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Ram S, Verleden SE, Kumar M, Bell AJ, Pal R, Ordies S, Vanstapel A, Dubbeldam A, Vos R, Galban S, Ceulemans LJ, Frick AE, Van Raemdonck DE, Verschakelen J, Vanaudenaerde BM, Verleden GM, Lama VN, Neyrinck AP, Galban CJ. Computed tomography-based machine learning for donor lung screening before transplantation. J Heart Lung Transplant 2024; 43:394-402. [PMID: 37778525 DOI: 10.1016/j.healun.2023.09.018] [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: 03/22/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. METHODS Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. RESULTS Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. CONCLUSIONS We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.
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Affiliation(s)
- Sundaresh Ram
- Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Stijn E Verleden
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; Department of ASTARC, University of Antwerp, Wilrijk, Belgium
| | - Madhav Kumar
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Alexander J Bell
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Ravi Pal
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Sofie Ordies
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Arno Vanstapel
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
| | | | - Robin Vos
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Stefanie Galban
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Laurens J Ceulemans
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Anna E Frick
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Dirk E Van Raemdonck
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | | | - Bart M Vanaudenaerde
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Geert M Verleden
- Lung Transplant Unit, Department of Chronic Diseases and Metabolism, Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium
| | - Vibha N Lama
- Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan
| | - Arne P Neyrinck
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Craig J Galban
- Department of Radiology, University of Michigan, Ann Arbor, Michigan; Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan.
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Monier E, Oberlin T, Brun N, Li X, Tencé M, Dobigeon N. Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling. Ultramicroscopy 2020; 215:112993. [PMID: 32516700 DOI: 10.1016/j.ultramic.2020.112993] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/04/2020] [Accepted: 04/04/2020] [Indexed: 11/15/2022]
Abstract
This paper discusses the reconstruction of partially sampled spectrum-images to accelerate the acquisition in scanning transmission electron microscopy (STEM). The problem of image reconstruction has been widely considered in the literature for many imaging modalities, but only a few attempts handled 3D data such as spectral images acquired by STEM electron energy loss spectroscopy (EELS). Besides, among the methods proposed in the microscopy literature, some are fast but inaccurate while others provide accurate reconstruction but at the price of a high computation burden. Thus none of the proposed reconstruction methods fulfills our expectations in terms of accuracy and computation complexity. In this paper, we propose a fast and accurate reconstruction method suited for atomic-scale EELS. This method is compared to popular solutions such as beta process factor analysis (BPFA) which is used for the first time on STEM-EELS images. Experiments based on real as synthetic data will be conducted.
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Affiliation(s)
- Etienne Monier
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France.
| | - Thomas Oberlin
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France; University of Toulouse, ISAE-SUPAERO, Toulouse 31400, France.
| | - Nathalie Brun
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Xiaoyan Li
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Marcel Tencé
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, 91405, France.
| | - Nicolas Dobigeon
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France; Institut Universitaire de France (IUF), France.
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