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Vicente EM, Grande Gutierrez N, Oakes JM, Cammin J, Gopal A, Kipritidis J, Modiri A, Mossahebi S, Mohindra P, Citron WK, Matuszak MM, Timmerman R, Sawant A. Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss. Med Phys 2024. [PMID: 38820385 DOI: 10.1002/mp.17187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/04/2024] [Accepted: 05/11/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects. PURPOSE We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways. METHODS In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed. RESULTS We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions. CONCLUSIONS These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.
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Affiliation(s)
- Esther M Vicente
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Noelia Grande Gutierrez
- Mechanical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Jessica M Oakes
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Jochen Cammin
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Arun Gopal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - John Kipritidis
- Department of Radiotherapy, Northern Sydney Cancer Centre, Sydney, Australia
| | - Arezoo Modiri
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sina Mossahebi
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Wendla K Citron
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Chen Y, Pahlavian SH, Jacobs P, Neupane T, Forghani-Arani F, Castillo E, Castillo R, Vinogradskiy Y. Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database. Int J Radiat Oncol Biol Phys 2024; 118:242-252. [PMID: 37607642 PMCID: PMC10842520 DOI: 10.1016/j.ijrobp.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023]
Abstract
PURPOSE A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods. METHODS AND MATERIALS The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]). RESULTS Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively. CONCLUSIONS Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.
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Affiliation(s)
- Yingxuan Chen
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | | | - Taindra Neupane
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | - Edward Castillo
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
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Gu J, Qiu Q, Zhu J, Cao Q, Hou Z, Li B, Shu H. Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image. Med Phys 2023; 50:7779-7790. [PMID: 37387645 DOI: 10.1002/mp.16566] [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: 01/04/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. PURPOSE To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI). METHODS Pulmonary technetium-99 m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes. RESULTS The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001). CONCLUSIONS The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.
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Affiliation(s)
- Jiabing Gu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Qingtao Qiu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, P.R. China
| | - Qiang Cao
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Zhen Hou
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, P.R. China
| | - Baosheng Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
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Xiao H, Xue X, Zhu M, Jiang X, Xia Q, Chen K, Li H, Long L, Peng K. Deep learning-based lung image registration: A review. Comput Biol Med 2023; 165:107434. [PMID: 37696177 DOI: 10.1016/j.compbiomed.2023.107434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/13/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.
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Affiliation(s)
- Hanguang Xiao
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Xufeng Xue
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Mi Zhu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
| | - Xin Jiang
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Qingling Xia
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Kai Chen
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Huanqi Li
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Li Long
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Ke Peng
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
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Flakus MJ, Kent SP, Wallat EM, Wuschner AE, Tennant E, Yadav P, Burr A, Yu M, Christensen GE, Reinhardt JM, Bayouth JE, Baschnagel AM. Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients. Radiother Oncol 2023; 182:109553. [PMID: 36813178 PMCID: PMC10283046 DOI: 10.1016/j.radonc.2023.109553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023]
Abstract
PURPOSE To identify metrics of radiation dose delivered to highly ventilated lung that are predictive of radiation-induced pneumonitis. METHODS AND MATERIALS A cohort of 90 patients with locally advanced non-small cell lung cancer treated with standard fractionated radiation therapy (RT) (60-66 Gy in 30-33 fractions) were evaluated. Regional lung ventilation was determined from pre-RT 4-dimensional computed tomography (4DCT) using the Jacobian determinant of a B-spline deformable image registration to estimate lung tissue expansion during respiration. Multiple voxel-wise population- and individual-based thresholds for defining high functioning lung were considered. Mean dose and volumes receiving dose ≥ 5-60 Gy were analyzed for both total lung-ITV (MLD,V5-V60) and highly ventilated functional lung-ITV (fMLD,fV5-fV60). The primary endpoint was symptomatic grade 2+ (G2+) pneumonitis. Receiver operator curve (ROC) analyses were used to identify predictors of pneumonitis. RESULTS G2+ pneumonitis occurred in 22.2% of patients, with no differences between stage, smoking status, COPD, or chemo/immunotherapy use between G<2 and G2+ patients (P≥ 0.18). Highly ventilated lung was defined as voxels exceeding the population-wide median of 18% voxel-level expansion. All total and functional metrics were significantly different between patients with and without pneumonitis (P≤ 0.039). Optimal ROC points predicting pneumonitis from functional lung dose were fMLD ≤ 12.3 Gy, fV5 ≤ 54% and fV20 ≤ 19 %. Patients with fMLD ≤ 12.3 Gy had a 14% risk of developing G2+ pneumonitis whereas risk significantly increased to 35% for those with fMLD > 12.3 Gy (P = 0.035). CONCLUSIONS Dose to highly ventilated lung is associated with symptomatic pneumonitis and treatment planning strategies should focus on limiting dose to functional regions. These findings provide important metrics to be used in functional lung avoidance RT planning and designing clinical trials.
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Affiliation(s)
- Mattison J. Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Sean P. Kent
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Eric M. Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Antonia E. Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Erica Tennant
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Poonam Yadav
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago Illinois
| | - Adam Burr
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
| | - Joseph M. Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - John E. Bayouth
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon
| | - Andrew M. Baschnagel
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
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Katsuta Y, Kadoya N, Kajikawa T, Mouri S, Kimura T, Takeda K, Yamamoto T, Imano N, Tanaka S, Ito K, Kanai T, Nakajima Y, Jingu K. Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. Phys Med 2023; 105:102505. [PMID: 36535238 DOI: 10.1016/j.ejmp.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO). METHODS Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation. RESULTS We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01). CONCLUSIONS The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy.
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Affiliation(s)
- Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomohiro Kajikawa
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shina Mouri
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomoki Kimura
- Department of Radiation Oncology, Kochi Medical School, Kochi University, Nangoku, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University, Yamagata, Japan
| | - Yujiro Nakajima
- Department of Radiological Sciences, Komazawa University, Tokyo, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Ren G, Li B, Lam SK, Xiao H, Huang YH, Cheung ALY, Lu Y, Mao R, Ge H, Kong FM(S, Ho WY, Cai J. A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients. Front Oncol 2022; 12:883516. [PMID: 35847874 PMCID: PMC9283770 DOI: 10.3389/fonc.2022.883516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/02/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.
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Affiliation(s)
- Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Sai-kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Yufei Lu
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Ronghu Mao
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Feng-Ming (Spring) Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wai-yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, 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
- *Correspondence: Jing Cai,
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Yaremko BP, Capaldi DP, Sheikh K, Palma DA, Warner A, Dar AR, Yu E, Rodrigues GB, Louie AV, Landis M, Sanatani M, Vincent MD, Younus J, Kuruvilla S, Chen JZ, Erickson A, Gaede S, Parraga G, Hoover DA. Functional Lung Avoidance for Individualized Radiotherapy (FLAIR): Results of a Double-Blind, Randomized Controlled Trial. Int J Radiat Oncol Biol Phys 2022; 113:1072-1084. [DOI: 10.1016/j.ijrobp.2022.04.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 10/18/2022]
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Katsuta Y, Kadoya N, Mouri S, Tanaka S, Kanai T, Takeda K, Yamamoto T, Ito K, Kajikawa T, Nakajima Y, Jingu K. Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features. JOURNAL OF RADIATION RESEARCH 2022; 63:71-79. [PMID: 34718683 PMCID: PMC8776701 DOI: 10.1093/jrr/rrab097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/20/2021] [Indexed: 06/13/2023]
Abstract
In this article, we highlight the fundamental importance of the simultaneous use of dose-volume histogram (DVH) and dose-function histogram (DFH) features based on functional images calculated from 4-dimensional computed tomography (4D-CT) and deformable image registration (DIR) in developing a multivariate radiation pneumonitis (RP) prediction model. The patient characteristics, DVH features and DFH features were calculated from functional images by Hounsfield unit (HU) and Jacobian metrics, for an RP grade ≥ 2 multivariate prediction models were computed from 85 non-small cell lung cancer patients. The prediction model is developed using machine learning via a kernel-based support vector machine (SVM) machine. In the patient cohort, 21 of the 85 patients (24.7%) presented with RP grade ≥ 2. The median area under curve (AUC) was 0.58 for the generated 50 prediction models with patient clinical features and DVH features. When HU metric and Jacobian metric DFH features were added, the AUC improved to 0.73 and 0.68, respectively. We conclude that predictive RP models that incorporate DFH features were successfully developed via kernel-based SVM. These results demonstrate that effectiveness of the simultaneous use of DVH features and DFH features calculated from 4D-CT and DIR on functional image-guided radiotherapy.
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Affiliation(s)
- Yoshiyuki Katsuta
- Corresponding author. Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan, Tel: +81-22-717-7312, Fax: +81-22-717-7316, E-mail:
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10
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Castillo E, Nair G, Turner-Lawrence D, Myziuk N, Emerson S, Al-Katib S, Westergaard S, Castillo R, Vinogradskiy Y, Quinn T, Guerrero T, Stevens C. Quantifying pulmonary perfusion from noncontrast computed tomography. Med Phys 2021; 48:1804-1814. [PMID: 33608933 PMCID: PMC8252085 DOI: 10.1002/mp.14792] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/14/2021] [Accepted: 02/08/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose Computed tomography (CT)‐derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT‐Perfusion (CT‐P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion. Methods CT‐Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT‐P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT‐P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT‐ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ. Spatial Spearman correlation between single photon emission CT perfusion (SPECT‐P) and the proposed CT‐P method was assessed in two patient cohorts via a parameter sweep of τ. The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT‐P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT‐P and 4DCT images acquired prior to radiotherapy. For each test case, CT‐P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT‐P and the resulting CT‐P images were computed. Results The median correlations between CT‐P and SPECT‐P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT‐P and SPECT‐P correlations across all 30 test cases ranged between 0.02 and 0.82. A one‐sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two‐sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One‐sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one‐sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001. Conclusion CT‐Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT‐P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT‐P imaging.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Girish Nair
- Department of Internal Medicine, Beaumont Health, Royal Oak, MI, USA
| | | | - Nicholas Myziuk
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Scott Emerson
- Department of Diagnostic Radiology, Beaumont Health, Royal Oak, MI, USA
| | - Sayf Al-Katib
- Department of Diagnostic Radiology, Beaumont Health, Royal Oak, MI, USA
| | - Sarah Westergaard
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Thomas Quinn
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
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11
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Nair GB, Galban CJ, Al-Katib S, Podolsky R, van den Berge M, Stevens C, Castillo E. An assessment of the correlation between robust CT-derived ventilation and pulmonary function test in a cohort with no respiratory symptoms. Br J Radiol 2020; 94:20201218. [PMID: 33320729 DOI: 10.1259/bjr.20201218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate CT-ventilation imaging (CTVI) within a well-characterized, healthy cohort with no respiratory symptoms and examine the correlation between CTVI and concurrent pulmonary function test (PFT). METHODS CT scans and PFTs from 77 Caucasian participants in the NORM dataset (clinicaltrials.gov NCT00848406) were analyzed. CTVI was generated using the robust Integrated Jacobian Formulation (IJF) method. IJF estimated total lung capacity (TLC) was computed from CTVI. Bias-adjusted Pearson's correlation between PFT and IJF-based TLC was computed. RESULTS IJF- and PFT-measured TLC showed a good correlation for both males and females [males: 0.657, 95% CI (0.438-0.797); females: 0.667, 95% CI (0.416-0.817)]. When adjusting for age, height, smoking, and abnormal CT scan, correlation moderated [males: 0.432, 95% CI (0.129-0.655); females: 0.540, 95% CI (0.207-0.753)]. Visual inspection of CTVI revealed participants who had functional defects, despite the fact that all participant had normal high-resolution CT scan. CONCLUSION In this study, we demonstrate that IJF computed CTVI has good correlation with concurrent PFT in a well-validated patient cohort with no respiratory symptoms. ADVANCES IN KNOWLEDGE IJF-computed CTVI's overall numerical robustness and consistency with PFT support its potential as a method for providing spatiotemporal assessment of high and low function areas on volumetric non-contrast CT scan.
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Affiliation(s)
- Girish B Nair
- Division of Pulmonary and Critical Care, Beaumont Health, OUWB School of Medicine, Auburn Hills, MI, USA
| | - Craig J Galban
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Sayf Al-Katib
- Department of Radiology and Molecular Imaging, Beaumont Health, OUWB School of Medicine, Auburn Hills, MI, USA
| | - Robert Podolsky
- Division of Informatics & Biostatistics, Beaumont Research Institute, Beaumont Health, Beaumont, TX, USA
| | - Maarten van den Berge
- Department of Pulmonary Disease, University Medical Center Groningen, Groningen, The Netherlands
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health, OUWB School of Medicine, Auburn Hills, MI, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health, OUWB School of Medicine, Auburn Hills, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
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12
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Kadoya N, Nemoto H, Kajikawa T, Nakajima Y, Kanai T, Ieko Y, Ikeda R, Sato K, Dobashi S, Takeda K, Jingu K. Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom. Phys Med 2020; 77:75-83. [PMID: 32795891 DOI: 10.1016/j.ejmp.2020.07.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/24/2020] [Accepted: 07/26/2020] [Indexed: 10/23/2022] Open
Abstract
We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VICBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VICBCT- and 4D-CT-based ventilation images (VICT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VICBCT. Correlations between the VICBCT and VICT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VICBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VICBCT was greater or equal to that of the HU-based VICBCT. In addition, the ventilation accuracy of VICBCT increased at low gantry speeds. Thus, the image quality of VICBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VICBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VICBCT imaging into the functional avoidance planning process.
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Affiliation(s)
- Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Hikaru Nemoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Tomohiro Kajikawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Yoshiro Ieko
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiation Oncology, Iwate Medical University School of Medicine, Iwate, Japan
| | - Ryutaro Ikeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiology, Japanese Red Cross Ishinomaki Hospital, Ishinomaki, Japan
| | - Kiyokazu Sato
- Radiation Technology, Tohoku University Hospital, Sendai, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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13
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O’Reilly S, Jain V, Huang Q, Cheng C, Teo BKK, Yin L, Zhang M, Diffenderfer E, Li T, Levin W, Xiao Y, Dong L, Feigenberg S, Berman AT, Zou W. Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiation Therapy. Int J Radiat Oncol Biol Phys 2020; 107:79-87. [DOI: 10.1016/j.ijrobp.2020.01.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/08/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
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14
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Nakajima Y, Kadoya N, Kimura T, Hioki K, Jingu K, Yamamoto T. Variations Between Dose-Ventilation and Dose-Perfusion Metrics in Radiation Therapy Planning for Lung Cancer. Adv Radiat Oncol 2020; 5:459-465. [PMID: 32529141 PMCID: PMC7280081 DOI: 10.1016/j.adro.2020.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 02/20/2020] [Accepted: 03/05/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose Currently, several active clinical trials of functional lung avoidance radiation therapy using different imaging modalities for ventilation or perfusion are underway. Patients with lung cancer often show ventilation-perfusion mismatch, whereas the significance of dose-function metric remains unclear. The aim of the present study was to compare dose-ventilation metrics with dose-perfusion metrics for radiation therapy plan evaluation. Methods and Materials Pretreatment 4-dimensional computed tomography and 99mTc-macroaggregated albumin single-photon emission computed tomography perfusion images of 60 patients with lung cancer treated with radiation therapy were analyzed. Ventilation images were created using the deformable image registration of 4-dimensional computed tomography image sets and image analysis for regional volume changes as a surrogate for ventilation. Ventilation and perfusion images were converted into percentile distribution images. Analyses included Pearson’s correlation coefficient and comparison of agreements between the following dose-ventilation and dose-perfusion metrics: functional mean lung dose and functional percent lung function receiving 5, 10, 20, 30, and 40 Gy (fV5, fV10, fV20, fV30, and fV40, respectively). Results Overall, the dose-ventilation metrics were greater than the dose-perfusion metrics (ie, fV20, 26.3% ± 9.9% vs 23.9% ± 9.8%). Correlations between the dose-ventilation and dose-perfusion metrics were strong (range, r = 0.94-0.97), whereas the agreements widely varied among patients, with differences as large as 6.6 Gy for functional mean lung dose and 11.1% for fV20. Paired t test indicated that the dose-ventilation and dose-perfusion metrics were significantly different. Conclusions Strong correlations were present between the dose-ventilation and dose-perfusion metrics. However, the agreement between the dose-ventilation and dose-perfusion metrics widely varied among patients, suggesting that ventilation-based radiation therapy plan evaluation may not be comparable to that based on perfusion. Future studies should elucidate the correlation of dose-function metrics with clinical pulmonary toxicity metrics.
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Affiliation(s)
- Yujiro Nakajima
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan.,Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomoki Kimura
- Department of Radiation Oncology, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan
| | - Kazunari Hioki
- Department of Clinical Support, Hiroshima University Hospital, Hiroshima, Japan.,Graduate School of Health Science, Kumamoto University, Kumamoto, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
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15
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Evaluation of the effect of user-guided deformable image registration of thoracic images on registration accuracy among users. Med Dosim 2020; 45:206-212. [PMID: 32014379 DOI: 10.1016/j.meddos.2019.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 11/22/2019] [Accepted: 12/10/2019] [Indexed: 11/20/2022]
Abstract
User-guided deformable image registration (DIR) has allowed users to actively participate in the DIR process and is expected to improve DIR accuracy. The purpose of this study was to evaluate the time required for and effect of user-guided DIR on registration accuracy for thoracic images among users. In this study, 4-dimensional computed tomographic images of 10 thoracic cancer patients were used. The dataset for these patients was provided by DIR-Lab (www.dir-lab.com) and included a coordinate list of anatomical landmarks (300 bronchial bifurcations). Four medical physicists from different institutions performed DIR between peak-inhale and peak-exhale images with/without the user-guided DIR tool, Reg Refine, implemented in MIM Maestro (MIM software, Cleveland, OH). DIR accuracy was quantified by using target registration errors (TREs) for 300 anatomical landmarks in each patient. The average TREs with user-guided DIR in the 10 images by the 4 medical physicists were 1.48, 1.80, 3.46, and 3.55 mm, respectively, whereas the TREs without user-guided DIR were 3.28, 3.45, 3.56, and 3.28 mm, respectively. The average times taken by the 4 physicists to use the user-guided DIR were 10.0, 6.7, 7.1, and 8.0 min, respectively. This study demonstrated that user-guided DIR can improve DIR accuracy and requires only a moderate amount of time (<10 min). However, 2 of the 4 users did not show much improvement in DIR accuracy, which indicated the necessity of training prior to use of user-guided DIR.
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16
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Liu Z, Miao J, Huang P, Wang W, Wang X, Zhai Y, Wang J, Zhou Z, Bi N, Tian Y, Dai J. A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation. Med Phys 2020; 47:1249-1257. [PMID: 31883382 DOI: 10.1002/mp.14004] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 12/13/2019] [Accepted: 12/23/2019] [Indexed: 01/19/2023] Open
Abstract
PURPOSE The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against single-photon emission-computed tomography (SPECT) ventilation imaging (SPECT-VI). The performance of the DL-based method is assessed by comparing with density change- and Jacobian-based (HU and JAC) methods. MATERIALS AND METHODS Fifty patients with esophagus or lung cancer who underwent thoracic radiotherapy were enrolled in this study. For each patient, 4DCT scans paired with 99mTc-Technegas SPECT/CT were acquired before the first radiotherapy treatment. 4DCT and SPECT/CT were first rigidly registered using MIMvista and converted to data matrix using MATLAB, and then transferred to a DL model based on U-net for correlating 4DCT features and SPECT-VI. Two forms of 4DCT dataset [(a) ten phases and (b) two phases of peak-exhalation and peak-inhalation] as input are studied. Tenfold cross-validation procedure was used to evaluate the performance of the DL model. For comparative evaluation, HU and JAC methodologies are used to calculate specific ventilation imaging based on 4DCT (CTVI) for each patient. The voxel-wise Spearman's correlation was evaluated over the whole lung between each of CTVI and corresponding SPECT-VI. The SPECT-VI and produced CTVIs were segmented into high, median, and low functional lung (HFL, MFL, and LFL) regions. The spatial overlap of corresponding HFL, MFL, and LFL for each CTVI against SPECT-VI was also evaluated using the dice similarity coefficient (DSC). The averaged DSC of functional lung regions was calculated and statistically analyzed with a one-factor ANONA model among different methods. RESULTS The voxel-wise Spearman rs values were (0.22 ± 0.31), (-0.09 ± 0.18), and (0.73 ± 0.16)/(0.71 ± 0.17) for the CTVIHU , CTVIJAC , and CTVIDL(1) /CTVIDL(2) . These results showed the DL method yielded the strongest correlation with SPECT-VI. Using the DSC as the spatial overlap metric, we found that the CTVIHU , CTVIJAC , and CTVIDL(1) /CTVIDL(2) methods achieved averaged DSC values for all patients to be (0.45 ± 0.08), (0.33 ± 0.04), and (0.73 ± 0.09)/(0.71 ± 0.09), respectively. The results demonstrated that the DL method yielded the highest similarity with SPECT-VI with the prominently significant difference (P < 10-7 ). CONCLUSIONS This study developed a DL method for producing CTVI and performed a validation against SPECT-VI. The results demonstrated that DL method can derive CTVI with greatly improved accuracy in comparison to HU and JAC methods. The produced ventilation images can be more accurate and useful for lung functional avoidance radiotherapy and treatment response modeling.
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Affiliation(s)
- Zhiqiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Junjie Miao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Peng Huang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Wenqing Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Xin Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Yirui Zhai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Jingbo Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Zongmei Zhou
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Nan Bi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Yuan Tian
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China
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17
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Castillo E, Vinogradskiy Y, Castillo R. Robust HU-based CT ventilation from an integrated mass conservation formulation. Med Phys 2019; 46:5036-5046. [PMID: 31514235 PMCID: PMC6842051 DOI: 10.1002/mp.13817] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/26/2019] [Accepted: 08/20/2019] [Indexed: 11/07/2022] Open
Abstract
Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel. PURPOSE We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing. METHODS Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method. RESULTS The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11). CONCLUSION While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.
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Affiliation(s)
- Edward Castillo
- Department of Radiation OncologyBeaumont Health SystemsRoyal OakMIUSA
- Department of Computational and Applied MathematicsRice UniversityHoustonTXUSA
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Rigaud B, Simon A, Castelli J, Lafond C, Acosta O, Haigron P, Cazoulat G, de Crevoisier R. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol 2019; 58:1225-1237. [PMID: 31155990 DOI: 10.1080/0284186x.2019.1620331] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.
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Affiliation(s)
- Bastien Rigaud
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Antoine Simon
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Joël Castelli
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Caroline Lafond
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Oscar Acosta
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Pascal Haigron
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , TX , USA
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19
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Szmul A, Matin T, Gleeson FV, Schnabel JA, Grau V, Papież BW. Patch-based lung ventilation estimation using multi-layer supervoxels. Comput Med Imaging Graph 2019; 74:49-60. [PMID: 31009928 DOI: 10.1016/j.compmedimag.2019.04.002] [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: 04/05/2018] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 01/03/2023]
Abstract
Patch-based approaches have received substantial attention over the recent years in medical imaging. One of their potential applications may be to provide more anatomically consistent ventilation maps estimated on dynamic lung CT. An assessment of regional lung function may act as a guide for radiotherapy, ensuring a more accurate treatment plan. This in turn, could spare well-functioning parts of the lungs. We present a novel method for lung ventilation estimation from dynamic lung CT imaging, combining a supervoxel-based image representation with deformations estimated during deformable image registration, performed between peak breathing phases. For this we propose a method that tracks changes of the intensity of previously extracted supervoxels. For the evaluation of the method we calculate correlation of the estimated ventilation maps with static ventilation images acquired from hyperpolarized Xenon129 MRI. We also investigate the influence of different image registration methods used to estimate deformations between the peak breathing phases in the dynamic CT imaging. We show that our method performs favorably to other ventilation estimation methods commonly used in the field, independently of the image registration method applied to dynamic CT. Due to the patch-based approach of our method, it may be physiologically more consistent with lung anatomy than previous methods relying on voxel-wise relationships. In our method the ventilation is estimated for supervoxels, which tend to group spatially close voxels with similar intensity values. The proposed method was evaluated on a dataset consisting of three lung cancer patients undergoing radiotherapy treatment, and this resulted in a correlation of 0.485 with XeMRI ventilation images, compared with 0.393 for the intensity-based approach, 0.231 for the Jacobian-based method and 0.386 for the Hounsfield units averaging method, on average. Within the limitation of the small number of cases analyzed, results suggest that the presented technique may be advantageous for CT-based ventilation estimation. The results showing higher values of correlation of the proposed method demonstrate the potential of our method to more accurately mimic the lung physiology.
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Affiliation(s)
- Adam Szmul
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.
| | - Tahreema Matin
- Department of Radiology, Oxford University Hospitals NHS FT, Oxford, UK
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, UK; Department of Radiology, Oxford University Hospitals NHS FT, Oxford, UK
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Bartłomiej W Papież
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
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20
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Vinogradskiy Y. CT-based ventilation imaging in radiation oncology. BJR Open 2019; 1:20180035. [PMID: 33178925 PMCID: PMC7592480 DOI: 10.1259/bjro.20180035] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/28/2019] [Accepted: 03/11/2019] [Indexed: 11/06/2022] Open
Abstract
A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose-response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose-response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO
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21
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Castillo E, Castillo R, Vinogradskiy Y, Dougherty M, Solis D, Myziuk N, Thompson A, Guerra R, Nair G, Guerrero T. Robust CT ventilation from the integral formulation of the Jacobian. Med Phys 2019; 46:2115-2125. [PMID: 30779353 PMCID: PMC6510605 DOI: 10.1002/mp.13453] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/03/2019] [Accepted: 02/12/2019] [Indexed: 11/18/2022] Open
Abstract
Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation‐based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation‐based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method. Purpose We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR‐recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit‐or‐miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible. Methods Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert‐determined landmark point pairs and inhale/exhale phases from 10 four‐dimensional computed tomographies (4DCTs) available on http://www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy. Results The ten Dir‐Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method. Conclusion The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR‐measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | | | - David Solis
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Nicholas Myziuk
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Andrew Thompson
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Rudy Guerra
- Department of Statistics, Rice University, Houston, TX, USA
| | - Girish Nair
- Department of Internal Medicine, Beaumont Health Systems, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
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22
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Tahir BA, Marshall H, Hughes PJC, Brightling CE, Collier G, Ireland RH, Wild JM. Comparison of CT ventilation imaging and hyperpolarised gas MRI: effects of breathing manoeuvre. Phys Med Biol 2019; 64:055013. [PMID: 30673634 DOI: 10.1088/1361-6560/ab0145] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Image registration of lung CT images acquired at different inflation levels has been proposed as a surrogate method to map lung 'ventilation'. Prior to clinical use, it is important to understand how this technique compares with direct ventilation imaging modalities such as hyperpolarised gas MRI. However, variations in lung inflation level have been shown to affect regional ventilation distributions. Therefore, the aim of this study was to evaluate the impact of lung inflation levels when comparing CT ventilation imaging to ventilation from 3He-MRI. Seven asthma patients underwent breath-hold CT at total lung capacity (TLC) and functional residual capacity (FRC). 3He-MRI and a same-breath 1H-MRI were acquired at FRC+1L and TLC. Percentage ventilated volumes (%VVs) were calculated for FRC+1L and TLC 3He-MRI. TLC-CT and registered FRC-CT were used to compute a surrogate ventilation map from voxel-wise intensity differences in Hounsfield unit values, which was thresholded at the 10th and 20th percentiles. For direct comparison of CT and 3He-MRI ventilation, FRC+1L and TLC 3He-MRI were registered to TLC-CT indirectly via the corresponding same-breath 1H-MRI data. For 3He-MRI and CT ventilation comparison, Dice similarity coefficients (DSCs) between the binary segmentations were computed. The median (range) of %VVs for FRC+1L and TLC 3He-MRI were 90.5 (54.9-93.6) and 91.8 (67.8-96.2), respectively (p = 0.018). For MRI versus CT ventilation comparison, statistically significant improvements in DSCs were observed for TLC 3He MRI when compared with FRC+1L, with median (range) values of 0.93 (0.86-0.93) and 0.86 (0.68-0.92), respectively (p = 0.017), for the 10-100th percentile and 0.87 (0.83-0.88) and 0.81 (0.66-0.87), respectively (p = 0.027), for the 20-100th percentile. Correlation of CT ventilation imaging and hyperpolarised gas MRI is sensitive to lung inflation level. For ventilation maps derived from CT acquired at FRC and TLC, a higher correlation with gas ventilation MRI can be achieved if the MRI is acquired at TLC.
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Affiliation(s)
- Bilal A Tahir
- POLARIS, Academic Radiology, University of Sheffield, Sheffield, United Kingdom. Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, United Kingdom. Author to whom any correspondence should be addressed
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23
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Kipritidis J, Tahir BA, Cazoulat G, Hofman MS, Siva S, Callahan J, Hardcastle N, Yamamoto T, Christensen GE, Reinhardt JM, Kadoya N, Patton TJ, Gerard SE, Duarte I, Archibald-Heeren B, Byrne M, Sims R, Ramsay S, Booth JT, Eslick E, Hegi-Johnson F, Woodruff HC, Ireland RH, Wild JM, Cai J, Bayouth JE, Brock K, Keall PJ. The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging. Med Phys 2019; 46:1198-1217. [PMID: 30575051 DOI: 10.1002/mp.13346] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/18/2018] [Accepted: 11/23/2018] [Indexed: 01/31/2023] Open
Abstract
PURPOSE CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. METHODS The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient r S , and Dice similarity coefficients evaluated for low function lung ( DSC low ) and high function lung ( DSC high ). RESULTS A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The r S and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for r S , 0.52 (0.36-0.67) for DSC low , and 0.45 (0.28-0.62) for DSC high . All other algorithms exhibited at least one negative r S value, and/or one DSC value less than 0.5. CONCLUSIONS The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.
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Affiliation(s)
- John Kipritidis
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Bilal A Tahir
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK.,Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Guillaume Cazoulat
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | | | - Shankar Siva
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Jason Callahan
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | | | - Tokihiro Yamamoto
- University of California Davis School of Medicine, Sacramento, CA, USA
| | | | | | - Noriyuki Kadoya
- Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | | | | | - Ben Archibald-Heeren
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia.,University of Wollongong, Wollongong, NSW, Australia
| | - Mikel Byrne
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia
| | - Rick Sims
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Scott Ramsay
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Jeremy T Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Enid Eslick
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Fiona Hegi-Johnson
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia.,Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Henry C Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Rob H Ireland
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Jing Cai
- Duke University Medical Center, Durham, NC, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
| | | | - Kristy Brock
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | - Paul J Keall
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
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24
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Cui T, Miller GW, Mugler JP, Cates GD, Mata JF, de Lange EE, Huang Q, Altes TA, Yin FF, Cai J. An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration-based lung ventilation. Med Phys 2018; 45:5535-5542. [PMID: 30276819 DOI: 10.1002/mp.13223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 08/21/2018] [Accepted: 09/19/2018] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Deformable image registration (DIR)-based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR-based lung ventilation. METHOD AND MATERIAL Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton MRI (p-MRI) using balanced steady-state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t-MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR-based DVFs from the p-MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR-based DVFs using the Jacobian method and then compared using cross correlation and mutual information. RESULTS The DIR-based lung ventilations calculated using p-MRI varied considerably from the reference lung ventilations based on t-MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323). CONCLUSION We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR-based lung ventilation.
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Affiliation(s)
- Taoran Cui
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, 08901, USA
| | - G Wilson Miller
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA
| | - John P Mugler
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA
| | - Gordon D Cates
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jaime F Mata
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA
| | - Eduard E de Lange
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA
| | - Qijie Huang
- Department of Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Talissa A Altes
- Department of Radiology, University of Missouri School of Medicine, Columbia, Missouri, 65212, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Jing Cai
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, USA.,Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong, China
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25
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Vinogradskiy Y, Rusthoven CG, Schubert L, Jones B, Faught A, Castillo R, Castillo E, Gaspar LE, Kwak J, Waxweiler T, Dougherty M, Gao D, Stevens C, Miften M, Kavanagh B, Guerrero T, Grills I. Interim Analysis of a Two-Institution, Prospective Clinical Trial of 4DCT-Ventilation-based Functional Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1357-1365. [PMID: 30353873 PMCID: PMC6919556 DOI: 10.1016/j.ijrobp.2018.07.186] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 06/13/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE Functional imaging has been proposed that uses 4DCT images to calculate 4DCT-based lung ventilation (4DCT-ventilation). We have started a 2-institution, phase 2 prospective trial evaluating the feasibility, safety, and preliminary efficacy of 4DCT-ventilation functional avoidance. The trial hypothesis is that the rate of grade ≥2 radiation pneumonitis could be reduced to 12% with functional avoidance, compared with a 25% rate of pneumonitis with a historical control. The trial employed a Simon 2-stage design with a planned futility analysis after 17 evaluable patients. The purpose of this work is to present the trial design and implementation, dosimetric data, and clinical results for the planned futility analysis. METHODS AND MATERIALS Eligible patients were patients with lung cancer who were prescribed doses of 45 to 75 Gy. For each patient, the 4DCT data were used to generate a 4DCT-ventilation image using the Hounsfield unit technique along with a compressible flow-based image registration algorithm. Two intensity modulated radiation therapy treatment plans were generated: (1) a standard lung plan and (2) a functional avoidance treatment plan that aimed to reduce dose to functional lung while meeting target and normal tissue constraints. Patients were treated with the functional avoidance plan and evaluated for thoracic toxicity (presented as rate and 95% confidence intervals [CI]) with a 1-year follow-up. RESULTS The V20 to functional lung was 21.6% ± 9.5% (mean ± standard deviation) with functional avoidance, representing a decrease of 3.2% (P < .01) relative to standard, nonfunctional treatment plans. The rates of grade ≥2 and grade ≥3 radiation pneumonitis were 17.6% (95% CI, 3.8%-43.4%) and 5.9% (95% CI, 0.1%-28.7%), respectively. CONCLUSIONS Dosimetrically, functional avoidance achieved reduction in doses to functional lung while meeting target and organ at risk constraints. On the basis of Simon's 2-stage design and the 17.6% grade ≥2 pneumonitis rate, the trial met its futility criteria and has continued accrual.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Bernard Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Austin Faught
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, Tennessee
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | | | - Dexiang Gao
- Department of Pediatrics and Department of Biostatistics and Informatics, University of Colorado School of Medicine, Aurora, Colorado
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
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26
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Tahir BA, Hughes PJ, Robinson SD, Marshall H, Stewart NJ, Norquay G, Biancardi A, Chan HF, Collier GJ, Hart KA, Swinscoe JA, Hatton MQ, Wild JM, Ireland RH. Spatial Comparison of CT-Based Surrogates of Lung Ventilation With Hyperpolarized Helium-3 and Xenon-129 Gas MRI in Patients Undergoing Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1276-1286. [DOI: 10.1016/j.ijrobp.2018.04.077] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 03/21/2018] [Accepted: 04/26/2018] [Indexed: 11/30/2022]
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Otsuka M, Monzen H, Matsumoto K, Tamura M, Inada M, Kadoya N, Nishimura Y. Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients. PLoS One 2018; 13:e0204721. [PMID: 30281625 PMCID: PMC6169903 DOI: 10.1371/journal.pone.0204721] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 09/13/2018] [Indexed: 11/18/2022] Open
Abstract
Background Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Methods Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. Results For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. Conclusions Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy.
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Affiliation(s)
- Masakazu Otsuka
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, Osakasayama, Japan
| | - Hajime Monzen
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, Osakasayama, Japan
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osakasayama, Japan
- * E-mail:
| | - Kenji Matsumoto
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, Osakasayama, Japan
| | - Mikoto Tamura
- Department of Medical Physics, Graduate School of Medical Science, Kindai University, Osakasayama, Japan
| | - Masahiro Inada
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osakasayama, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yasumasa Nishimura
- Department of Radiation Oncology, Kindai University Faculty of Medicine, Osakasayama, Japan
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Rankine LJ, Wang Z, Driehuys B, Marks LB, Kelsey CR, Das SK. Correlation of Regional Lung Ventilation and Gas Transfer to Red Blood Cells: Implications for Functional-Avoidance Radiation Therapy Planning. Int J Radiat Oncol Biol Phys 2018; 101:1113-1122. [PMID: 29907488 PMCID: PMC6689416 DOI: 10.1016/j.ijrobp.2018.04.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/02/2018] [Accepted: 04/05/2018] [Indexed: 02/08/2023]
Abstract
PURPOSE To investigate the degree to which lung ventilation and gas exchange are regionally correlated, using the emerging technology of hyperpolarized (HP)-129Xe magnetic resonance imaging (MRI). METHODS AND MATERIALS Hyperpolarized-129Xe MRI studies were performed on 17 institutional review board-approved human subjects, including 13 healthy volunteers, 1 emphysema patient, and 3 non-small cell lung cancer patients imaged before and approximately 11 weeks after radiation therapy (RT). Subjects inhaled 1 L of HP-129Xe mixture, followed by the acquisition of interleaved ventilation and gas exchange images, from which maps were obtained of the relative HP-129Xe distribution in three states: (1) gaseous, in lung airspaces; (2) dissolved interstitially, in alveolar barrier tissue; and (3) transferred to red blood cells (RBCs), in the capillary vasculature. The relative spatial distributions of HP-129Xe in airspaces (regional ventilation) and RBCs (regional gas transfer) were compared. Further, we investigated the degree to which ventilation and RBC transfer images identified similar functional regions of interest (ROIs) suitable for functionally guided RT. For the RT patients, both ventilation and RBC functional images were used to calculate differences in the lung dose-function histogram and functional effective uniform dose. RESULTS The correlation of ventilation and RBC transfer was ρ = 0.39 ± 0.15 in healthy volunteers. For the RT patients, this correlation was ρ = 0.53 ± 0.02 before treatment and ρ = 0.39 ± 0.07 after treatment; for the emphysema patient it was ρ = 0.24. Comparing functional ROIs, ventilation and RBC transfer demonstrated poor spatial agreement: Dice similarity coefficient = 0.50 ± 0.07 and 0.26 ± 0.12 for the highest-33%- and highest-10%-function ROIs in healthy volunteers, and in RT patients (before treatment) these were 0.58 ± 0.04 and 0.40 ± 0.04. The average magnitude of the differences between RBC- and ventilation-derived functional effective uniform dose, fV20Gy, fV10Gy, and fV5Gy were 1.5 ± 1.4 Gy, 4.1% ± 3.8%, 5.0% ± 3.8%, and 5.3% ± 3.9%, respectively. CONCLUSION Ventilation may not be an effective surrogate for true regional lung function for all patients.
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Affiliation(s)
- Leith J Rankine
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina; Medical Physics Graduate Program, Duke University, Durham, North Carolina.
| | - Ziyi Wang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Bastiaan Driehuys
- Medical Physics Graduate Program, Duke University, Durham, North Carolina; Department of Biomedical Engineering, Duke University, Durham, North Carolina; Radiology, Duke University, Durham, North Carolina
| | - Lawrence B Marks
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Chris R Kelsey
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Shiva K Das
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina
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Evaluation of functionally weighted dose-volume parameters for thoracic stereotactic ablative radiotherapy (SABR) using CT ventilation. Phys Med 2018; 49:47-51. [PMID: 29866342 DOI: 10.1016/j.ejmp.2018.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 04/27/2018] [Accepted: 05/01/2018] [Indexed: 01/04/2023] Open
Abstract
For the purpose of reducing radiation pneumontisis (RP), four-dimensional CT (4DCT)-based ventilation can be used to reduce functionally weighted lung dose. This study aimed to evaluate the functionally weighted dose-volume parameters and to investigate an optimal weighting method to realize effective planning optimization in thoracic stereotactic ablative radiotherapy (SABR). Forty patients treated with SABR were analyzed. Ventilation images were obtained from 4DCT using deformable registration and Hounsfield unit-based calculation. Functionally-weighted mean lung dose (fMLD) and functional lung fraction receiving at least x Gy (fVx) were calculated by two weighting methods: thresholding and linear weighting. Various ventilation thresholds (5th-95th, every 5th percentile) were tested. The predictive accuracy for CTCAE grade ≧ 2 pneumonitis was evaluated by area under the curve (AUC) of receiver operating characteristic analysis. AUC values varied from 0.459 to 0.570 in accordance with threshold and dose-volume metrics. A combination of 25th percentile threshold and fV30 showed the best result (AUC: 0.570). AUC values with fMLD, fV10, fV20, and fV40 were 0.541, 0.487, 0.548 and 0.563 using a 25th percentile threshold. Although conventional MLD, V10, V20, V30 and V40 showed lower AUC values (0.516, 0.477, 0.534, 0.552 and 0.527), the differences were not statistically significant. fV30 with 25th percentile threshold was the best predictor of RP. Our results suggested that the appropriate weighting should be used for better treatment outcomes in thoracic SABR.
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Huang Q, Jabbour SK, Xiao Z, Yue N, Wang X, Cao H, Kuang Y, Zhang Y, Nie K. Dosimetric feasibility of 4DCT-ventilation imaging guided proton therapy for locally advanced non-small-cell lung cancer. Radiat Oncol 2018; 13:78. [PMID: 29695284 PMCID: PMC5918906 DOI: 10.1186/s13014-018-1018-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 04/05/2018] [Indexed: 12/18/2022] Open
Abstract
Background The principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy. Methods Eight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues. Results Our results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met. Conclusions Incorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.
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Affiliation(s)
- Qijie Huang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Salma K Jabbour
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Zhiyan Xiao
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.,Proton Therapy Center, University of Cincinnati Medical Center, Cincinnati, OH, 45044, USA
| | - Ning Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Hongbin Cao
- Department of Radiation Oncology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
| | - Yu Kuang
- Department of Medical Physics, University of Nevada, Las Vegas, NV, USA
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
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Sawant A, Yamamoto T, Cai J. Treatment planning based on lung functional avoidance is not ready for clinical deployment. Med Phys 2018; 45:2353-2356. [PMID: 29570812 DOI: 10.1002/mp.12881] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 03/15/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Amit Sawant
- Department of Radiation Oncology, University of Maryland, Baltimore, Maryland, 21201
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California, 95817
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Jensen KR, Brink C, Hansen O, Bernchou U. Ventilation measured on clinical 4D-CBCT: Increased ventilation accuracy through improved image quality. Radiother Oncol 2017; 125:459-463. [DOI: 10.1016/j.radonc.2017.10.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 09/05/2017] [Accepted: 10/17/2017] [Indexed: 12/25/2022]
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Yamamoto T, Kabus S. Technical Note: Correction for the effect of breathing variations in CT pulmonary ventilation imaging. Med Phys 2017; 45:322-327. [PMID: 29072320 DOI: 10.1002/mp.12634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/17/2017] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The accuracy and precision of computed tomography (CT) pulmonary ventilation imaging with conventional CT scanners are limited by breathing variations. We propose a method to correct for the effect of breathing variations in CT ventilation imaging based on external respiratory signals acquired throughout a scan. METHODS The proposed method is based on: (a) calculating voxel-by-voxel abdominal surface motion ranges using four-dimensional (4D) CT image datasets spatiotemporally correlated with external respiratory monitor data, and (b) applying the correction factor, which is defined as the ratio of the overall mean of the abdominal surface motion range in the lungs to that of each voxel, to the CT ventilation value. The performance of the proposed method was investigated by comparing voxel-wise correlations of the uncorrected and corrected CT ventilation images with single-photon emission CT (SPECT) ventilation images as a ground truth for nine patients. CT ventilation images were calculated by deformable image registration of the 4D-CT image datasets, followed by calculation of regional volume changes. A Steiger's Z-test was used to determine the statistical significance of the difference between the correlations for the uncorrected and corrected CT ventilation images. RESULTS The proposed correction method resulted in significant increases (P < 0.05) in the correlation between CT and SPECT ventilation in three patients, trends toward significant increase (P: 0.13-0.18) in two patients, no significant differences in three patients, and a significantly decreased correlation in one patient. The average standard deviation of the abdominal surface motion range in three patients showing significant increases was 0.27 (range 0.10-0.37), which was greater than 0.17 (range 0.07-0.38) in the other six patients. CONCLUSIONS The proposed method to correct for the effect of breathing variations could be readily implemented and has the potential to improve the accuracy of CT ventilation imaging as demonstrated in a nine-patient study.
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Affiliation(s)
- Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
| | - Sven Kabus
- Department of Digital Imaging, Philips Research, 22335, Hamburg, Germany
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Miura H, Ozawa S, Nakao M, Furukawa K, Doi Y, Kawabata H, Kenjou M, Nagata Y. Impact of deformable image registration accuracy on thoracic images with different regularization weight parameter settings. Phys Med 2017; 42:108-111. [DOI: 10.1016/j.ejmp.2017.09.122] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 07/20/2017] [Accepted: 09/09/2017] [Indexed: 10/18/2022] Open
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Vinogradskiy Y, Schubert L, Diot Q, Waxweiller T, Koo P, Castillo R, Castillo E, Guerrero T, Rusthoven C, Gaspar L, Kavanagh B, Miften M. Regional Lung Function Profiles of Stage I and III Lung Cancer Patients: An Evaluation for Functional Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2017; 95:1273-80. [PMID: 27354134 DOI: 10.1016/j.ijrobp.2016.02.058] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 02/17/2016] [Accepted: 02/25/2016] [Indexed: 02/01/2023]
Abstract
PURPOSE The development of clinical trials is underway to use 4-dimensional computed tomography (4DCT) ventilation imaging to preferentially spare functional lung in patients undergoing radiation therapy. The purpose of this work was to generate data to aide with clinical trial design by retrospectively characterizing dosimetric and functional profiles for patients with different stages of lung cancer. METHODS AND MATERIALS A total of 118 lung cancer patients (36% stage I and 64% stage III) from 2 institutions were used for the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging, deformable image registration, and a density-change-based algorithm. To assess each patient's spatial ventilation profile both quantitative and qualitative metrics were developed, including an observer-based defect observation and metrics based on the ventilation in each lung third. For each patient we used the clinical doses to calculate functionally weighted mean lung doses and metrics that assessed the interplay between the spatial location of the dose and high-functioning lung. RESULTS Both qualitative and quantitative metrics revealed a significant difference in functional profiles between the 2 stage groups (P<.01). We determined that 65% of stage III and 28% of stage I patients had ventilation defects. Average functionally weighted mean lung dose was 19.6 Gy and 5.4 Gy for stage III and I patients, respectively, with both groups containing patients with large spatial overlap between dose and high-function regions. CONCLUSION Our 118-patient retrospective study found that 65% of stage III patients have regionally variant ventilation profiles that are suitable for functional avoidance. Our results suggest that regardless of disease stage, it is possible to have unique spatial interplay between dose and high-functional lung, highlighting the importance of evaluating the function of each patient and developing a personalized functional avoidance treatment approach.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Timothy Waxweiller
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Phillip Koo
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch, Galveston, Texas
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Laurie Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
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Waxweiler T, Schubert L, Diot Q, Faught A, Stuhr K, Castillo R, Castillo E, Guerrero T, Rusthoven C, Gaspar L, Kavanagh B, Miften M, Vinogradskiy Y. A complete 4DCT-ventilation functional avoidance virtual trial: Developing strategies for prospective clinical trials. J Appl Clin Med Phys 2017; 18:144-152. [PMID: 28436107 PMCID: PMC5689844 DOI: 10.1002/acm2.12086] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/30/2017] [Accepted: 03/08/2017] [Indexed: 12/25/2022] Open
Abstract
Introduction 4DCT‐ventilation is an exciting new imaging modality that uses 4DCT data to calculate lung‐function maps. Because 4DCTs are acquired as standard of care for lung cancer patients undergoing radiotherapy, 4DCT‐ventiltation provides functional information at no extra dosimetric or monetary cost to the patient. The development of clinical trials is underway to use 4DCT‐ventilation imaging to spare functional lung in patients undergoing radiotherapy. The purpose of this work was to perform a virtual trial using retrospective data to develop the practical aspects of a 4DCT‐ventilation functional avoidance clinical trial. Methods The study included 96 stage III lung cancer patients. A 4DCT‐ventilation map was calculated using the patient's 4DCT‐imaging, deformable registration, and a density‐change‐based algorithm. Clinical trial inclusion assessment used quantitative and qualitative metrics based on the patient's spatial ventilation profile. Clinical and functional plans were generated for 25 patients. The functional plan aimed to reduce dose to functional lung while meeting standard target and critical structure constraints. Standard and dose‐function metrics were compared between the clinical and functional plans. Results Our data showed that 69% and 59% of stage III patients have regional variability in function based on qualitative and quantitative metrics, respectively. Functional planning demonstrated an average reduction of 2.8 Gy (maximum 8.2 Gy) in the mean dose to functional lung. Conclusions Our work demonstrated that 60–70% of stage III patients would be eligible for functional planning and that a typical functional lung mean dose reduction of 2.8 Gy can be expected relative to standard clinical plans. These findings provide salient data for the development of functional clinical trials.
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Affiliation(s)
- Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Austin Faught
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kelly Stuhr
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch, Galveston, TX, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Laurie Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
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Vinogradskiy Y, Jackson M, Schubert L, Jones B, Castillo R, Castillo E, Guerrero T, Mitchell J, Rusthoven C, Miften M, Kavanagh B. Assessing the use of 4DCT-ventilation in pre-operative surgical lung cancer evaluation. Med Phys 2017; 44:200-208. [PMID: 28102961 DOI: 10.1002/mp.12026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 10/03/2016] [Accepted: 11/13/2016] [Indexed: 12/25/2022] Open
Abstract
PURPOSE A primary treatment option for lung cancer patients is surgical resection. Patients who have poor lung function prior to surgery are at increased risk of developing serious and life-threatening complications after surgical resection. Surgeons use nuclear medicine ventilation-perfusion (VQ) scans along with pulmonary function test (PFT) information to assess a patient's pre-surgical lung function. The nuclear medicine images and pre-surgery PFTs are used to calculate percent predicted postoperative (%PPO) PFT values by estimating the amount of functioning lung tissue that would be lost with surgical resection. Nuclear medicine imaging is currently considered the standard of care when evaluating the amount of ventilation that would be lost due to surgery. A novel lung function imaging modality has been developed in radiation oncology that uses 4-Dimensional computed tomography data to calculate ventilation maps (4DCT-ventilation). Compared to nuclear medicine, 4DCT-ventilation is cheaper, does not require a radioactive contrast agent, provides a faster imaging procedure, and has improved spatial resolution. In this work we perform a retrospective study to assess the use of 4DCT-ventilation as a pre-operative surgical lung function evaluation tool. Specifically, the purpose of our study was to compare %PPO PFT values calculated with 4DCT-ventilation and %PPO PFT values calculated with nuclear medicine ventilation-perfusion imaging. METHODS The study included 16 lung cancer patients that had undergone 4DCT imaging, nuclear medicine imaging, and had Forced Expiratory Volume in 1 second (FEV1 ) acquired as part of a standard PFT. The 4DCT datasets, spatial registration, and a density-change-based model were used to compute 4DCT-ventilation maps. Both 4DCT-ventilation and nuclear medicine images were used to calculate %PPO FEV1 . The %PPO FEV1 was calculated by scaling the pre-surgical FEV1 by (1-fraction of total resected ventilation); where the resected ventilation was determined using either the 4DCT-ventilation or nuclear medicine imaging. Calculations were done assuming both lobectomy and pneumonectomy resections. The %PPO FEV1 values were compared between the 4DCT-ventilation-based calculations and the nuclear medicine-based calculations using correlation coefficients, average differences, and Receiver Operating Characteristic (ROC) analysis. RESULTS Overall the 4DCT-ventilation derived %PPO FEV1 values agreed well with nuclear medicine-derived %PPO FEV1 data with correlations of 0.99 and 0.81 for lobectomy and pneumonectomy, respectively. The average differences between the 4DCT-ventilation and nuclear medicine-based calculation for %PPO FEV1 were less than 5%. ROC analysis revealed predictive accuracy that ranged from 87.5% to 100% when assessing the ability of 4DCT-ventilation to predict for nuclear medicine-based %PPO FEV1 values. CONCLUSIONS 4DCT-ventilation is an innovative technology developed in radiation oncology that has great potential to translate to the surgical domain. The high correlation results when comparing 4DCT-ventilation to the current standard of care provide a strong rationale for a prospective clinical trial assessing 4DCT-ventilation in the clinical setting. 4DCT-ventilation can reduce the cost and imaging time for patients while providing improved spatial accuracy and quantitative results for surgeons.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Matthew Jackson
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Bernard Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Richard Castillo
- Department of Radiation Oncology, The University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, 3601 W 13 Mile Rd, Royal Oak, MI, 48073, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, 3601 W 13 Mile Rd, Royal Oak, MI, 48073, USA
| | - John Mitchell
- Department of Surgery, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, 1665 Aurora Court, Aurora, CO, 80045, USA
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Castillo E, Castillo R, Vinogradskiy Y, Guerrero T. The numerical stability of transformation-based CT ventilation. Int J Comput Assist Radiol Surg 2017; 12:569-580. [PMID: 28058533 PMCID: PMC5362676 DOI: 10.1007/s11548-016-1509-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 12/03/2016] [Indexed: 12/31/2022]
Abstract
Abstract Computed tomography (CT)-derived ventilation imaging utilizes deformable image registration (DIR) to recover respiratory-induced tissue volume changes from inhale/exhale 4DCT phases. While current strategies for validating CT ventilation rely on analyzing its correlation with existing functional imaging modalities, the numerical stability of the CT ventilation calculation has not been characterized. Purpose The purpose of this study is to examine how small changes in the DIR displacement field can affect the calculation of transformation-based CT ventilation. Methods First, we derive a mathematical theorem, which states that the change in ventilation metric induced by a perturbation to single displacement vector is bounded by the perturbation magnitude. Second, we introduce a novel Jacobian constrained optimization method for computing user-defined CT ventilation images. Results Using the Jacobian constrained method, we demonstrate that for the same inhale/exhale CT pair, it is possible to compute two DIR transformations that have similar spatial accuracies, but generate ventilation images with significantly different physical characteristics. In particular, we compute a CT ventilation image that perfectly correlates with a single-photon emission CT perfusion scan. Conclusion The analysis and experiments indicate that while transformation-based CT ventilation is a promising modality, small changes in the DIR displacement field can result in large relative changes in the ventilation image. As such, approaches for improving the reproducibility of CT ventilation are still needed.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA.
- Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA.
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch, Galveston, TX, USA
| | | | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
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Ireland R, Tahir B, Wild J, Lee C, Hatton M. Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance. Clin Oncol (R Coll Radiol) 2016; 28:695-707. [DOI: 10.1016/j.clon.2016.08.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 07/19/2016] [Accepted: 07/20/2016] [Indexed: 12/25/2022]
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Zhang GG, Latifi K, Du K, Reinhardt JM, Christensen GE, Ding K, Feygelman V, Moros EG. Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods. J Appl Clin Med Phys 2016; 17:550-560. [PMID: 27074479 PMCID: PMC5874808 DOI: 10.1120/jacmp.v17i2.5985] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 11/30/2015] [Accepted: 11/25/2015] [Indexed: 12/25/2022] Open
Abstract
Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon-enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published meth-ods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44 ± 0.13 with a range between 0.29 and 0.61 between the XeCT and ΔV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55 ± 0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV-4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution.
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Kipritidis J, Hofman MS, Siva S, Callahan J, Le Roux PY, Woodruff HC, Counter WB, Keall PJ. Estimating lung ventilation directly from 4D CT Hounsfield unit values. Med Phys 2015; 43:33. [DOI: 10.1118/1.4937599] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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Tahir BA, Van Holsbeke C, Ireland RH, Swift AJ, Horn FC, Marshall H, Kenworthy JC, Parra-Robles J, Hartley R, Kay R, Brightling CE, De Backer J, Vos W, Wild JM. Comparison of CT-based Lobar Ventilation with 3He MR Imaging Ventilation Measurements. Radiology 2015; 278:585-92. [PMID: 26322908 DOI: 10.1148/radiol.2015142278] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To compare lobar lung ventilation computed from expiratory and inspiratory computed tomographic (CT) data with direct measurements of ventilation at hyperpolarized helium 3 ((3)He) magnetic resonance (MR) imaging by using same-breath hydrogen 1 ((1)H) MR imaging examinations to coregister the multimodality images. MATERIALS AND METHODS The study was approved by the national research ethics committee, and written patient consent was obtained. Thirty patients with asthma underwent breath-hold CT at total lung capacity and functional residual capacity. (3)He and (1)H MR images were acquired during the same breath hold at a lung volume of functional residual capacity plus 1 L. Lobar segmentations delineated by major fissures on both CT scans were used to calculate the percentage of ventilation per lobe from the change in inspiratory and expiratory lobar volumes. CT-based ventilation was compared with (3)He MR imaging ventilation by using diffeomorphic image registration of (1)H MR imaging to CT, which enabled indirect registration of (3)He MR imaging to CT. Statistical analysis was performed by using the Wilcoxon signed-rank test, Pearson correlation coefficient, and Bland-Altman analysis. RESULTS The mean ± standard deviation absolute difference between the CT and (3)He MR imaging percentage of ventilation volume in all lobes was 4.0% (right upper and right middle lobes, 5.4% ± 3.3; right lower lobe, 3.7% ± 3.9; left upper lobe, 2.8% ± 2.7; left lower lobe, 3.9% ± 2.6; Wilcoxon signed-rank test, P < .05). The Pearson correlation coefficient between the two techniques in all lobes was 0.65 (P < .001). Greater percentage of ventilation was seen in the upper lobes with (3)He MR imaging and in the lower lobes with CT. This was confirmed with Bland-Altman analysis, with 95% limits of agreement for right upper and middle lobes, -2.4, 12.7; right lower lobe, -11.7, 4.6; left upper lobe, -4.9, 8.7; and left lower lobe, -9.8, 2.8. CONCLUSION The percentage of regional ventilation per lobe calculated at CT was comparable to a direct measurement of lung ventilation at hyperpolarized (3)He MR imaging. This work provides evidence for the validity of the CT model, and same-breath (1)H MR imaging enables regional interpretation of (3)He ventilation MR imaging on the underlying lung anatomy at thin-section CT.
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Affiliation(s)
- Bilal A Tahir
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Cedric Van Holsbeke
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Rob H Ireland
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Andrew J Swift
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Felix C Horn
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Helen Marshall
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - John C Kenworthy
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Juan Parra-Robles
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Ruth Hartley
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Richard Kay
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Chris E Brightling
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Jan De Backer
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Wim Vos
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
| | - Jim M Wild
- From the Academic Units of Academic Radiology (B.A.T., A.J.S., F.C.H., H.M., J.C.K., J.P.R., J.M.W.) and Clinical Oncology (B.A.T., R.H.I.), University of Sheffield, C Floor, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, England; Fluidda, Kontich, Belgium (C.V.H., J.D.B., W.V.); Institute for Lung Health, University of Leicester, Leicester, England (R.H., C.E.B.); Novartis, Basel, Switzerland (R.K.); and INSIGNEO Institute of In-silico Medicine, Sheffield, England (A.J.S., J.M.W.)
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Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation After Radiation Therapy. Int J Radiat Oncol Biol Phys 2015; 92:683-90. [DOI: 10.1016/j.ijrobp.2015.02.037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 02/14/2015] [Accepted: 02/18/2015] [Indexed: 11/17/2022]
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Brennan D, Schubert L, Diot Q, Castillo R, Castillo E, Guerrero T, Martel MK, Linderman D, Gaspar LE, Miften M, Kavanagh BD, Vinogradskiy Y. Clinical validation of 4-dimensional computed tomography ventilation with pulmonary function test data. Int J Radiat Oncol Biol Phys 2015; 92:423-9. [PMID: 25817531 DOI: 10.1016/j.ijrobp.2015.01.019] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Revised: 01/09/2015] [Accepted: 01/13/2015] [Indexed: 12/25/2022]
Abstract
PURPOSE A new form of functional imaging has been proposed in the form of 4-dimensional computed tomography (4DCT) ventilation. Because 4DCTs are acquired as part of routine care for lung cancer patients, calculating ventilation maps from 4DCTs provides spatial lung function information without added dosimetric or monetary cost to the patient. Before 4DCT-ventilation is implemented it needs to be clinically validated. Pulmonary function tests (PFTs) provide a clinically established way of evaluating lung function. The purpose of our work was to perform a clinical validation by comparing 4DCT-ventilation metrics with PFT data. METHODS AND MATERIALS Ninety-eight lung cancer patients with pretreatment 4DCT and PFT data were included in the study. Pulmonary function test metrics used to diagnose obstructive lung disease were recorded: forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity. Four-dimensional CT data sets and spatial registration were used to compute 4DCT-ventilation images using a density change-based and a Jacobian-based model. The ventilation maps were reduced to single metrics intended to reflect the degree of ventilation obstruction. Specifically, we computed the coefficient of variation (SD/mean), ventilation V20 (volume of lung ≤20% ventilation), and correlated the ventilation metrics with PFT data. Regression analysis was used to determine whether 4DCT ventilation data could predict for normal versus abnormal lung function using PFT thresholds. RESULTS Correlation coefficients comparing 4DCT-ventilation with PFT data ranged from 0.63 to 0.72, with the best agreement between FEV1 and coefficient of variation. Four-dimensional CT ventilation metrics were able to significantly delineate between clinically normal versus abnormal PFT results. CONCLUSIONS Validation of 4DCT ventilation with clinically relevant metrics is essential. We demonstrate good global agreement between PFTs and 4DCT-ventilation, indicating that 4DCT-ventilation provides a reliable assessment of lung function. Four-dimensional CT ventilation enables exciting opportunities to assess lung function and create functional avoidance radiation therapy plans. The present work provides supporting evidence for the integration of 4DCT-ventilation into clinical trials.
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Affiliation(s)
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Richard Castillo
- Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, Texas
| | - Edward Castillo
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan
| | - Thomas Guerrero
- Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, Michigan
| | - Mary K Martel
- Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
| | - Derek Linderman
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian D Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
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Fatyga M, Dogan N, Weiss E, Sleeman WC, Zhang B, Lehman WJ, Williamson JF, Wijesooriya K, Christensen GE. A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies. Front Oncol 2015; 5:17. [PMID: 25699238 PMCID: PMC4316695 DOI: 10.3389/fonc.2015.00017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 01/14/2015] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs. METHODS A voxel-by-voxel comparison of three DIR algorithms applied to lung patients is presented. Registrations are compared by comparing volume histograms formed both with individual DIR maps and with a voxel-by-voxel subtraction of the two maps. When two DIR maps agree one concludes that both maps are interchangeable in treatment planning applications, though one cannot conclude that either one agrees with the ground truth. If two DIR maps significantly disagree one concludes that at least one of the maps deviates from the ground truth. We use the method to compare 3 DIR algorithms applied to peak inhale-peak exhale registrations of 4DFBCT data obtained from 13 patients. RESULTS All three algorithms appear to be nearly equivalent when compared using DICE similarity coefficients. A comparison based on Jacobian volume histograms shows that all three algorithms measure changes in total volume of the lungs with reasonable accuracy, but show large differences in the variance of Jacobian distribution on contoured structures. Analysis of voxel-by-voxel subtraction of DIR maps shows differences between algorithms that exceed a centimeter for some registrations. CONCLUSION Deformation maps produced by DIR algorithms must be treated as mathematical approximations of physical tissue deformation that are not self-consistent and may thus be useful only in applications for which they have been specifically validated. The three algorithms tested in this work perform fairly robustly for the task of contour propagation, but produce potentially unreliable results for the task of DVH accumulation or measurement of local volume change. Performance of DIR algorithms varies significantly from one image pair to the next hence validation efforts, which are exhaustive but performed on a small number of image pairs may not reflect the performance of the same algorithm in practical clinical situations. Such efforts should be supplemented by validation based on a longer series of images of clinical quality.
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Affiliation(s)
- Mirek Fatyga
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - Elizabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - William C. Sleeman
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - Baoshe Zhang
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - William J. Lehman
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - Jeffrey F. Williamson
- Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, USA
| | - Krishni Wijesooriya
- Department of Radiation Oncology, University of Virginia Health Systems, Charlottesville, VA, USA
| | - Gary E. Christensen
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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Kanai T, Kadoya N, Ito K, Onozato Y, Cho SY, Kishi K, Dobashi S, Umezawa R, Matsushita H, Takeda K, Jingu K. Evaluation of accuracy of B-spline transformation-based deformable image registration with different parameter settings for thoracic images. JOURNAL OF RADIATION RESEARCH 2014; 55:1163-70. [PMID: 25053349 PMCID: PMC4229927 DOI: 10.1093/jrr/rru062] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Revised: 06/10/2014] [Accepted: 06/10/2014] [Indexed: 05/11/2023]
Abstract
Deformable image registration (DIR) is fundamental technique for adaptive radiotherapy and image-guided radiotherapy. However, further improvement of DIR is still needed. We evaluated the accuracy of B-spline transformation-based DIR implemented in elastix. This registration package is largely based on the Insight Segmentation and Registration Toolkit (ITK), and several new functions were implemented to achieve high DIR accuracy. The purpose of this study was to clarify whether new functions implemented in elastix are useful for improving DIR accuracy. Thoracic 4D computed tomography images of ten patients with esophageal or lung cancer were studied. Datasets for these patients were provided by DIR-lab (dir-lab.com) and included a coordinate list of anatomical landmarks that had been manually identified. DIR between peak-inhale and peak-exhale images was performed with four types of parameter settings. The first one represents original ITK (Parameter 1). The second employs the new function of elastix (Parameter 2), and the third was created to verify whether new functions improve DIR accuracy while keeping computational time (Parameter 3). The last one partially employs a new function (Parameter 4). Registration errors for these parameter settings were calculated using the manually determined landmark pairs. 3D registration errors with standard deviation over all cases were 1.78 (1.57), 1.28 (1.10), 1.44 (1.09) and 1.36 (1.35) mm for Parameter 1, 2, 3 and 4, respectively, indicating that the new functions are useful for improving DIR accuracy, even while maintaining the computational time, and this B-spline-based DIR could be used clinically to achieve high-accuracy adaptive radiotherapy.
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Affiliation(s)
- Takayuki Kanai
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yusuke Onozato
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Sang Yong Cho
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kazuma Kishi
- Radiation Technology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Haruo Matsushita
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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Kida S. [Toward physiologically-adaptive radiotherapy with lung functional imaging based on 4D CT]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2014; 70:1353-1359. [PMID: 25410344 DOI: 10.6009/jjrt.2014_jsrt_70.11.1353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
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Yip S, Chen AB, Aerts HJWL, Berbeco R. Sensitivity study of voxel-based PET image comparison to image registration algorithms. Med Phys 2014; 41:111714. [DOI: 10.1118/1.4898125] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
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Werner R, Schmidt-Richberg A, Handels H, Ehrhardt J. Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study. Phys Med Biol 2014; 59:4247-60. [PMID: 25017631 DOI: 10.1088/0031-9155/59/15/4247] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Accurate and robust estimation of motion fields in respiration-correlated CT (4D CT) images, usually performed by non-linear registration of the temporal CT frames, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported diagnostics and treatment planning. In this work, we present a comprehensive comparison and evaluation study of non-linear registration variants applied to the task of lung motion estimation in thoracic 4D CT data. In contrast to existing multi-institutional comparison studies (e.g. MIDRAS and EMPIRE10), we focus on the specific but common class of variational intensity-based non-parametric registration and analyze the impact of the different main building blocks of the underlying optimization problem: the distance measure to be minimized, the regularization approach and the transformation space considered during optimization. In total, 90 different combinations of building block instances are compared. Evaluated on proprietary and publicly accessible 4D CT images, landmark-based registration errors (TRE) between 1.14 and 1.20 mm for the most accurate registration variants demonstrate competitive performance of the applied general registration framework compared to other state-of-the-art approaches for lung CT registration. Although some specific trends can be observed, effects of interchanging individual instances of the building blocks on the TRE are in general rather small (no single outstanding registration variant existing); the same level of accuracy is, however, associated with significantly different degrees of motion field smoothness and computational demands. Consequently, the building block combination of choice will depend on application-specific requirements on motion field characteristics.
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Affiliation(s)
- René Werner
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Germany. Institute of Medical Informatics, University of Lübeck, Germany
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Vinogradskiy Y, Koo PJ, Castillo R, Castillo E, Guerrero T, Gaspar LE, Miften M, Kavanagh BD. Comparison of 4-dimensional computed tomography ventilation with nuclear medicine ventilation-perfusion imaging: a clinical validation study. Int J Radiat Oncol Biol Phys 2014; 89:199-205. [PMID: 24725702 DOI: 10.1016/j.ijrobp.2014.01.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 12/11/2013] [Accepted: 01/08/2014] [Indexed: 11/18/2022]
Abstract
PURPOSE Four-dimensional computed tomography (4DCT) ventilation imaging provides lung function information for lung cancer patients undergoing radiation therapy. Before 4DCT-ventilation can be implemented clinically it needs to be validated against an established imaging modality. The purpose of this work was to compare 4DCT-ventilation to nuclear medicine ventilation, using clinically relevant global metrics and radiologist observations. METHODS AND MATERIALS Fifteen lung cancer patients with 16 sets of 4DCT and nuclear medicine ventilation-perfusion (VQ) images were used for the study. The VQ-ventilation images were acquired in planar mode using Tc-99m-labeled diethylenetriamine-pentaacetic acid aerosol inhalation. 4DCT data, spatial registration, and a density-change-based model were used to compute a 4DCT-based ventilation map for each patient. The percent ventilation was calculated in each lung and each lung third for both the 4DCT and VQ-ventilation scans. A nuclear medicine radiologist assessed the VQ and 4DCT scans for the presence of ventilation defects. The VQ and 4DCT-based images were compared using regional percent ventilation and radiologist clinical observations. RESULTS Individual patient examples demonstrate good qualitative agreement between the 4DCT and VQ-ventilation scans. The correlation coefficients were 0.68 and 0.45, using the percent ventilation in each individual lung and lung third, respectively. Using radiologist-noted presence of ventilation defects and receiver operating characteristic analysis, the sensitivity, specificity, and accuracy of the 4DCT-ventilation were 90%, 64%, and 81%, respectively. CONCLUSIONS The current work compared 4DCT with VQ-based ventilation using clinically relevant global metrics and radiologist observations. We found good agreement between the radiologist's assessment of the 4DCT and VQ-ventilation images as well as the percent ventilation in each lung. The agreement lessened when the data were analyzed on a regional level. Our study presents an important step for the integration of 4DCT-ventilation into thoracic clinical practice.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
| | - Phillip J Koo
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Richard Castillo
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Edward Castillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Computational and Applied Mathematics, Rice University, Houston, Texas
| | - Thomas Guerrero
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Computational and Applied Mathematics, Rice University, Houston, Texas
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian D Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
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