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Foo CT, Donovan GM, Thien F, Langton D, Noble PB. Bronchial Thermoplasty Improves Ventilation Heterogeneity Measured by Functional Respiratory Imaging in Severe Asthma. J Asthma Allergy 2024; 17:399-409. [PMID: 38681236 PMCID: PMC11048211 DOI: 10.2147/jaa.s454951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 03/22/2024] [Indexed: 05/01/2024] Open
Abstract
Purpose Bronchial thermoplasty (BT) is a bronchoscopic intervention for the treatment of severe asthma. Despite demonstrated symptomatic benefit, the underlying mechanisms by which this is achieved remain uncertain. We hypothesize that the effects of BT are driven by improvements in ventilation heterogeneity as assessed using functional respiratory imaging (FRI). Patient and Methods Eighteen consecutive patients with severe asthma who underwent clinically indicated BT were recruited. Patients were assessed at baseline, 4-week after treatment of the left lung, and 12-month after treatment of the right lung. Data collected included short-acting beta-agonist (SABA) and oral prednisolone (OCS) use, asthma control questionnaire (ACQ-5) and exacerbation history. Patients also underwent lung function tests and chest computed tomography. Ventilation parameters including interquartile distance (IQD; measure of ventilation heterogeneity) were derived using FRI. Results 12 months after BT, significant improvements were seen in SABA and OCS use, ACQ-5, and number of OCS-requiring exacerbations. Apart from pre-bronchodilator FEV1, no other significant changes were observed in lung function. Ventilation heterogeneity significantly improved after treatment of the left lung (0.18 ± 0.04 vs 0.20 ± 0.04, p=0.045), with treatment effect persisting up to 12 months later (0.18 ± 0.05 vs 0.20 ± 0.04, p=0.028). Ventilation heterogeneity also improved after treatment of the right lung, although this did not reach statistical significance (0.18 ± 0.05 vs 0.19 ± 0.04, p=0.06). Conclusion Clinical benefits after BT are accompanied by improvements in ventilation heterogeneity, advancing our understanding of its mechanism of action. Beyond BT, FRI has the potential to be expanded into other clinical applications.
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Affiliation(s)
- Chuan T Foo
- Department of Respiratory Medicine, Eastern Health, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Graham M Donovan
- Department of Mathematics, University of Auckland, Auckland, New Zealand
| | - Francis Thien
- Department of Respiratory Medicine, Eastern Health, Melbourne, VIC, Australia
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - David Langton
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- Department of Thoracic Medicine, Peninsula Health, Frankston, VIC, Australia
| | - Peter B Noble
- School of Human Sciences, The University of Western Australia, Crawley, WA, Australia
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Kim M, Hwang J, Grist JT, Abueid G, Yoon SH, Grau V, Fraser E, Gleeson FV. Functional Impairment in Small Airways Associated With the Breathlessness Symptoms in Long-Coronavirus Disease. J Thorac Imaging 2024; 39:79-85. [PMID: 37889567 DOI: 10.1097/rti.0000000000000748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
PURPOSE This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis. PATIENTS AND METHODS Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models. RESULTS The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056). CONCLUSIONS This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.
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Affiliation(s)
- Minsuok Kim
- School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough
| | - Jeongeun Hwang
- Department of Engineering Science, Institute of Biomedical Engineering, Oxford e-Research Centre
- Department of Medical IT Engineering, Soonchunhyang University, Chungcheonnam-do
| | - James T Grist
- Department of Physiology, Anatomy, and Genetics
- Department of Radiology
- Oxford Centre for Clinical MR Research, John Radcliffe Hospital, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Vicente Grau
- Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine
| | - Emily Fraser
- Oxford Interstitial Lung Disease Service, The Churchill Hospital
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford
- Department of Radiology
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3
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Flakus MJ, Wuschner AE, Wallat EM, Shao W, Meudt J, Shanmuganayagam D, Christensen GE, Reinhardt JM, Bayouth JE. Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model. Med Phys 2023; 50:6366-6378. [PMID: 36999913 PMCID: PMC10544701 DOI: 10.1002/mp.16400] [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: 11/24/2022] [Accepted: 03/13/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remaining variables. PURPOSE To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilated pigs. METHODS Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques.L E R 2 $LER_2$ measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images.L E R N $LER_N$ measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. RESULTS Biomarkers showed strong agreement with voxel-wise Spearman correlationρ > 0.9 $\rho > 0.9$ for intraday repeatability andρ > 0.8 $\rho > 0.8$ for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. CONCLUSIONS 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhuman subjects.
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Affiliation(s)
- Mattison J Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Antonia E Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Eric M Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Wei Shao
- Department of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jen Meudt
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Dhanansayan Shanmuganayagam
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, Oregon, USA
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Huang YH, Teng X, Zhang J, Chen Z, Ma Z, Ren G, Kong FMS, Ge H, Cai J. Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging 2023; 38:286-296. [PMID: 37265243 DOI: 10.1097/rti.0000000000000717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
PURPOSE The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.
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Affiliation(s)
- Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Feng-Ming Spring Kong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
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5
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Astley JR, Biancardi AM, Marshall H, Hughes PJC, Collier GJ, Hatton MQ, Wild JM, Tahir BA. A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI. Med Phys 2023; 50:5657-5670. [PMID: 36932692 PMCID: PMC10946819 DOI: 10.1002/mp.16369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 02/25/2023] [Accepted: 03/04/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.
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Affiliation(s)
- Joshua R Astley
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Alberto M Biancardi
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Helen Marshall
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Paul J C Hughes
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Guilhem J Collier
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Matthew Q Hatton
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Jim M Wild
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Bilal A Tahir
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
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6
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Huang YH, Ren G, Xiao H, Yang D, Kong FMS, Ho WY, Cai J. Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy. Med Phys 2022; 49:7237-7246. [PMID: 35841346 DOI: 10.1002/mp.15847] [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: 10/22/2021] [Revised: 04/20/2022] [Accepted: 06/23/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). METHODS A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method. RESULTS The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. CONCLUSIONS In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.
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Affiliation(s)
- Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Dongrong Yang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
| | - Feng-Ming Spring Kong
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Wai Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR
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7
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Astley JR, Wild JM, Tahir BA. Deep learning in structural and functional lung image analysis. Br J Radiol 2022; 95:20201107. [PMID: 33877878 PMCID: PMC9153705 DOI: 10.1259/bjr.20201107] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow.
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Affiliation(s)
| | - Jim M Wild
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom
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8
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Lauwers E, Snoeckx A, Ides K, Van Hoorenbeeck K, Lanclus M, De Backer W, De Backer J, Verhulst S. Functional respiratory imaging in relation to classical outcome measures in cystic fibrosis: a cross-sectional study. BMC Pulm Med 2021; 21:256. [PMID: 34348676 PMCID: PMC8336350 DOI: 10.1186/s12890-021-01622-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/29/2021] [Indexed: 12/17/2022] Open
Abstract
Background Functional Respiratory Imaging (FRI) combines HRCT scans with computational fluid dynamics to provide objective and quantitative information about lung structure and function. FRI has proven its value in pulmonary diseases such as COPD and asthma, but limited studies have focused on cystic fibrosis (CF). This study aims to investigate the relation of multiple FRI parameters to validated imaging parameters and classical respiratory outcomes in a CF population. Methods CF patients aged > 5 years scheduled for a chest CT were recruited in a cross-sectional study. FRI outcomes included regional airway volume, airway wall volume, airway resistance, lobar volume, air trapping and pulmonary blood distribution. Besides FRI, CT scans were independently evaluated by 2 readers using the CF-CT score. Spirometry and the 6-Minute Walk Test (6MWT) were also performed. Statistical tests included linear mixed-effects models, repeated measures correlations, Pearson and Spearman correlations. Results 39 CT scans of 24 (17M/7F) subjects were analyzed. Patients were 24 ± 9 years old and had a ppFEV1 of 71 ± 25% at the time of the first CT. All FRI parameters showed significant low-to-moderate correlations with the total CF-CT score, except for lobar volume. When considering the relation between FRI parameters and similar CF-CT subscores, significant correlations were found between parameters related to airway volume, air trapping and airway wall thickening. Air trapping, lobar volume after normal expiration and pulmonary blood distribution showed significant associations with all spirometric parameters and oxygen saturation at the end of 6MWT. In addition, air trapping was the only parameter related to the distance covered during 6MWT. A subgroup analysis showed considerably higher correlations in patients with mild lung disease (ppFEV1 ≥ 70%) compared to patients with moderate to severe lung disease (ppFEV1 < 70%) when comparing FRI to CF-CT scores. Conclusions Multiple structural characteristics determined by FRI were associated with abnormalities determined by CF-CT score. Air trapping and pulmonary blood distribution appeared to be the most clinically relevant FRI parameters for CF patients due to their associations with classical outcome measures. The FRI methodology could particularly be of interest for patients with mild lung disease, although this should be confirmed in future research. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-021-01622-3.
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Affiliation(s)
- Eline Lauwers
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2160, Wilrijk, Belgium. .,Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium.
| | - Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital, Edegem, Belgium.,Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Ides
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2160, Wilrijk, Belgium.,Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium.,Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium.,CoSys Research Lab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium.,Flanders Make Strategic Research Center, Lommel, Belgium
| | - Kim Van Hoorenbeeck
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2160, Wilrijk, Belgium.,Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium.,Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
| | | | - Wilfried De Backer
- FLUIDDA NV, Kontich, Belgium.,Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Stijn Verhulst
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2160, Wilrijk, Belgium.,Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium.,Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
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9
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Debray MP, Ghanem M, Khalil A, Taillé C. [Lung imaging in severe asthma]. Rev Mal Respir 2021; 38:41-57. [PMID: 33423858 DOI: 10.1016/j.rmr.2020.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 09/02/2020] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Asthma is a common disease whose diagnosis does not typically rely on the results of imaging. However, chest CT has gained a key place over the last decade to support the management of patients with difficult to treat and severe asthma. STATE OF THE ART Bronchial wall thickening and mild dilatation or narrowing of bronchial lumen are frequently observed on chest CT in people with asthma. Bronchial wall thickening is correlated to the degree of obstruction and to bronchial wall remodeling and inflammation. Diverse conditions which can mimic asthma should be recognized on CT, including endobronchial tumours, interstitial pneumonias, bronchiectasis and bronchiolitis. Ground-glass opacities and consolidation may be related to transient eosinophilic infiltrates, infection or an associated disease (vasculitis, chronic eosinophilic pneumonia). Hyperdense mucous plugging is highly specific for allergic bronchopulmonary aspergillosis. PERSPECTIVES Airway morphometry, air trapping and quantitative analysis of ventilatory defects, with CT or MRI, can help to identify different morphological subgroups of patients with different functional or inflammatory characteristics. These imaging tools could emerge as new biomarkers for the evaluation of treatment response. CONCLUSION Chest CT is indicated in people with severe asthma to search for additional or alternative diagnoses. Quantitative imaging may contribute to phenotyping this patient group.
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Affiliation(s)
- M-P Debray
- Service de Radiologie, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, 46, rue Henri Huchard, 75018 Paris; Inserm UMR1152, France.
| | - M Ghanem
- Service de Pneumologie et Centre de Référence constitutif des Maladies Pulmonaires Rares, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, France
| | - A Khalil
- Service de Radiologie, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, 46, rue Henri Huchard, 75018 Paris; Université de Paris, Inserm UMR1152, France
| | - C Taillé
- Service de Pneumologie et Centre de Référence constitutif des Maladies Pulmonaires Rares, Assistance Publique-Hôpitaux de Paris, Hôpital Bichat, France; Département Hospitalo-Universitaire FIRE ; Université de Paris ; Inserm UMR 1152 ; LabEx Inflamex, 75018 Paris, France
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10
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Lauwers E, Belmans D, Mignot B, Ides K, Van Hoorenbeeck K, Snoeckx A, Van Holsbeke C, Nowé V, Van Braeckel E, De Backer W, De Backer J, Verhulst S. The short-term effects of ORKAMBI (lumacaftor/ivacaftor) on regional and distal lung structures using functional respiratory imaging. Ther Adv Respir Dis 2021; 15:17534666211046774. [PMID: 34541955 PMCID: PMC8461124 DOI: 10.1177/17534666211046774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/11/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Lumacaftor/ivacaftor (LUM/IVA) has shown modest benefits in previous research, but the exact effects in the cystic fibrosis (CF) lung remain unclear. This study aims to offer novel information on the mode of action of the cystic fibrosis transmembrane conductance regulator (CFTR)-modulating drug by assessing lung structure and function using functional respiratory imaging (FRI). METHODS CF patients aged ⩾12 years homozygous for F508del were recruited in an open-label study. Before and after 12 weeks of treatment with LUM/IVA, FRI was used to visualize regional information, such as air trapping, lobar volume and airway wall volume. Secondary outcomes included the CF-CT scoring system, spirometry, the Cystic Fibrosis Questionnaire-Revised (CFQ-R) questionnaire, exercise tolerance and nutritional status. RESULTS Of the 12 patients enrolled in the study, 11 completed all study visits. Concerning the FRI parameters, hyperinflation of the lung decreased, indicated by a reduction in air trapping and lobar volume at expiration. Also, a decrease in airway wall volume and a redistribution of pulmonary blood volume were noted, which might be related to a decrease in mucus impaction. Airway resistance, airway volume, internal airflow distribution and aerosol deposition pattern did not show significant changes. No significant improvements were found in any of the CF-CT scores or in the spirometric parameters. Other secondary outcomes showed similar results compared with previous research. Correlations at baseline were found between FRI and conventional outcomes, including physical functioning, spirometry and CF-CT scores. CONCLUSIONS LUM/IVA decreased lung hyperinflation in combination with a potential decrease in mucus impaction, which can be related to an improved mucociliary transport. These results indicate that several FRI parameters, reflecting regional and distal lung structures, are more sensitive to changes caused by LUM/IVA than conventional respiratory outcomes.
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Affiliation(s)
- Eline Lauwers
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, Wilrijk, 2160 Antwerp, Belgium
- Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium
| | | | | | - Kris Ides
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium
- Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
- CoSys Research Lab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium
- Flanders Make Strategic Research Center, Lommel, Belgium
| | - Kim Van Hoorenbeeck
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium
- Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
| | - Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital, Edegem, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Vicky Nowé
- Department of Pulmonology, GZA Hospital, Antwerp, Belgium
| | - Eva Van Braeckel
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
- Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Wilfried De Backer
- FLUIDDA NV, Kontich, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Stijn Verhulst
- Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- Infla-Med Research Consortium of Excellence, University of Antwerp, Antwerp, Belgium
- Department of Pediatrics, Antwerp University Hospital, Edegem, Belgium
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11
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Kim M, Doganay O, Hwang HJ, Seo JB, Gleeson FV. Lobar Ventilation in Patients with COPD Assessed with the Full-Scale Airway Network Flow Model and Xenon-enhanced Dual-Energy CT. Radiology 2020; 298:201-209. [PMID: 33231530 DOI: 10.1148/radiol.2020202485] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background The full-scale airway network (FAN) flow model shows excellent agreement with limited functional imaging data but requires further validation prior to clinical use. Purpose To validate the ventilation distributions computed with the FAN flow model with xenon ventilation from xenon-enhanced dual-energy (DE) CT in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods In this prospective study, the FAN model extracted structural data from xenon-enhanced DE CT images of men with COPD scanned between June 2012 and July 2013 to compute gas ventilation dynamics. The ventilation distributions on the middle cross-section plane, percentage lobar ventilation, and ventilation heterogeneity quantified by the coefficient of variation (CV) were compared between xenon-enhanced DE CT imaging and the FAN model. The relationship between the ventilation parameters with the densitometry and pulmonary function test results was demonstrated. The agreements and correlations between the parameters were measured using the concordance correlation coefficient and the Pearson correlation coefficient. Results Twenty-two men with COPD (mean age, 67 years ± 7 [standard deviation]) were evaluated. The percentage lobar ventilation computed with FAN showed a strong positive correlation with xenon-enhanced DE CT data (r = 0.7, P < .001). Ninety-five percent of lobar ventilation CV differences lay within 95% confidence intervals. Correlations of the percentage lobar ventilation were negative for percentage emphysema (xenon-enhanced DE CT: r = -0.38, P < .001; FAN: r = -0.23, P = .02) but were positive for percentage normal tissue volume (xenon-enhanced DE CT: r = 0.78, P < .001; FAN: r = 0.45, P < .001). Lung CVs of FAN revealed negative correlations with the spirometry results (CVFAN vs percentage predicted forced expiratory volume in 1 second: r = -0.75, P < .001; CVFAN vs ratio of forced expiratory volume in 1 second to forced vital capacity: r = -0.67, P < .001). Conclusion The full-scale airway network modeled lobar ventilation in patients with chronic obstructive pulmonary disease correlated with the xenon-enhanced dual-energy CT imaging data. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Parraga and Eddy in this issue.
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Affiliation(s)
- Minsuok Kim
- From the School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, England (M.K.); Healthy Science Institute, Ege University, Izmir, Turkey (O.D.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.J.H., J.B.S.); Department of Oncology, University of Oxford, Oxford, England (F.V.G.); and Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, England (F.V.G.)
| | - Ozkan Doganay
- From the School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, England (M.K.); Healthy Science Institute, Ege University, Izmir, Turkey (O.D.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.J.H., J.B.S.); Department of Oncology, University of Oxford, Oxford, England (F.V.G.); and Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, England (F.V.G.)
| | - Hye Jeon Hwang
- From the School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, England (M.K.); Healthy Science Institute, Ege University, Izmir, Turkey (O.D.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.J.H., J.B.S.); Department of Oncology, University of Oxford, Oxford, England (F.V.G.); and Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, England (F.V.G.)
| | - Joon Beom Seo
- From the School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, England (M.K.); Healthy Science Institute, Ege University, Izmir, Turkey (O.D.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.J.H., J.B.S.); Department of Oncology, University of Oxford, Oxford, England (F.V.G.); and Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, England (F.V.G.)
| | - Fergus V Gleeson
- From the School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, England (M.K.); Healthy Science Institute, Ege University, Izmir, Turkey (O.D.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (H.J.H., J.B.S.); Department of Oncology, University of Oxford, Oxford, England (F.V.G.); and Department of Radiology, The Churchill Hospital, Oxford University Hospitals NHS Trust, Headington, England (F.V.G.)
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12
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Cox CW, Chung JH, Ackman JB, Berry MF, Carter BW, de Groot PM, Hobbs SB, Johnson GB, Maldonado F, McComb BL, Tong BC, Walker CM, Kanne JP. ACR Appropriateness Criteria® Occupational Lung Diseases. J Am Coll Radiol 2020; 17:S188-S197. [PMID: 32370962 DOI: 10.1016/j.jacr.2020.01.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 01/22/2020] [Indexed: 11/28/2022]
Abstract
Ordering the appropriate diagnostic imaging for occupational lung disease requires a firm understanding of the relationship between occupational exposure and expected lower respiratory track manifestation. Where particular inorganic dust exposures typically lead to nodular and interstitial lung disease, other occupational exposures may lead to isolated small airway obstruction. Certain workplace exposures, like asbestos, increase the risk of malignancy, but also produce pulmonary findings that mimic malignancy. This publication aims to delineate the common and special considerations associated with occupational lung disease to assist the ordering physician in selecting the most appropriate imaging study, while still stressing the importance of a multidisciplinary approach. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
| | | | - Jeanne B Ackman
- Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mark F Berry
- Stanford University Medical Center, Stanford, California; The Society of Thoracic Surgeons
| | - Brett W Carter
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | | | | | - Fabien Maldonado
- Vanderbilt University Medical Center, Nashville, Tennessee; American College of Chest Physicians
| | | | - Betty C Tong
- Duke University School of Medicine, Durham, North Carolina; The Society of Thoracic Surgeons
| | | | - Jeffrey P Kanne
- Specialty Chair, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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13
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Hegi-Johnson F, de Ruysscher D, Keall P, Hendriks L, Vinogradskiy Y, Yamamoto T, Tahir B, Kipritidis J. Imaging of regional ventilation: Is CT ventilation imaging the answer? A systematic review of the validation data. Radiother Oncol 2019; 137:175-185. [DOI: 10.1016/j.radonc.2019.03.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/08/2019] [Accepted: 03/10/2019] [Indexed: 01/08/2023]
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14
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Duan C, Deng H, Xiao S, Xie J, Li H, Sun X, Ma L, Lou X, Ye C, Zhou X. Fast and accurate reconstruction of human lung gas MRI with deep learning. Magn Reson Med 2019; 82:2273-2285. [PMID: 31322298 DOI: 10.1002/mrm.27889] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 06/03/2019] [Accepted: 06/11/2019] [Indexed: 12/17/2022]
Abstract
PURPOSE To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning. METHODS The scheme was comprised of coarse-to-fine nets (C-net and F-net). Zero-filling images from retrospectively undersampled k-space at an acceleration factor of 4 were used as input for C-net, and then output intermediate results which were fed into F-net. During training, a L2 loss function was adopted in C-net, while a function that united L2 loss with proton prior knowledge was used in F-net. The 871 hyperpolarized 129 Xe pulmonary ventilation images from 72 volunteers were randomly arranged as training (90%) and testing (10%) data. Ventilation defect percentage comparisons were implemented using a paired 2-tailed Student's t-test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers. RESULTS Each image with size of 96 × 84 could be reconstructed within 31 ms (mean absolute error was 4.35% and structural similarity was 0.7558). Compared with conventional compressed sensing MRI, the mean absolute error decreased by 17.92%, but the structural similarity increased by 6.33%. For ventilation defect percentage, there were no significant differences between the fully sampled and reconstructed images through the proposed algorithm (P = 0.932), but had significant correlations (r = 0.975; P < 0.001). The prospectively undersampled results validated a good agreement with fully sampled images, with no significant differences in ventilation defect percentage but significantly higher signal-to-noise ratio values. CONCLUSION The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real-time and accurate reconstruction of gas MRI.
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Affiliation(s)
- Caohui Duan
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - He Deng
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Sa Xiao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Junshuai Xie
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Haidong Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Xianping Sun
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Lin Ma
- Department of Radiology, Chinese PLA General Hospital, Beijing, P. R. China
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, Beijing, P. R. China
| | - Chaohui Ye
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, P. R. China.,University of Chinese Academy of Sciences, Beijing, P. R. China
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15
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Langton D, Sloan G, Banks C, Bennetts K, Plummer V, Thien F. Bronchial thermoplasty increases airway volume measured by functional respiratory imaging. Respir Res 2019; 20:157. [PMID: 31311604 PMCID: PMC6636001 DOI: 10.1186/s12931-019-1132-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 07/10/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND The purpose of this study was to use CT scanning with computational fluid dynamics to evaluate the mechanisms by which Bronchial Thermoplasty (BT) improves asthmatic symptoms. METHODS The study was conducted in a university teaching hospital, experienced in performing BT. Imaging studies were performed before, and after, BT of the left lung, and prior to treatment of the right lung, which therefore acted as a control. On each occasion, two high-resolution CT scans were performed, one at full inspiration (TLC) and the other at Functional Residual Capacity (FRC). The study protocol was offered to 10 patients, all of whom met the definition of severe asthma, despite high dose inhaled corticosteroids and dual long acting bronchodilators. RESULTS Significant increases in airway luminal volume were observed on the treated side, compared with control, at both full inspiration (by 27%) and at FRC (by 17%). The ratio of distal airway volume to lung volume significantly increased on the treated side. The change in airway volume with inspiration from FRC increased by 48% on the treated side compared to 5% in the control lung, suggesting treatment increased airway distensibility. No effect was observed on airway wall thickness, nor air trapping. There was a trend towards correlation between the improvement in airway volume at TLC and improvement in symptoms. CONCLUSION This study demonstrates that BT increases the luminal airway volume on the treated side compared to the control lung. We suggest that this is an important link between the airway smooth muscle atrophy demonstrated pathologically, and the improvement in symptoms observed clinically.
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Affiliation(s)
- D Langton
- Department of Thoracic Medicine, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston, VIC, 3199, Australia. .,Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Vic, Australia.
| | - G Sloan
- Department of Medical Imaging, Frankston Hospital, Peninsula Health, Frankston, Vic, Australia
| | - C Banks
- Department of Thoracic Medicine, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston, VIC, 3199, Australia
| | - K Bennetts
- Department of Thoracic Medicine, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston, VIC, 3199, Australia
| | - V Plummer
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Vic, Australia
| | - F Thien
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Vic, Australia.,Department of Respiratory Medicine, Eastern Health, Box Hill, Vic, Australia
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16
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Bell AJ, Foy BH, Richardson M, Singapuri A, Mirkes E, van den Berge M, Kay D, Brightling C, Gorban AN, Galbán CJ, Siddiqui S. Functional CT imaging for identification of the spatial determinants of small-airways disease in adults with asthma. J Allergy Clin Immunol 2019; 144:83-93. [PMID: 30682455 DOI: 10.1016/j.jaci.2019.01.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 01/09/2019] [Accepted: 01/14/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND Asthma is a disease characterized by ventilation heterogeneity (VH). A number of studies have demonstrated that VH markers derived by using impulse oscillometry (IOS) or multiple-breath washout (MBW) are associated with key asthmatic patient-related outcome measures and airways hyperresponsiveness. However, the topographical mechanisms of VH in the lung remain poorly understood. OBJECTIVES We hypothesized that specific regionalization of topographical small-airway disease would best account for IOS- and MBW-measured indices in patients. METHODS We evaluated the results of paired expiratory/inspiratory computed tomography in a cohort of asthmatic (n = 41) and healthy (n = 11) volunteers to understand the determinants of clinical VH indices commonly reported by using IOS and MBW. Parametric response mapping (PRM) was used to calculate the functional small-airways disease marker PRMfSAD and Hounsfield unit (HU)-based density changes from total lung capacity to functional residual capacity (ΔHU); gradients of ΔHU in gravitationally perpendicular (parallel) inferior-superior (anterior-posterior) axes were quantified. RESULTS The ΔHU gradient in the inferior-superior axis provided the highest level of discrimination of both acinar VH (measured by using phase 3 slope analysis of multiple-breath washout data) and resistance at 5 Hz minus resistance at 20 Hz measured by using impulse oscillometry (R5-R20) values. Patients with a high inferior-superior ΔHU gradient demonstrated evidence of reduced specific ventilation in the lower lobes of the lungs and high levels of PRMfSAD. A computational small-airway tree model confirmed that constriction of gravitationally dependent, lower-zone, small-airway branches would promote the largest increases in R5-R20 values. Ventilation gradients correlated with asthma control and quality of life but not with exacerbation frequency. CONCLUSIONS Lower lobe-predominant small-airways disease is a major driver of clinically measured VH in adults with asthma.
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Affiliation(s)
- Alex J Bell
- NIHR Respiratory Biomedical Research Centre (BRC), Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Brody H Foy
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Matthew Richardson
- NIHR Respiratory Biomedical Research Centre (BRC), Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Amisha Singapuri
- NIHR Respiratory Biomedical Research Centre (BRC), Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Evgeny Mirkes
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
| | - Maarten van den Berge
- Department of Pulmonology, University Medical Centre Groningen, Groningen, the Netherlands
| | - David Kay
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Chris Brightling
- NIHR Respiratory Biomedical Research Centre (BRC), Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Alexander N Gorban
- Department of Mathematics, University of Leicester, Leicester, United Kingdom
| | - Craig J Galbán
- Department of Radiology, University of Michigan, Ann Arbor, Mich
| | - Salman Siddiqui
- NIHR Respiratory Biomedical Research Centre (BRC), Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom.
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17
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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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18
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Zhong Y, Vinogradskiy Y, Chen L, Myziuk N, Castillo R, Castillo E, Guerrero T, Jiang S, Wang J. Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys 2019; 46:2323-2329. [PMID: 30714159 DOI: 10.1002/mp.13421] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/20/2018] [Accepted: 01/22/2019] [Indexed: 01/10/2023] Open
Abstract
PURPOSE Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration. METHODS A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images. RESULTS The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively. CONCLUSIONS The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.
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Affiliation(s)
- Yuncheng Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nick Myziuk
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, 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
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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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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Wu X, Kim GH, Salisbury ML, Barber D, Bartholmai BJ, Brown KK, Conoscenti CS, De Backer J, Flaherty KR, Gruden JF, Hoffman EA, Humphries SM, Jacob J, Maher TM, Raghu G, Richeldi L, Ross BD, Schlenker-Herceg R, Sverzellati N, Wells AU, Martinez FJ, Lynch DA, Goldin J, Walsh SLF. Computed Tomographic Biomarkers in Idiopathic Pulmonary Fibrosis. The Future of Quantitative Analysis. Am J Respir Crit Care Med 2019; 199:12-21. [DOI: 10.1164/rccm.201803-0444pp] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | - Grace H. Kim
- Radiological Science, University of California Los Angeles School of Medicine, Los Angeles, California
| | | | | | | | - Kevin K. Brown
- Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, Colorado
| | | | | | | | | | - Eric A. Hoffman
- Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa
| | | | - Joseph Jacob
- Respiratory Medicine and
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Toby M. Maher
- Interstitial Lung Disease Unit, Royal Brompton Hospital, London, United Kingdom
- Associate Editor, AJRCCM
| | - Ganesh Raghu
- Pulmonary and Critical Care Medicine, University of Washington Medical Center, Seattle, Washington
| | - Luca Richeldi
- Fondazione Policlinico Universitario A. Gemelli, Universita Cattolica del Sacro Cuore, Rome, Italy
| | - Brian D. Ross
- Radiology, University of Michigan Hospital, Ann Arbor, Michigan
| | | | - Nicola Sverzellati
- Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Athol U. Wells
- Interstitial Lung Disease Unit, Royal Brompton Hospital, London, United Kingdom
| | | | | | - Jonathan Goldin
- Radiological Science, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Simon L. F. Walsh
- Radiology, Kings College Hospital National Health Service Foundation Trust, London, United Kingdom
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Doganay O, Matin T, Chen M, Kim M, McIntyre A, McGowan DR, Bradley KM, Povey T, Gleeson FV. Time-series hyperpolarized xenon-129 MRI of lobar lung ventilation of COPD in comparison to V/Q-SPECT/CT and CT. Eur Radiol 2018; 29:4058-4067. [PMID: 30552482 PMCID: PMC6610266 DOI: 10.1007/s00330-018-5888-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 10/08/2018] [Accepted: 11/13/2018] [Indexed: 12/23/2022]
Abstract
Purpose To derive lobar ventilation in patients with chronic obstructive pulmonary disease (COPD) using a rapid time-series hyperpolarized xenon-129 (HPX) magnetic resonance imaging (MRI) technique and compare this to ventilation/perfusion single-photon emission computed tomography (V/Q-SPECT), correlating the results with high-resolution computed tomography (CT) and pulmonary function tests (PFTs). Materials and methods Twelve COPD subjects (GOLD stages I–IV) participated in this study and underwent HPX-MRI, V/Q-SPECT/CT, high-resolution CT, and PFTs. HPX-MRI was performed using a novel time-series spiral k-space sampling approach. Relative percentage ventilations were calculated for individual lobe for comparison to the relative SPECT lobar ventilation and perfusion. The absolute HPX-MRI percentage ventilation in each lobe was compared to the absolute CT percentage emphysema score calculated using a signal threshold method. Pearson’s correlation and linear regression tests were performed to compare each imaging modality. Results Strong correlations were found between the relative lobar percentage ventilation with HPX-MRI and percentage ventilation SPECT (r = 0.644; p < 0.001) and percentage perfusion SPECT (r = 0.767; p < 0.001). The absolute CT percentage emphysema and HPX percentage ventilation correlation was also statistically significant (r = 0.695, p < 0.001). The whole lung HPX percentage ventilation correlated with the PFT measurements (FEV1 with r = − 0.886, p < 0.001*, and FEV1/FVC with r = − 0.861, p < 0.001*) better than the whole lung CT percentage emphysema score (FEV1 with r = − 0.635, p = 0.027; and FEV1/FVC with r = − 0.652, p = 0.021). Conclusion Lobar ventilation with HPX-MRI showed a strong correlation with lobar ventilation and perfusion measurements derived from SPECT/CT, and is better than the emphysema score obtained with high-resolution CT. Key Points • The ventilation hyperpolarized xenon-129 MRI correlates well with ventilation and perfusion with SPECT/CT with the advantage of higher temporal and spatial resolution. • The hyperpolarized xenon-129 MRI correlates with the PFT measurements better than the high-resolution CT with the advantage of avoiding the use of ionizing radiation. Electronic supplementary material The online version of this article (10.1007/s00330-018-5888-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ozkan Doganay
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK.
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK.
| | - Tahreema Matin
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Mitchell Chen
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Minsuok Kim
- Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK
| | - Anthony McIntyre
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Thomas Povey
- Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
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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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CT ventilation imaging derived from breath hold CT exhibits good regional accuracy with Galligas PET. Radiother Oncol 2018; 127:267-273. [DOI: 10.1016/j.radonc.2017.12.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 12/16/2017] [Accepted: 12/16/2017] [Indexed: 11/23/2022]
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Abstract
PURPOSE OF REVIEW The present review aims to summarize the most recent evidence related to imaging and severe asthma, both with regard to advances in imaging research and to their current and potential clinical implications. RECENT FINDINGS Recent work in imaging in severe asthma has principally been using computed tomography (CT) and MRI, as well as the integration of the two. Some of the most notable findings include the use of CT imaging biomarkers to create unique clusters of asthmatics, and the use of co-registration to link CT images of airways with regional variation in ventilation in MRI. In addition, temporal studies have shown that some the ventilation defects found using MRI in asthmatics are intermittent and others are persistent, but both are associated with lower lung function. SUMMARY The role of imaging in severe asthma currently is primarily in the exclusion of comorbid or other conditions, or in the assessment for complications in the setting of acute decompensation. A rapidly expanding body of literature using CT and MRI suggests that these tools may soon be of utility in the chronic management of the disease.
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Regional Heterogeneity of Lobar Ventilation in Asthma Using Hyperpolarized Helium-3 MRI. Acad Radiol 2018; 25:169-178. [PMID: 29174189 DOI: 10.1016/j.acra.2017.09.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Revised: 09/07/2017] [Accepted: 09/21/2017] [Indexed: 12/26/2022]
Abstract
RATIONALE AND OBJECTIVES To determine lobar ventilation patterns in asthmatic lungs with hyperpolarized 3He magnetic resonance imaging (HP 3He MRI). MATERIALS AND METHODS Eighty-two subjects (14 normal, 48 mild-to-moderate asthma, and 20 severe asthma) underwent HP 3He MRI, computed tomography (CT), and pulmonary function testing. After registering proton to 3He images, we segmented the lungs from proton MRI and further segmented the five lung lobes (right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]; left upper lobe and left lower lobe [LLL]) by referring to the lobar segmentation from CT. We classified the gas volume into four signal intensity levels as follows: ventilation defect percent (VDP), low ventilation percent, medium ventilation percent, and high ventilation percent. The local signal intensity variations in the ventilated volume were estimated using heterogeneity score (Hs). We compared each ventilation level and Hs measured in the whole lung and lobar regions across the three subject groups. RESULTS In mild-to-moderate asthma, the RML and RUL showed significantly greater VDP than the two lower lobes (RLL and LLL) (P ≤ .047). In severe asthma, the pattern was more variable with the VDP in the RUL significantly greater than in the RLL (P = .026). In both asthma groups, the lower lobes (RLL and LLL) showed significantly higher high ventilation percent and Hs compared to the three upper lobes (all P ≤ .015). CONCLUSIONS In asthma, the RML and RUL showed greater ventilation abnormalities, and the RLL and LLL were more highly ventilated with greater local heterogeneity. These findings may facilitate guided bronchoscopic sampling and localized airway treatment in future studies.
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Abstract
Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.
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Affiliation(s)
- Alys R Clark
- 1 Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- 1 Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kelly Burrowes
- 2 Department of Chemical and Materials Engineering, The University of Auckland, Auckland, New Zealand
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Burrowes KS, De Backer J, Kumar H. Image-based computational fluid dynamics in the lung: virtual reality or new clinical practice? WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017; 9. [PMID: 28608962 DOI: 10.1002/wsbm.1392] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/12/2017] [Accepted: 04/19/2017] [Indexed: 11/05/2022]
Abstract
The development and implementation of personalized medicine is paramount to improving the efficiency and efficacy of patient care. In the respiratory system, function is largely dictated by the choreographed movement of air and blood to the gas exchange surface. The passage of air begins in the upper airways, either via the mouth or nose, and terminates at the alveolar interface, while blood flows from the heart to the alveoli and back again. Computational fluid dynamics (CFD) is a well-established tool for predicting fluid flows and pressure distributions within complex systems. Traditionally CFD has been used to aid in the effective or improved design of a system or device; however, it has become increasingly exploited in biological and medical-based applications further broadening the scope of this computational technique. In this review, we discuss the advancement in application of CFD to the respiratory system and the contributions CFD is currently making toward improving precision medicine. The key areas CFD has been applied to in the pulmonary system are in predicting fluid transport and aerosol distribution within the airways. Here we focus our discussion on fluid flows and in particular on image-based clinically focused CFD in the ventilatory system. We discuss studies spanning from the paranasal sinuses through the conducting airways down to the level of the alveolar airways. The combination of imaging and CFD is enabling improved device design in aerosol transport, improved biomarkers of lung function in clinical trials, and improved predictions and assessment of surgical interventions in the nasal sinuses. WIREs Syst Biol Med 2017, 9:e1392. doi: 10.1002/wsbm.1392 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Kelly S Burrowes
- Department of Chemical and Materials Engineering, University of Auckland, Auckland, New Zealand.,Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Matin TN, Rahman N, Nickol AH, Chen M, Xu X, Stewart NJ, Doel T, Grau V, Wild JM, Gleeson FV. Chronic Obstructive Pulmonary Disease: Lobar Analysis with Hyperpolarized 129Xe MR Imaging. Radiology 2017; 282:857-868. [PMID: 27732160 DOI: 10.1148/radiol.2016152299] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2024]
Abstract
Purpose To compare lobar ventilation and apparent diffusion coefficient (ADC) values obtained with hyperpolarized xenon 129 (129Xe) magnetic resonance (MR) imaging to quantitative computed tomography (CT) metrics on a lobar basis and pulmonary function test (PFT) results on a whole-lung basis in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods The study was approved by the National Research Ethics Service Committee; written informed consent was obtained from all patients. Twenty-two patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage II-IV) underwent hyperpolarized 129Xe MR imaging at 1.5 T, quantitative CT, and PFTs. Whole-lung and lobar 129Xe MR imaging parameters were obtained by using automated segmentation of multisection hyperpolarized 129Xe MR ventilation images and hyperpolarized 129Xe MR diffusion-weighted images after coregistration to CT scans. Whole-lung and lobar quantitative CT-derived metrics for emphysema and bronchial wall thickness were calculated. Pearson correlation coefficients were used to evaluate the relationship between imaging measures and PFT results. Results Percentage ventilated volume and average ADC at lobar 129Xe MR imaging showed correlation with percentage emphysema at lobar quantitative CT (r = -0.32, P < .001 and r = 0.75, P < .0001, respectively). The average ADC at whole-lung 129Xe MR imaging showed moderate correlation with PFT results (percentage predicted transfer factor of the lung for carbon monoxide [Tlco]: r = -0.61, P < .005) and percentage predicted functional residual capacity (r = 0.47, P < .05). Whole-lung quantitative CT percentage emphysema also showed statistically significant correlation with percentage predicted Tlco (r = -0.65, P < .005). Conclusion Lobar ventilation and ADC values obtained from hyperpolarized 129Xe MR imaging demonstrated correlation with quantitative CT percentage emphysema on a lobar basis and with PFT results on a whole-lung basis. © RSNA, 2016.
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Affiliation(s)
- Tahreema N Matin
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Najib Rahman
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Annabel H Nickol
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Mitchell Chen
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Xiaojun Xu
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Neil J Stewart
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Tom Doel
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Vicente Grau
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - James M Wild
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
| | - Fergus V Gleeson
- From the Department of Radiology (T.N.M., M.C., X.X., F.V.G.) and Oxford Centre for Respiratory Medicine (N.R., A.H.N.), The Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, Headington, OX3 7LE, England; Unit of Academic Radiology, Royal Hallamshire Hospital, University of Sheffield, Sheffield, England (N.J.S., J.M.W.); and Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Headington, England (T.D., V.G.)
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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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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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