States
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28
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Papadimitroulas P. Dosimetry applications in GATE Monte Carlo toolkit. Phys Med 2017; 41:136-140. [DOI: 10.1016/j.ejmp.2017.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Revised: 02/08/2017] [Accepted: 02/10/2017] [Indexed: 10/20/2022] Open
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29
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Wang C, Yin FF, Segars WP, Chang Z, Ren L. Development of a Computerized 4-D MRI Phantom for Liver Motion Study. Technol Cancer Res Treat 2017; 16:1051-1059. [PMID: 28789598 PMCID: PMC5575982 DOI: 10.1177/1533034617723753] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose: To develop a 4-dimensional computerized magnetic resonance imaging phantom with image textures extracted from real patient scans for liver motion studies. Methods: The proposed phantom was developed based on the current version of 4-dimensional extended cardiac-torso computerized phantom and a clinical magnetic resonance scan. Initially, the extended cardiac-torso phantom was voxelized in abdominal–chest region at the end of exhalation phase. Structures/tissues were classified into 4 categories: (1) Seven key textured organs, including liver, gallbladder, spleen, stomach, heart, kidneys, and pancreas, were mapped from a clinical T1-weighted liver magnetic resonance scan using deformable registration. (2) Large textured soft tissue volumes were simulated via an iterative pattern generation method using the same magnetic resonance scan. (3) Lung and intestine structures were generated by assigning uniform intensity with proper noise modeling. (4) Bony structures were generated by assigning the magnetic resonance values. A spherical hypointensity tumor was inserted into the liver. Other respiratory phases of the 4-dimensional phantom were generated using the backward deformation vector fields exported by the extended cardiac-torso program, except that bony structures were generated separately for each phase. A weighted image filtering process was utilized to improve the overall tissue smoothness at each phase. Results: Three 4-dimensional series with different motion amplitudes were generated. The developed motion phantom produced good illustrations of abdominal–chest region with anatomical structures in key organs and texture patterns in large soft tissue volumes. In a standard series, the tumor volume was measured as 13.90 ± 0.11 cm3 in a respiratory cycle and the tumor’s maximum center-of-mass shift was 2.95 cm/1.84 cm on superior–inferior/anterior–posterior directions. The organ motion during the respiratory cycle was well rendered. The developed motion phantom has the flexibility of motion pattern variation, organ geometry variation, and tumor modeling variation. Conclusions: A 4-D computerized phantom was developed and could be used to produce image series with synthetic magnetic resonance textures for magnetic resonance imaging research of liver motion.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - W P Segars
- Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Lei Ren
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
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30
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Qi W, Yang Y, Song C, Wernick MN, Pretorius PH, King MA. 4-D Reconstruction With Respiratory Correction for Gated Myocardial Perfusion SPECT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1626-1635. [PMID: 28391190 PMCID: PMC5595423 DOI: 10.1109/tmi.2017.2690819] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Cardiac single photon emission computed tomography (SPECT) images are known to suffer from both cardiac and respiratory motion blur. In this paper, we investigate a 4-D reconstruction approach to suppress the effect of respiratory motion in gated cardiac SPECT imaging. In this approach, the sequence of cardiac gated images is reconstructed with respect to a reference respiratory amplitude bin in the respiratory cycle. To combat the challenge of inherent high-imaging noise, we utilize the data counts acquired during the entire respiratory cycle by making use of a motion-compensated scheme, in which both cardiac motion and respiratory motion are taken into account. In the experiments, we first use Monte Carlo simulated imaging data, wherein the ground truth is known for quantitative comparison. We then demonstrate the proposed approach on eight sets of clinical acquisitions, in which the subjects exhibit different degrees of respiratory motion blur. The quantitative evaluation results show that the 4-D reconstruction with respiratory correction could effectively reduce the effect of motion blur and lead to a more accurate reconstruction of the myocardium. The mean-squared error of the myocardium is reduced by 22%, and the left ventricle (LV) resolution is improved by 21%. Such improvement is also demonstrated with the clinical acquisitions, where the motion blur is markedly improved in the reconstructed LV wall and blood pool. The proposed approach is also noted to be effective on correcting the spill-over effect in the myocardium from nearby bowel or liver activities.
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Wang B, van Roosmalen J, Piët L, van Schie MA, Beekman FJ, Goorden MC. Voxelized ray-tracing simulation dedicated to multi-pinhole molecular breast tomosynthesis. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa8012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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32
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Automatic evaluation of vessel diameter variation from 2D X-ray angiography. Int J Comput Assist Radiol Surg 2017; 12:1867-1876. [DOI: 10.1007/s11548-017-1639-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 06/29/2017] [Indexed: 10/19/2022]
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Son K, Kim JS, Lee H, Cho S. IMAGING DOSE OF HUMAN ORGANS FROM kV-CBCT IN IMAGE-GUIDED RADIATION THERAPY. RADIATION PROTECTION DOSIMETRY 2017; 175:194-200. [PMID: 27765832 DOI: 10.1093/rpd/ncw285] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 09/26/2016] [Indexed: 06/06/2023]
Abstract
This study investigates dose distribution due to kV cone-beam computed tomography (CBCT) for the patients undergoing CBCT-based image-guided radiation therapy (IGRT). The kV-CBCT provides an efficient image-guidance tool for acquiring the latest volumetric image of a patient's anatomy, and has been being routinely used in clinics for an accurate treatment setup. Imaging radiation doses resulting from six different acquisition protocols of the on-board imager (OBI) were calculated using a Geant4 Application for Tomographic Emission (GATE) Monte Carlo simulation toolkit, and the absorbed doses by various organs were analyzed for the adult and pediatric numerical XCAT phantoms in this study. The calculated organ doses range from 0.1 to 24.1 mGy in the adult phantom, and from 0.1 to 36.8 mGy in the pediatric one. The imaging organ doses to the pediatric phantom turn out to be consistently higher than those to the adult phantom. It is believed that our results would provide reliable data to the clinicians for their making better decisions on CBCT scanning options and would also provide a platform for developing a new kV-CBCT scanning protocol in conjunction with a low-dose capability.
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Affiliation(s)
- Kihong Son
- Department of Nuclear and Quantum engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jin Sung Kim
- Department of Radiation Oncology Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea
| | - Hoyeon Lee
- Department of Nuclear and Quantum engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Seungryong Cho
- Department of Nuclear and Quantum engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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Hamdi M, Mimi M, Bentourkia M. Impact of X-ray energy on absorbed dose assessed with Monte Carlo simulations in a mouse tumor and in nearest organs irradiated with kilovoltage X-ray beams. Cancer Radiother 2017; 21:190-198. [DOI: 10.1016/j.canrad.2017.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 12/24/2016] [Accepted: 01/09/2017] [Indexed: 02/07/2023]
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35
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Parages FM, Denney TS, Gupta H, Lloyd SG, Dell'Italia LJ, Brankov JG. Estimation of Left Ventricular Motion from Cardiac Gated Tagged MRI Using an Image-Matching Deformable Mesh Model. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2017. [DOI: 10.1109/tns.2017.2670619] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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36
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Abstract
This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and distinctive features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.
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Affiliation(s)
- Vasant Kearney
- Department of Radiation Oncology, University of California, San Francisco, CA, USA. Department of Bioengineering, University of Texas Arlington, Arlington, TX, USA
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37
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Liu Y, Zhong X, Czito BG, Palta M, Bashir MR, Dale BM, Yin FF, Cai J. Four-dimensional diffusion-weighted MR imaging (4D-DWI): a feasibility study. Med Phys 2017; 44:397-406. [PMID: 28121369 DOI: 10.1002/mp.12037] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Revised: 10/04/2016] [Accepted: 11/23/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Diffusion-weighted Magnetic Resonance Imaging (DWI) has been shown to be a powerful tool for cancer detection with high tumor-to-tissue contrast. This study aims to investigate the feasibility of developing a four-dimensional DWI technique (4D-DWI) for imaging respiratory motion for radiation therapy applications. MATERIALS/METHODS Image acquisition was performed by repeatedly imaging a volume of interest (VOI) using an interleaved multislice single-shot echo-planar imaging (EPI) 2D-DWI sequence in the axial plane. Each 2D-DWI image was acquired with an intermediately low b-value (b = 500 s/mm2 ) and with diffusion-encoding gradients in x, y, and z diffusion directions. Respiratory motion was simultaneously recorded using a respiratory bellow, and the synchronized respiratory signal was used to retrospectively sort the 2D images to generate 4D-DWI. Cine MRI using steady-state free precession was also acquired as a motion reference. As a preliminary feasibility study, this technique was implemented on a 4D digital human phantom (XCAT) with a simulated pancreas tumor. The respiratory motion of the phantom was controlled by regular sinusoidal motion profile. 4D-DWI tumor motion trajectories were extracted and compared with the input breathing curve. The mean absolute amplitude differences (D) were calculated in superior-inferior (SI) direction and anterior-posterior (AP) direction. The technique was then evaluated on two healthy volunteers. Finally, the effects of 4D-DWI on apparent diffusion coefficient (ADC) measurements were investigated for hypothetical heterogeneous tumors via simulations. RESULTS Tumor trajectories extracted from XCAT 4D-DWI were consistent with the input signal: the average D value was 1.9 mm (SI) and 0.4 mm (AP). The average D value was 2.6 mm (SI) and 1.7 mm (AP) for the two healthy volunteers. CONCLUSION A 4D-DWI technique has been developed and evaluated on digital phantom and human subjects. 4D-DWI can lead to more accurate respiratory motion measurement. This has a great potential to improve the visualization and delineation of cancer tumors for radiotherapy.
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Affiliation(s)
- Yilin Liu
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthcare, Atlanta, GA, 30354, USA
| | - Brian G Czito
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Manisha Palta
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.,Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, 27710, USA
| | - Brian M Dale
- MR R&D Collaborations, Siemens Healthcare, Cary, NC, 27511, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, NC, 27710, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
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Bae S, Chun J, Cha H, Yeom JY, Lee K, Lee H. Simulation study of a novel target oriented SPECT design using a variable pinhole collimator. Med Phys 2016; 44:470-478. [PMID: 28032904 DOI: 10.1002/mp.12075] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 10/08/2016] [Accepted: 12/19/2016] [Indexed: 11/05/2022] Open
Abstract
PURPOSE In the past decade, demands for organ specific (target oriented) single-photon emission computed tomography (SPECT) is increasing, and several groups have conducted studies on developing clinical dedicated SPECT with pinhole collimator to improve the spatial resolution. However, acceptance angle of the collimator cannot be adjusted to fit the different ROIs of target objects because the shape of pinhole could not be changed, and the magnifying factor cannot be maximized as the collimator-to-detector distance is fixed. Furthermore, those dedicated pinhole SPECTs are typically made for a single purpose and therefore possess a drawback in that it cannot be utilized for any other purpose. In this study, we propose a novel SPECT system using variable pinhole collimator (VP SPECT) whose parameters are flexible. METHODS The proposed variable pinhole collimator is modeled on conventional pinhole by piling several tungsten layers of different apertures. Depending on the combination of the holes in each layer, a variety of hole diameters and acceptance angles of the pinhole can be made. In addition, VP SPECT system allows attaching the collimator to the object as close as possible to maximize the sensitivity and adjust the distance of the pinhole from the scintillation detector to optimize the system resolution for each rotation angle, automatically. For quantitative measurement, we compared the sensitivity and spatial resolution of VP SPECT with those of conventional pinhole SPECT. To determine the possibility of the clinical and preclinical use of proposed system, a digital mouse whole-body (MOBY) phantom is used for simulating the live mouse model. RESULTS The result of simulation using ultra-micro hot spot phantom shows that the sensitivity of the proposed VP SPECT system is about 297% of that of the conventional system. While hot rods of diameter 0.6 mm can be distinguished in the image with the proposed VP SPECT system, 1.2-mm hot rods are barely discernible in the conventional pinhole SPECT image. According to the result of MOBY phantom simulation, heart walls separated by 3 mm were not distinguished in conventional pinhole SPECT images, but were clearly discerned in VP SPECT images. CONCLUSIONS In this study, we designed a novel pinhole collimator for SPECT and presented preliminary results of target oriented imaging with a simulation study. Currently, we are pursuing strategies to realize the proposed system, with the goal to apply the technology into a high-sensitivity and high-resolution preclinical SPECT. Should VP SPECT be applied to the clinical setting, we anticipate a high-sensitivity, high-resolution system for applications such as heart dedicated SPECT or related fields.
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Affiliation(s)
- Seungbin Bae
- Bio-convergence Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
| | - Jaehee Chun
- Bio-convergence Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
| | - Hyemi Cha
- Bio-convergence Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Yeol Yeom
- Bio-convergence Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea.,School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
| | - Kisung Lee
- Bio-convergence Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea.,School of Biomedical Engineering, College of Health Science, Korea University, Seoul, 02841, Republic of Korea
| | - Hakjae Lee
- Research Institute of Global Health Tech., College of Health Science, Korea University, Seoul, 02841, Republic of Korea
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M'hiri F, Duong L, Desrosiers C, Leye M, Miró J, Cheriet M. A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences. Comput Biol Med 2016; 79:45-58. [PMID: 27744180 DOI: 10.1016/j.compbiomed.2016.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 09/30/2016] [Accepted: 10/01/2016] [Indexed: 01/10/2023]
Abstract
The segmentation and tracking of coronary arteries (CAs) are critical steps for the computation of biophysical measurements in pediatric interventional cardiology. In the literature, most methods are focused on either segmenting the vessel lumen or on tracking the vessel centerline. However, they do not simultaneously combine the segmentation and tracking of a specific CA. This paper introduces a novel algorithm for CA segmentation and tracking from 2D X-ray angiography sequences. The proposed algorithm is based on the Temporal Vessel Walker (TVW) segmentation method, which combines graph-based formulation and temporal priors. Moreover, superpixel groups are used by TVW as image primitives to ensure a better extraction of the CA. The proposed algorithm, TVW with superpixels (SP-TVW), returns an accurate result to segment and track the artery along the angiogram. Quantitative results over 12 sequences of young patients show the accuracy of the proposed framework. The results return a mean recall of 84% in the dataset. In addition, the proposed method returned a Dice index of 70% in segmenting and tracking right coronary arteries and circumflex arteries. The performance of the proposed method surpasses the existing polyline method in tracking the centerline of CA with a more precise localization of the centerline, resulting in a smaller distance error of 0.23mm compared to 0.94mm.
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Affiliation(s)
- Faten M'hiri
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada.
| | - Luc Duong
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada
| | - Christian Desrosiers
- Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada
| | - Mohamed Leye
- Department of Cardiology, Sainte-Justine Hospital, Montreal, Canada
| | - Joaquim Miró
- Department of Cardiology, Sainte-Justine Hospital, Montreal, Canada
| | - Mohamed Cheriet
- Automated Production Engineering, École de technologie supérieure, Montreal, Canada
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Liu Y, Yin FF, Rhee D, Cai J. Accuracy of respiratory motion measurement of 4D-MRI: A comparison between cine and sequential acquisition. Med Phys 2016; 43:179. [PMID: 26745910 DOI: 10.1118/1.4938066] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors have recently developed a cine-mode T2*/T1-weighted 4D-MRI technique and a sequential-mode T2-weighted 4D-MRI technique for imaging respiratory motion. This study aims at investigating which 4D-MRI image acquisition mode, cine or sequential, provides more accurate measurement of organ motion during respiration. METHODS A 4D digital extended cardiac-torso (XCAT) human phantom with a hypothesized tumor was used to simulate the image acquisition and the 4D-MRI reconstruction. The respiratory motion was controlled by the given breathing signal profiles. The tumor was manipulated to move continuously with the surrounding tissue. The motion trajectories were measured from both sequential- and cine-mode 4D-MRI images. The measured trajectories were compared with the average trajectory calculated from the input profiles, which was used as references. The error in 4D-MRI tumor motion trajectory (E) was determined. In addition, the corresponding respiratory motion amplitudes of all the selected 2D images for 4D reconstruction were recorded. Each of the amplitude was compared with the amplitude of its associated bin on the average breathing curve. The mean differences from the average breathing curve across all slice positions (D) were calculated. A total of 500 simulated respiratory profiles with a wide range of irregularity (Ir) were used to investigate the relationship between D and Ir. Furthermore, statistical analysis of E and D using XCAT controlled by 20 cancer patients' breathing profiles was conducted. Wilcoxon Signed Rank test was conducted to compare two modes. RESULTS D increased faster for cine-mode (D = 1.17 × Ir + 0.23) than sequential-mode (D = 0.47 × Ir + 0.23) as irregularity increased. For the XCAT study using 20 cancer patients' breathing profiles, the median E values were significantly different: 0.12 and 0.10 cm for cine- and sequential-modes, respectively, with a p-value of 0.02. The median D values were significantly different: 0.47 and 0.24 cm for cine- and sequential-modes, respectively, with a p-value < 0.001. CONCLUSIONS Respiratory motion measurement may be more accurate and less susceptible to breathing irregularity in sequential-mode 4D-MRI than that in cine-mode 4D-MRI.
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Affiliation(s)
- Yilin Liu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
| | - DongJoo Rhee
- Dongnam Institute of Radiological and Medical Sciences, Gijang-gun, Busan 619-953, South Korea
| | - Jing Cai
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27710 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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Evaluation of Stationary and Semi-stationary Acquisitions from Dual-head Multi-pinhole Collimator for Myocardial Perfusion SPECT. J Med Biol Eng 2016. [DOI: 10.1007/s40846-016-0169-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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42
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Denisova NV, Terekhov IN. A study of myocardial perfusion SPECT imaging with reduced radiation dose using maximum likelihood and entropy-based maximum
a posteriori
approaches. Biomed Phys Eng Express 2016. [DOI: 10.1088/2057-1976/2/5/055015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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van Roosmalen J, Goorden MC, Beekman FJ. Molecular breast tomosynthesis with scanning focus multi-pinhole cameras. Phys Med Biol 2016; 61:5508-28. [PMID: 27384301 DOI: 10.1088/0031-9155/61/15/5508] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Planar molecular breast imaging (MBI) is rapidly gaining in popularity in diagnostic oncology. To add 3D capabilities, we introduce a novel molecular breast tomosynthesis (MBT) scanner concept based on multi-pinhole collimation. In our design, the patient lies prone with the pendant breast lightly compressed between transparent plates. Integrated webcams view the breast through these plates and allow the operator to designate the scan volume (e.g. a whole breast or a suspected region). The breast is then scanned by translating focusing multi-pinhole plates and NaI(Tl) gamma detectors together in a sequence that optimizes count yield from the volume-of-interest. With simulations, we compared MBT with existing planar MBI. In a breast phantom containing different lesions, MBT improved tumour-to-background contrast-to-noise ratio (CNR) over planar MBI by 12% and 111% for 4.0 and 6.0 mm lesions respectively in case of whole breast scanning. For the same lesions, much larger CNR improvements of 92% and 241% over planar MBI were found in a scan that focused on a breast region containing several lesions. MBT resolved 3.0 mm rods in a Derenzo resolution phantom in the transverse plane compared to 2.5 mm rods distinguished by planar MBI. While planar MBI cannot provide depth information, MBT offered 4.0 mm depth resolution. Our simulations indicate that besides offering 3D localization of increased tracer uptake, multi-pinhole MBT can significantly increase tumour-to-background CNR compared to planar MBI. These properties could be promising for better estimating the position, extend and shape of lesions and distinguishing between single and multiple lesions.
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Affiliation(s)
- Jarno van Roosmalen
- Section Radiation, Detection & Medical Imaging, Delft University of Technology, Delft, The Netherlands
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Sen A, Kalantari F, Gifford HC. Task Equivalence for Model and Human-Observer Comparisons in SPECT Localization Studies. IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2016; 63:1426-1434. [PMID: 27980345 PMCID: PMC5152772 DOI: 10.1109/tns.2016.2542042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
While mathematical model observers are intended for efficient assessment of medical imaging systems, their findings should be relevant for human observers as the primary clinical end users. We have investigated whether pursuing equivalence between the model and human-observer tasks can help ensure this goal. A localization ROC (LROC) study tested prostate lesion detection in simulated In-111 SPECT imaging with anthropomorphic phantoms. The test images were 2D slices extracted from reconstructed volumes. The iterative OSEM reconstruction method was used with Gaussian postsmoothing. Variations in the number of iterations and the level of postfiltering defined the test strategies in the study. Human-observer performance was compared with that of a visual-search (VS) observer, a scanning channelized Hotelling observer, and a scanning nonprewhitening (CNPW) observer. These model observers were applied with precise information about the target regions of interest (ROIs). ROI knowledge was a study variable for the human observers. In one study format, the humans read the SPECT image alone. With a dual-modality format, the SPECT image was presented alongside an anatomical image slice extracted from the density map of the phantom. Performance was scored by area under the LROC curve. The human observers performed significantly better with the dual-modality format, and correlation with the model observers was also improved. Given the human-observer data from the SPECT study format, the Pearson correlation coefficients for the model observers were 0.58 (VS), -0.12 (CH), and -0.23 (CNPW). The respective coefficients based on the human-observer data from the dual-modality study were 0.72, 0.27, and -0.11. These results point towards the continued development of the VS observer for enhancing task equivalence in model-observer studies.
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Affiliation(s)
- Anando Sen
- Department of Biomedical Informatics, Columbia University, New York City, NY, USA
| | - Faraz Kalantari
- Department of Radiation Oncology, University of Texas Southwestern, Dallas, TX, USA
| | - Howard C. Gifford
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
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3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4382854. [PMID: 27019849 PMCID: PMC4785510 DOI: 10.1155/2016/4382854] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Accepted: 12/28/2015] [Indexed: 11/17/2022]
Abstract
By using prior information of planning CT images and feature-based nonuniform meshes, this paper demonstrates that volumetric images can be efficiently registered with a very small portion of 2D projection images of a Cone-Beam Computed Tomography (CBCT) scan. After a density field is computed based on the extracted feature edges from planning CT images, nonuniform tetrahedral meshes will be automatically generated to better characterize the image features according to the density field; that is, finer meshes are generated for features. The displacement vector fields (DVFs) are specified at the mesh vertices to drive the deformation of original CT images. Digitally reconstructed radiographs (DRRs) of the deformed anatomy are generated and compared with corresponding 2D projections. DVFs are optimized to minimize the objective function including differences between DRRs and projections and the regularity. To further accelerate the above 3D-2D registration, a procedure to obtain good initial deformations by deforming the volume surface to match 2D body boundary on projections has been developed. This complete method is evaluated quantitatively by using several digital phantoms and data from head and neck cancer patients. The feature-based nonuniform meshing method leads to better results than either uniform orthogonal grid or uniform tetrahedral meshes.
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Elshahaby FEA, Ghaly M, Jha AK, Frey EC. Factors affecting the normality of channel outputs of channelized model observers: an investigation using realistic myocardial perfusion SPECT images. J Med Imaging (Bellingham) 2016; 3:015503. [PMID: 26839913 DOI: 10.1117/1.jmi.3.1.015503] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 12/10/2015] [Indexed: 11/14/2022] Open
Abstract
The channelized Hotelling observer (CHO) uses the first- and second-order statistics of channel outputs under both hypotheses to compute test statistics used in binary classification tasks. If these input data deviate from a multivariate normal (MVN) distribution, the classification performance will be suboptimal compared to an ideal observer operating on the same channel outputs. We conducted a comprehensive investigation to rigorously study the validity of the MVN assumption under various kinds of background and signal variability in a realistic population of phantoms. The study was performed in the context of myocardial perfusion SPECT imaging; anatomical, uptake (intensity), and signal variability were simulated. Quantitative measures and graphical approaches applied to the outputs of each channel were used to investigate the amount and type of deviation from normality. For some types of background and signal variations, the channel outputs, under both hypotheses, were non-normal (i.e., skewed or multimodal). This indicates that, for realistic medical images in cases where there is signal or background variability, the normality of the channel outputs should be evaluated before applying a CHO. Finally, the different degrees of departure from normality of the various channels are explained in terms of violations of the central limit theorem.
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Affiliation(s)
- Fatma E A Elshahaby
- Johns Hopkins University, Whiting School of Engineering, Department of Electrical and Computer Engineering, 3400 North Charles street, Baltimore, Maryland 21218, United States; Johns Hopkins Hospital, Russell H. Morgan Department of Radiology and Radiological Science, 601 North Caroline street, Baltimore, Maryland 21287, United States
| | - Michael Ghaly
- Johns Hopkins Hospital , Russell H. Morgan Department of Radiology and Radiological Science, 601 North Caroline street, Baltimore, Maryland 21287, United States
| | - Abhinav K Jha
- Johns Hopkins Hospital , Russell H. Morgan Department of Radiology and Radiological Science, 601 North Caroline street, Baltimore, Maryland 21287, United States
| | - Eric C Frey
- Johns Hopkins Hospital , Russell H. Morgan Department of Radiology and Radiological Science, 601 North Caroline street, Baltimore, Maryland 21287, United States
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Registration and Summation of Respiratory-Gated or Breath-Hold PET Images Based on Deformation Estimation of Lung from CT Image. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:9713280. [PMID: 28096896 PMCID: PMC5206782 DOI: 10.1155/2016/9713280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 11/16/2016] [Indexed: 11/17/2022]
Abstract
Lung motion due to respiration causes image degradation in medical imaging, especially in nuclear medicine which requires long acquisition times. We have developed a method for image correction between the respiratory-gated (RG) PET images in different respiration phases or breath-hold (BH) PET images in an inconsistent respiration phase. In the method, the RG or BH-PET images in different respiration phases are deformed under two criteria: similarity of the image intensity distribution and smoothness of the estimated motion vector field (MVF). However, only these criteria may cause unnatural motion estimation of lung. In this paper, assuming the use of a PET-CT scanner, we add another criterion that is the similarity for the motion direction estimated from inhalation and exhalation CT images. The proposed method was first applied to a numerical phantom XCAT with tumors and then applied to BH-PET image data for seven patients. The resultant tumor contrasts and the estimated motion vector fields were compared with those obtained by our previous method. Through those experiments we confirmed that the proposed method can provide an improved and more stable image quality for both RG and BH-PET images.
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Sen A, Gifford HC. Accounting for anatomical noise in search-capable model observers for planar nuclear imaging. J Med Imaging (Bellingham) 2016; 3:015502. [PMID: 26835503 PMCID: PMC4726820 DOI: 10.1117/1.jmi.3.1.015502] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 12/23/2015] [Indexed: 11/14/2022] Open
Abstract
Model observers intended to predict the diagnostic performance of human observers should account for the effects of both quantum and anatomical noise. We compared the abilities of several visual-search (VS) and scanning Hotelling-type models to account for anatomical noise in a localization receiver operating characteristic (LROC) study involving simulated nuclear medicine images. Our VS observer invoked a two-stage process of search and analysis. The images featured lesions in the prostate and pelvic lymph nodes. Lesion contrast and the geometric resolution and sensitivity of the imaging collimator were the study variables. A set of anthropomorphic mathematical phantoms was imaged with an analytic projector based on eight parallel-hole collimators with different sensitivity and resolution properties. The LROC study was conducted with human observers and the channelized nonprewhitening, channelized Hotelling (CH) and VS model observers. The CH observer was applied in a "background-known-statistically" protocol while the VS observer performed a quasi-background-known-exactly task. Both of these models were applied with and without internal noise in the decision variables. A perceptual search threshold was also tested with the VS observer. The model observers without inefficiencies failed to mimic the average performance trend for the humans. The CH and VS observers with internal noise matched the humans primarily at low collimator sensitivities. With both internal noise and the search threshold, the VS observer attained quantitative agreement with the human observers. Computational efficiency is an important advantage of the VS observer.
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Affiliation(s)
- Anando Sen
- University of Houston, Department of Biomedical Engineering, 3605 Cullen Boulevard, Houston, Texas 77004, United States
| | - Howard C. Gifford
- University of Houston, Department of Biomedical Engineering, 3605 Cullen Boulevard, Houston, Texas 77004, United States
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Shrestha UM, Seo Y, Botvinick EH, Gullberg GT. Image reconstruction in higher dimensions: myocardial perfusion imaging of tracer dynamics with cardiac motion due to deformation and respiration. Phys Med Biol 2015; 60:8275-301. [PMID: 26450115 DOI: 10.1088/0031-9155/60/21/8275] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Myocardial perfusion imaging (MPI) using slow rotating large field of view cameras requires spatiotemporal reconstruction of dynamically acquired data to capture the time variation of the radiotracer concentration. In vivo, MPI contains additional degrees of freedom involving unavoidable motion of the heart due to quasiperiodic beating and the effects of respiration, which can severely degrade the quality of the images. This work develops a technique for a single photon emission computed tomography (SPECT) that reconstructs the distribution of the radiotracer concentration in the myocardium using a tensor product of different sets of basis functions that approximately describe the spatiotemporal variation of the radiotracer concentration and the motion of the heart. In this study the temporal B-spline basis functions are chosen to reflect the dynamics of the radiotracer, while the intrinsic deformation and the extrinsic motion of the heart are described by a product of a discrete set of Gaussian basis functions. Reconstruction results are presented showing the dynamics of the tracer in the myocardium as it deforms due to cardiac beating, and is displaced due to respiratory motion. These results are compared with the conventional 4D-spatiotemporal reconstruction method that models only the temporal changes of the tracer activity. The higher dimensional reconstruction method proposed here improves bias, yet the signal-to-noise ratio (SNR) decreases slightly due to redistribution of the counts over the cardiac-respiratory gates. Additionally, there is a trade-off between the number of gates and the number of projections per gate to achieve high contrast images.
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Affiliation(s)
- Uttam M Shrestha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. Structural Biology and Imaging Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Vieira L, Costa D, Almeida P. The influence of number of counts in the myocardium in the determination of reproducible functional parameters in gated-SPECT studies simulated with GATE. Rev Esp Med Nucl Imagen Mol 2015. [DOI: 10.1016/j.remnie.2015.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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