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Salehi M, Maiter A, Strickland S, Aldabbagh Z, Karunasaagarar K, Thomas R, Lopez-Dee T, Capener D, Dwivedi K, Sharkey M, Metherall P, van der Geest R, Alabed S, Swift AJ. Clinical assessment of an AI tool for measuring biventricular parameters on cardiac MR. Front Cardiovasc Med 2024; 11:1279298. [PMID: 38374997 PMCID: PMC10875016 DOI: 10.3389/fcvm.2024.1279298] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
Introduction Cardiac magnetic resonance (CMR) is of diagnostic and prognostic value in a range of cardiopulmonary conditions. Current methods for evaluating CMR studies are laborious and time-consuming, contributing to delays for patients. As the demand for CMR increases, there is a growing need to automate this process. The application of artificial intelligence (AI) to CMR is promising, but the evaluation of these tools in clinical practice has been limited. This study assessed the clinical viability of an automatic tool for measuring cardiac volumes on CMR. Methods Consecutive patients who underwent CMR for any indication between January 2022 and October 2022 at a single tertiary centre were included prospectively. For each case, short-axis CMR images were segmented by the AI tool and manually to yield volume, mass and ejection fraction measurements for both ventricles. Automated and manual measurements were compared for agreement and the quality of the automated contours was assessed visually by cardiac radiologists. Results 462 CMR studies were included. No statistically significant difference was demonstrated between any automated and manual measurements (p > 0.05; independent T-test). Intraclass correlation coefficient and Bland-Altman analysis showed excellent agreement across all metrics (ICC > 0.85). The automated contours were evaluated visually in 251 cases, with agreement or minor disagreement in 229 cases (91.2%) and failed segmentation in only a single case (0.4%). The AI tool was able to provide automated contours in under 90 s. Conclusions Automated segmentation of both ventricles on CMR by an automatic tool shows excellent agreement with manual segmentation performed by CMR experts in a retrospective real-world clinical cohort. Implementation of the tool could improve the efficiency of CMR reporting and reduce delays between imaging and diagnosis.
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Affiliation(s)
- Mahan Salehi
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
| | - Scarlett Strickland
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Ziad Aldabbagh
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Kavita Karunasaagarar
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Richard Thomas
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Tristan Lopez-Dee
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Dave Capener
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Pete Metherall
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Rob van der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Samer Alabed
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
| | - Andrew J. Swift
- Department of Radiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Cardiovascular Disease, NIHR Sheffield Biomedical Research Centre, Sheffield, United Kingdom
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Alandejani F, Alabed S, Garg P, Goh ZM, Karunasaagarar K, Sharkey M, Salehi M, Aldabbagh Z, Dwivedi K, Mamalakis M, Metherall P, Uthoff J, Johns C, Rothman A, Condliffe R, Hameed A, Charalampoplous A, Lu H, Plein S, Greenwood JP, Lawrie A, Wild JM, de Koning PJH, Kiely DG, Van Der Geest R, Swift AJ. Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements. J Cardiovasc Magn Reson 2022; 24:25. [PMID: 35387651 PMCID: PMC8988415 DOI: 10.1186/s12968-022-00855-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 09/15/2021] [Accepted: 03/19/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value. METHODS A deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018). RESULTS All deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84. CONCLUSION Automatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.
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Affiliation(s)
- Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Pankaj Garg
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Ze Ming Goh
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Kavita Karunasaagarar
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ziad Aldabbagh
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Pete Metherall
- Radiology Department, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Johanna Uthoff
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Chris Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Alexander Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Robin Condliffe
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Abdul Hameed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Athanasios Charalampoplous
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Haiping Lu
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Sven Plein
- Multidisciplinary Cardiovascular Research Centre (MCRC) &, Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds, UK
| | - John P Greenwood
- Multidisciplinary Cardiovascular Research Centre (MCRC) &, Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Clarendon Way, Leeds, UK
| | - Allan Lawrie
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Patrick J H de Koning
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Rob Van Der Geest
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK.
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Alabed S, Karunasaagarar K, Alandejani F, Garg P, Uthoff J, Metherall P, Sharkey M, Lu H, Wild JM, Kiely DG, Van Der Geest RJ, Swift AJ. High interstudy repeatability of automatic deep learnt biventricular CMR measurements. Eur Heart J Cardiovasc Imaging 2021. [DOI: 10.1093/ehjci/jeab090.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: Foundation. Main funding source(s): Wellcome Trust (UK), NIHR (UK)
Introduction
Cardiac magnetic resonance (CMR) measurements have significant diagnostic and prognostic value. Accurate and repeatable measurements are essential to assess disease severity, evaluate therapy response and monitor disease progression. Deep learning approaches have shown promise for automatic left ventricular (LV) segmentation on CMR, however fully automatic right ventricular (RV) segmentation remains challenging. We aimed to develop a biventricular automatic contouring model and evaluate the interstudy repeatability of the model in a prospectively recruited cohort.
Methods
A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, pulmonary hypertension and lung diseases (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. To test the accuracy of the automatic segmentation, 30 ASPIRE CMRs were segmented independently by two CMR experts. Each segmentation was compared to the automatic contouring with agreement assessed using the Dice similarity coefficient (DSC).
A prospective validation cohort of 46 subjects (10 healthy volunteers and 36 patients with pulmonary hypertension) were recruited to assess interstudy agreement of automatic and manual CMR assessments. Two CMR studies were performed during separate sessions on the same day. Interstudy repeatability was assessed using intraclass correlation coefficient (ICC) and Bland-Altman plots.
Results
DSC showed high agreement (figure 1) comparing automatic and expert CMR readers, with minimal bias towards either CMR expert. The scan-scan repeatability CMR measurements were higher for all automatic RV measurements (ICC 0.89 to 0.98) compared to manual RV measurements (0.78 to 0.98). LV automatic and manual measurements were similarly repeatable (figure 2). Bland-Altman plots showed strong agreement with small mean differences between the scan-scan measurements (figure 2).
Conclusion
Fully automatic biventricular short-axis segmentations are comparable with expert manual segmentations, and have shown excellent interstudy repeatability.
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Affiliation(s)
- S Alabed
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - K Karunasaagarar
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - F Alandejani
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Garg
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - J Uthoff
- University of Sheffield, Department of Computer Science, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Metherall
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - M Sharkey
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - H Lu
- University of Sheffield, Department of Computer Science, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - JM Wild
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - DG Kiely
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | | | - AJ Swift
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
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Alabed S, Karunasaagarar K, Alandejani F, Garg P, Uthoff J, Metherall P, Sharkey M, Lu H, Wild JM, Kiely DG, Van Der Geest RJ, Swift AJ. Fully automated CMR derived stroke volume correlates with right heart catheter measurements in patients with suspected pulmonary hypertension. Eur Heart J Cardiovasc Imaging 2021. [DOI: 10.1093/ehjci/jeab090.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: Foundation. Main funding source(s): Welcome Trust (UK), NIHR (UK)
Introduction
Cardiac magnetic resonance (CMR) assessment plays a significant role in the diagnosis, prognosis and monitoring of patients with pulmonary hypertension (PH). We developed a deep learning model to automatically generate biventricular contours and validated its result in a prospective cohort of patients with suspected PH who underwent right heart catheterization (RHC).
Methods
A deep learning CMR contouring model was developed in a retrospective multi-vendor (Siemens and General Electric), multi-pathology cohort of patients, predominantly with heart failure, lung disease and PH (n = 400, ASPIRE registry). Biventricular segmentations were made on all CMR studies across cardiac phases. A prospective validation cohort of 102 suspected PH patients was recruited and they had RHC within 24 hours of the CMR. To test the accuracy of the automatic segmentation, the RHC-thermodilution and CMR-derived measures of stroke volume (SV) were compared for manual and automated measurements.
Results
The mean and standard deviation for the derived SV was 59 ml ± 21 measured by RHC and 75 ml ± 25 for automated and 79 ml ± 26 for manual CMR measurements. Automatic and manual CMR measurement correlated strongly with RHC derived SV; 0.73, 95% CI [0.62, 0.81] and 0.78, 95% CI [0.69, 0.85], respectively (figure 1). The agreement between automatic and manual SV was high; interclass correlation coefficient (ICC) = 0.88, 95% CI [0.83, 0.92] and Bland-Altman plots showed a narrow spread of mean differences between manual and automatic measurements (figure 2).
Conclusion
In a prospective cohort, fully automatic CMR assessments corresponded accurately to invasive hemodynamics performed within 24 hours of a CMR study.
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Affiliation(s)
- S Alabed
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - K Karunasaagarar
- University of Sheffield, Academic Unit of Radiology, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - F Alandejani
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Garg
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - J Uthoff
- University of Sheffield, Department of Computer Science, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Metherall
- University of Sheffield, Academic Unit of Radiology, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - M Sharkey
- University of Sheffield, Academic Unit of Radiology, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - H Lu
- University of Sheffield, Department of Computer Science, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - JM Wild
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - DG Kiely
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | | | - AJ Swift
- University of Sheffield, Department of Infection, Immunity & Cardiovascular Disease, Sheffield, United Kingdom of Great Britain & Northern Ireland
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Varley I, Metherall P. Supine scapular flap harvest planning using staging imaging of the thorax. Br J Oral Maxillofac Surg 2021; 60:363-364. [PMID: 34266701 DOI: 10.1016/j.bjoms.2021.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 10/21/2022]
Affiliation(s)
- I Varley
- Department of Oral & Maxillofacial Surgery, Sheffield Teaching Hospitals, Charles Clifford Dental Hospital, Wellesley Road, Sheffield S10 2SZ, UK.
| | - P Metherall
- Department of Medical Physics, Sheffield Teaching Hospitals, Northern General Hospital, Herries Road, Sheffield S5 7AU, UK
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Stravrakas M, Metherall P, Ray J. Preoperative 3D Virtual Preplanning for Bonebridge Implantation: Our Experience. Ear Nose Throat J 2020; 101:145561320940075. [PMID: 32662671 DOI: 10.1177/0145561320940075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Marios Stravrakas
- Regional Department of Neurotology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Pete Metherall
- Medical Imaging and Medical Physics, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Jaydip Ray
- Regional Department of Neurotology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
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Swift AJ, Lu H, Garg P, Taylor J, Metherall P, Zhou S, Johns C, Condliffe RA, Lawrie A, Wild JM, Kiely DG. 543A machine-learning CMR approach to extract disease features and automate pulmonary arterial hypertension diagnosis. Eur Heart J Cardiovasc Imaging 2019. [DOI: 10.1093/ehjci/jez104.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 11/13/2022] Open
Affiliation(s)
- A J Swift
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - H Lu
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Garg
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - J Taylor
- Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - P Metherall
- Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - S Zhou
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - C Johns
- Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - R A Condliffe
- Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - A Lawrie
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - J M Wild
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
| | - D G Kiely
- University of Sheffield, IICD, Sheffield, United Kingdom of Great Britain & Northern Ireland
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Kelland NF, Metherall P, Sugden J, Nelson T, Sahu J, Kyriacou AL, Lee JMS. 38Multimodality image reconstruction and fusion to guide VT ablation. Europace 2017. [DOI: 10.1093/europace/eux283.047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Frezzini C, Thomas M, Metherall P, Lee N. Change of globe position on computed tomography head and cone-beam computed tomography—implications for management. Int J Oral Maxillofac Surg 2017. [DOI: 10.1016/j.ijom.2017.02.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Birks S, Altinkaya M, Altinkaya A, Pilkington G, Kurian KM, Crosby C, Hopkins K, Williams M, Donovan L, Birks S, Eason A, Bosak V, Pilkington G, Birks S, Holliday J, Corbett I, Pilkington G, Keeling M, Bambrough J, Simpson J, Higgins S, Dogra H, Pilkington G, Kurian KM, Zhang Y, Bradley M, Schmidberger C, Hafizi S, Noorani I, Price S, Dubocq A, Jaunky T, Chatelain C, Evans L, Gaissmaier T, Pilkington GJ, An Q, Hurwitz V, Logan J, Bhangoo R, Ashkan K, Gullan A, Beaney R, Brazil L, Kokkinos S, Blake R, Singleton A, Shaw A, Iyer V, Kurian KM, Jeyapalan JN, Morley IC, Hill AA, Mumin MA, Tatevossian RG, Qaddoumi I, Ellison DW, Sheer D, Frary A, Price S, Jefferies S, Harris F, Burnet N, Jena R, Watts C, Haylock B, Leow-Dyke S, Rathi N, Wong H, Dunn J, Baborie A, Crooks D, Husband D, Shenoy A, Brodbelt A, Walker C, Bahl A, Larsen J, Craven I, Metherall P, McKevitt F, Romanowski C, Hoggard N, Jellinek DA, Bell S, Murray E, Muirhead R, James A, Hanzely Z, Jackson R, Stewart W, O'Brien A, Young A, Bell S, Hanzely Z, Stewart W, Shepherd S, Cavers D, Wallace L, Hacking B, Scott S, Bowyer D, Elmahdi A, Frary AJ, O'Donovan DG, Price SJ, Kia A, Przystal JM, Nianiaris N, Mazarakis ND, Mintz PJ, Hajitou A, Karakoula K, Phipps K, Harkness W, Hayward R, Thompson D, Jacques T, Harding B, Darling J, Warr T, Leow-Dyke S, Rathi N, Haylock B, Crooks D, Jenkinson M, Walker C, Brodbelt A, Zhou L, Ercolano E, Ammoun S, Schmid MC, Barczyk M, Hanemann CO, Rowther F, Dawson T, Ashton K, Darling J, Warr T, Maherally Z, Hatherell KE, Kroese K, Hafizi S, Pilkington GJ, Singh P, McQuaid S, Al-Rashid S, Prise K, Herron B, Healy E, Shoakazemi A, Donnelly M, McConnell R, Harney J, Conkey D, McGrath E, Lunsford L, Kondziolka D, Niranjan A, Kano H, Hamilton R, Flannery T, Majani Y, Smith S, Grundy R, Rahman R, Saini S, Hall G, Davis C, Rowther F, Lawson T, Ashton K, Potter N, Goessl E, Darling J, Warr T, Brodbelt A, Jenkinson M, Walker C, Leow-Dyke S, Haylock B, Dunn J, Wilkins S, Smith T, Petinou V, Nicholl I, Singh J, Lea R, Welsby P, Spiteri I, Sottoriva A, Marko N, Tavare S, Collins P, Price SJ, Watts C, Su Z, Gerhard A, Hinz R, Roncaroli F, Coope D, Thompson G, Karabatsou K, Sofat A, Leggate J, du Plessis D, Turkheimer F, Jackson A, Brodbelt A, Jenkinson M, Das K, Crooks D, Herholz K, Price SJ, Whittle IR, Ashkan K, Grundy P, Cruickshank G, Berry V, Elder D, Iyer V, Hopkins K, Cohen N, Tavare J, Zilidis G, Tibarewal P, Spinelli L, Leslie NR, Coope DJ, Karabatsou K, Green S, Wall G, Bambrough J, Brennan P, Baily J, Diaz M, Ironside J, Sansom O, Brunton V, Frame M, Young A, Thomas O, Mohsen L, Frary A, Lupson V, McLean M, Price S, Arora M, Shaw L, Lawrence C, Alder J, Dawson T, Hall G, Rada L, Chen K, Shivane A, Ammoun S, Parkinson D, Hanemann C, Pangeni RP, Warr TJ, Morris MR, Mackinnon M, Williamson A, James A, Chalmers A, Beckett V, Joannides A, Brock R, McCarthy K, Price S, Singh A, Karakoula K, Dawson T, Ashton K, Darling J, Warr T, Kardooni H, Morris M, Rowther F, Darling J, Warr T, Watts C, Syed N, Roncaroli F, Janczar K, Singh P, O'Neil K, Nigro CL, Lattanzio L, Coley H, Hatzimichael E, Bomalaski J, Szlosarek P, Crook T, Pullen NA, Anand M, Birks S, Van Meter T, Pullen NA, Anand M, Williams S, Boissinot M, Steele L, Williams S, Chiocca EA, Lawler S, Al Rashid ST, Mashal S, Taggart L, Clarke E, Flannery T, Prise KM. Abstracts from the 2012 BNOS Conference. Neuro Oncol 2012. [DOI: 10.1093/neuonc/nos198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Bluml S, Panigrahy A, Laskov M, Dhall G, Nelson MD, Finlay JL, Gilles FH, Arita H, Kinoshita M, Kagawa N, Fujimoto Y, Hashimoto N, Yoshimine T, Kinoshita M, Arita H, Kagawa N, Fujimoto Y, Hashimoto N, Yoshimine T, Hamilton JD, Wang J, Levin VA, Hou P, Loghin ME, Gilbert MR, Leeds NE, deGroot JF, Puduvalli V, Jackson EF, Yung WKA, Kumar AJ, Ellingson BM, Cloughesy TF, Pope WB, Zaw T, Phillips H, Lalezari S, Nghiemphu PL, Ibrahim H, Motevalibashinaeini K, Lai A, Ellingson BM, Cloughesy TF, Zaw T, Harris R, Lalezari S, Nghiemphu PL, Motevalibashinaeini K, Lai A, Pope WB, Douw L, Van de Nieuwenhuijzen ME, Heimans JJ, Baayen JC, Stam CJ, Reijneveld JC, Juhasz C, Mittal S, Altinok D, Robinette NL, Muzik O, Chakraborty PK, Barger GR, Ellingson BM, Cloughesy TF, Zaw TM, Lalezari S, Nghiemphu PL, Motevalibashinaeini K, Lai A, Goldin J, Pope WB, Ellingson BM, Cloughesy TF, Harris R, Pope WB, Nghiemphu PL, Lai A, Zaw T, Chen W, Ahlman MA, Giglio P, Kaufmann TJ, Anderson SK, Jaeckle KA, Uhm JH, Northfelt DW, Flynn PJ, Buckner JC, Galanis E, Zalatimo O, Weston C, Allison D, Bota D, Kesari S, Glantz M, Sheehan J, Harbaugh RE, Chiba Y, Kinoshita M, Kagawa N, Fujimoto Y, Tsuboi A, Hatazawa J, Sugiyama H, Hashimoto N, Yoshimine T, Nariai T, Toyohara J, Tanaka Y, Inaji M, Aoyagi M, Yamamoto M, Ishiwara K, Ohno K, Jalilian L, Essock-Burns E, Cha S, Chang S, Prados M, Butowski N, Nelson S, Kawahara Y, Nakada M, Hayashi Y, Kai Y, Hayashi Y, Uchiyama N, Kuratsu JI, Hamada JI, Yeom K, Rosenberg J, Andre JB, Fisher PG, Edwards MS, Barnes PD, Partap S, Essock-Burns E, Jalilian L, Lupo JM, Crane JC, Cha S, Chang SM, Nelson SJ, Romanowski CA, Hoggard N, Jellinek DA, Clenton S, McKevitt F, Wharton S, Craven I, Buller A, Waddle C, Bigley J, Wilkinson ID, Metherall P, Eckel LJ, Keating GF, Wetjen NM, Giannini C, Wetmore C, Jain R, Narang J, Arbab AS, Schultz L, Scarpace L, Mikkelsen T, Babajni-Feremi A, Jain R, Poisson L, Narang J, Scarpace L, Gutman D, Jaffe C, Saltz J, Flanders A, Daniel B, Mikkelsen T, Zach L, Guez D, Last D, Daniels D, Hoffman C, Mardor Y, Guha-Thakurta N, Debnam JM, Kotsarini C, Wilkinson ID, Jellinek D, Griffiths PD, Khandanpour N, Hoggard N, Kotsarini C, Wilkinson ID, Jellinek D, Griffiths PD, Bambrough P, Hoggard N, Hamilton JD, Levin VA, Hou P, Prabhu S, Loghin ME, Gilbert MR, Bassett RL, Wang J, Yung WA, Jackson EF, Kumar AJ, Campen CJ, Soman S, Fisher PG, Edwards MS, Yeom KW, Vos MJ, Berkhof J, Postma TJ, Sanchez E, Sizoo EM, Heimans JJ, Lagerwaard FJ, Buter J, Noske DP, Reijneveld JC, Colen RR, Mahajan B, Jolesz FA, Zinn PO, Lupo JM, Molinaro A, Chang S, Lawton K, Cha S, Nelson SJ, Alexandru D, Bota D, Linskey ME, Chaumeil MM, Gini B, Yang H, Iwanami A, Subramanian S, Ozawa T, Read EJ, Pieper RO, Mischel P, James CD, Ronen SM, LaViolette PS, Cochran E, Al-Gizawiy M, Connelly JM, Malkin MG, Rand SD, Mueller WM, Schmainda KM, LaViolette PS, Cohen AD, Cochran E, Prah M, Hartman CJ, Connelly JM, Rand SD, Malkin MG, Mueller WM, Schmainda KM, Qiao XJ, He R, Brown M, Goldin J, Cloughesy T, Pope WB. RADIOLOGY. Neuro Oncol 2011; 13:iii136-iii144. [PMCID: PMC3222969 DOI: 10.1093/neuonc/nor162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
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Abstract
In this paper, we discuss issues involved in defining an optimum placement of four electrodes for impedance pneumography. We observed a general trend where the change in impedance (delta Z) decreased while the sensitivity (delta Z/Z) increased with distance between the drive and receive electrode pairs. However, the theoretical study indicated that delta Z/Z should decrease with distance. The scatter of points in the plots indicated that sensitivity was influenced by factors other than distance. The correlation coefficient between the theoretical and measured delta Z/Z was low, but significant. This suggested that the best electrode configuration can be derived from the theoretical data. High delta Z/Z was obtained when the drive and receive electrode pairs were placed close to the lungs and in different horizontal planes.
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Affiliation(s)
- N Khambete
- Department of Medical Physics, University of Sheffield Royal Hallamshire Hospital, United Kingdom
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Abstract
The electrical resistivity of mammalian tissues varies widely and is correlated with physiological function. Electrical impedance tomography (EIT) can be used to probe such variations in vivo, and offers a non-invasive means of imaging the internal conductivity distribution of the human body. But the computational complexity of EIT has severe practical limitations, and previous work has been restricted to considering image reconstruction as an essentially two-dimensional problem. This simplification can limit significantly the imaging capabilities of EIT, as the electric currents used to determine the conductivity variations will not in general be confined to a two-dimensional plane. A few studies have attempted three-dimensional EIT image reconstruction, but have not yet succeeded in generating images of a quality suitable for clinical applications. Here we report the development of a three-dimensional EIT system with greatly improved imaging capabilities, which combines our 64-electrode data-collection apparatus with customized matrix inversion techniques. Our results demonstrate the practical potential of EIT for clinical applications, such as lung or brain imaging and diagnostic screening.
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Affiliation(s)
- P Metherall
- Department of Medical Physics and Clinical Engineering, University of Sheffield, UK
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Abstract
The investigation studied the effects of biofeedback on the sitting posture of a 14 year old girl with cerebral palsy. The subject's posture was quantified using a video analysis technique which established the threshold of poor posture at 30 degrees from the vertical plane. A stimulator system was designed using an adapted drop foot stimulator and a custom made controller with a mercury tilt switch as the posture angle transducer. If posture became greater than 30 degrees tactile electrical stimulation was administered to the subject's lower back. Repetitive stimuli occurred on non-correction of posture, with a maximum of 4 consecutive stimuli, upon which an alarm was activated. 10 training sessions of 20 min duration were completed over a 4 week period, monitored using a data logger. Following initial improvement the daily results show a gradual deterioration in posture, whilst post-trial video analysis indicates a significant improvement in posture. An improved response to the alarm stimulus is observed. Reasons for these conflicting findings are discussed.
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Affiliation(s)
- P Metherall
- Lincoln Medical Physics and Computing Services, County Hospital, Lincoln, UK
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