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Yang J, Afaq A, Sibley R, McMilan A, Pirasteh A. Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01199-y. [PMID: 39167304 DOI: 10.1007/s10334-024-01199-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 08/06/2024] [Accepted: 08/08/2024] [Indexed: 08/23/2024]
Abstract
We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.
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Affiliation(s)
- Jaewon Yang
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA.
| | - Asim Afaq
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Robert Sibley
- Department of Radiology, University of Texas Southwestern, 5323 Harry Hines Blvd., Dallas, TX, USA
| | - Alan McMilan
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, USA
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Wong YL, Li T, Liu C, Lee HFV, Cheung LYA, Hui ESK, Cao P, Cai J. Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities. Med Phys 2024; 51:4721-4735. [PMID: 38386904 DOI: 10.1002/mp.17001] [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: 03/21/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase. PURPOSE To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities. METHODS The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner. RESULTS In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively. CONCLUSIONS Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.
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Affiliation(s)
- Yat Lam Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Lai-Yin Andy Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Oncology Center, St. Paul's Hospital, Hong Kong, China
| | - Edward Sai Kam Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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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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Manini C, Nemchyna O, Akansel S, Walczak L, Tautz L, Kolbitsch C, Falk V, Sündermann S, Kühne T, Schulz-Menger J, Hennemuth A. A simulation-based phantom model for generating synthetic mitral valve image data-application to MRI acquisition planning. Int J Comput Assist Radiol Surg 2024; 19:553-569. [PMID: 37679657 PMCID: PMC10881710 DOI: 10.1007/s11548-023-03012-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
PURPOSE Numerical phantom methods are widely used in the development of medical imaging methods. They enable quantitative evaluation and direct comparison with controlled and known ground truth information. Cardiac magnetic resonance has the potential for a comprehensive evaluation of the mitral valve (MV). The goal of this work is the development of a numerical simulation framework that supports the investigation of MRI imaging strategies for the mitral valve. METHODS We present a pipeline for synthetic image generation based on the combination of individual anatomical 3D models with a position-based dynamics simulation of the mitral valve closure. The corresponding images are generated using modality-specific intensity models and spatiotemporal sampling concepts. We test the applicability in the context of MRI imaging strategies for the assessment of the mitral valve. Synthetic images are generated with different strategies regarding image orientation (SAX and rLAX) and spatial sampling density. RESULTS The suitability of the imaging strategy is evaluated by comparing MV segmentations against ground truth annotations. The generated synthetic images were compared to ones acquired with similar parameters, and the result is promising. The quantitative analysis of annotation results suggests that the rLAX sampling strategy is preferable for MV assessment, reaching accuracy values that are comparable to or even outperform literature values. CONCLUSION The proposed approach provides a valuable tool for the evaluation and optimization of cardiac valve image acquisition. Its application to the use case identifies the radial image sampling strategy as the most suitable for MV assessment through MRI.
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Affiliation(s)
- Chiara Manini
- Institute of Computer-Assisted Cardiovascular Medicine, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany.
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany.
| | - Olena Nemchyna
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
| | - Serdar Akansel
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
| | - Lars Walczak
- Institute of Computer-Assisted Cardiovascular Medicine, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- Fraunhofer MEVIS, Berlin, Germany
| | | | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Volkmar Falk
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Simon Sündermann
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Titus Kühne
- Institute of Computer-Assisted Cardiovascular Medicine, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany
| | - Anja Hennemuth
- Institute of Computer-Assisted Cardiovascular Medicine, Deutsches Herzzentrum Der Charité (DHZC), Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
- Fraunhofer MEVIS, Berlin, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Wang L, Li T, Cai J, Chang HC. Motion-resolved four-dimensional abdominal diffusion-weighted imaging using PROPELLER EPI (4D-DW-PROPELLER-EPI). Magn Reson Med 2023; 90:2454-2471. [PMID: 37486854 DOI: 10.1002/mrm.29802] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/22/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
PURPOSE To develop a distortion-free motion-resolved four-dimensional diffusion-weighted PROPELLER EPI (4D-DW-PROPELLER-EPI) technique for benefiting clinical abdominal radiotherapy (RT). METHODS An improved abdominal 4D-DWI technique based on 2D diffusion-weighted PROPELLER-EPI (2D-DW-PROPELLER-EPI), termed 4D-DW-PROPELLER-EPI, was proposed to improve the frame rate of repeated data acquisition and produce distortion-free 4D-DWI images. Since the radial or PROPELLER sampling with golden-angle rotation can achieve an efficient k-space coverage with a flexible time-resolved acquisition, the golden-angle multi-blade acquisition was used in the proposed 4D-DW-PROPELLER-EPI to improve the performance of data sorting. A new k-space and blade (K-B) amplitude binning method was developed for the proposed 4D-DW-PROPELLER-EPI to optimize the number of blades and the k-space uniformity before performing conventional PROPELLER-EPI reconstruction, by using two metrics to evaluate the adequacy of the acquired data. The proposed 4D-DW-PROPELLER-EPI was preliminarily evaluated in both simulation experiments and in vivo experiments with varying frame rates and different numbers of repeated acquisition. RESULTS The feasibility of achieving distortion-free 4D-DWI images by using the proposed 4D-DW-PROPELLER-EPI technique was demonstrated in both digital phantom and healthy subjects. Evaluation of the 4D completeness metrics shows that the K-B amplitude binning method could simultaneously improve the acquisition efficiency and data reconstruction performance for 4D-DW-PROPELLER-EPI. CONCLUSION 4D-DW-PROPELLER-EPI with K-B amplitude binning is an advanced technique that can provide distortion-free 4D-DWI images for resolving respiratory motion, and may benefit the application of image-guided abdominal RT.
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Affiliation(s)
- Lu Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- Multi-Scale Medical Robotics Center, The Chinese University of Hong Kong, Hong Kong, Hong Kong
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Liu C, Li T, Cao P, Hui ES, Wong YL, Wang Z, Xiao H, Zhi S, Zhou T, Li W, Lam SK, Cheung ALY, Lee VHF, Ying M, Cai J. Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management. Int J Radiat Oncol Biol Phys 2023; 117:493-504. [PMID: 37116591 DOI: 10.1016/j.ijrobp.2023.04.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/04/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023]
Abstract
PURPOSE The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.
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Affiliation(s)
- Chenyang Liu
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yat-Lam Wong
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zuojun Wang
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shaohua Zhi
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sai Kit Lam
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Victor Ho-Fun Lee
- Department of Clinical Oncology, University of Hong Kong, Hong Kong SAR, China
| | - Michael Ying
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Jing Cai
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China.
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Whitehead JF, Laeseke PF, Periyasamy S, Speidel MA, Wagner MG. In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation. Med Phys 2023; 50:5505-5517. [PMID: 36950870 PMCID: PMC10517083 DOI: 10.1002/mp.16379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/28/2023] [Accepted: 03/13/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random. PURPOSE The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. METHODS The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented. RESULTS The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law= 1.2 ± 1 . 2 o $ = \;1.2 \pm {1.2^o}$ ), radii (median Murray deviation= 0.08 $ = \;0.08$ ), and smoothly curved, non-intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01). CONCLUSIONS This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.
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Affiliation(s)
- Joseph F Whitehead
- Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Paul F Laeseke
- Department of Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Sarvesh Periyasamy
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Michael A Speidel
- Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Martin G Wagner
- Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
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Dong Z, Yu S, Szmul A, Wang J, Qi J, Wu H, Li J, Lu Z, Zhang Y. Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy. Comput Biol Med 2023; 162:107073. [PMID: 37290392 PMCID: PMC10311359 DOI: 10.1016/j.compbiomed.2023.107073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/09/2023] [Accepted: 05/27/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy. METHOD Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively. RESULTS High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively. CONCLUSIONS This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.
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Affiliation(s)
- Zhengkun Dong
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Shutong Yu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Adam Szmul
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Jingyuan Wang
- Department of Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Junfeng Qi
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Junyu Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Zihong Lu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
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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: 8] [Impact Index Per Article: 8.0] [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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10
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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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11
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Karimipourfard M, Sina S, Khodadai Shoshtari F, Alavi M. Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network. Nuklearmedizin 2023; 62:61-72. [PMID: 36878470 DOI: 10.1055/a-2026-0784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
The cumulative activity map estimation are essential tools for patient specific dosimetry with high accuracy, which is estimated using biokinetic models instead of patient dynamic data or the number of static PET scans, owing to economical and time-consuming points of view. In the era of deep learning applications in medicine, the pix-to-pix (p2 p) GAN neural networks play a significant role in image translation between imaging modalities. In this pilot study, we extended the p2 p GAN networks to generate PET images of patients at different times according to a 60 min scan time after the injection of F-18 FDG. In this regard, the study was conducted in two sections: phantom and patient studies. In the phantom study section, the SSIM, PSNR, and MSE metric results of the generated images varied from 0.98-0.99, 31-34 and 1-2 respectively and the fine-tuned Resnet-50 network classified the different timing images with high performance. In the patient study, these values varied from 0.88-0.93, 36-41 and 1.7-2.2, respectively and the classification network classified the generated images in the true group with high accuracy. The results of phantom studies showed high values of evaluation metrics owing to ideal image quality conditions. However, in the patient study, promising results were achieved which showed that the image quality and training data number affected the network performance. This study aims to assess the feasibility of p2 p GAN network application for different timing image generation.
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Affiliation(s)
| | | | | | - Mehrsadat Alavi
- Shiraz University of Medical Sciences, Shiraz, Iran (the Islamic Republic of)
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12
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Tadesse GF, Geramifar P, Abbasi M, Tsegaw EM, Amin M, Salimi A, Mohammadi M, Teimourianfard B, Ay MR. Attenuation Correction for Dedicated Cardiac SPECT Imaging Without Using Transmission Data. Mol Imaging Radionucl Ther 2023; 32:42-53. [PMID: 36818953 PMCID: PMC9950684 DOI: 10.4274/mirt.galenos.2022.55476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Objectives Attenuation correction (AC) using transmission scanning-like computed tomography (CT) is the standard method to increase the accuracy of cardiac single-photon emission computed tomography (SPECT) images. Recently developed dedicated cardiac SPECT do not support CT, and thus, scans on these systems are vulnerable to attenuation artifacts. This study presented a new method for generating an attenuation map directly from emission data by segmentation of precisely non-rigid registration extended cardiac-torso (XCAT)-digital phantom with cardiac SPECT images. Methods In-house developed non-rigid registration algorithm automatically aligns the XCAT- phantom with cardiac SPECT image to precisely segment the contour of organs. Pre-defined attenuation coefficients for given photon energies were assigned to generate attenuation maps. The CT-based attenuation maps were used for validation with which cardiac SPECT/CT data of 38 patients were included. Segmental myocardial counts of a 17-segment model from these databases were compared based on the basis of the paired t-test. Results The mean, and standard deviation of the mean square error and structural similarity index measure of the female stress phase between the proposed attenuation maps and the CT attenuation maps were 6.99±1.23% and 92±2.0%, of the male stress were 6.87±3.8% and 96±1.0%. Proposed attenuation correction and computed tomography based attenuation correction average myocardial perfusion count was significantly higher than that in non-AC in the mid-inferior, mid-lateral, basal-inferior, and lateral regions (p<0.001). Conclusion The proposed attenuation maps showed good agreement with the CT-based attenuation map. Therefore, it is feasible to enable AC for a dedicated cardiac SPECT or SPECT standalone scanners.
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Affiliation(s)
- Getu Ferenji Tadesse
- Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran,Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran,St. Paul’s Hospital Millennium Medical College, Department of Internal Medicine, Addis Ababa, Ethiopia
| | - Parham Geramifar
- Tehran University of Medical Sciences, Shariati Hospital, Research Center for Nuclear Medicine, Tehran, Iran
| | - Mehrshad Abbasi
- Tehran University of Medical Sciences, Department of Nuclear Medicine, Vali-Asr Hospital, Tehran, Iran
| | - Eyachew Misganew Tsegaw
- Debre Tabor University Faculty of Natural and Computational Sciences, Department of Physics, Debre Tabor, Ethiopia
| | - Mohammad Amin
- Shahed University Faculty of Science, Department of Computer Science, Tehran, Iran
| | - Ali Salimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Mohammadi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mohammed Reza Ay
- Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran,Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran,* Address for Correspondence: Research Center for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS); Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran Phone: +989125789765 E-mail:
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13
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Gillen R, Erlandsson K, Denis-Bacelar AM, Thielemans K, Hutton BF, McQuaid SJ. Towards accurate partial volume correction in 99mTc oncology SPECT: perturbation for case-specific resolution estimation. EJNMMI Phys 2022; 9:59. [PMID: 36064882 PMCID: PMC9445108 DOI: 10.1186/s40658-022-00489-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Currently, there is no consensus on the optimal partial volume correction (PVC) algorithm for oncology imaging. Several existing PVC methods require knowledge of the reconstructed resolution, usually as the point spread function (PSF)-often assumed to be spatially invariant. However, this is not the case for SPECT imaging. This work aimed to assess the accuracy of SPECT quantification when PVC is applied using a case-specific PSF. METHODS Simulations of SPECT [Formula: see text]Tc imaging were performed for a range of activity distributions, including those replicating typical clinical oncology studies. Gaussian PSFs in reconstructed images were estimated using perturbation with a small point source. Estimates of the PSF were made in situations which could be encountered in a patient study, including; different positions in the field of view, different lesion shapes, sizes and contrasts, noise-free and noisy data. Ground truth images were convolved with the perturbation-estimated PSF, and with a PSF reflecting the resolution at the centre of the field of view. Both were compared with reconstructed images and the root-mean-square error calculated to assess the accuracy of the estimated PSF. PVC was applied using Single Target Correction, incorporating the perturbation-estimated PSF. Corrected regional mean values were assessed for quantitative accuracy. RESULTS Perturbation-estimated PSF values demonstrated dependence on the position in the Field of View and the number of OSEM iterations. A lower root mean squared error was observed when convolution of the ground truth image was performed with the perturbation-estimated PSF, compared with convolution using a different PSF. Regional mean values following PVC using the perturbation-estimated PSF were more accurate than uncorrected data, or data corrected with PVC using an unsuitable PSF. This was the case for both simple and anthropomorphic phantoms. For the simple phantom, regional mean values were within 0.7% of the ground truth values. Accuracy improved after 5 or more OSEM iterations (10 subsets). For the anthropomorphic phantoms, post-correction regional mean values were within 1.6% of the ground truth values for noise-free uniform lesions. CONCLUSION Perturbation using a simulated point source could potentially improve quantitative SPECT accuracy via the application of PVC, provided that sufficient reconstruction iterations are used.
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Affiliation(s)
- Rebecca Gillen
- Institute of Nuclear Medicine, University College London, London, UK.
- National Physical Laboratory, Teddington, UK.
- Department of Clinical Physics and Bioengineering, Nuclear Medicine, North East Sector, NHS Greater Glasgow and Clyde, Glasgow, UK.
| | - Kjell Erlandsson
- Institute of Nuclear Medicine, University College London, London, UK
| | | | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, UK
| | - Sarah J McQuaid
- Institute of Nuclear Medicine, University College London, London, UK
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14
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Zhang L, Yin FF, Lu K, Moore B, Han S, Cai J. Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion. PRECISION RADIATION ONCOLOGY 2022; 6:190-198. [PMID: 36590077 PMCID: PMC9797133 DOI: 10.1002/pro6.1167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/23/2022] [Indexed: 01/05/2023] Open
Abstract
Purpose Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims to propose an adaptive multiparametric MRI (mpMRI) fusion method and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients. Methods An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. Linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic-user-interface (GUI) was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion. Results The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-w, from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for DWI. The coefficient of variation (CV) of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w and T2/T1-w MRI, respectively. Conclusion A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features such as tumor contrast and liver signal. Synthesis of novel image contrasts including the composition of multiple image features into single image set was achieved.
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Affiliation(s)
- Lei Zhang
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316 China
| | - Ke Lu
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Brittany Moore
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Silu Han
- Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
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Piruzan E, Vosoughi N, Mahani H. Modeling and optimization of respiratory-gated partial breast irradiation with proton beams - A Monte Carlo study. Comput Biol Med 2022; 147:105666. [DOI: 10.1016/j.compbiomed.2022.105666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/24/2022] [Accepted: 05/21/2022] [Indexed: 11/03/2022]
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16
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He L. Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method. Front Public Health 2022; 10:863307. [PMID: 35719652 PMCID: PMC9198292 DOI: 10.3389/fpubh.2022.863307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.
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Affiliation(s)
- Liting He
- School of Computer and Information Science, Southwest University, Chongqing, China
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17
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Taguchi K, Polster C, Segars WP, Aygun N, Stierstorfer K. Model-based pulse pileup and charge sharing compensation for photon counting detectors: A simulation study. Med Phys 2022; 49:5038-5051. [PMID: 35722721 PMCID: PMC9541674 DOI: 10.1002/mp.15779] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose We aim at developing a model‐based algorithm that compensates for the effect of both pulse pileup (PP) and charge sharing (CS) and evaluates the performance using computer simulations. Methods The proposed PCP algorithm for PP and CS compensation uses cascaded models for CS and PP we previously developed, maximizes Poisson log‐likelihood, and uses an efficient three‐step exhaustive search. For comparison, we also developed an LCP algorithm that combines models for a loss of counts (LCs) and CS. Two types of computer simulations, slab‐ and computed tomography (CT)‐based, were performed to assess the performance of both PCP and LCP with 200 and 800 mA, (300 µm)2 × 1.6‐mm cadmium telluride detector, and a dead‐time of 23 ns. A slab‐based assessment used a pair of adipose and iodine with different thicknesses, attenuated X‐rays, and assessed the bias and noise of the outputs from one detector pixel; a CT‐based assessment simulated a chest/cardiac scan and a head‐and‐neck scan using 3D phantom and noisy cone‐beam projections. Results With the slab simulation, the PCP had little or no biases when the expected counts were sufficiently large, even though a probability of count loss (PCL) due to dead‐time loss or PP was as high as 0.8. In contrast, the LCP had significant biases (>±2 cm of adipose) when the PCL was higher than 0.15. Biases were present with both PCP and LCP when the expected counts were less than 10–120 per datum, which was attributed to the fact that the maximum likelihood did not approach the asymptote. The noise of PCP was within 8% from the Cramér–Rao lower bounds for most cases when no significant bias was present. The two CT studies essentially agreed with the slab simulation study. PCP had little or no biases in the estimated basis line integrals, reconstructed basis density maps, and synthesized monoenergetic CT images. But the LCP had significant biases in basis line integrals when X‐ray beams passed through lungs and near the body and neck contours, where the PCLs were above 0.15. As a consequence, basis density maps and monoenergetic CT images obtained by LCP had biases throughout the imaged space. Conclusion We have developed the PCP algorithm that uses the PP–CS model. When the expected counts are more than 10–120 per datum, the PCP algorithm is statistically efficient and successfully compensates for the effect of the spectral distortion due to both PP and CS providing little or no biases in basis line integrals, basis density maps, and monoenergetic CT images regardless of count‐rates. In contrast, the LCP algorithm, which models an LC due to pileup, produces severe biases when incident count‐rates are high and the PCL is 0.15 or higher.
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Affiliation(s)
- Katsuyuki Taguchi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, JHOC 4267, Baltimore, Maryland, 21287, USA
| | - Christoph Polster
- Computed Tomography, Siemens Healthineers, Siemensstr. 3, Forchheim, 91301, Germany
| | - W Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories and Department of Radiology, Institution: Duke University, North Caroline, 2424 Erwin Road, Suite 302, Durham, 27705, USA
| | - N Aygun
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline St., JHOC 4269, Baltimore, Maryland, 21287, USA.,Dr. Aygun is currently with Moffitt Cancer Center (Tampa, FL)
| | - Karl Stierstorfer
- Computed Tomography, Siemens Healthineers, Siemensstr. 3, Forchheim, 91301, Germany
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Cheng X, Yang D, Zhong Y, Shao Y. Real-time marker-less tumor tracking with TOF PET: in silico feasibility study. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6d9f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/06/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Purpose. Although positron emission tomography (PET) can provide a functional image of static tumors for RT guidance, it’s conventionally very challenging for PET to track a moving tumor in real-time with a multiple frame/s sampling rate. In this study, we developed a novel method to enable PET based three-dimension (3D) real-time marker-less tumor tracking (RMTT) and demonstrated its feasibility with a simulation study. Methods. For each line-of-response (LOR) acquired, its positron-electron annihilation position is calculated based on the time difference between the two gamma interactions detected by the TOF PET detectors. The accumulation of these annihilation positions from data acquired within a single sampling frame forms a coarsely measured 3D distribution of positron-emitter radiotracer uptakes of the lung tumor and other organs and tissues (background). With clinically relevant tumor size and sufficient differential radiotracer uptake concentrations between the tumor and background, the high-uptake tumor can be differentiated from the surrounding low-uptake background in the measured distribution of radiotracer uptakes. With a volume-of-interest (VOI) that closely encloses the tumor, the count-weighted centroid of the annihilation positions within the VOI can be calculated as the tumor position. All these data processes can be conducted online. The feasibility of the new method was investigated with a simulated cardiac-torso digital phantom and stationary dual-panel TOF PET detectors to track a 28 mm diameter lung tumor with a 4:1 tumor-to-background 18FDG activity concentration ratio. Results. The initial study shows TOF PET based RMTT can achieve <2.0 mm tumor tracking accuracy with 5 frame s−1 sampling rate under the simulated conditions. In comparison, using reconstructed PET images to track a similar size tumor would require >30 s acquisition time to achieve the same tracking accuracy. Conclusion. With the demonstrated feasibility, the new method may enable TOF PET based RMTT for practical RT applications.
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Cheung ALY, Zhang L, Liu C, Li T, Cheung AHY, Leung C, Leung AKC, Lam SK, Lee VHF, Cai J. Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma. Front Oncol 2022; 12:816678. [PMID: 35280780 PMCID: PMC8913492 DOI: 10.3389/fonc.2022.816678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/27/2022] [Indexed: 12/22/2022] Open
Abstract
Purpose Tumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation. Methods Ten patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1WPP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1WDP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated. Results Fused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Conclusion The results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1WPP, T1WDP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.
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Affiliation(s)
- Andy Lai-Yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Lei Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.,Medical Physics Graduate Program, Duke University, Durham, NC, United States.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Anson Ho-Yin Cheung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | - Chun Leung
- Radiotherapy and Oncology Centre, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | | | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
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20
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Cheung AHY, Wu VWC, Cheung ALY, Cai J. Respiratory 4D-Gating F-18 FDG PET/CT Scan for Liver Malignancies: Feasibility in Liver Cancer Patient and Tumor Quantitative Analysis. Front Oncol 2022; 12:789506. [PMID: 35223472 PMCID: PMC8864173 DOI: 10.3389/fonc.2022.789506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/12/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose To evaluate the potential clinical role and effectiveness of respiratory 4D-gating F-18 FDG PET/CT scan for liver malignancies, relative to routine (3D) F-18 FDG PET/CT scan. Materials and Methods This study presented a prospective clinical study of 16 patients who received F-18 FDG PET/CT scan for known or suspected malignant liver lesions. Ethics approvals were obtained from the ethics committees of the Hong Kong Baptist Hospital and The Hong Kong Polytechnic University. Liver lesions were compared between the gated and ungated image sets, in terms of 1) volume measurement of PET image, 2) accuracy of maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and 3) accuracy of total lesion glycoses (TLG). Statistical analysis was performed by using a two-tailed paired Student t-test and Pearson correlation test. Results The study population consisted of 16 patients (9 males and 7 females; mean age of 65) with a total number of 89 lesions. The SUVmax and SUVmean measurement of the gated PET images was more accurate than that of the ungated PET images, compared to the static reference images. An average of 21.48% (p < 0.001) reduction of the tumor volume was also observed. The SUVmax and SUVmean of the gated PET images were improved by 19.81% (p < 0.001) and 25.53% (p < 0.001), compared to the ungated PET images. Conclusions We have demonstrated the feasibility of implementing 4D PET/CT scan for liver malignancies in a prospective clinical study. The 4D PET/CT scan for liver malignancies could improve the quality of PET image by improving the SUV accuracy of the lesions and reducing image blurring. The improved accuracy in the classification and identification of liver tumors with 4D PET image would potentially lead to its increased utilization in target delineation of GTV, ITV, and PTV for liver radiotherapy treatment planning in the future.
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Affiliation(s)
- Anson H Y Cheung
- Department of Health Technology & Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Radiotherapy and Oncology Department, Hong Kong Baptist Hospital, Hong Kong, Hong Kong SAR, China
| | - Vincent W C Wu
- Department of Health Technology & Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy L Y Cheung
- Department of Health Technology & Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology & Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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21
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Role of nanoparticles in transarterial radioembolization with glass microspheres. Ann Nucl Med 2022; 36:479-487. [PMID: 35199286 DOI: 10.1007/s12149-022-01727-7] [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: 09/21/2021] [Accepted: 02/06/2022] [Indexed: 11/01/2022]
Abstract
OBJECTIVE Transarterial Radioembolization (TARE) with 90Y-loaded glass microspheres is a locoregional treatment option for Hepatocellular Carcinoma (HCC). Post-treatment 90Y bremsstrahlung imaging using Single-Photon Emission Tomography (SPECT) is currently a gold-standard imaging modality for quantifying the delivered dose. However, the nature of bremsstrahlung photons causes difficulty for dose estimation using SPECT imaging. This work aimed to investigate the possibility of using glass microspheres loaded with 90Y and Nanoparticles (NPs) to improve the quantification of delivered doses. METHODS The Monte Carlo codes were used to simulate the post-TARE 90Y planar imaging. Planar images from bremsstrahlung photons and characteristic X-rays are acquired when 0, 1.2 mol/L, 2.4 mol/L, and 4.8 mol/L of Gold (Au), Hafnium (Hf), and Gadolinium (Gd) NPs are incorporated into the glass microspheres. We evaluated the quality of acquired images by calculating sensitivity and Signal-to-Background Ratio (SBR). Therapeutic effects of NPs were evaluated by calculation of Dose Enhancement Ratio (DER) in tumoral and non-tumoral liver tissues. RESULTS The in silico results showed that the sensitivity values of bremsstrahlung and characteristic X-ray planar images increased significantly as the NPs concentration increased in the glass microspheres. The SBR values decreased as the NPs concentration increased for the bremsstrahlung planar images. In contrast, the SBR values increased for the characteristic X-ray planar images when Hf and Gd were incorporated into the glass microspheres. The DER values decreased in the tumoral and non-tumoral liver tissues as the NPs concentration increased. The maximum dose reduction was observed at the NPs concentration of 4.8 mol/L (≈ 7%). CONCLUSIONS The incorporation of Au, Hf, and Gd NPs into the glass microspheres improved the quality and quantity of post-TARE planar images. Also, treatment efficiency was decreased significantly at NPs concentration > 4.8 mol/L.
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22
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Xiao H, Ni R, Zhi S, Li W, Liu C, Ren G, Teng X, Liu W, Wang W, Zhang Y, Wu H, Lee HFV, Cheung LYA, Chang HCC, Li T, Cai J. A Dual-supervised Deformation Estimation Model (DDEM) for constructing ultra-quality 4D-MRI based on a commercial low-quality 4D-MRI for liver cancer radiation therapy. Med Phys 2022; 49:3159-3170. [PMID: 35171511 DOI: 10.1002/mp.15542] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/09/2022] [Accepted: 02/09/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Most available 4D-MRI techniques are limited by insufficient image quality and long acquisition times or require specially designed sequences or hardware that are not available in the clinic. These limitations have greatly hindered the clinical implementation of 4D-MRI. PURPOSE This study aims to develop a fast ultra-quality (UQ) 4D-MRI reconstruction method using a commercially available 4D-MRI sequence and dual-supervised deformation estimation model (DDEM). METHODS Thirty-nine patients receiving radiotherapy for liver tumors were included. Each patient was scanned using a TWIST-VIBE MRI sequence to acquire 4D-MR images. They also received 3D T1-/T2-weighted MRI scans as prior images and UQ 4D-MRI at any instant was considered a deformation of them. A DDEM was developed to obtain a 4D deformable vector field (DVF) from 4D-MRI data, and the prior images were deformed using this 4D-DVF to generate UQ 4D-MR images. The registration accuracies of the DDEM, VoxelMorph (normalized cross-correlation (NCC) supervised), VoxelMorph (end-to-end point error (EPE) supervised), and the parametric total variation (pTV) algorithm were compared. Tumor motion on UQ 4D-MRI was evaluated quantitatively using region-of-interest (ROI) tracking errors, while image quality was evaluated using the contrast-to-noise ratio (CNR), lung-liver edge sharpness, and perceptual blur metric (PBM). RESULTS The registration accuracy of the DDEM was significantly better than those of VoxelMorph (NCC supervised), VoxelMorph (EPE supervised) and the pTV algorithm (all, p < 0.001), with an inference time of 69.3 ± 5.9 ms. UQ 4D-MRI yielded ROI tracking errors of 0.79 ± 0.65, 0.50 ± 0.55, and 0.51 ± 0.58 mm in the superior-inferior, anterior-posterior, and mid-lateral directions, respectively. From the original 4D-MRI to UQ 4D-MRI, the CNR increased from 7.25 ± 4.89 to 18.86 ± 15.81; the lung-liver edge full-width-at-half-maximum decreased from 8.22 ± 3.17 to 3.65 ± 1.66 mm in the in-plane direction and from 8.79 ± 2.78 to 5.04 ± 1.67 mm in the cross-plane direction, and the PBM decreased from 0.68 ± 0.07 to 0.38 ± 0.01. CONCLUSION This novel DDEM method successfully generated UQ 4D-MR images based on a commercial 4D-MRI sequence. It shows great promise for improving liver tumor motion management during radiation therapy. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Ruiyan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Shaohua Zhi
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Weiwei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Beijing Cancer Hospital & Institute, Peking University Cancer Hospital & Institute, Beijing, 100000, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong SAR, 999077, China
| | - Lai-Yin Andy Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong SAR, 999077, China
| | | | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
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23
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Zarei M, Sotoudeh-Paima S, Abadi E, Samei E. A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12031:120313B. [PMID: 35574204 PMCID: PMC9101919 DOI: 10.1117/12.2613168] [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
Inherent to Computed tomography (CT) is image reconstruction, constructing 3D voxel values from noisy projection data. Modeling this inverse operation is not straightforward. Given the ill-posed nature of inverse problem in CT reconstruction, data-driven methods need regularization to enhance the accuracy of the reconstructed images. Besides, generalization of the results hinges upon the availability of large training datasets with access to ground truth. This paper offers a new strategy to reconstruct CT images with the advantage of ground truth accessible through a virtual imaging trial (VIT) platform. A learned primal-dual deep neural network (LPD-DNN) employed the forward model and its adjoint as a surrogate of the imaging's geometry and physics. VIT offered simulated CT projections paired with ground truth labels from anthropomorphic human models without image noise and resolution degradation. The models included a library of anthropomorphic, computational patient models (XCAT). The DukeSim simulator was utilized to form realistic projection data emulating the impact of the physics and geometry of a commercial-equivalent CT scanner. The resultant noisy sinogram data associated with each slice was thus generated for training. Corresponding linear attenuation coefficients of phantoms' materials at the effective energy of the x-ray spectrum were used as the ground truth labels. The LPD-DNN was deployed to learn the complex operators and hyper-parameters in the proximal primal-dual optimization. The obtained validation results showed a 12% normalized root mean square error with respect to the ground truth labels, a peak signal-to-noise ratio of 32 dB, a signal-to-noise ratio of 1.5, and a structural similarity index of 96%. These results were highly favorable compared to standard filtered-back projection reconstruction (65%, 17 dB, 1.0, 26%).
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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
- 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
- Department of Radiology, Duke University School of Medicine
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
| | - Ehsan Abadi
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories
- Department of Radiology, Duke University School of Medicine
- Dept. 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
- Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University
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24
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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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25
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Akhavanallaf A, Fayad H, Salimi Y, Aly A, Kharita H, Al Naemi H, Zaidi H. An update on computational anthropomorphic anatomical models. Digit Health 2022; 8:20552076221111941. [PMID: 35847523 PMCID: PMC9277432 DOI: 10.1177/20552076221111941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/19/2022] [Indexed: 11/15/2022] Open
Abstract
The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.
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Affiliation(s)
- Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Hadi Fayad
- Hamad Medical Corporation, Doha, Qatar
- Weill Cornell Medicine, Doha, Qatar
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Antar Aly
- Hamad Medical Corporation, Doha, Qatar
- Weill Cornell Medicine, Doha, Qatar
| | | | - Huda Al Naemi
- Hamad Medical Corporation, Doha, Qatar
- Weill Cornell Medicine, Doha, Qatar
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Geneva University Neurocenter, Geneva University, Geneva,
Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University
Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark,
Odense, Denmark
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26
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Papadopoulos C, Kolokithas-Ntoukas A, Moreno R, Fuentes D, Loudos G, Loukopoulos VC, Kagadis GC. Using kinetic Monte Carlo simulations to design efficient magnetic nanoparticles for clinical hyperthermia. Med Phys 2021; 49:547-567. [PMID: 34724215 DOI: 10.1002/mp.15317] [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: 04/14/2021] [Revised: 10/22/2021] [Accepted: 10/22/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE The purpose of this study was to identify the properties of magnetite nanoparticles that deliver optimal heating efficiency, predict the geometrical characteristics to get these target properties, and determine the concentrations of nanoparticles required to optimize thermotherapy. METHODS Kinetic Monte Carlo simulations were employed to identify the properties of magnetic nanoparticles that deliver high Specific Absorption Rate (SAR) values. Optimal volumes were determined for anisotropies ranging between 11 and 40 kJ/m3 under clinically relevant magnetic field conditions. Atomistic spin simulations were employed to determine the aspect ratios of ellipsoidal magnetite nanoparticles that deliver the target properties. A numerical model was developed using the extended cardiac-torso (XCAT) phantom to simulate low-field (4 kA/m) and high-field (18 kA/m) prostate cancer thermotherapy. A stationary optimization study exploiting the Method of Moving Asymptotes (MMA) was carried out to calculate the concentration fields that deliver homogenous temperature distributions within target thermotherapy range constrained by the optimization objective function. A time-dependent study was used to compute the thermal dose of a 30-min session. RESULTS Prolate ellipsoidal magnetite nanoparticles with a volume of 3922 ± 35 nm3 and aspect ratio of 1.56, which yields an effective anisotropy of 20 kJ/m3 , constituted the optimal design at current maximum clinical field properties (H0 = 18 kA/m, f = 100 kHz), with SAR = 342.0 ± 2.7 W/g, while nanoparticles with a volume of 4147 ± 36 nm3 , aspect ratio of 1.29, and effective anisotropy 11 kJ/m3 were optimal for low-field applications (H0 = 4 kA/m, f = 100 kHz), with SAR = 50.2 ± 0.5 W/g. The average concentration of 3.86 ± 0.10 and 0.57 ± 0.01 mg/cm3 at 4 and 18 kA/m, respectively, were sufficient to reach therapeutic temperatures of 42-44°C throughout the prostate volume. The thermal dose delivered during a 30-min session exceeded 5.8 Cumulative Equivalent Minutes at 43°C within 90% of the prostate volume (CEM43T90 ). CONCLUSION The optimal properties and design specifications of magnetite nanoparticles vary with magnetic field properties. Application-specific magnetic nanoparticles or nanoparticles that are optimized at low fields are indicated for optimal thermal dose delivery at low concentrations.
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Affiliation(s)
- Costas Papadopoulos
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR, Greece
| | - Argiris Kolokithas-Ntoukas
- Department of Materials Science, School of Natural Sciences, University of Patras, Rion, GR, Greece.,Department of Pharmacy, School of Health Sciences, University of Patras, Rion, GR, Greece
| | - Roberto Moreno
- Earth and Planetary Science, School of Geosciences, University of Edinburgh, Edinburgh, UK
| | - David Fuentes
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - George Loudos
- BIOEMTECH, Lefkippos Attica Technology Park NCSR "Demokritos", Athens, Greece
| | | | - George C Kagadis
- 3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR, Greece.,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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27
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Wells RG, Klein R. Dynamic phantoms: Making the right tool for the job. J Nucl Cardiol 2021; 28:2310-2312. [PMID: 32124249 DOI: 10.1007/s12350-020-02083-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 02/13/2020] [Indexed: 10/24/2022]
Affiliation(s)
- R Glenn Wells
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Canada.
| | - Ran Klein
- Division Nuclear Medicine, Department of Medicine, University of Ottawa, Ottawa, Canada
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28
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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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29
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Fu W, Sharma S, Abadi E, Iliopoulos AS, Wang Q, Lo JY, Sun X, Segars WP, Samei E. iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry. IEEE J Biomed Health Inform 2021; 25:3061-3072. [PMID: 33651703 DOI: 10.1109/jbhi.2021.3063080] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. METHOD Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. RESULT iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). CONCLUSION iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. SIGNIFICANCE The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.
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Shopa RY, Klimaszewski K, Kopka P, Kowalski P, Krzemień W, Raczyński L, Wiślicki W, Chug N, Curceanu C, Czerwiński E, Dadgar M, Dulski K, Gajos A, Hiesmayr BC, Kacprzak K, Kapłon Ł, Kisielewska D, Korcyl G, Krawczyk N, Kubicz E, Niedźwiecki S, Raj J, Sharma S, Shivani, Stȩpień EŁ, Tayefi F, Moskal P. Optimisation of the event-based TOF filtered back-projection for online imaging in total-body J-PET. Med Image Anal 2021; 73:102199. [PMID: 34365143 DOI: 10.1016/j.media.2021.102199] [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: 09/24/2020] [Revised: 07/21/2021] [Accepted: 07/23/2021] [Indexed: 10/20/2022]
Abstract
We perform a parametric study of the newly developed time-of-flight (TOF) image reconstruction algorithm, proposed for the real-time imaging in total-body Jagiellonian PET (J-PET) scanners. The asymmetric 3D filtering kernel is applied at each most likely position of electron-positron annihilation, estimated from the emissions of back-to-back γ-photons. The optimisation of its parameters is studied using Monte Carlo simulations of a 1-mm spherical source, NEMA IEC and XCAT phantoms inside the ideal J-PET scanner. The combination of high-pass filters which included the TOF filtered back-projection (FBP), resulted in spatial resolution, 1.5 times higher in the axial direction than for the conventional 3D FBP. For realistic 10-minute scans of NEMA IEC and XCAT, which require a trade-off between the noise and spatial resolution, the need for Gaussian TOF kernel components, coupled with median post-filtering, is demonstrated. The best sets of 3D filter parameters were obtained by the Nelder-Mead minimisation of the mean squared error between the resulting and reference images. The approach allows training the reconstruction algorithm for custom scans, using the IEC phantom, when the temporal resolution is below 50 ps. The image quality parameters, estimated for the best outcomes, were systematically better than for the non-TOF FBP.
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Affiliation(s)
- R Y Shopa
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland.
| | - K Klimaszewski
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - P Kopka
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - P Kowalski
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - W Krzemień
- High Energy Physics Division, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - L Raczyński
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - W Wiślicki
- Department of Complex Systems, National Centre for Nuclear Research, 05-400 Otwock-Świerk, Poland
| | - N Chug
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - C Curceanu
- INFN, Laboratori Nazionali di Frascati, Frascati 00044, Italy
| | - E Czerwiński
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - M Dadgar
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - K Dulski
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - A Gajos
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - B C Hiesmayr
- Faculty of Physics, University of Vienna, Vienna 1090, Austria
| | - K Kacprzak
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - Ł Kapłon
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - D Kisielewska
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - G Korcyl
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - N Krawczyk
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - E Kubicz
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - Sz Niedźwiecki
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - J Raj
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - S Sharma
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - Shivani
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - E Ł Stȩpień
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - F Tayefi
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
| | - P Moskal
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Cracow, Poland; Total-Body Jagiellonian-PET Laboratory, Jagiellonian University, Poland
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Bodner J, Baxter W, Leung C, Falkner P. A New Medical Device Modeling Framework for Predicting the Performance of Indwelling Continence Care Devices and Improving Patient Care. J Med Device 2021. [DOI: 10.1115/1.4051441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Abstract
A computational model of the human torso has been developed to study the stability of implanted leads that are part of a sacral nerve stimulation system. The model was built using presegmented anatomies that were themselves built from imaging of human patients. The sacral leads are represented using beam elements, and their interaction with the tissue is defined using a function that relates frictional force to the amount of slip between the lead and tissue. Displacements to the skin in the sacral region are applied to simulate activities of daily living, and the resulting displacement of the tip of the lead is indicative of its tendency to dislodge in real patients. Validation of the model was performed using experimental results collected in human cadavers. In these experiments, analogous displacements of the skin were applied after implantation of the leads per normal implant procedures. The displacement of the distal tip of the lead was measured using computed tomography (CT) imaging, allowing direct comparison to the predictions of the model. Recognizing that many model inputs were informed by sparse literature values, a novel application of uncertainty quantification methodology was developed wherein all model inputs were treated as uncertain intervals. This allowed an optimization approach to be used for estimating the uncertain interval for the model outputs. The computational model and cadaver results were used to study the performance of a new sacral lead design, relative to a predicate product. The results showed that the reduction in lead axial stiffness in the new design leads to less lead tip displacement, such that the lead is more likely to remain near the therapeutic target in patients.
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Affiliation(s)
- Jeff Bodner
- Neuromodulation Operating Unit, Medtronic, plc, Minneapolis, MN 55432
| | - Walt Baxter
- Neuromodulation Operating Unit, Medtronic, plc, Santa Ana, CA 92705
| | - Christina Leung
- Neuromodulation Operating Unit, Medtronic, plc, Santa Ana, CA 92705
| | - Phillip Falkner
- Neuromodulation Operating Unit, Medtronic, plc, Minneapolis, MN 55432
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Chang Y, Jiang Z, Segars WP, Zhang Z, Lafata K, Cai J, Yin FF, Ren L. A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms. Phys Med Biol 2021; 66. [PMID: 34061044 DOI: 10.1088/1361-6560/ac01b4] [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: 08/22/2020] [Accepted: 05/14/2021] [Indexed: 11/12/2022]
Abstract
Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated.Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along thex/y/zdirection in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm.Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies.
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Affiliation(s)
- Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Zhuoran Jiang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America
| | - William Paul Segars
- Medical Physics Graduate Program, Duke University Durham, NC, United States of America.,Department of Radiology, Duke University Medical Center, Durham, NC, United States of America.,Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, NC, United States of America
| | - Zeyu Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Kyle Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America
| | - Jing Cai
- Hong Kong Polytechnic University, Hong Kong, HK, CN, Hong Kong, People's Republic of China
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC,United States of America.,Medical Physics Graduate Program, Duke University Durham, NC, United States of America
| | - Lei Ren
- School of Medicine, University of Maryland, Baltimore, MD, United States of America
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Razdevsek G, Dolenec R, Krizan P, Majewski S, Studen A, Korpar S, El Fakhri G, Pestotnik R. Multi-panel limited angle PET system with 50 ps FWHM coincidence time resolution: a simulation study. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2021.3115704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Han S, Liang X, Li T, Yin FF, Cai J. Slice-stacking T2-weighted MRI for fast determination of internal target volume for liver tumor. Quant Imaging Med Surg 2021; 11:32-42. [PMID: 33392009 DOI: 10.21037/qims-20-41] [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] [Indexed: 12/25/2022]
Abstract
Background To investigate the feasibility of generating maximum intensity projection (MIP) images to determine internal target volume (ITV) using slice-stacking MRI (SS-MRI) technique. Methods Slice-stacking is a technique which applies a multi-slice MRI acquisition to generate a 3D MIP for ITV contouring, without reconstructing 4D-MRI. 4D digital extended cardiac-torso (XCAT) phantom was used to generate MIP images with sequential 2D HASTE sequence, with different tumor diameters (10, 30 and 50 mm) and with simulated regular and irregular (patient) breathing motions. A reference MIP was generated using all acquisition images. Consecutive repetitions were then used to generate MIP to analyze the relationship between Dice's similarity coefficient (DSC) and the number of repetitions, and the relationship between the relative ITV volume difference and the number of repetitions. Images from XCAT phantom and from three hepatic carcinoma patients were collected in this study to demonstrate the feasibility of this technique. Results For both regular and irregular breathing motion, the average DSC of ITV is >0.94 and the average relative ITV volume difference is <10% (approximately 0.15 cm3) when using 5 repeated scanning images to reconstruct MIP for tumor diameter of 10 mm. As tumor diameter increases, the DSC of ITV is >0.97 and the relative ITV volume difference is <5% for regular breathing motion, and the DSC of ITV is >0.97 and the relative ITV volume difference is <5.5% for irregular breathing motion when using 5 repeated scanning images to reconstruct MIP. In patient image study, the mean relative ITV volume difference is <3% and the mean DSC is 0.99 when using 5 repeated scanning images to reconstruct MIP. Conclusions The number of scans required to generate tumor ITV for slice-stacking method (5-7 repetition) is 3-4 times less than that of 4D-MRI (15-20 repetitions). It is feasible to generate a fast clinically acceptable ITV using slice-stacking method with sequential 2D MR images.
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Affiliation(s)
- Silu Han
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA.,Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, USA
| | - Xiao Liang
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University Medical Center, Durham, NNC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Myelin detection in fluorescence microscopy images using machine learning. J Neurosci Methods 2020; 346:108946. [PMID: 32931810 DOI: 10.1016/j.jneumeth.2020.108946] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/28/2020] [Accepted: 09/10/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens. NEW METHOD In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total. RESULTS Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84±0.09% and 98.46±0.11% were achieved by Boosted Trees and customized-CNN, respectively. COMPARISON WITH EXISTING METHODS Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images. CONCLUSIONS Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.
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Taguchi K, Sauer TJ, Segars WP, Frey EC, Xu J, Liapi E, Stayman JW, Hong K, Hui FK, Unberath M, Du Y. Three-dimensional regions-of-interest-based intra-operative four-dimensional soft tissue perfusion imaging using a standard x-ray system with no gantry rotation: A simulation study for a proof of concept. Med Phys 2020; 47:6087-6102. [PMID: 33006759 DOI: 10.1002/mp.14514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 09/01/2020] [Accepted: 09/25/2020] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4-10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative four-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation. METHODS IPEN uses two input datasets: (a) the contours and locations of three-dimensional regions-of-interest (ROIs) such as arteries and sub-sections of cancerous lesions, and (b) a series of x-ray projection data obtained from an intra-arterial contrast injection to contrast enhancement to wash-out. IPEN then estimates a time-enhancement curve (TEC) for each ROI directly from projections without reconstructing cross-sectional images by maximizing the agreement between synthesized and measured projections with a temporal roughness penalty. When path lengths through ROIs are known for each x-ray beam, the ROI-specific enhancement can be accurately estimated from projections. Computer simulations are performed to assess the performance of the IPEN algorithm. Intra-arterial contrast-enhanced liver scans over 25 s were simulated using XCAT phantom version 2.0 with heterogeneous tissue textures and cancerous lesions. The following four sub-studies were performed: (a) The accuracy of the estimated TECs with overlapped lesions was evaluated at various noise (dose) levels with either homogeneous or heterogeneous lesion enhancement patterns; (b) the accuracy of IPEN with inaccurate ROI contours was assessed; (c) we investigated how overlapping ROIs and noise in projections affected the accuracy of the IPEN algorithm; and (d) the accuracy of the perfusion indices was assessed. RESULTS The TECs estimated by IPEN were sufficiently accurate at a reference dose level with the root-mean-square deviation (RMSD) of 0.0027 ± 0.0001 cm-1 or 13 ± 1 Hounsfield unit (mean ± standard deviation) for the homogeneous lesion enhancement and 0.0032 ± 0.0005 cm-1 for the heterogeneous enhancement (N = 20 each). The accuracy was degraded with decreasing doses: The RMSD with homogeneous enhancement was 0.0220 ± 0.0003 cm-1 for 20% of the reference dose level. Performing 3 × 3 pixel averaging on projection data improved the RMSDs to 0.0051 ± 0.0002 cm-1 for 20% dose. When the ROI contours were inaccurate, smaller ROI contours resulted in positive biases in TECs, whereas larger ROI contours produced negative biases. The bias remained small, within ± 0.0070 cm-1 , when the Sorenson-Dice coefficients (SDCs) were larger than 0.81. The RMSD of the TEC estimation was strongly associated with the condition of the problem, which can be empirically quantified using the condition number of a matrix A z that maps a vector of ROI enhancement values z to projection data and a weighted variance of projection data: a linear correlation coefficient (R) was 0.794 (P < 0.001). The perfusion index values computed from the estimated TECs agreed well with the true values (R ≥ 0.985, P < 0.0001). CONCLUSION The IPEN algorithm can estimate ROI-specific TECs with high accuracy especially when 3 × 3 pixel averaging is applied, even when lesion enhancement is heterogeneous, or ROI contours are inaccurate but the SDC is at least 0.81.
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Affiliation(s)
- Katsuyuki Taguchi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Thomas J Sauer
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, USA
| | - W Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, USA
| | - Eric C Frey
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Jingyan Xu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Eleni Liapi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - J Webster Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Kelvin Hong
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ferdinand K Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Yong Du
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
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Eiben B, Bertholet J, Menten MJ, Nill S, Oelfke U, McClelland JR. Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom. Phys Med Biol 2020; 65:165005. [PMID: 32235043 DOI: 10.1088/1361-6560/ab8533] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum [Formula: see text]-percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.
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Affiliation(s)
- Björn Eiben
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Martin J Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland
| | - Simeon Nill
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Jamie R McClelland
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
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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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Chang Y, Lafata K, Segars WP, Yin FF, Ren L. Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN). Phys Med Biol 2020; 65:065009. [PMID: 32023555 DOI: 10.1088/1361-6560/ab7309] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were [Formula: see text] and [Formula: see text], compared with that of real CT ([Formula: see text]) and CBCT ([Formula: see text]). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials.
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Affiliation(s)
- Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States of America. Medical Physics Graduate Program, Duke University, Durham, NC, United States of America
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Okuda K, Ito T. [10. Simulation of Nuclear Medicine Experiments Using ImageJ]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2019; 75:1205-1210. [PMID: 31631116 DOI: 10.6009/jjrt.2019_jsrt_75.10.1205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Affiliation(s)
- Koichi Okuda
- Department of Physics, Kanazawa Medical University
| | - Toshimune Ito
- Department of Radiology, Saiseikai Yokohamashi Tobu Hospital
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Kainz W, Neufeld E, Bolch WE, Graff CG, Kim CH, Kuster N, Lloyd B, Morrison T, Segars P, Yeom YS, Zankl M, Xu XG, Tsui BMW. Advances in Computational Human Phantoms and Their Applications in Biomedical Engineering - A Topical Review. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:1-23. [PMID: 30740582 PMCID: PMC6362464 DOI: 10.1109/trpms.2018.2883437] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Over the past decades, significant improvements have been made in the field of computational human phantoms (CHPs) and their applications in biomedical engineering. Their sophistication has dramatically increased. The very first CHPs were composed of simple geometric volumes, e.g., cylinders and spheres, while current CHPs have a high resolution, cover a substantial range of the patient population, have high anatomical accuracy, are poseable, morphable, and are augmented with various details to perform functionalized computations. Advances in imaging techniques and semi-automated segmentation tools allow fast and personalized development of CHPs. These advances open the door to quickly develop personalized CHPs, inherently including the disease of the patient. Because many of these CHPs are increasingly providing data for regulatory submissions of various medical devices, the validity, anatomical accuracy, and availability to cover the entire patient population is of utmost importance. The article is organized into two main sections: the first section reviews the different modeling techniques used to create CHPs, whereas the second section discusses various applications of CHPs in biomedical engineering. Each topic gives an overview, a brief history, recent developments, and an outlook into the future.
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Affiliation(s)
- Wolfgang Kainz
- Food and Drug Administration (FDA), Center for Devices and Radiological Health (CDRH), Silver Spring, MD 20993 USA
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | | | - Christian G Graff
- Food and Drug Administration (FDA), Center for Devices and Radiological Health (CDRH), Silver Spring, MD 20993 USA
| | | | - Niels Kuster
- Swiss Federal Institute of Technology, ETH Zürich, and the Foundation for Research on Information Technologies in Society (IT'IS), Zürich, Switzerland
| | - Bryn Lloyd
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Tina Morrison
- Food and Drug Administration (FDA), Center for Devices and Radiological Health (CDRH), Silver Spring, MD 20993 USA
| | | | | | - Maria Zankl
- Helmholtz Zentrum München German Research Center for Environmental Health, Munich, Germany
| | - X George Xu
- Rensselaer Polytechnic Institute, Troy, NY, USA
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Källman HE, Traneus E, Ahnesjö A. Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System. Med Phys 2018; 46:1012-1023. [PMID: 30582891 DOI: 10.1002/mp.13357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 11/14/2018] [Accepted: 12/16/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated. METHODS Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry. RESULTS The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs. CONCLUSIONS After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.
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Affiliation(s)
- Hans-Erik Källman
- Medical radiation sciences, Department of Immunology, Genetics and Pathology, Uppsala University, and Center for Clinical Research, Uppsala, County Dalarna, Sweden.,Bild och Funktionsmedicin, Falu lasarett, SE-791 82, Falun, Sweden
| | - Erik Traneus
- Raysearch Laboratories AB, Box 3297, SE-103 65, Stockholm, Sweden
| | - Anders Ahnesjö
- Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Sjukhusfysik Ing. 82, Akademiska Sjukhuset, SE-751 85, Uppsala, Sweden
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Neufeld E, Lloyd B, Schneider B, Kainz W, Kuster N. Functionalized Anatomical Models for Computational Life Sciences. Front Physiol 2018; 9:1594. [PMID: 30505279 PMCID: PMC6250781 DOI: 10.3389/fphys.2018.01594] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 10/24/2018] [Indexed: 11/20/2022] Open
Abstract
The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.
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Affiliation(s)
- Esra Neufeld
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Bryn Lloyd
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | | | - Wolfgang Kainz
- Division of Biomedical Physics, OSEL, CDRH, Food and Drug Administration, Silver Spring, MD, United States
| | - Niels Kuster
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland.,Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland
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Vignero J, Marshall NW, Bliznakova K, Bosmans H. A hybrid simulation framework for computer simulation and modelling studies of grating-based x-ray phase-contrast images. ACTA ACUST UNITED AC 2018; 63:14NT03. [DOI: 10.1088/1361-6560/aaceb8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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45
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Abadi E, Segars WP, Sturgeon GM, Harrawood B, Kapadia A, Samei E. Modeling "Textured" Bones in Virtual Human Phantoms. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 3:47-53. [PMID: 31559375 DOI: 10.1109/trpms.2018.2828083] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The purpose of this study was to develop detailed and realistic models of the cortical and trabecular bones in the spine, ribs, and sternum and incorporate them into the library of virtual human phantoms (XCAT). Cortical bone was modeled by 3D morphological erosion of XCAT homogenously defined bones with an average thickness measured from the CT dataset upon which each individual XCAT phantom was based. The trabecular texture was modeled using a power law synthesis algorithm where the parameters were tuned using high-resolution anatomical images of the Human Visible Female. The synthesized bone textures were added into the XCAT phantoms. To qualitatively evaluate the improved realism of the bone modeling, CT simulations of the XCAT phantoms were acquired with and without the textured bone modeling. The 3D power spectrum of the anatomical images exhibited a power law behavior (R2 = 0.84), as expected in fractal and porous textures. The proposed texture synthesis algorithm was able to synthesize textures emulating real anatomical images, with the simulated CT images with the prototyped bones were more realistic than those simulated with the original XCAT models. Incorporating intra-organ structures, the "textured" phantoms are envisioned to be used to conduct virtual clinical trials in the context of medical imaging in cases where the actual trials are infeasible due to the lack of ground truth, cost, or potential risks to the patients.
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Affiliation(s)
- Ehsan Abadi
- Department of Electrical and Computer Engineering, and the Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, 27705 USA
| | - William P Segars
- Carl E. Ravin Advanced Imaging Laboratories, the Department of Radiology, the Medical Physics Graduate Program, and the Department of Biomedical Engineering, Duke University, Durham, NC, 27705 USA
| | - Gregory M Sturgeon
- Carl E. Ravin Advanced Imaging Laboratories and the Department of Radiology, Duke University Medical Center, Durham, NC, 27705 USA
| | - Brian Harrawood
- Carl E. Ravin Advanced Imaging Laboratories and Department of Radiology, Duke University Medical Center, Durham, NC, 27705 USA
| | - Anuj Kapadia
- Carl E. Ravin Advanced Imaging Laboratories, the Department of Radiology, and the Medical Physics Graduate Program, Durham, NC, 27705 USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, the Department of Electrical and Computer Engineering, the Department of Radiology, the Department of Biomedical Engineering, the Medical Physics Graduate Program, and the Department of Physics, Duke University, Durham, NC, 27705 USA
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Segars WP, Veress AI, Sturgeon GM, Samei E. Incorporation of the Living Heart Model into the 4D XCAT Phantom for Cardiac Imaging Research. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 3:54-60. [PMID: 30766954 DOI: 10.1109/trpms.2018.2823060] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The 4D extended cardiac-torso (XCAT) phantom has provided a valuable tool to study the effects of anatomy and motion on medical images, especially cardiac motion. One limitation of the XCAT was that it did not have a physiological basis which to realistically simulate variations in cardiac function. In this work, we incorporate into the XCAT anatomy the four-chamber FE Living Heart Model (LHM) developed by the Living Heart Project (LHP). The LHM represents the state of the art in cardiac FE simulation because of its ability to accurately replicate the biomechanical motion of the entire heart and its variations. We create a new series of 4D phantoms capable of simulating patients with varying body sizes and shapes; cardiac positions, orientations, and dynamics. While extendable to other imaging modalities and technologies, our goal is to use the FE-enhanced XCAT models to investigate the optimal use of computed tomography (CT) for the evaluation of coronary artery disease (CAD). With the ability to simulate realistic, predictive, patient quality 4D imaging data, the enhanced XCAT models will enable optimization studies to identify the most promising systems or system parameters for further clinical validation.
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Affiliation(s)
- W Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories, the Department of Radiology, the Medical Physics Graduate Program, and the Department of Biomedical Engineering, Duke University, Durham, NC, 27705 USA
| | - Alexander I Veress
- Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195 USA
| | - Gregory M Sturgeon
- Carl E. Ravin Advanced Imaging Laboratories and the Department of Radiology, Duke University Medical Center, Durham, NC, 27705 USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, the Department of Electrical and Computer Engineering, the Department of Radiology, the Department of Biomedical Engineering, the Medical Physics Graduate Program, and the Department of Physics, Duke University, Durham, NC, 27705 USA
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