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Rajagopal JR, Farhadi F, Solomon J, Saboury B, Sahbaee P, Negussie AH, Pritchard WF, Jones EC, Samei E. Development of a separability index for task specific characterization of spectral computed tomography. Phys Med 2024; 122:103382. [PMID: 38820805 PMCID: PMC11185224 DOI: 10.1016/j.ejmp.2024.103382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 01/26/2024] [Accepted: 05/21/2024] [Indexed: 06/02/2024] Open
Abstract
PURPOSE In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions. METHOD Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated. RESULTS Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability. CONCLUSION The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.
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Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC 27705, United States; Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States; Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States.
| | - Faraz Farhadi
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States; Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC 27705, United States; Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States; Clinical Imaging Physics Group, Duke University Medical Center, Durham, NC 27705, United States
| | - Babak Saboury
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States
| | - Pooyan Sahbaee
- Siemens Medical Solutions USA, Malvern, PA 19335, United States
| | - Ayele H Negussie
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States
| | - William F Pritchard
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, United States
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC 27705, United States; Medical Physics Graduate Program, Duke University Medical Center, Durham, NC 27705, United States; Clinical Imaging Physics Group, Duke University Medical Center, Durham, NC 27705, United States.
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Tushar FI, Vancoillie L, McCabe C, Kavuri A, Dahal L, Harrawood B, Fryling M, Zarei M, Sotoudeh-Paima S, Ho FC, Ghosh D, Luo S, Segars WP, Abadi E, Lafata KJ, Samei E, Lo JY. VLST: Virtual Lung Screening Trial for Lung Cancer Detection Using Virtual Imaging Trial. ARXIV 2024:arXiv:2404.11221v1. [PMID: 38699170 PMCID: PMC11065052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Importance The efficacy of lung cancer screening can be significantly impacted by the imaging modality used. This Virtual Lung Screening Trial (VLST) addresses the critical need for precision in lung cancer diagnostics and the potential for reducing unnecessary radiation exposure in clinical settings. Objectives To establish a virtual imaging trial (VIT) platform that accurately simulates real-world lung screening trials (LSTs) to assess the diagnostic accuracy of CT and CXR modalities. Design Setting and Participants Utilizing computational models and machine learning algorithms, we created a diverse virtual patient population. The cohort, designed to mirror real-world demographics, was assessed using virtual imaging techniques that reflect historical imaging technologies. Main Outcomes and Measures The primary outcome was the difference in the Area Under the Curve (AUC) for CT and CXR modalities across lesion types and sizes. Results The study analyzed 298 CT and 313 CXR simulated images from 313 virtual patients, with a lesion-level AUC of 0.81 (95% CI: 0.78-0.84) for CT and 0.55 (95% CI: 0.53-0.56) for CXR. At the patient level, CT demonstrated an AUC of 0.85 (95% CI: 0.80-0.89), compared to 0.53 (95% CI: 0.47-0.60) for CXR. Subgroup analyses indicated CT's superior performance in detecting homogeneous lesions (AUC of 0.97 for lesion-level) and heterogeneous lesions (AUC of 0.71 for lesion-level) as well as in identifying larger nodules (AUC of 0.98 for nodules > 8 mm). Conclusion and Relevance The VIT platform validated the superior diagnostic accuracy of CT over CXR, especially for smaller nodules, underscoring its potential to replicate real clinical imaging trials. These findings advocate for the integration of virtual trials in the evaluation and improvement of imaging-based diagnostic tools.
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Affiliation(s)
- Fakrul Islam Tushar
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Liesbeth Vancoillie
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
| | - Cindy McCabe
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Medical Physics Graduate Program, Duke University
| | - Amareswararao Kavuri
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
| | - Lavsen Dahal
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Brian Harrawood
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
| | - Milo Fryling
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
| | - Mojtaba Zarei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Fong Chi Ho
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Dhrubajyoti Ghosh
- Dept. of Biostatistics and Bioinformatics, Duke University School of Medicine
| | - Sheng Luo
- Dept. of Biostatistics and Bioinformatics, Duke University School of Medicine
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
- Medical Physics Graduate Program, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
- Medical Physics Graduate Program, Duke University
| | - Kyle J Lafata
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
- Medical Physics Graduate Program, Duke University
- Dept. of Biostatistics and Bioinformatics, Duke University School of Medicine
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
- Medical Physics Graduate Program, Duke University
| | - Joseph Y Lo
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
- Medical Physics Graduate Program, Duke University
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Rajagopal JR, Schwartz FR, McCabe C, Farhadi F, Zarei M, Ria F, Abadi E, Segars P, Ramirez-Giraldo JC, Jones EC, Henry T, Marin D, Samei E. Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography. J Comput Assist Tomogr 2024:00004728-990000000-00312. [PMID: 38626754 DOI: 10.1097/rct.0000000000001608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
OBJECTIVE Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.
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Affiliation(s)
| | - Fides R Schwartz
- Duke University Health System, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Cindy McCabe
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
| | | | - Mojtaba Zarei
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Francesco Ria
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Ehsan Abadi
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Paul Segars
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
| | | | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Travis Henry
- Duke University Health System, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Daniele Marin
- Duke University Health System, Department of Radiology, Duke University Medical Center, Durham, NC
| | - Ehsan Samei
- From the Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC
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Sotoudeh-Paima S, Segars WP, Ghosh D, Luo S, Samei E, Abadi E. A systematic assessment and optimization of photon-counting CT for lung density quantifications. Med Phys 2024; 51:2893-2904. [PMID: 38368605 PMCID: PMC11055522 DOI: 10.1002/mp.16987] [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: 07/21/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. PURPOSE To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. METHODS This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. RESULTS Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied. CONCLUSION Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.
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Affiliation(s)
- Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
| | - W. Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
| | - Dhrubajyoti Ghosh
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
- Department of Biomedical Engineering, Duke University, Durham, USA
- Department of Physics, Duke University, Durham, USA
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, USA
- Department of Electrical & Computer Engineering, Duke University, Durham, USA
- Medical Physics Graduate Program, Duke University, Durham, USA
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Jadick G, Schlafly G, La Rivière PJ. Dual-energy computed tomography imaging with megavoltage and kilovoltage X-ray spectra. J Med Imaging (Bellingham) 2024; 11:023501. [PMID: 38445223 PMCID: PMC10910563 DOI: 10.1117/1.jmi.11.2.023501] [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: 06/26/2023] [Revised: 12/26/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024] Open
Abstract
Purpose Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts. Approach A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory. Five dose-matched spectra (3 kV, 2 MV) and three variables were considered: spectral combination, spectral dose allocation, and object material composition. The single-line model was extended to a simulated CT acquisition of an anthropomorphic phantom with and without a metal implant. Basis material sinograms were computed and synthesized into virtual monoenergetic images (VMIs). MV-kV and kV-kV VMIs were compared with single-energy images. Results The 80 kV-140 kV pair typically yielded the best SNRs, but for bone thicknesses > 8 cm , the detunedMV-80 kV pair surpassed it. Peak MV-kV SNR was achieved with ∼ 90 % dose allocated to the MV spectrum. In CT simulations of the pelvis with a steel implant, MV-kV VMIs yielded a higher contrast-to-noise ratio (CNR) than single-energy CT and kV-kV DE-CT. Without steel, the MV-kV VMIs produced higher contrast but lower CNR than single-energy CT. Conclusions This work analyzes MV-kV DE-CT imaging and assesses its potential advantages. The technique may be used for metal artifact correction and generation of VMIs with higher native contrast than single-energy CT. Improved denoising is generally necessary for greater CNR without metal.
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Affiliation(s)
- Giavanna Jadick
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Geneva Schlafly
- University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Tunissen SAM, Oostveen LJ, Moriakov N, Teuwen J, Michielsen K, Smit EJ, Sechopoulos I. Development, validation, and simplification of a scanner-specific CT simulator. Med Phys 2024; 51:2081-2095. [PMID: 37656009 PMCID: PMC10904672 DOI: 10.1002/mp.16679] [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: 10/31/2022] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. PURPOSE To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. METHODS To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. RESULTS The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2 HU and the maximum was 22.5 HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. CONCLUSION The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.
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Affiliation(s)
| | - Luuk J. Oostveen
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
| | - Nikita Moriakov
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jonas Teuwen
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Koen Michielsen
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
| | - Ewoud J. Smit
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Technical Medicine Centre, University of Twente, Enschede, The Netherlands
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Kavuri A, Ho FC, Ghojogh-Nejad M, Sotoudeh-Paima S, Samei E, Segars WP, Abadi E. Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12927:129270B. [PMID: 38765483 PMCID: PMC11100024 DOI: 10.1117/12.3006833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Parametric response mapping (PRM) is a voxel-based quantitative CT imaging biomarker that measures the severity of chronic obstructive pulmonary disease (COPD) by analyzing both inspiratory and expiratory CT scans. Although PRM-derived measurements have been shown to predict disease severity and phenotyping, their quantitative accuracy is impacted by the variability of scanner settings and patient conditions. The aim of this study was to evaluate the variability of PRM-based measurements due to the changes in the scanner types and configurations. We developed 10 human chest models with emphysema and air-trapping at end-inspiration and end-expiration states. These models were virtually imaged using a scanner-specific CT simulator (DukeSim) to create CT images at different acquisition settings for energy-integrating and photon-counting CT systems. The CT images were used to estimate PRM maps. The quantified measurements were compared with ground truth values to evaluate the deviations in the measurements. Results showed that PRM measurements varied with scanner type and configurations. The emphysema volume was overestimated by 3 ± 9.5 % (mean ± standard deviation) of the lung volume, and the functional small airway disease (fSAD) volume was underestimated by 7.5±19 % of the lung volume. PRM measurements were more accurate and precise when the acquired settings were photon-counting CT, higher dose, smoother kernel, and larger pixel size. This study demonstrates the development and utility of virtual imaging tools for systematic assessment of a quantitative biomarker accuracy.
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Affiliation(s)
- Amar Kavuri
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - Fong Chi Ho
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - Mobina Ghojogh-Nejad
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - W Paul Segars
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Department of Radiology, Duke University, United States
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Sharma S, Pal D, Abadi E, Segars P, Hsieh J, Samei E. Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies. Eur J Radiol 2024; 171:111279. [PMID: 38194843 PMCID: PMC10922475 DOI: 10.1016/j.ejrad.2023.111279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/26/2023] [Accepted: 12/20/2023] [Indexed: 01/11/2024]
Abstract
OBJECTIVES To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions. MATERIALS AND METHODS Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT. RESULTS Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear. CONCLUSIONS Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.
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Affiliation(s)
- Shobhit Sharma
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, NC 27705, USA; Department of Physics, Duke University, Science Drive, Durham, NC 27708, USA
| | - Debashish Pal
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA
| | - Ehsan Abadi
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, NC 27705, USA; Department of Radiology, Duke University, 2301 Erwin Rd, Durham, NC 27705, USA.
| | - Paul Segars
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, NC 27705, USA; Department of Radiology, Duke University, 2301 Erwin Rd, Durham, NC 27705, USA
| | - Jiang Hsieh
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA
| | - Ehsan Samei
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, 2424 Erwin Rd, Suite 302, Durham, NC 27705, USA; Department of Physics, Duke University, Science Drive, Durham, NC 27708, USA; Department of Radiology, Duke University, 2301 Erwin Rd, Durham, NC 27705, USA
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9
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Sotoudeh-Paima S, Ho FC, Nejad MG, Kavuri A, O'Sullivan-Murphy B, Lynch DA, Segars WP, Samei E, Abadi E. Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12925:129251H. [PMID: 38741597 PMCID: PMC11090051 DOI: 10.1117/12.3006925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Pulmonary emphysema is a progressive lung disease that requires accurate evaluation for optimal management. This task, possible using quantitative CT, is particularly challenging as scanner and patient attributes change over time, negatively impacting the CT-derived quantitative measures. Efforts to minimize such variations have been limited by the absence of ground truth in clinical data, thus necessitating reliance on clinical surrogates, which may not have one-to-one correspondence to CT-based findings. This study aimed to develop the first suite of human models with emphysema at multiple time points, enabling longitudinal assessment of disease progression with access to ground truth. A total of 14 virtual subjects were modeled across three time points. Each human model was virtually imaged using a validated imaging simulator (DukeSim), modeling an energy-integrating CT scanner. The models were scanned at two dose levels and reconstructed with two reconstruction kernels, slice thicknesses, and pixel sizes. The developed longitudinal models were further utilized to demonstrate utility in algorithm testing and development. Two previously developed image processing algorithms (CT-HARMONICA, EmphysemaSeg) were evaluated. The results demonstrated the efficacy of both algorithms in improving the accuracy and precision of longitudinal quantifications, from 6.1±6.3% to 1.1±1.1% and 1.6±2.2% across years 0-5. Further investigation in EmphysemaSeg identified that baseline emphysema severity, defined as >5% emphysema at year 0, contributed to its reduced performance. This finding highlights the value of virtual imaging trials in enhancing the explainability of algorithms. Overall, the developed longitudinal human models enabled ground-truth based assessment of image processing algorithms for lung quantifications.
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Affiliation(s)
- Saman Sotoudeh-Paima
- Department of Radiology, Duke University School of Medicine, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | - Fong Chi Ho
- Department of Radiology, Duke University School of Medicine, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
| | | | - Amar Kavuri
- Department of Radiology, Duke University School of Medicine, Durham, NC
| | | | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO, USA
| | - W Paul Segars
- Department of Radiology, Duke University School of Medicine, Durham, NC
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department Physics, Duke University, Durham, NC
| | - Ehsan Samei
- Department of Radiology, Duke University School of Medicine, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department Physics, Duke University, Durham, NC
- Medical Physics Graduate Program, Duke University, Durham, NC
| | - Ehsan Abadi
- Department of Radiology, Duke University School of Medicine, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
- Medical Physics Graduate Program, Duke University, Durham, NC
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10
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Shimomura T, Fujiwara D, Inoue Y, Takeya A, Ohta T, Nozawa Y, Imae T, Nawa K, Nakagawa K, Haga A. Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition. Phys Med 2023; 113:102648. [PMID: 37672845 DOI: 10.1016/j.ejmp.2023.102648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/19/2023] [Accepted: 07/29/2023] [Indexed: 09/08/2023] Open
Abstract
PURPOSE The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator. METHODS The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient. RESULTS The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable. CONCLUSIONS This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy.
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Affiliation(s)
- Taisei Shimomura
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan; Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Daiyu Fujiwara
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Yuki Inoue
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Atsushi Takeya
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Takeshi Ohta
- Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Yuki Nozawa
- Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Toshikazu Imae
- Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Kanabu Nawa
- Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Keiichi Nakagawa
- Department of Radiology, The University of Tokyo Hospital, Bunkyo, Tokyo 113-8655, Japan
| | - Akihiro Haga
- Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan.
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11
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Clark DP, Badea CT. MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Med Phys 2023; 50:4775-4796. [PMID: 37285215 PMCID: PMC10756497 DOI: 10.1002/mp.16532] [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: 11/20/2022] [Revised: 05/07/2023] [Accepted: 05/19/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains. PURPOSE We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). METHODS The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times. RESULTS Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems. CONCLUSIONS The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.
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Affiliation(s)
- Darin P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, North Carolina, USA
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Cristian T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, North Carolina, USA
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12
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Sharma S, Pal D, Abadi E, Sauer T, Segars P, Hsieh J, Samei E. Can Photon-Counting CT Improve Estimation Accuracy of Morphological Radiomics Features? A Simulation Study for Assessing the Quantitative Benefits from Improved Spatial Resolution in Deep Silicon-Based Photon-Counting CT. Acad Radiol 2023; 30:1153-1163. [PMID: 35871908 PMCID: PMC9859937 DOI: 10.1016/j.acra.2022.06.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/17/2022] [Accepted: 06/24/2022] [Indexed: 01/25/2023]
Abstract
RATIONALE AND OBJECTIVES Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions. MATERIALS AND METHODS A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions. RESULTS Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect. CONCLUSION For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging.
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Affiliation(s)
- Shobhit Sharma
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC; Department of Physics, Duke University, Durham, NC.
| | | | - Ehsan Abadi
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC; Department of Radiology, Duke University, Durham, NC
| | - Thomas Sauer
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC
| | - Paul Segars
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC; Department of Radiology, Duke University, Durham, NC
| | | | - Ehsan Samei
- Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC; Department of Physics, Duke University, Durham, NC; Department of Radiology, Duke University, Durham, NC
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13
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Abadi E, Jadick G, Lynch DA, Segars WP, Samei E. Emphysema Quantifications With CT Scan: Assessing the Effects of Acquisition Protocols and Imaging Parameters Using Virtual Imaging Trials. Chest 2023; 163:1084-1100. [PMID: 36462532 PMCID: PMC10206513 DOI: 10.1016/j.chest.2022.11.033] [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: 04/17/2022] [Revised: 11/01/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND CT scan has notable potential to quantify the severity and progression of emphysema in patients. Such quantification should ideally reflect the true attributes and pathologic conditions of subjects, not scanner parameters. To achieve such an objective, the effects of the scanner conditions need to be understood so the influence can be mitigated. RESEARCH QUESTION How do CT scan imaging parameters affect the accuracy of emphysema-based quantifications and biomarkers? STUDY DESIGN AND METHODS Twenty anthropomorphic digital phantoms were developed with diverse anatomic attributes and emphysema abnormalities informed by a real COPD cohort. The phantoms were input to a validated CT scan simulator (DukeSim), modeling a commercial scanner (Siemens Flash). Virtual images were acquired under various clinical conditions of dose levels, tube current modulations (TCM), and reconstruction techniques and kernels. The images were analyzed to evaluate the effects of imaging parameters on the accuracy of density-based quantifications (percent of lung voxels with HU < -950 [LAA-950] and 15th percentile of lung histogram HU [Perc15]) across varied subjects. Paired t tests were performed to explore statistical differences between any two imaging conditions. RESULTS The most accurate imaging condition corresponded to the highest acquired dose (100 mAs) and iterative reconstruction (SAFIRE) with the smooth kernel of I31, where the measurement errors (difference between measurement and ground truth) were 35 ± 3 Hounsfield Units (HU), -4% ± 5%, and 26 ± 10 HU (average ± SD), for the mean lung HU, LAA-950, and Perc15, respectively. Without TCM and at the I31 kernel, increase of dose (20 to 100 mAs) improved the lung mean absolute error (MAE) by 4.2 ± 2.3 HU (average ± SD). TCM did not contribute to a systematic improvement of lung MAE. INTERPRETATION The results highlight that although CT scan quantification is possible, its reliability is impacted by the choice of imaging parameters. The developed virtual imaging trial platform in this study enables comprehensive evaluation of CT scan methods in reliable quantifications, an effort that cannot be readily made with patient images or simplistic physical phantoms.
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Affiliation(s)
- Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC; Department of Electrical & Computer Engineering, Duke University, Durham, NC; Medical Physics Graduate Program, Duke University, Durham, NC.
| | - Giavanna Jadick
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC; Medical Physics Graduate Program, Duke University, Durham, NC; Department of Biomedical Engineering, Duke University, Durham, NC
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC; Department of Electrical & Computer Engineering, Duke University, Durham, NC; Medical Physics Graduate Program, Duke University, Durham, NC; Department of Biomedical Engineering, Duke University, Durham, NC; Department of Physics, Duke University, Durham, NC
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14
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Vegas Sanchez-Ferrero G, San José Estépar R. The Value of Virtual Clinical Trials for the Assessment of the Effect of Acquisition Protocols in Emphysema Quantification. Chest 2023; 163:1001-1002. [PMID: 37164565 DOI: 10.1016/j.chest.2023.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 01/30/2023] [Accepted: 02/05/2023] [Indexed: 05/12/2023] Open
Affiliation(s)
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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15
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Zhang D, Mu C, Zhang X, Yan J, Xu M, Wang Y, Wang Y, Xue H, Chen Y, Jin Z. Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction. BMC Med Imaging 2023; 23:33. [PMID: 36800947 PMCID: PMC9940378 DOI: 10.1186/s12880-023-00988-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/06/2023] [Indexed: 02/21/2023] Open
Abstract
BACKGROUND To evaluate the image quality of lower extremity computed tomography angiography (CTA) with deep learning-based reconstruction (DLR) compared to model-based iterative reconstruction (MBIR), hybrid-iterative reconstruction (HIR), and filtered back projection (FBP). METHODS Fifty patients (38 males, average age 59.8 ± 19.2 years) who underwent lower extremity CTA between January and May 2021 were included. Images were reconstructed with DLR, MBIR, HIR, and FBP. The standard deviation (SD), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), noise power spectrum (NPS) curves, and the blur effect, were calculated. The subjective image quality was independently evaluated by two radiologists. The diagnostic accuracy of DLR, MBIR, HIR, and FBP reconstruction algorithms was calculated. RESULTS The CNR and SNR were significantly higher in DLR images than in the other three reconstruction algorithms, and the SD was significantly lower in DLR images of the soft tissues. The noise magnitude was the lowest with DLR. The NPS average spatial frequency (fav) values were higher using DLR than HIR. For blur effect evaluation, DLR and FBP were similar for soft tissues and the popliteal artery, which was better than HIR and worse than MBIR. In the aorta and femoral arteries, the blur effect of DLR was worse than MBIR and FBP and better than HIR. The subjective image quality score of DLR was the highest. The sensitivity and specificity of the lower extremity CTA with DLR were the highest in the four reconstruction algorithms with 98.4% and 97.2%, respectively. CONCLUSIONS Compared to the other three reconstruction algorithms, DLR showed better objective and subjective image quality. The blur effect of the DLR was better than that of the HIR. The diagnostic accuracy of lower extremity CTA with DLR was the best among the four reconstruction algorithms.
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Affiliation(s)
- Daming Zhang
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China
| | - Chunlin Mu
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China ,Department of Radiology, Beijing Sixth Hospital, Beijing, 100007 China
| | - Xinyue Zhang
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China
| | - Jing Yan
- Canon Medical Systems, Beijing, 100015 China
| | - Min Xu
- Canon Medical Systems, Beijing, 100015 China
| | - Yun Wang
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China
| | - Yining Wang
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China
| | - Huadan Xue
- grid.506261.60000 0001 0706 7839Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730 China
| | - Yuexin Chen
- Department of Vascular Surgery, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Bejing, 100730, China.
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16
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Zarei M, Sotoudeh-Paima S, McCabe C, Abadi E, Samei E. Harmonizing CT Images via Physics-based Deep Neural Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12463:124631Q. [PMID: 37131954 PMCID: PMC10149034 DOI: 10.1117/12.2654215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The rendition of medical images influences the accuracy and precision of quantifications. Image variations or biases make measuring imaging biomarkers challenging. The objective of this paper is to reduce the variability of computed tomography (CT) quantifications for radiomics and biomarkers using physics-based deep neural networks (DNNs). With the proposed framework, it is possible to harmonize the different renditions of a single CT scan (with variations in reconstruction kernel and dose) into an image that is in close agreement with the ground truth. To this end, a generative adversarial network (GAN) model was developed where the generator is informed by the scanner's modulation transfer function (MTF). To train the network, a virtual imaging trial (VIT) platform was used to acquire CT images, from a set of forty computational models (XCAT) serving as the patient model. Phantoms with varying levels of pulmonary disease, such as lung nodules and emphysema, were used. We scanned the patient models with a validated CT simulator (DukeSim) modeling a commercial CT scanner at 20 and 100 mAs dose levels and then reconstructed the images by twelve kernels representing smooth to sharp kernels. An evaluation of the harmonized virtual images was conducted in four different ways: 1) visual quality of the images, 2) bias and variation in density-based biomarkers, 3) bias and variation in morphological-based biomarkers, and 4) Noise Power Spectrum (NPS) and lung histogram. The trained model harmonized the test set images with a structural similarity index of 0.95±0.1, a normalized mean squared error of 10.2±1.5%, and a peak signal-to-noise ratio of 31.8±1.5 dB. Moreover, emphysema-based imaging biomarkers of LAA-950 (-1.5±1.8), Perc15 (13.65±9.3), and Lung mass (0.1±0.3) had more precise quantifications.
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Affiliation(s)
- Mojtaba Zarei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine
- Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine
- Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Cindy McCabe
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine
- Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine
- Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
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17
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Ho FC, Sotoudeh-Paima S, Segars WP, Samei E, Abadi E. Development and Application of a Virtual Imaging Trial framework for Airway Quantifications via CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12463:124631B. [PMID: 37125262 PMCID: PMC10142146 DOI: 10.1117/12.2654263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, characterized by emphysema and bronchitis. Airway measurements reflect the severity of bronchitis and other airway-related diseases. Airway structures can be objectively evaluated with quantitative computed tomography (CT). The accuracy of such quantifications is limited by the spatial resolution and image noise characteristics of the imaging system and can be potentially improved with the emerging photon-counting CT (PCCT) technology. This study evaluated the quantitative performance of PCCT against energy-integrating CT (EICT) systems for airway measurements, and further identified optimum CT imaging parameters for such quantifications. The study was performed using a novel virtual imaging framework by developing the first library of virtual patients with bronchitis. These virtual patients were developed based on CT images of confirmed COPD patients with varied bronchitis severity. The human models were virtually imaged at 6.3 and 12.6 mGy dose levels using a scanner-specific simulator (DukeSim), synthesizing clinical PCCT and EICT scanners (NAEOTOM Alpha, FLASH, Siemens). The projections were reconstructed with two algorithms and kernels at different matrix sizes and slice thicknesses. The CT images were used to quantify clinically relevant airway measurements ("Pi10" and "WA%") and compared against their ground truth values. Compared to EICT, PCCT provided more accurate Pi10 and WA% measurements by 63.1% and 68.2%, respectively. For both technologies, sharper kernels and larger matrix sizes led to more reliable bronchitis quantifications. This study highlights the potential advantages of PCCT against EICT in characterizing bronchitis utilizing a virtual imaging platform.
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Affiliation(s)
- Fong Chi Ho
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
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Shankar SS, Felice N, Hoffman EA, Atha J, Sieren JC, Samei E, Abadi E. Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom. Med Phys 2022; 49:7447-7457. [PMID: 36097259 PMCID: PMC9792443 DOI: 10.1002/mp.15967] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. PURPOSE To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. METHODS An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom. RESULTS The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94 HU, lung volume of 797-3785 cm3 , and lung mass of -800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84 HU, lung volume of 66-3785 cm3 , and lung mass of 170-1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. CONCLUSION We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.
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Affiliation(s)
- Sachin S. Shankar
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
| | - Nicholas Felice
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Eric A. Hoffman
- Department of Radiology, University of Iowa
- Department of Biomedical Engineering, University of Iowa
| | | | - Jessica C. Sieren
- Department of Radiology, University of Iowa
- Department of Biomedical Engineering, University of Iowa
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
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Wu M, FitzGerald P, Zhang J, Segars WP, Yu H, Xu Y, De Man B. XCIST-an open access x-ray/CT simulation toolkit. Phys Med Biol 2022; 67:10.1088/1361-6560/ac9174. [PMID: 36096127 PMCID: PMC10151073 DOI: 10.1088/1361-6560/ac9174] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/12/2022] [Indexed: 11/12/2022]
Abstract
Objective. X-ray-based imaging modalities including mammography and computed tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment planning, and therapy response monitoring. Over the past few decades, improvements to these modalities have resulted in substantially improved efficacy and efficiency, and substantially reduced radiation dose and cost. However, such improvements have evolved more slowly than would be ideal because lengthy preclinical and clinical evaluation is required. In many cases, new ideas cannot be evaluated due to the high cost of fabricating and testing prototypes. Wider availability of computer simulation tools could accelerate development of new imaging technologies. This paper introduces the development of a new open-access simulation environment for x-ray-based imaging. The main motivation of this work is to publicly distribute a fast but accurate ray-tracing x-ray and CT simulation tool along with realistic phantoms and 3D reconstruction capability, building on decades of developments in industry and academia.Approach. The x-ray-based Cancer Imaging Simulation Toolkit (XCIST) is developed in the context of cancer imaging, but can more broadly be applied. XCIST is physics-based, written in Python and C/C++, and currently consists of three major subsets: digital phantoms, the simulator itself (CatSim), and image reconstruction algorithms; planned future features include a fast dose-estimation tool and rigorous validation. To enable broad usage and to model and evaluate new technologies, XCIST is easily extendable by other researchers. To demonstrate XCIST's ability to produce realistic images and to show the benefits of using XCIST for insight into the impact of separate physics effects on image quality, we present exemplary simulations by varying contributing factors such as noise and sampling.Main results. The capabilities and flexibility of XCIST are demonstrated, showing easy applicability to specific simulation problems. Geometric and x-ray attenuation accuracy are shown, as well as XCIST's ability to model multiple scanner and protocol parameters, and to attribute fundamental image quality characteristics to specific parameters.Significance. This work represents an important first step toward the goal of creating an open-access platform for simulating existing and emerging x-ray-based imaging systems. While numerous simulation tools exist, we believe the combined XCIST toolset provides a unique advantage in terms of modeling capabilities versus ease of use and compute time. We publicly share this toolset to provide an environment for scientists to accelerate and improve the relevance of their research in x-ray and CT.
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Affiliation(s)
| | | | | | | | - Hengyong Yu
- University of Massachusetts Lowell, Lowell, MA
| | - Yongshun Xu
- University of Massachusetts Lowell, Lowell, MA
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [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: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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Narita A, Okamoto K, Toyonaga K, Ohkubo M. [An Easy-to-use Method for CT Image Simulation with Parameter Optimization Using a Water Phantom]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:732-740. [PMID: 35705316 DOI: 10.6009/jjrt.2022-1222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE We developed an easy-to-use method to generate computed tomography (CT) images that simulate the images obtained when using an actual scanner. METHODS The developed method generates images by simulating the data acquisition and image reconstruction processes of a scanner from a linear attenuation coefficient map of an object numerically generated. This approach is similar to general image simulation methods. However, we introduced adjustable parameters for the CT data acquisition process, for example, parameters related to X-ray attenuation in the anode of the X-ray tube and the bowtie filter. These parameters were optimized in advance by minimizing the difference between the simulated and measured images of a water phantom. To verify the validity of the developed method, a simulated image was generated for a torso phantom and then compared with the measured image of the phantom obtained using the scanner. RESULTS The simulated and measured images of the torso phantom were in good agreement. The spatial resolution and noise characteristics of these two images were also comparable, further indicating the accuracy of the developed method. CONCLUSION In the existing methods, various information/data related to an actual scanner, including difficult-to-acquire ones, were essential for image simulation. In the developed method, instead of determining the difficult-to-acquire information/data, we introduced adjustable parameters. Therefore, the developed method was easier to use than the existing methods.
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Affiliation(s)
- Akihiro Narita
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University
| | - Kazuki Okamoto
- Department of Radiological Technology, Seikei-kai Chiba Medical Center
| | - Kengo Toyonaga
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University
| | - Masaki Ohkubo
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University
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Sauer TJ, Abadi E, Segars P, Samei E. Anatomically- and physiologically-informed computational model of hepatic contrast perfusion for virtual imaging trials. Med Phys 2022; 49:2938-2951. [PMID: 35195901 PMCID: PMC9547339 DOI: 10.1002/mp.15562] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 02/02/2022] [Accepted: 02/02/2022] [Indexed: 12/10/2022] Open
Abstract
PURPOSE Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver. METHODS Intra-organ contrast perfusion depends on the organ's vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood-contrast mixture was compared to clinical data. RESULTS The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves (TCC). Measurements of simulated CT ROIs were found to agree with clinically-observed values of early arterial phase contrast enhancement of the parenchyma (∼5 HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement (180 HU). CONCLUSIONS The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Thomas J Sauer
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Ehsan Abadi
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Paul Segars
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Ehsan Samei
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
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Abadi E, McCabe C, Harrawood B, Sotoudeh-Paima S, Segars WP, Samei E. Development and Clinical Applications of a Virtual Imaging Framework for Optimizing Photon-counting CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12031:120311Q. [PMID: 35611365 PMCID: PMC9125732 DOI: 10.1117/12.2612079] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The purpose of this study was to develop a virtual imaging framework that simulates a new photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens). The PCCT simulator was built upon the DukeSim platform, which generates projection images of computational phantoms given the geometry and physics of the scanner and imaging parameters. DukeSim was adapted to account for the geometry of the PCCT prototype. To model the photon-counting detection process, we utilized a Monte Carlo-based detector model with the known properties of the detectors. We validated the simulation platform against experimental measurements. The images were acquired at four dose levels (CTDIvol of 1.5, 3.0, 6.0, and 12.0 mGy) and reconstructed with three kernels (Br36, Br40, Br48). The experimental acquisitions were replicated using our developed simulation platform. The real and simulated images were quantitatively compared in terms of image quality metrics (HU values, noise magnitude, noise power spectrum, and modulation transfer function). The clinical utility of our framework was demonstrated by conducting two clinical applications (COPD quantifications and lung nodule radiomics). The phantoms with relevant pathologies were imaged with DukeSim modeling the PCCT systems. Different imaging parameters (e.g., dose, reconstruction techniques, pixel size, and slice thickness) were altered to investigate their effects on task-based quantifications. We successfully implemented the acquisition and physics attributes of the PCCT prototype into the DukeSim platform. The discrepancy between the real and simulated data was on average about 2 HU in terms of noise magnitude, 0.002 mm-1 in terms of noise power spectrum peak frequency and 0.005 mm-1 in terms of the frequency at 50% MTF. Analysis suggested that lung lesion radiomics to be more accurate with reduced pixel size and slice thickness. For COPD quantifications, higher doses, thinner slices, and softer kernels yielded more accurate quantification of density-based biomarkers. Our developed virtual imaging platform enables systematic comparison of new PCCT technologies as well as optimization of the imaging parameters for specific clinical tasks.
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Affiliation(s)
- Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
| | - Cindy McCabe
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
| | - Brian Harrawood
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
| | - Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, NC, United States
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Sotoudeh-Paima S, Segars WP, Samei E, Abadi E. Photon-counting CT versus conventional CT for COPD quantifications: intra-scanner optimization and inter-scanner assessments using virtual imaging trials. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12031:120312I. [PMID: 35574205 PMCID: PMC9097858 DOI: 10.1117/12.2613003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease and a major cause of death and disability worldwide. Quantitative CT is a powerful tool to better understand the heterogeneity and severity of this disease. Quantitative CT is being increasingly used in COPD research, and the recent advancements in CT technology have made it even more encouraging. One recent advancement has been the development of photon-counting detectors, offering higher spatial resolution, higher image contrast, and lower noise levels in the images. However, the quantification performance of this new technology compared to conventional scanners remains unknown. Additionally, different protocol settings (e.g., different dose levels, slice thicknesses, reconstruction kernels and algorithms) affect quantifications in an unsimilar fashion. This study investigates the potential advantages of photon-counting CT (PCCT) against conventional energy-integrating detector (EID) CT and explores the effects of protocol settings on lung density quantifications in COPD patients. This study was made possible using a virtual imaging platform, taking advantage of anthropomorphic phantoms with COPD (COPD-XCAT) and a scanner-specific CT simulator (DukeSim). Having the physical and geometrical properties of three Siemens commercial scanners (Flash, Force for EID and NAEOTOM Alpha for PCCT) modeled, we simulated CT images of ten COPD-XCAT phantoms at 0.63 and 3.17 mGy dose levels and reconstructed at three levels of kernel sharpness. The simulated CT images were quantified in terms of "Lung mean absolute error (MAE)," "LAA -950," "Perc 15," "Lung mass" imaging biomarkers and compared against the ground truth values of the phantoms. The intra-scanner assessment demonstrated the superior qualitative and quantitative performance of the PCCT scanner over the conventional scanners (21.01% and 22.74% mean lung MAE improvement, and 53.97% and 68.13% mean LAA -950 error improvement compared to Flash and Force). The results also showed that higher mAs, thinner slices, smoother kernels, and iterative reconstruction could lead to more accurate and precise quantification scores. This study underscored the qualitative and quantitative benefits of PCCT against conventional EID scanners as well as the importance of optimal protocol choice within scanners for more accurate quantifications.
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Affiliation(s)
- Saman Sotoudeh-Paima
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
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Shankar SS, Jadick GL, Hoffman EA, Atha J, Sieren JC, Samei E, Abadi E. Scanner-specific validation of a CT simulator using a COPD-emulated anthropomorphic phantom. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12031:120313R. [PMID: 35547178 PMCID: PMC9087304 DOI: 10.1117/12.2613212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.
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Affiliation(s)
- Sachin S Shankar
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
| | - Giavanna L Jadick
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
| | - Eric A Hoffman
- Department of Radiology, University of Iowa
- Department of Biomedical Engineering, University of Iowa
| | | | - Jessica C Sieren
- Department of Radiology, University of Iowa
- Department of Biomedical Engineering, University of Iowa
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University
- Department of Electrical and Computer Engineering, Duke University
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Costa PR, Nersissian DY, Umisedo NK, Gonzales AHL, Fernández-Varea JM. A comprehensive Monte Carlo study of CT dose metrics proposed by the AAPM Reports 111 and 200. Med Phys 2021; 49:201-218. [PMID: 34800303 DOI: 10.1002/mp.15306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 09/22/2021] [Accepted: 10/10/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE A Monte Carlo (MC) modeling of single axial and helical CT scan modes has been developed to compute single and accumulated dose distributions. The radiation emission characteristics of an MDCT scanner has been modeled and used to evaluate the dose deposition in infinitely long head and body PMMA phantoms. The simulated accumulated dose distributions determined the approach to equilibrium function, H(L). From these H ( L ) curves, dose-related information was calculated for different head and body clinical protocols. METHODS The PENELOPE/penEasy package has been used to model the single axial and helical procedures and the radiation transport of photons and electrons in the phantoms. The bowtie filters, heel effect, focal-spot angle, and fan-beam geometry were incorporated. Head and body protocols with different pitch values were modeled for x-ray spectra corresponding to 80, 100, 120, and 140 kV. The analytical formulation for the single dose distributions and experimental measurements of single and accumulated dose distributions were employed to validate the MC results. The experimental dose distributions were measured with OSLDs and a thimble ion chamber inserted into PMMA phantoms. Also, the experimental values of the C T D I 100 along the center and peripheral axes of the CTDI phantom served to calibrate the simulated single and accumulated dose distributions. RESULTS The match of the simulated dose distributions with the reference data supports the correct modeling of the heel effect and the radiation transport in the phantom material reflected in the tails of the dose distributions. The validation of the x-ray source model was done comparing the CTDI ratios between simulated, measured and CTDosimetry data. The average difference of these ratios for head and body protocols between the simulated and measured data was in the range of 13-17% and between simulated and CTDosimetry data varied 10-13%. The distributions of simulated doses and those measured with the thimble ion chamber are compatible within 3%. In this study, it was demonstrated that the efficiencies of the C T D I 100 measurements in head phantoms with nT = 20 mm and 120 kV are 80.6% and 87.8% at central and peripheral axes, respectively. In the body phantoms with n T = 40 mm and 120 kV, the efficiencies are 56.5% and 86.2% at central and peripheral axes, respectively. In general terms, the clinical parameters such as pitch, beam intensity, and voltage affect the Deq values with the increase of the pitch decreasing the Deq and the beam intensity and the voltage increasing its value. The H(L) function does not change with the pitch values, but depends on the phantom axis (central or peripheral). CONCLUSIONS The computation of the pitch-equilibrium dose product, D ̂ eq , evidenced the limitations of the C T D I 100 method to determine the dose delivered by a CT scanner. Therefore, quantities derived from the C T D I 100 propagate this limitation. The developed MC model shows excellent compatibility with both measurements and literature quantities defined by AAPM Reports 111 and 200. These results demonstrate the robustness and versatility of the proposed modeling method.
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Affiliation(s)
- Paulo R Costa
- Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
| | | | - Nancy K Umisedo
- Institute of Physics, University of São Paulo, São Paulo, SP, Brazil
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Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11198961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Purpose: The purpose of this study was to preliminarily estimate patient-specific organ doses in chest CT examinations for Chinese adults, and to investigate the effect of patient size on organ doses. Methods: By considering the body-size and body-build effects on the organ doses and taking the mid-chest water equivalent diameter (WED) as a body-size indicator, the chest scan images of 18 Chinese adults were acquired on a multi-detector CT to generate the regional voxel models. For each patient, the lungs, heart, and breasts (glandular breast tissues for both breasts) were segmented, and other organs were semi-automated segmented based on their HU values. The CT scanner and patient models simulated by MCNPX 2.4.0 software (Los Alamos National LaboratoryLos Alamos, USA) were used to calculate lung, breast, and heart doses. CTDIvol values were used to normalize simulated organ doses, and the exponential estimation model between the normalized organ dose and WED was investigated. Results: Among the 18 patients in this study, the simulated doses of lung, heart, and breast were 18.15 ± 2.69 mGy, 18.68 ± 2.87 mGy, and 16.11 ± 3.08 mGy, respectively. Larger patients received higher organ doses than smaller ones due to the higher tube current used. The ratios of lung, heart, and breast doses to the CTDIvol were 1.48 ± 0.22, 1.54 ± 0.20, and 1.41 ± 0.13, respectively. The normalized organ doses of all the three organs decreased with the increase in WED, and the normalized doses decreased more obviously in the lung and the heart than that in the breasts. Conclusions: The output of CT scanner under ATCM is positively related to the attenuation of patients, larger-size patients receive higher organ doses. The organ dose normalized by CTDIvol was negatively correlated with patient size. The organ doses could be estimated by using the indicated CTDIvol combined with the estimated WED.
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O'Connell J, Lindsay C, Bazalova-Carter M. Experimental validation of Fastcat kV and MV cone beam CT (CBCT) simulator. Med Phys 2021; 48:6869-6880. [PMID: 34559406 DOI: 10.1002/mp.15243] [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: 04/28/2021] [Revised: 09/05/2021] [Accepted: 09/14/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To experimentally validate the Fastcat cone beam computed tomography (CBCT) simulator against kV and MV CBCT images acquired with a Varian Truebeam linac. METHODS kV and MV CBCT images of a Catphan 504 phantom were acquired using a 100 kVp beam with the on-board imager (OBI) and a 6 MV treatment beam with the electronic portal imaging device (EPID), respectively. The kV Fastcat simulation was performed using detailed models of the x-ray source, bowtie filter, a high resolution voxelized virtual Catphan phantom, anti-scatter grid, and the CsI scintillating detector. Likewise, an MV Fastcat CBCT was simulated with detailed models for the beam energy spectrum, flattening filter, a high-resolution voxelized virtual Catphan phantom, and the gadolinium oxysulfide (GOS) scintillating detector. Experimental and simulated CBCT images of the phantom were compared with respect to HU values, contrast to noise ratio (CNR), and dose linearity. Detector modulation transfer function (MTF) for the two detectors were also experimentally validated. Fastcat's dose calculations were compared to Monte Carlo (MC) dose calculations performed with Topas. RESULTS For the kV and MV simulations, respectively: Contrast agreed within 14 and 9 HUs and detector MTF agreed within 4.2% and 2.5%. Likewise, CNR had a root mean squared error (RMSE) of 2.6% and 1.4%. Dose agreed within 2.4% and 1.6% of MC values. The kV and MV CBCT images took 71 and 72 s to simulate in Fastcat with 887 and 493 projections, respectively. CONCLUSIONS We present a multienergy experimental validation of a fast and accurate CBCT simulator against a commercial linac. The simulator is open source and all models found in this work can be downloaded from https://github.com/jerichooconnell/fastcat.git.
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Affiliation(s)
- Jericho O'Connell
- Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada
| | - Clayton Lindsay
- Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada
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Jadick G, Abadi E, Harrawood B, Sharma S, Segars WP, Samei E. A scanner-specific framework for simulating CT images with tube current modulation. Phys Med Biol 2021; 66. [PMID: 34464942 DOI: 10.1088/1361-6560/ac2269] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/31/2021] [Indexed: 11/12/2022]
Abstract
Although tube current modulation (TCM) is routinely implemented in modern computed tomography (CT) scans, no existing CT simulator is capable of generating realistic images with TCM. The goal of this study was to develop such a framework to (1) facilitate patient-specific optimization of TCM parameters and (2) enable future virtual imaging trials (VITs) with more clinically realistic image quality and x-ray flux distributions. The framework was created by developing a TCM module and integrating it with an existing CT simulator (DukeSim). The developed module utilizes scanner-calibrated TCM parameters and two localizer radiographs to compute the mAs for each simulated CT projection. This simulation pipeline was validated in two parts. First, DukeSim was validated in the context of a commercial scanner with TCM (SOMATOM Force, Siemens Healthineers) by imaging a physical CT phantom (Mercury, Sun Nuclear) and its computational analogue. Second, the TCM module was validated by imaging a computational anthropomorphic phantom (ATOM, CIRS) using DukeSim with real and module-generated TCM profiles. The validation demonstrated DukeSim's realism in terms of noise magnitude, noise texture, spatial resolution, and image contrast (with average differences of 0.38%, 6.31%, 0.43%, and -9 HU, respectively). It also demonstrated the TCM module's realism in terms of projection-level mAs and resulting noise magnitude (2.86% and -2.60%, respectively). Finally, the framework was applied to a pilot VIT simulating images of three computational anthropomorphic phantoms (XCAT, with body mass indices (BMIs) of 24.3, 28.2, and 33.0) under five different TCM settings. The optimal TCM for each phantom was characterized based on various criteria, such as minimizing mAs or maximizing image quality. 'Very Weak' TCM minimized noise for the 24.3 BMI phantom, while 'Very Strong' TCM minimized noise for the 33.0 BMI phantom. This illustrates the utility of the developed framework for future optimization studies of TCM parameters and, more broadly, large-scale VITs with scanner-specific TCM.
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Affiliation(s)
- Giavanna Jadick
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America.,Medical Physics Graduate Program, Duke University School of Medicine, NC, United States of America.,Department of Electrical and Computer Engineering, Duke University, NC, United States of America
| | - Brian Harrawood
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America
| | - Shobhit Sharma
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America.,Department of Physics, Duke University, NC, United States of America
| | - W Paul Segars
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America.,Medical Physics Graduate Program, Duke University School of Medicine, NC, United States of America.,Department of Biomedical Engineering, Duke University, NC, United States of America
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, NC, United States of America.,Medical Physics Graduate Program, Duke University School of Medicine, NC, United States of America.,Department of Electrical and Computer Engineering, Duke University, NC, United States of America.,Department of Physics, Duke University, NC, United States of America.,Department of Biomedical Engineering, Duke University, NC, United States of America
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Smith TB, Abadi E, Sauer TJ, Fu W, Solomon J, Samei E. Development and validation of an automated methodology to assess perceptual in vivo noise texture in liver CT. J Med Imaging (Bellingham) 2021; 8:052113. [PMID: 34712744 PMCID: PMC8543153 DOI: 10.1117/1.jmi.8.5.052113] [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: 04/07/2021] [Accepted: 10/11/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo). Approach: The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise-non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency (f avg ), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results: Pearson and Spearman correlation coefficients 0.97 and 0.96 forf avg indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with ∼ 10 6 pixels . Conclusions: Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.
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Affiliation(s)
- Taylor Brunton Smith
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
| | - Ehsan Abadi
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
| | - Thomas J. Sauer
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
| | - Wanyi Fu
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
| | - Justin Solomon
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University Medical Center, Durham, North Carolina, United States
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Schultheiss M, Schmette P, Bodden J, Aichele J, Müller-Leisse C, Gassert FG, Gassert FT, Gawlitza JF, Hofmann FC, Sasse D, von Schacky CE, Ziegelmayer S, De Marco F, Renger B, Makowski MR, Pfeiffer F, Pfeiffer D. Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance. Sci Rep 2021; 11:15857. [PMID: 34349135 PMCID: PMC8339004 DOI: 10.1038/s41598-021-94750-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 07/15/2021] [Indexed: 12/24/2022] Open
Abstract
We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75-0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area.
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Affiliation(s)
- Manuel Schultheiss
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany.
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany.
| | - Philipp Schmette
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Jannis Bodden
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Juliane Aichele
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Christina Müller-Leisse
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Felix G Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Florian T Gassert
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Joshua F Gawlitza
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Felix C Hofmann
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Daniel Sasse
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Claudio E von Schacky
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Sebastian Ziegelmayer
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Fabio De Marco
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Bernhard Renger
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Franz Pfeiffer
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Daniela Pfeiffer
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
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O'Connell J, Bazalova-Carter M. fastCAT: Fast cone beam CT (CBCT) simulation. Med Phys 2021; 48:4448-4458. [PMID: 34053094 DOI: 10.1002/mp.15007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE To develop fastCAT, a fast cone-beam computed tomography (CBCT) simulator. fastCAT uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. METHODS Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5- and 6 MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled-a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy-dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B-100. RESULTS fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 × 1024 × 10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. CONCLUSIONS We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.
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Affiliation(s)
- Jericho O'Connell
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, V8P 5C2, Canada
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Radiation Dose to the Fetus From Computed Tomography of Pregnant Patients-Development and Validation of a Web-Based Tool. Invest Radiol 2021; 55:762-768. [PMID: 32604386 DOI: 10.1097/rli.0000000000000701] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Estimations of radiation dose absorbed by the fetus from computed tomography (CT) in pregnant patients is mandatory, but currently available methods are not feasible in clinical routine. The aims of this study were to develop and validate a tool for assessment of fetal dose from CT of pregnant patients and to develop a user-friendly web interface for fast fetal dose calculations. METHODS In the first study part, 750 Monte Carlo (MC) simulations were performed on phantoms representing pregnant patients at various gestational stages. The MC code simulating vendor-independent dose distributions was validated against CT dose index (CTDI) measurements performed on CT scanners of 2 vendors. The volume CTDI-normalized fetal dose values from MC simulations were used for developing the computational algorithm enabling fetal dose assessments from CT of various body regions at different exposure settings. In the institutional review board-approved second part, the algorithm was validated against patient-specific MC simulations performed on CT data of 29 pregnant patients (gestational ages 8-35 weeks) who underwent CT. Furthermore, the tool was compared with a commercially available software. A user-friendly web-based interface for fetal dose calculations was created. RESULTS Weighted CTDI values obtained from MC simulations were in excellent agreement with measurements performed on the 2 CT systems (average error, 4%). The median fetal dose from abdominal CT in pregnant patients was 2.7 mGy, showing moderate correlation with maternal perimeter (r = 0.69). The algorithm provided accurate estimates of fetal doses (average error, 11%), being more accurate than the commercially available tool. The web-based interface (www.fetaldose.org) enabling vendor-independent calculations of fetal doses from CT requires the input of gestational age, volume CTDI, tube voltage, and scan region. CONCLUSIONS A tool for fetal dose assessments from CT of pregnant patients was developed and validated being freely available on a user-friendly web interface.
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Divel SE, Christensen S, Segars WP, Lansberg MG, Pelc NJ. A dynamic simulation framework for CT perfusion in stroke assessment built from first principles. Med Phys 2021; 48:3500-3510. [PMID: 33877693 DOI: 10.1002/mp.14887] [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: 06/29/2020] [Revised: 01/24/2021] [Accepted: 04/02/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Physicians utilize cerebral perfusion maps (e.g., cerebral blood flow, cerebral blood volume, transit time) to prescribe the plan of care for stroke patients. Variability in scanning techniques and post-processing software can result in differences between these perfusion maps. To determine which techniques are acceptable for clinical care, it is important to validate the accuracy and reproducibility of the perfusion maps. Validation using clinical data is challenging due to the lack of a gold standard to assess cerebral perfusion and the impracticality of scanning patients multiple times with different scanning techniques. In contrast, simulated data from a realistic digital phantom of the cerebral perfusion in acute stroke patients would enable studies to optimize and validate the scanning and post-processing techniques. METHODS We describe a complete framework to simulate CT perfusion studies for stroke assessment. We begin by expanding the XCAT brain phantom to enable spatially varying contrast agent dynamics and incorporate a realistic model of the dynamics in the cerebral vasculature derived from first principles. A dynamic CT simulator utilizes the time-concentration curves to define the contrast agent concentration in the object at each time point and generates CT perfusion images compatible with commercially available post-processing software. We also generate ground truth perfusion maps to which the maps generated by post-processing software can be compared. RESULTS We demonstrate a dynamic CT perfusion study of a simulated patient with an ischemic stroke and the resulting perfusion maps generated by post-processing software. We include a visual comparison between the computer-generated perfusion maps and the ground truth perfusion maps. The framework is highly tunable; users can modify the perfusion properties (e.g., occlusion location, CBF, CBV, and MTT), scanner specifications (e.g., focal spot size and detector configuration), scanning protocol (e.g., kVp and mAs), and reconstruction parameters (e.g., slice thickness and reconstruction filter). CONCLUSIONS This framework provides realistic test data with the underlying ground truth that enables a robust assessment of CT perfusion techniques and post-processing methods for stroke assessment.
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Affiliation(s)
- Sarah E Divel
- Departments of Electrical Engineering and Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Soren Christensen
- Stanford Stroke Center, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - William P Segars
- Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology and Biomedical Engineering, Medical Physics Graduate Program, Duke University, Durham, NC, 27705, USA
| | - Maarten G Lansberg
- Department of Neurology and Neurological Sciences and the Stanford Stroke Center, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Norbert J Pelc
- Departments of Bioengineering and Radiology, Stanford University, Stanford, CA, 94305, USA
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Sharma S, Abadi E, Kapadia A, Segars WP, Samei E. A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials. Phys Med Biol 2021; 66. [PMID: 33652421 DOI: 10.1088/1361-6560/abeb32] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 03/02/2021] [Indexed: 01/27/2023]
Abstract
Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.
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Affiliation(s)
- Shobhit Sharma
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, NC, United States of America.,Department of Physics, Duke University, NC, United States of America
| | - Ehsan Abadi
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, NC, United States of America.,Department of Radiology, Duke University, NC, United States of America
| | - Anuj Kapadia
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, NC, United States of America.,Department of Physics, Duke University, NC, United States of America.,Department of Radiology, Duke University, NC, United States of America
| | - W Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, NC, United States of America.,Department of Radiology, Duke University, NC, United States of America.,Department of Biomedical Engineering, Duke University, NC, United States of America
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, NC, United States of America.,Department of Physics, Duke University, NC, United States of America.,Department of Radiology, Duke University, NC, United States of America.,Department of Biomedical Engineering, Duke University, NC, United States of America.,Department of Electrical and Computer Engineering, Duke University, NC, United States of America
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FitzGerald P, Araujo S, Wu M, De Man B. Semiempirical, parameterized spectrum estimation for x-ray computed tomography. Med Phys 2021; 48:2199-2213. [PMID: 33426704 DOI: 10.1002/mp.14715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/10/2020] [Accepted: 12/29/2020] [Indexed: 01/06/2023] Open
Abstract
PURPOSE To develop a tool to produce accurate, well-validated x-ray spectra for standalone use or for use in an open-access x-ray/CT simulation tool. Spectrum models will be developed for tube voltages in the range of 80 kVp through 140 kVp and for anode takeoff angles in the range of 5° to 9°. METHODS Spectra were initialized based on physics models, then refined using empirical measurements, as follows. A new spectrum-parameterization method was developed, including 13 spline knots to represent the bremsstrahlung component and 4 values to represent characteristic lines. Initial spectra at 80, 100, 120, and 140 kVp and at takeoff angles from 5° to 9° were produced using physics-based spectrum estimation tools XSPECT and SpekPy. Empirical experiments were systematically designed with careful selection of attenuator materials and thicknesses, and by reducing measurement contamination from scatter to <1%. Measurements were made on a 64-row CT scanner using the scanner's detector and using multiple layers of polymethylmethacrylate (PMMA), aluminum, titanium, tin, and neodymium. Measurements were made at 80, 100, 120, and 140 kVp and covering the entire 64-row detector (takeoff angles from 5° to 9°); a total of 6,144 unique measurements were made. After accounting for the detector's energy response, parameterized representations of the initial spectra were refined for best agreement with measurements using two proposed optimization schemes: based on modulation and based on gradient descent. X-ray transmission errors were computed for measurements vs calculations using the nonoptimized and optimized spectra. Half-value, tenth-value, and hundredth-value layers for PMMA, Al, and Ti were calculated. RESULTS Spectra before and after parameterization were in excellent agreement (e.g., R2 values of 0.995 and 0.997). Empirical measurements produced smoothly varying curves with x-ray transmission covering a range of up to 3.5 orders of magnitude. Spectra from the two optimization schemes, compared with the unoptimized physic-based spectra, each improved agreement with measurements by twofold through tenfold, for both postlog transmission data and for fractional value layers. CONCLUSION The resulting well-validated spectra are appropriate for use in the open-access x-ray/CT simulator under development, the x-ray-based Cancer Imaging Toolkit (XCIST), or for standalone use. These spectra can be readily interpolated to produce spectra at arbitrary kVps over the range of 80 to 140 kVp and arbitrary takeoff angles over the range of 5° to 9°. Furthermore, interpolated spectra over these ranges can be obtained by applying the standalone Matlab function available at https://github.com/xcist/documentation/blob/master/XCISTspectrum.m.
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Affiliation(s)
| | | | - Mingye Wu
- GE Research, Niskayuna, NY, 12309, USA
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Precision medicine in human heart modeling : Perspectives, challenges, and opportunities. Biomech Model Mechanobiol 2021; 20:803-831. [PMID: 33580313 PMCID: PMC8154814 DOI: 10.1007/s10237-021-01421-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023]
Abstract
Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.
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Abadi E, Paul Segars W, Chalian H, Samei E. Virtual Imaging Trials for Coronavirus Disease (COVID-19). AJR Am J Roentgenol 2021; 216:362-368. [PMID: 32822224 PMCID: PMC8080437 DOI: 10.2214/ajr.20.23429] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. CONCLUSION. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.
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Affiliation(s)
- Ehsan Abadi
- Department of Radiology, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705
- Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC
| | - W Paul Segars
- Department of Radiology, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705
- Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC
- Department of Biomedical Engineering, Duke University, Durham, NC
| | - Hamid Chalian
- Department of Radiology, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705
| | - Ehsan Samei
- Department of Radiology, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705
- Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department of Physics, Duke University, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
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Elhamiasl M, Nuyts J. Low-dose x-ray CT simulation from an available higher-dose scan. ACTA ACUST UNITED AC 2020; 65:135010. [DOI: 10.1088/1361-6560/ab8953] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Abadi E, Segars WP, Tsui BMW, Kinahan PE, Bottenus N, Frangi AF, Maidment A, Lo J, Samei E. Virtual clinical trials in medical imaging: a review. J Med Imaging (Bellingham) 2020; 7:042805. [PMID: 32313817 PMCID: PMC7148435 DOI: 10.1117/1.jmi.7.4.042805] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 03/23/2020] [Indexed: 12/13/2022] Open
Abstract
The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.
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Affiliation(s)
- Ehsan Abadi
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - William P. Segars
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Benjamin M. W. Tsui
- Johns Hopkins University, Department of Radiology, Baltimore, Maryland, United States
| | - Paul E. Kinahan
- University of Washington, Department of Radiology, Seattle, Washington, United States
| | - Nick Bottenus
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- University of Colorado Boulder, Department of Mechanical Engineering, Boulder, Colorado, United States
| | - Alejandro F. Frangi
- University of Leeds, School of Computing, Leeds, United Kingdom
- University of Leeds, School of Medicine, Leeds, United Kingdom
| | - Andrew Maidment
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Joseph Lo
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University, Department of Radiology, Durham, North Carolina, United States
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Abadi E, Segars WP, Harrawood B, Sharma S, Kapadia A, Samei E. Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography. J Med Imaging (Bellingham) 2020; 7:042806. [PMID: 32509918 PMCID: PMC7262564 DOI: 10.1117/1.jmi.7.4.042806] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 05/19/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose: To utilize a virtual clinical trial (VCT) construct to investigate the effects of beam collimation and pitch on image quality (IQ) in computed tomography (CT) under different respiratory and cardiac motion rates. Approach: A computational human model [extended cardiac-torso (XCAT) phantom] with added lung lesions was used to simulate seven different rates of cardiac and respiratory motions. A validated CT simulator (DukeSim) was used in this study. A supplemental validation was done to ensure the accuracy of DukeSim across different pitches and beam collimations. Each XCAT phantom was imaged using the CT simulator at multiple pitches (0.5 to 1.5) and beam collimations (19.2 to 57.6 mm) at a constant dose level. The images were compared against the ground truth using three task-generic IQ metrics in the lungs. Additionally, the bias and variability in radiomics (morphological) feature measurements were quantified for task-specific lung lesion quantification across the studied imaging conditions. Results: All task-generic metrics degraded by 1.6% to 13.3% with increasing pitch. When imaged with motion, increasing pitch reduced motion artifacts. The IQ slightly degraded (1.3%) with changes in the studied beam collimations. Patient motion exhibited negative effects (within 7%) on the IQ. Among all features across all imaging conditions studies, compactness2 and elongation showed the largest ( - 26.5 % , 7.8%) and smallest ( - 0.8 % , 2.7%) relative bias and variability. The radiomics results were robust across the motion profiles studied. Conclusions: While high pitch and large beam collimations can negatively affect the quality of CT images, they are desirable for fast imaging. Further, our results showed no major adverse effects in morphology quantification of lung lesions with the increase in pitch or beam collimation. VCTs, such as the one demonstrated in this study, represent a viable methodology for experiments in CT.
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Affiliation(s)
- Ehsan Abadi
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
| | - William P. Segars
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University School of Medicine, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Brian Harrawood
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
| | - Shobhit Sharma
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Anuj Kapadia
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University School of Medicine, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Ehsan Samei
- Duke University School of Medicine, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States
- Duke University School of Medicine, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
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Design of a Monte Carlo model based on dual-source computed tomography (DSCT) scanners for dose and image quality assessment using the Monte Carlo N-Particle (MCNP5) code. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2020. [DOI: 10.2478/pjmpe-2020-0002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
The purpose of this work was to develop and validate a Monte Carlo model for a Dual Source Computed Tomography (DSCT) scanner based on the Monte Carlo N-particle radiation transport computer code (MCNP5). The geometry of the Siemens Somatom Definition CT scanner was modeled, taking into consideration the x-ray spectrum, bowtie filter, collimator, and detector system. The accuracy of the simulation from the dosimetry point of view was tested by calculating the Computed Tomography Dose Index (CTDI) values. Furthermore, typical quality assurance phantoms were modeled in order to assess the imaging aspects of the simulation. Simulated projection data were processed, using the MATLAB software, in order to reconstruct slices, using a Filtered Back Projection algorithm. CTDI, image noise, CT-number linearity, spatial and low contrast resolution were calculated using the simulated test phantoms. The results were compared using several published values including IMPACT, NIST and actual measurements. Bowtie filter shapes are in agreement with those theoretically expected. Results show that low contrast and spatial resolution are comparable with expected ones, taking into consideration the relatively limited number of events used for the simulation. The differences between simulated and nominal CT-number values were small. The present attempt to simulate a DSCT scanner could provide a powerful tool for dose assessment and support the training of clinical scientists in the imaging performance characteristics of Computed Tomography scanners.
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Sharma S, Kapadia A, Fu W, Abadi E, Segars WP, Samei E. A real-time Monte Carlo tool for individualized dose estimations in clinical CT. Phys Med Biol 2019; 64:215020. [PMID: 31539892 DOI: 10.1088/1361-6560/ab467f] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The increasing awareness of the adverse effects associated with radiation exposure in computed tomography (CT) has necessesitated the quantification of dose delivered to patients for better risk assessment in the clinic. The current methods for dose quantification used in the clinic are approximations, lacking realistic models for the irradiation conditions utilized in the scan and the anatomy of the patient being imaged, which limits their relevance for a particular patient. The established gold-standard technique for individualized dose quantification uses Monte Carlo (MC) simulations to obtain patient-specific estimates of organ dose in anatomically realistic computational phantoms to provide patient-specific estimates of organ dose. Although accurate, MC simulations are computationally expensive, which limits their utility for time-constrained applications in the clinic. To overcome these shortcomings, a real-time GPU-based MC tool based on FDA's MC-GPU framework was developed for patient and scanner-specific dosimetry in the clinic. The tool was validated against (1) AAPM's TG-195 reference datasets and (2) physical measurements of dose acquired using TLD chips in adult and pediatric anthropomorphic phantoms. To demonstrate its utility towards providing individualized dose estimates, it was integrated with an automatic segmentation method for generating patient-specific models, which were then used to estimate patient- and scanner-specific organ doses for a select population of 50 adult patients using a clinically relevant CT protocol. The organ dose estimates were compared to corresponding dose estimates from a previously validated MC method based on Penelope. The dose estimates from our MC tool agreed within 5% for all organs (except thyroid) tabulated by TG-195 and within 10% for all TLD locations in the adult and pediactric phantoms, across all validation cases. Compared against Penelope, the organ dose estimates agreed within 3% on average for all organs in the patient population study. The average run duration for each patient was estimated at 23.79 s, representing a significant speedup (~700×) over existing non-parallelized MC methods. The accuracy of dose estimates combined with a significant improvement in execution times suggests a feasible solution utilizing the proposed MC tool for real-time individualized dosimetry in the clinic.
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Affiliation(s)
- Shobhit Sharma
- Department of Physics, Duke University, Durham, NC 27705, United States of America. Carl E Ravin Advanced Imaging Laboratories, Duke University, Durham, NC 27705, United States of America. Author to whom any correspondence should be addressed
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Abadi E, Harrawood B, Rajagopal JR, Sharma S, Kapadia A, Segars WP, Stierstorfer K, Sedlmair M, Jones E, Samei E. Development of a scanner-specific simulation framework for photon-counting computed tomography. Biomed Phys Eng Express 2019; 5:055008. [PMID: 33304618 PMCID: PMC7725233 DOI: 10.1088/2057-1976/ab37e9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.
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Affiliation(s)
- Ehsan Abadi
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Brian Harrawood
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Shobhit Sharma
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Anuj Kapadia
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - William Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Karl Stierstorfer
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Martin Sedlmair
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Elizabeth Jones
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, United States of America
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