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Tran MC, Crockett DC, Tran TK, Phan PA, Federico F, Bruce R, Perchiazzi G, Payne SJ, Farmery AD. Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography. Sci Rep 2024; 14:4897. [PMID: 38418516 PMCID: PMC10902369 DOI: 10.1038/s41598-024-55144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 02/20/2024] [Indexed: 03/01/2024] Open
Abstract
The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. Paired measurements of lung heterogeneity were then taken with both the IST and CT. Lung heterogeneity measured by the IST was calculated by (a) the ratio of tracer gas outputs measured at oscillation periods of 180 s and 60 s, and (b) by the standard deviation of the modelled log-normal distribution of ventilations and perfusions in the simulation lung. In the CT images, lungs were manually segmented and divided into different regions according to voxel density. A quantitative CT method to calculate the heterogeneity (the Cressoni method) was applied. The IST and CT show good Pearson correlation coefficients in lung heterogeneity measurements (ventilation: 0.71, and perfusion, 0.60, p < 0.001). Within individual animals, the coefficients of determination average ventilation (R2 = 0.53) and perfusion (R2 = 0.68) heterogeneity. Strong concordance rates of 98% in ventilation and 89% when the heterogeneity changes were reported in pairs measured by CT scanning and IST methods. This quantitative method to identify heterogeneity has the potential to replicate CT lung heterogeneity, and to aid individualised care in ARDS.
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Affiliation(s)
- Minh C Tran
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
| | - Douglas C Crockett
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
| | - Tu K Tran
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
| | - Formenti Federico
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Centre for Human and Applied Physiology, King's College London, London, UK
- Department of Biomechanics, The University of Nebraska Omaha, Omaha, USA
| | - Richard Bruce
- Centre for Human and Applied Physiology, King's College London, London, UK
| | - Gaetano Perchiazzi
- Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Stephen J Payne
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
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Babaeipour R, Ouriadov A, Fox MS. Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review. Bioengineering (Basel) 2023; 10:1349. [PMID: 38135940 PMCID: PMC10740978 DOI: 10.3390/bioengineering10121349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/06/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.
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Affiliation(s)
- Ramtin Babaeipour
- School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, Canada;
| | - Alexei Ouriadov
- School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, Canada;
- Department of Physics and Astronomy, The University of Western Ontario, London, ON N6A 3K7, Canada;
- Lawson Health Research Institute, London, ON N6C 2R5, Canada
| | - Matthew S. Fox
- Department of Physics and Astronomy, The University of Western Ontario, London, ON N6A 3K7, Canada;
- Lawson Health Research Institute, London, ON N6C 2R5, Canada
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Garcea F, Serra A, Lamberti F, Morra L. Data augmentation for medical imaging: A systematic literature review. Comput Biol Med 2023; 152:106391. [PMID: 36549032 DOI: 10.1016/j.compbiomed.2022.106391] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/22/2022] [Accepted: 11/29/2022] [Indexed: 12/13/2022]
Abstract
Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. However, collecting large datasets for medical imaging is still a challenge due to privacy concerns and labeling costs. Data augmentation makes it possible to greatly expand the amount and variety of data available for training without actually collecting new samples. Data augmentation techniques range from simple yet surprisingly effective transformations such as cropping, padding, and flipping, to complex generative models. Depending on the nature of the input and the visual task, different data augmentation strategies are likely to perform differently. For this reason, it is conceivable that medical imaging requires specific augmentation strategies that generate plausible data samples and enable effective regularization of deep neural networks. Data augmentation can also be used to augment specific classes that are underrepresented in the training set, e.g., to generate artificial lesions. The goal of this systematic literature review is to investigate which data augmentation strategies are used in the medical domain and how they affect the performance of clinical tasks such as classification, segmentation, and lesion detection. To this end, a comprehensive analysis of more than 300 articles published in recent years (2018-2022) was conducted. The results highlight the effectiveness of data augmentation across organs, modalities, tasks, and dataset sizes, and suggest potential avenues for future research.
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Affiliation(s)
- Fabio Garcea
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Alessio Serra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Fabrizio Lamberti
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy
| | - Lia Morra
- Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi, 24, Torino, 10129, Italy.
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Taskiran NP, Hiura GT, Zhang X, Barr RG, Dashnaw SM, Hoffman EA, Malinsky D, Oelsner EC, Prince MR, Smith BM, Sun Y, Sun Y, Wild JM, Shen W, Hughes EW. Mapping Alveolar Oxygen Partial Pressure in COPD Using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study. Tomography 2022; 8:2268-2284. [PMID: 36136886 PMCID: PMC9498778 DOI: 10.3390/tomography8050190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/05/2022] [Accepted: 09/05/2022] [Indexed: 11/24/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) and emphysema are characterized by functional and structural damage which increases the spaces for gaseous diffusion and impairs oxygen exchange. Here we explore the potential for hyperpolarized (HP) 3He MRI to characterize lung structure and function in a large-scale population-based study. Participants (n = 54) from the Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study, a nested case-control study of COPD among participants with 10+ packyears underwent HP 3He MRI measuring pAO2, apparent diffusion coefficient (ADC), and ventilation. HP MRI measures were compared to full-lung CT and pulmonary function testing. High ADC values (>0.4 cm2/s) correlated with emphysema and heterogeneity in pAO2 measurements. Strong correlations were found between the heterogeneity of global pAO2 as summarized by its standard deviation (SD) (p < 0.0002) and non-physiologic pAO2 values (p < 0.0001) with percent emphysema on CT. A regional study revealed a strong association between pAO2 SD and visual emphysema severity (p < 0.003) and an association with the paraseptal emphysema subtype (p < 0.04) after adjustment for demographics and smoking status. HP noble gas pAO2 heterogeneity and the fraction of non-physiological pAO2 results increase in mild to moderate COPD. Measurements of pAO2 are sensitive to regional emphysematous damage detected by CT and may be used to probe pulmonary emphysema subtypes. HP noble gas lung MRI provides non-invasive information about COPD severity and lung function without ionizing radiation.
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Affiliation(s)
- Naz P. Taskiran
- Department of Chemical Engineering, Columbia University, New York, NY 10027, USA
- Correspondence: (N.P.T.); (E.W.H.); Tel.: +1-347-3693052 (N.P.T.); +1-626-4838731 (E.W.H.)
| | - Grant T. Hiura
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
| | - Xuzhe Zhang
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - R. Graham Barr
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
| | - Stephen M. Dashnaw
- Neurological Institute, Radiology, Columbia University, New York, NY 10032, USA
| | - Eric A. Hoffman
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Daniel Malinsky
- Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Elizabeth C. Oelsner
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
| | - Martin R. Prince
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Benjamin M. Smith
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
| | - Yanping Sun
- Division of General Medicine, Columbia University Irving Medial Center, New York, NY 10032, USA
| | - Yifei Sun
- Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jim M. Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2TN, UK
| | - Wei Shen
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 10032, USA
- Institute of Human Nutrition, College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY 10032, USA
- Columbia Magnetic Resonance Research Center (CMRRC), Columbia University, New York, NY 10027, USA
| | - Emlyn W. Hughes
- Department of Physics, Columbia University, New York, NY 10027, USA
- Correspondence: (N.P.T.); (E.W.H.); Tel.: +1-347-3693052 (N.P.T.); +1-626-4838731 (E.W.H.)
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