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D’Sa K, Evans JR, Virdi GS, Vecchi G, Adam A, Bertolli O, Fleming J, Chang H, Leighton C, Horrocks MH, Athauda D, Choi ML, Gandhi S. Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models. NAT MACH INTELL 2023; 5:933-946. [PMID: 37615030 PMCID: PMC10442231 DOI: 10.1038/s42256-023-00702-9] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Accepted: 07/06/2023] [Indexed: 08/25/2023]
Abstract
Parkinson's disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson's. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future.
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Affiliation(s)
- Karishma D’Sa
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
| | - James R. Evans
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
| | - Gurvir S. Virdi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
| | | | | | | | - James Fleming
- The Francis Crick Institute, King’s Cross, London, UK
| | - Hojong Chang
- Institute for IT Convergence, KAIST, Daejeon, Republic of Korea
| | - Craig Leighton
- EaStCHEM School of Chemistry, The University of Edinburgh, Edinburgh, UK
- IRR Chemistry Hub, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Mathew H. Horrocks
- EaStCHEM School of Chemistry, The University of Edinburgh, Edinburgh, UK
- IRR Chemistry Hub, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Dilan Athauda
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
| | - Minee L. Choi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
- Department of Brain & Cognitive Sciences, KAIST, Daejeon, Republic of Korea
| | - Sonia Gandhi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, King’s Cross, London, UK
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Cushnan D, Berka R, Bertolli O, Williams P, Schofield D, Joshi I, Favaro A, Halling-Brown M, Imreh G, Jefferson E, Sebire NJ, Reilly G, Rodrigues JCL, Robinson G, Copley S, Malik R, Bloomfield C, Gleeson F, Crotty M, Denton E, Dickson J, Leeming G, Hardwick HE, Baillie K, Openshaw PJ, Semple MG, Rubin C, Howlett A, Rockall AG, Bhayat A, Fascia D, Sudlow C, Jacob J. Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic. Digit Health 2021; 7:20552076211048654. [PMID: 34868617 PMCID: PMC8637703 DOI: 10.1177/20552076211048654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/07/2021] [Indexed: 12/27/2022] Open
Abstract
The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the
unprecedented collection of health data to support research. Historically,
coordinating the collation of such datasets on a national scale has been
challenging to execute for several reasons, including issues with data privacy,
the lack of data reporting standards, interoperable technologies, and
distribution methods. The coronavirus SARS-CoV-2 disease pandemic has
highlighted the importance of collaboration between government bodies,
healthcare institutions, academic researchers and commercial companies in
overcoming these issues during times of urgency. The National COVID-19 Chest
Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey
NHS Foundation Trust and Faculty, is an example of such a national initiative.
Here, we summarise the experiences and challenges of setting up the National
COVID-19 Chest Imaging Database, and the implications for future ambitions of
national data curation in medical imaging to advance the safe adoption of
artificial intelligence in healthcare.
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Affiliation(s)
| | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust, UK.,CVSSP, University of Surrey, UK
| | | | - Emily Jefferson
- Health Data Research UK, UK.,Health Informatics Centre (HIC), School of Medicine, University of Dundee, UK
| | | | | | | | - Graham Robinson
- Department of Radiology, Royal United Hospitals Bath NHS Foundation Trust, UK
| | - Susan Copley
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK
| | - Rizwan Malik
- Department of Radiology, Bolton NHS Foundation Trust, UK
| | - Claire Bloomfield
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | - Fergus Gleeson
- National Consortium of Intelligent Medical Imaging (NCIMI), The Big Data Institute, University of Oxford, UK.,Dept of Oncology, University of Oxford, UK
| | | | - Erika Denton
- Norfolk and Norwich University Hospital Foundation Trust, UK
| | | | - Gary Leeming
- Institute of Population Health, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Hayley E Hardwick
- National Institute of Health Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections, UK
| | | | | | - Malcolm G Semple
- NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, UK
| | - Caroline Rubin
- Department of Radiology, University Hospital Southampton NHS Foundation Trust, UK
| | | | - Andrea G Rockall
- Imaging Department, Hammersmith Hospital, Imperial College NHS Healthcare Trust, UK.,Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, UK
| | - Ayub Bhayat
- NHS Arden & Greater East Midlands Commissioning Support Unit, UK
| | | | - Cathie Sudlow
- British Heart Foundation Data Science Centre Led by Health Data Research UK, UK
| | | | - Joseph Jacob
- Department of Respiratory Medicine, University College London, UK.,Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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Cushnan D, Bennett O, Berka R, Bertolli O, Chopra A, Dorgham S, Favaro A, Ganepola T, Halling-Brown M, Imreh G, Jacob J, Jefferson E, Lemarchand F, Schofield D, Wyatt JC, Collaborative NCCID. Erratum to: An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. Gigascience 2021; 10:giab083. [PMID: 34850874 PMCID: PMC8634578 DOI: 10.1093/gigascience/giab083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Dominic Cushnan
- AI Lab, NHSX, Skipton House, 80 London Road, London SE1 6LH, UK
| | | | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, UK
| | | | - Joseph Jacob
- UCL Respiratory, 1st Floor, Rayne Institute, University College London, London WC1E 6JF, UK
| | - Emily Jefferson
- Health Data Research UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, DD1 4HN, Dundee, UK
| | | | | | - Jeremy C Wyatt
- Emeritus Professor of Digital Healthcare, University of Southampton, Southampton SO17 1BJ, UK
- NHSX, Skipton House, 80 London Road, London SE1 6LH, UK
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Cushnan D, Bennett O, Berka R, Bertolli O, Chopra A, Dorgham S, Favaro A, Ganepola T, Halling-Brown M, Imreh G, Jacob J, Jefferson E, Lemarchand F, Schofield D, Wyatt JC. An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis. Gigascience 2021; 10:giab076. [PMID: 34849869 PMCID: PMC8633457 DOI: 10.1093/gigascience/giab076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 03/25/2021] [Revised: 08/04/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage. FINDINGS The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. CONCLUSION The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.
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Affiliation(s)
- Dominic Cushnan
- AI Lab, NHSX, Skipton House, 80 London Road, London SE1 6LH,
UK
| | | | | | | | | | | | | | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust,
Egerton Road, Guildford GU2 7XX, UK
| | | | - Joseph Jacob
- UCL Respiratory, 1st Floor, Rayne Institute, University College
London, London WC1E 6JF, UK
| | - Emily Jefferson
- Health Data Research UK, Gibbs Building, 215 Euston Road,
London NW1 2BE, UK
- Health Informatics Centre (HIC), School of Medicine, University of
Dundee, DD1 4HN, Dundee, UK
| | | | | | - Jeremy C Wyatt
- Emeritus Professor of Digital Healthcare, University of
Southampton, Southampton SO17 1BJ, UK
- NHSX, Skipton House, 80 London Road, London SE1 6LH, UK
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Jacob J, Alexander D, Baillie JK, Berka R, Bertolli O, Blackwood J, Buchan I, Bloomfield C, Cushnan D, Docherty A, Edey A, Favaro A, Gleeson F, Halling-Brown M, Hare S, Jefferson E, Johnstone A, Kirby M, McStay R, Nair A, Openshaw PJM, Parker G, Reilly G, Robinson G, Roditi G, Rodrigues JCL, Sebire N, Semple MG, Sudlow C, Woznitza N, Joshi I. Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database. Eur Respir J 2020; 56:2001809. [PMID: 32616598 PMCID: PMC7331656 DOI: 10.1183/13993003.01809-2020] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022]
Abstract
The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest radiographs, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research and development of AI technology and give insight into COVID-19 disease https://bit.ly/3eQeuha
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Affiliation(s)
- Joseph Jacob
- Dept of Respiratory Medicine, University College London, London, UK
- Centre for Medical Image Computing, Dept of Computer Science, University College London, London, UK
| | - Daniel Alexander
- Centre for Medical Image Computing, Dept of Computer Science, University College London, London, UK
| | - J Kenneth Baillie
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Edinburgh, UK
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | | | | | - James Blackwood
- The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD), Dept of eHealth, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Iain Buchan
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Claire Bloomfield
- National Consortium of Intelligent Medical Imaging (NCIMI), The University of Oxford, Big Data Institute, Oxford, UK
| | | | - Annemarie Docherty
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Anthony Edey
- Dept of Radiology, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
| | | | - Fergus Gleeson
- National Consortium of Intelligent Medical Imaging (NCIMI), The University of Oxford, Big Data Institute, Oxford, UK
- Dept of Oncology, University of Oxford, Oxford, UK
| | - Mark Halling-Brown
- Scientific Computing, Royal Surrey NHS Foundation Trust, Guildford, UK
- Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, UK
| | - Samanjit Hare
- Dept of Radiology, Royal Free London NHS Trust, London, UK
| | - Emily Jefferson
- Health Data Research UK, London, UK
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, Dundee, UK
| | - Annette Johnstone
- Dept of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds General Infirmary, Leeds, UK
| | | | - Ruth McStay
- Dept of Radiology, Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Arjun Nair
- Dept of Radiology, University College London Hospital, London, UK
| | - Peter J M Openshaw
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK
| | - Geoff Parker
- Centre for Medical Image Computing, Dept of Computer Science, University College London, London, UK
- Bioxydyn Limited, Manchester, UK
| | | | - Graham Robinson
- Dept of Radiology, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
| | - Giles Roditi
- Dept of Radiology, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK
| | | | | | - Malcolm G Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK
| | - Catherine Sudlow
- Usher Institute, University of Edinburgh, Edinburgh, UK
- British Heart Foundation (BHF) Data Science Centre, Health Data Research UK, Edinburgh, UK
| | - Nick Woznitza
- Radiology Dept, Homerton University Hospital, London, UK
- School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, UK
- 12 NHS Nightingale Hospital London, London, UK
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6
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Wadhwa P, Thielemans K, Efthimiou N, Wangerin K, Keat N, Emond E, Deller T, Bertolli O, Deidda D, Delso G, Tohme M, Jansen F, Gunn RN, Hallett W, Tsoumpas C. PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library. Methods 2020; 185:110-119. [PMID: 32006678 DOI: 10.1016/j.ymeth.2020.01.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/15/2019] [Accepted: 01/14/2020] [Indexed: 10/25/2022] Open
Abstract
This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using 'singles' rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.
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Affiliation(s)
- Palak Wadhwa
- Biomedical Imaging Science Department, School of Medicine, University of Leeds, UK; Invicro, London, UK.
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, UK
| | - Nikos Efthimiou
- PET Research Centre, Faculty of Health Sciences, University of Hull, UK
| | | | | | - Elise Emond
- Institute of Nuclear Medicine, University College London, UK
| | | | | | - Daniel Deidda
- Biomedical Imaging Science Department, School of Medicine, University of Leeds, UK; National Physical Laboratory, Teddington, UK
| | | | | | | | | | | | - Charalampos Tsoumpas
- Biomedical Imaging Science Department, School of Medicine, University of Leeds, UK; Invicro, London, UK.
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Ren S, Lu Y, Bertolli O, Thielemans K, Carson RE. Event-by-event non-rigid data-driven PET respiratory motion correction methods: comparison of principal component analysis and centroid of distribution. ACTA ACUST UNITED AC 2019; 64:165014. [DOI: 10.1088/1361-6560/ab0bc9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Lane T, Fontana M, Martinez-Naharro A, Quarta CC, Whelan CJ, Petrie A, Rowczenio DM, Gilbertson JA, Hutt DF, Rezk T, Strehina SG, Caringal-Galima J, Manwani R, Sharpley FA, Wechalekar AD, Lachmann HJ, Mahmood S, Sachchithanantham S, Drage EP, Jenner HD, McDonald R, Bertolli O, Calleja A, Hawkins PN, Gillmore JD. Natural History, Quality of Life, and Outcome in Cardiac Transthyretin Amyloidosis. Circulation 2019; 140:16-26. [DOI: 10.1161/circulationaha.118.038169] [Citation(s) in RCA: 168] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Thirusha Lane
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Marianna Fontana
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Ana Martinez-Naharro
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Candida Cristina Quarta
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Carol J. Whelan
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Aviva Petrie
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Dorota M. Rowczenio
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Janet A. Gilbertson
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - David F. Hutt
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Tamer Rezk
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Svetla G. Strehina
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Joan Caringal-Galima
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Richa Manwani
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Faye A. Sharpley
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Ashutosh D. Wechalekar
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Helen J. Lachmann
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Shameem Mahmood
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Sajitha Sachchithanantham
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Edmund P.S. Drage
- Eastman Dental Institute, University College London, United Kingdom (A.P.). IQVIA, London, United Kingdom (E.P.S.D., H.D.J., R. McDonald, O.B., A.C.)
| | - Harvey D. Jenner
- Eastman Dental Institute, University College London, United Kingdom (A.P.). IQVIA, London, United Kingdom (E.P.S.D., H.D.J., R. McDonald, O.B., A.C.)
| | - Rosie McDonald
- Eastman Dental Institute, University College London, United Kingdom (A.P.). IQVIA, London, United Kingdom (E.P.S.D., H.D.J., R. McDonald, O.B., A.C.)
| | - Ottavia Bertolli
- Eastman Dental Institute, University College London, United Kingdom (A.P.). IQVIA, London, United Kingdom (E.P.S.D., H.D.J., R. McDonald, O.B., A.C.)
| | - Alan Calleja
- Eastman Dental Institute, University College London, United Kingdom (A.P.). IQVIA, London, United Kingdom (E.P.S.D., H.D.J., R. McDonald, O.B., A.C.)
| | - Philip N. Hawkins
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
| | - Julian D. Gillmore
- National Amyloidosis Centre, Division of Medicine, University College London, United Kingdom (T.H., M.F., A.M.-N., C.C.Q., C.J.W., D.M.R., J.A.G., D.F.H., T.R., S.G.S., J.C.-G., R. Manwani, F.A.S., A.D.W., H.J.L., S.M., S.S., P.N.H., J.D.G.)
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9
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Bertolli O, Arridge S, Wollenweber SD, Stearns CW, Hutton BF, Thielemans K. Sign determination methods for the respiratory signal in data-driven PET gating. Phys Med Biol 2017; 62:3204-3220. [DOI: 10.1088/1361-6560/aa6052] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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10
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Bertolli O, Eleftheriou A, Cecchetti M, Camarlinghi N, Belcari N, Tsoumpas C. PET iterative reconstruction incorporating an efficient positron range correction method. Phys Med 2016; 32:323-30. [DOI: 10.1016/j.ejmp.2015.11.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 11/03/2015] [Accepted: 11/14/2015] [Indexed: 10/22/2022] Open
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11
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Bousse A, Bertolli O, Atkinson D, Arridge S, Ourselin S, Hutton BF, Thielemans K. Maximum-likelihood joint image reconstruction and motion estimation with misaligned attenuation in TOF-PET/CT. Phys Med Biol 2016; 61:L11-9. [PMID: 26789205 DOI: 10.1088/0031-9155/61/3/l11] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This work is an extension of our recent work on joint activity reconstruction/motion estimation (JRM) from positron emission tomography (PET) data. We performed JRM by maximization of the penalized log-likelihood in which the probabilistic model assumes that the same motion field affects both the activity distribution and the attenuation map. Our previous results showed that JRM can successfully reconstruct the activity distribution when the attenuation map is misaligned with the PET data, but converges slowly due to the significant cross-talk in the likelihood. In this paper, we utilize time-of-flight PET for JRM and demonstrate that the convergence speed is significantly improved compared to JRM with conventional PET data.
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Affiliation(s)
- Alexandre Bousse
- Institute of Nuclear Medicine, University College London, London NW1 2BU, UK
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12
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Bousse A, Bertolli O, Atkinson D, Arridge S, Ourselin S, Hutton BF, Thielemans K. Maximum-Likelihood Joint Image Reconstruction/Motion Estimation in Attenuation-Corrected Respiratory Gated PET/CT Using a Single Attenuation Map. IEEE Trans Med Imaging 2016; 35:217-28. [PMID: 26259017 DOI: 10.1109/tmi.2015.2464156] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
This work provides an insight into positron emission tomography (PET) joint image reconstruction/motion estimation (JRM) by maximization of the likelihood, where the probabilistic model accounts for warped attenuation. Our analysis shows that maximum-likelihood (ML) JRM returns the same reconstructed gates for any attenuation map (μ-map) that is a deformation of a given μ-map, regardless of its alignment with the PET gates. We derived a joint optimization algorithm accordingly, and applied it to simulated and patient gated PET data. We first evaluated the proposed algorithm on simulations of respiratory gated PET/CT data based on the XCAT phantom. Our results show that independently of which μ-map is used as input to JRM: (i) the warped μ-maps correspond to the gated μ-maps, (ii) JRM outperforms the traditional post-registration reconstruction and consolidation (PRRC) for hot lesion quantification and (iii) reconstructed gated PET images are similar to those obtained with gated μ-maps. This suggests that a breath-held μ-map can be used. We then applied JRM on patient data with a μ-map derived from a breath-held high resolution CT (HRCT), and compared the results with PRRC, where each reconstructed PET image was obtained with a corresponding cine-CT gated μ-map. Results show that JRM with breath-held HRCT achieves similar reconstruction to that using PRRC with cine-CT. This suggests a practical low-dose solution for implementation of motion-corrected respiratory gated PET/CT.
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Affiliation(s)
- Afroditi Eleftheriou
- Division of Medical Physics, University of Leeds, Kragujevac, UK.,Department of Physics, National and Kapodistrian University of Athens, Kragujevac, Greece
| | | | - Ottavia Bertolli
- Division of Medical Physics, University of Leeds, Kragujevac, UK
| | - Εfstathios Stiliaris
- Department of Physics, National and Kapodistrian University of Athens, Kragujevac, Greece.,Institute of Accelerating Systems & Applications (IASA), Athens, Greece
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Bertolli O, Cecchetti M, Camarlinghi N, Eleftheriou A, Belcari N, Tsoumpas C. Iterative reconstruction incorporating positron range correction within STIR framework. EJNMMI Phys 2014; 1:A42. [PMID: 26501630 PMCID: PMC4545625 DOI: 10.1186/2197-7364-1-s1-a42] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Ottavia Bertolli
- Division of Medical Physics, University of Leeds, Kragujevac, UK.,Department of Physics, University of Pisa and INFN Pisa, Kragujevac, Italy
| | - Matteo Cecchetti
- Department of Physics, University of Pisa and INFN Pisa, Kragujevac, Italy
| | | | - Afroditi Eleftheriou
- Division of Medical Physics, University of Leeds, Kragujevac, UK.,Department of Physics, National and Kapodistrian University of Athens, Kragujevac, Greece
| | - Nicola Belcari
- Department of Physics, University of Pisa and INFN Pisa, Kragujevac, Italy
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