Irum Kotadia | King's College London (original) (raw)

Papers by Irum Kotadia

Zenodo (CERN European Organization for Nuclear Research), Dec 13, 2022

medRxiv (Cold Spring Harbor Laboratory), Dec 28, 2023

Computing in Cardiology (CinC), 2012, Dec 31, 2022

OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. ... more OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. This paper provides an update on the features and tools added since initial release. Software development has been performed in Matlab (R2020a/R2021b) and Python3. Development is ongoing in the areas of data parsing and data analysis. Data parsing: The system continues to support parsers for Carto3, Velocity and Precision. In addition, a parser for the Kodex electroanatomic mapping system has been added. Data analysis: An extensible architecture for data interpolation has been added. This new architecture has exposed hitherto unrecognized variation in interpolation schemes in clinical mapping systems and will permit the optimization of interpolation methods against 'gold standard' simulation or histological data. Similarly, an architecture for conduction velocity vector measurement has been added. We have refined the ablation lesion quantification tools permitting time-domain analysis of ablation lesion formation. A Python-based graphical interface is now under development, with the beta-testing program for the interface planned. The interface provides the ability to visualize, manipulate and analyze electrophysiology data. To facilitate this development, we have implemented an open-source Python3 version of OpenEP: openep-py.

Journal of Clinical Medicine, Apr 22, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Europace, May 24, 2023

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis offers the poten... more Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis offers the potential to identify patterns unrecognisable to human interpreters and broaden the ECG's utility. However, current algorithms rely on waveform signals derived from digital ECGs for input data, and these cannot be readily obtained from paper-based ECGs. This potentially presents a barrier to adoption as numerous workplaces continue to use paper-based ECGs. The views of stakeholders on the current use of paper-based ECGs and the potential future application of AI-ECG analysis are unknown. Purpose: To explore stakeholders' views about current and future ECG use. To determine the perceived utility of AI-analysis of paper-based ECGs. Methods: A web-based survey was designed using Qualtrics and distributed to a variety of healthcare professionals from numerous locations across the United Kingdom (UK). The survey consisted of 12 questions about participants' perceptions relating to current and future paper-based ECG use and the perceived advantages and disadvantages of AI-ECG. Results: In total, 43 healthcare professionals from 15 health provider organisations in the National Health Service (NHS) completed the survey. Paper-based ECGs were in use in 86% (37/43) of the respondents' workplaces and 61% (26/43) felt that it would be useful if AI-based algorithms could analyse paper-based ECGs in addition to digital ECGs (Figure 1). Views on future prevalence of paper-based ECGs were split with 47% (20/43) responding that it is likely or extremely likely paper-based ECGs will still be in use in the next 5 years in the NHS. Perceived advantages of AI-based analysis included the potential to improve clinical decision making (51%, (22/43)) and optimisation of healthcare professionals' work (leaving more time for clinical patient management) (47%, (20/43)) (Figure 2A). The inability to explain how algorithms determine results (56%, (24/43)), a lack of clarity over the accountability for the results (44%, (19/43)), and a reduction in learning opportunities (44%, (19/43)) were identified as potential issues associated with use of AI-ECG (Figure 2B). Conclusions: Whilst AI-ECG offers potential to improve clinical care, there is currently a gap between research and the integration of AI-ECG into real-world practice. Paper-based ECGs remain prevalent within the NHS, and the current requirement for algorithms to receive signal data presents a barrier to current and future AI-ECG implementation. There is currently an unmet clinical need to develop algorithms capable of interpreting paper-based ECGs. AI-ECG analysis of paper-based ECGs could enable a wider range of healthcare professionals to capitalise on any benefits offered by AI-ECG.

Europace, May 24, 2023

Funding Acknowledgements: Type of funding sources: Private grant(s) and/or Sponsorship. Main fund... more Funding Acknowledgements: Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Wellcome/EPSRC Centre for Medical Engineering Background: Although primarily considered a respiratory virus, cardiovascular manifestations have been reported in patients with Covid-19 infection. Atrial fibrillation has been observed as the most common arrhythmia with the prevalence rate reportedly as high as 16.5%(1). Purpose: The aim of the study was to establish the incidence of atrial fibrillation in patients hospitalised with Covid-19 and evaluate the relationship between patient characteristics and disease severity with new-onset atrial fibrillation in patients with Covid-19. Methods: A single centre, retrospective study of 1241 patients with a confirmed PCR diagnosis of Covid-19 admitted during the 1st wave of the pandemic (1st March to 31st September 2020). Patient demographic data, medical history and clinical outcome data were manually collected. Results: The study population comprised of 1241 patients hospitalised with Covid-19. Of these, 94 (7.6%) patients were known to have pre-existing atrial fibrillation. In an unadjusted analysis, in-hospital mortality was twice as likely in patients with pre-existing atrial fibrillation compared to patients with no history of atrial fibrillation (odds ratio (OR): 2.18; 95% CI 1.29-3.59, p=0.002). However, after multi-variable matching for age, sex and CHA2DS2VASc score there was no significant difference between groups (OR: 1.13, 95% CI 0.57-2.21, p=0.732). During their admission, 42 (3.4%) patients developed new-onset atrial fibrillation. New-onset atrial fibrillation was associated with an increased

Europace

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Hea... more Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background Electrogram morphology is used to identify the atrio-ventricular annuli during catheter ablation procedures with a "balanced" signal considered to represent the annuli. However, anatomical studies highlight a significantly greater mass of ventricular myocardium compared to atrial myocardium at the valve plane suggesting the correct electrical signature of the anatomical annulus may not be a balanced signal. Purpose To identify the relationship between atrial and ventricular electrogram components at the anatomically defined mitral valve annulus. Methods Mapping of the mitral annulus was performed in patients undergoing ablation for paroxysmal atrial fibrillation. Following femoral venous access and trans-septal puncture, left atrial anatomy was defined using rotational angiography performed using a 100ml contrast injection 4 seconds prior to X-ray acqui...

Europace

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main fu... more Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Medical Research Council Fellowship Background In DECAAF II, left atrial (LA) fibrosis ablation plus PVI did not improve AF outcome compared to PVI alone across the study cohort. We hypothesize that biatrial fibrosis ablation could improve AF ablation therapy outcome in a subset of patients with properties identified through a large virtual in silico trial. Purpose To investigate the effects of anatomy, fibrosis distribution, and LGE-MRI threshold on ablation outcome using a virtual cohort of 4000 patients. Methods We constructed 1000 biatrial models from a statistical shape model and we mapped pectinate muscles, Bachmann’s bundle and fibers from an atlas. For each of the 1000 anatomies we applied a randomly selected fibrosis map from a library of 100 clinical maps. We then created four versions of each case by assigning one of four randomly selected right atrial (RA) fi...

Europace

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The authors... more Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The authors acknowledge the support of the British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216). SEW is supported by the British Heart Foundation (FS/20/26/34952). Background Atrial late gadolinium enhancement (Atrial-LGE) and electroanatomic voltage mapping (Atrial-EAVM) quantify the anatomical and functional extent of atrial cardiomyopathy. Prior studies provide conflicting information relating to the level of agreement between these modalities for the assessment of atrial cardiomyopathy disease severity. Purpose We aimed to explore the relationships between, and outcomes from, these contrasting measures of atrial cardiomyopathy disease severity in patients with atrial fibrillation undergoing ablation. Methods Atrial-LGE and Atrial-EAVM was performed in patients undergoing first-time ablation for atrial fibrillation. Atrial cardiomyopathy disease severities were quant...

Computers in Biology and Medicine

Journal of Clinical Medicine

There is increasing evidence to suggest that atrial fibrillation is associated with a heightened ... more There is increasing evidence to suggest that atrial fibrillation is associated with a heightened risk of dementia. The mechanism of interaction is unclear. Atrial fibrillation-induced cerebral infarcts, hypoperfusion, systemic inflammation, and anticoagulant therapy-induced cerebral microbleeds, have been proposed to explain the link between these conditions. An understanding of the pathogenesis of atrial fibrillation-associated cognitive decline may enable the development of treatment strategies targeted towards the prevention of dementia in atrial fibrillation patients. The aim of this review is to explore the impact that existing atrial fibrillation treatment strategies may have on cognition and the putative mechanisms linking the two conditions. This review examines how components of the ‘Atrial Fibrillation Better Care pathway’ (stroke risk reduction, rhythm control, rate control, and risk factor management) may influence the trajectory of atrial fibrillation-associated cogniti...

Computing in Cardiology Conference (CinC)

OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. ... more OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. This paper provides an update on the features and tools added since initial release. Software development has been performed in Matlab (R2020a/R2021b) and Python3. Development is ongoing in the areas of data parsing and data analysis. Data parsing: The system continues to support parsers for Carto3, Velocity and Precision. In addition, a parser for the Kodex electroanatomic mapping system has been added. Data analysis: An extensible architecture for data interpolation has been added. This new architecture has exposed hitherto unrecognized variation in interpolation schemes in clinical mapping systems and will permit the optimization of interpolation methods against 'gold standard' simulation or histological data. Similarly, an architecture for conduction velocity vector measurement has been added. We have refined the ablation lesion quantification tools permitting time-domain analysis of ablation lesion formation. A Python-based graphical interface is now under development, with the beta-testing program for the interface planned. The interface provides the ability to visualize, manipulate and analyze electrophysiology data. To facilitate this development, we have implemented an open-source Python3 version of OpenEP: openep-py.

arXiv (Cornell University), Jan 17, 2023

This work presents an open-source software pipeline to create patient-specific left atrial models... more This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72˘12.25 minutes. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median˘IQR of the absolute difference of the total activation times was 2.02 2.45 ms for inter, and 1.37˘2.45 ms for intra. Also, the average˘sd of the mean CV difference was-0.00404˘0.0155 m{s for inter, and 0.0021˘0.0115 m{s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean˘sd SSIM for inter and intra were 0.648˘0.21 and 0.608˘0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.

Zenodo (CERN European Organization for Nuclear Research), Dec 13, 2022

medRxiv (Cold Spring Harbor Laboratory), Dec 28, 2023

Computing in Cardiology (CinC), 2012, Dec 31, 2022

OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. ... more OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. This paper provides an update on the features and tools added since initial release. Software development has been performed in Matlab (R2020a/R2021b) and Python3. Development is ongoing in the areas of data parsing and data analysis. Data parsing: The system continues to support parsers for Carto3, Velocity and Precision. In addition, a parser for the Kodex electroanatomic mapping system has been added. Data analysis: An extensible architecture for data interpolation has been added. This new architecture has exposed hitherto unrecognized variation in interpolation schemes in clinical mapping systems and will permit the optimization of interpolation methods against 'gold standard' simulation or histological data. Similarly, an architecture for conduction velocity vector measurement has been added. We have refined the ablation lesion quantification tools permitting time-domain analysis of ablation lesion formation. A Python-based graphical interface is now under development, with the beta-testing program for the interface planned. The interface provides the ability to visualize, manipulate and analyze electrophysiology data. To facilitate this development, we have implemented an open-source Python3 version of OpenEP: openep-py.

Journal of Clinical Medicine, Apr 22, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Europace, May 24, 2023

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis offers the poten... more Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis offers the potential to identify patterns unrecognisable to human interpreters and broaden the ECG's utility. However, current algorithms rely on waveform signals derived from digital ECGs for input data, and these cannot be readily obtained from paper-based ECGs. This potentially presents a barrier to adoption as numerous workplaces continue to use paper-based ECGs. The views of stakeholders on the current use of paper-based ECGs and the potential future application of AI-ECG analysis are unknown. Purpose: To explore stakeholders' views about current and future ECG use. To determine the perceived utility of AI-analysis of paper-based ECGs. Methods: A web-based survey was designed using Qualtrics and distributed to a variety of healthcare professionals from numerous locations across the United Kingdom (UK). The survey consisted of 12 questions about participants' perceptions relating to current and future paper-based ECG use and the perceived advantages and disadvantages of AI-ECG. Results: In total, 43 healthcare professionals from 15 health provider organisations in the National Health Service (NHS) completed the survey. Paper-based ECGs were in use in 86% (37/43) of the respondents' workplaces and 61% (26/43) felt that it would be useful if AI-based algorithms could analyse paper-based ECGs in addition to digital ECGs (Figure 1). Views on future prevalence of paper-based ECGs were split with 47% (20/43) responding that it is likely or extremely likely paper-based ECGs will still be in use in the next 5 years in the NHS. Perceived advantages of AI-based analysis included the potential to improve clinical decision making (51%, (22/43)) and optimisation of healthcare professionals' work (leaving more time for clinical patient management) (47%, (20/43)) (Figure 2A). The inability to explain how algorithms determine results (56%, (24/43)), a lack of clarity over the accountability for the results (44%, (19/43)), and a reduction in learning opportunities (44%, (19/43)) were identified as potential issues associated with use of AI-ECG (Figure 2B). Conclusions: Whilst AI-ECG offers potential to improve clinical care, there is currently a gap between research and the integration of AI-ECG into real-world practice. Paper-based ECGs remain prevalent within the NHS, and the current requirement for algorithms to receive signal data presents a barrier to current and future AI-ECG implementation. There is currently an unmet clinical need to develop algorithms capable of interpreting paper-based ECGs. AI-ECG analysis of paper-based ECGs could enable a wider range of healthcare professionals to capitalise on any benefits offered by AI-ECG.

Europace, May 24, 2023

Funding Acknowledgements: Type of funding sources: Private grant(s) and/or Sponsorship. Main fund... more Funding Acknowledgements: Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Wellcome/EPSRC Centre for Medical Engineering Background: Although primarily considered a respiratory virus, cardiovascular manifestations have been reported in patients with Covid-19 infection. Atrial fibrillation has been observed as the most common arrhythmia with the prevalence rate reportedly as high as 16.5%(1). Purpose: The aim of the study was to establish the incidence of atrial fibrillation in patients hospitalised with Covid-19 and evaluate the relationship between patient characteristics and disease severity with new-onset atrial fibrillation in patients with Covid-19. Methods: A single centre, retrospective study of 1241 patients with a confirmed PCR diagnosis of Covid-19 admitted during the 1st wave of the pandemic (1st March to 31st September 2020). Patient demographic data, medical history and clinical outcome data were manually collected. Results: The study population comprised of 1241 patients hospitalised with Covid-19. Of these, 94 (7.6%) patients were known to have pre-existing atrial fibrillation. In an unadjusted analysis, in-hospital mortality was twice as likely in patients with pre-existing atrial fibrillation compared to patients with no history of atrial fibrillation (odds ratio (OR): 2.18; 95% CI 1.29-3.59, p=0.002). However, after multi-variable matching for age, sex and CHA2DS2VASc score there was no significant difference between groups (OR: 1.13, 95% CI 0.57-2.21, p=0.732). During their admission, 42 (3.4%) patients developed new-onset atrial fibrillation. New-onset atrial fibrillation was associated with an increased

Europace

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Hea... more Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background Electrogram morphology is used to identify the atrio-ventricular annuli during catheter ablation procedures with a "balanced" signal considered to represent the annuli. However, anatomical studies highlight a significantly greater mass of ventricular myocardium compared to atrial myocardium at the valve plane suggesting the correct electrical signature of the anatomical annulus may not be a balanced signal. Purpose To identify the relationship between atrial and ventricular electrogram components at the anatomically defined mitral valve annulus. Methods Mapping of the mitral annulus was performed in patients undergoing ablation for paroxysmal atrial fibrillation. Following femoral venous access and trans-septal puncture, left atrial anatomy was defined using rotational angiography performed using a 100ml contrast injection 4 seconds prior to X-ray acqui...

Europace

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main fu... more Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Medical Research Council Fellowship Background In DECAAF II, left atrial (LA) fibrosis ablation plus PVI did not improve AF outcome compared to PVI alone across the study cohort. We hypothesize that biatrial fibrosis ablation could improve AF ablation therapy outcome in a subset of patients with properties identified through a large virtual in silico trial. Purpose To investigate the effects of anatomy, fibrosis distribution, and LGE-MRI threshold on ablation outcome using a virtual cohort of 4000 patients. Methods We constructed 1000 biatrial models from a statistical shape model and we mapped pectinate muscles, Bachmann’s bundle and fibers from an atlas. For each of the 1000 anatomies we applied a randomly selected fibrosis map from a library of 100 clinical maps. We then created four versions of each case by assigning one of four randomly selected right atrial (RA) fi...

Europace

Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The authors... more Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): The authors acknowledge the support of the British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216). SEW is supported by the British Heart Foundation (FS/20/26/34952). Background Atrial late gadolinium enhancement (Atrial-LGE) and electroanatomic voltage mapping (Atrial-EAVM) quantify the anatomical and functional extent of atrial cardiomyopathy. Prior studies provide conflicting information relating to the level of agreement between these modalities for the assessment of atrial cardiomyopathy disease severity. Purpose We aimed to explore the relationships between, and outcomes from, these contrasting measures of atrial cardiomyopathy disease severity in patients with atrial fibrillation undergoing ablation. Methods Atrial-LGE and Atrial-EAVM was performed in patients undergoing first-time ablation for atrial fibrillation. Atrial cardiomyopathy disease severities were quant...

Computers in Biology and Medicine

Journal of Clinical Medicine

There is increasing evidence to suggest that atrial fibrillation is associated with a heightened ... more There is increasing evidence to suggest that atrial fibrillation is associated with a heightened risk of dementia. The mechanism of interaction is unclear. Atrial fibrillation-induced cerebral infarcts, hypoperfusion, systemic inflammation, and anticoagulant therapy-induced cerebral microbleeds, have been proposed to explain the link between these conditions. An understanding of the pathogenesis of atrial fibrillation-associated cognitive decline may enable the development of treatment strategies targeted towards the prevention of dementia in atrial fibrillation patients. The aim of this review is to explore the impact that existing atrial fibrillation treatment strategies may have on cognition and the putative mechanisms linking the two conditions. This review examines how components of the ‘Atrial Fibrillation Better Care pathway’ (stroke risk reduction, rhythm control, rate control, and risk factor management) may influence the trajectory of atrial fibrillation-associated cogniti...

Computing in Cardiology Conference (CinC)

OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. ... more OpenEP is an open-source library for electrophysiological data analysis, first released in 2020. This paper provides an update on the features and tools added since initial release. Software development has been performed in Matlab (R2020a/R2021b) and Python3. Development is ongoing in the areas of data parsing and data analysis. Data parsing: The system continues to support parsers for Carto3, Velocity and Precision. In addition, a parser for the Kodex electroanatomic mapping system has been added. Data analysis: An extensible architecture for data interpolation has been added. This new architecture has exposed hitherto unrecognized variation in interpolation schemes in clinical mapping systems and will permit the optimization of interpolation methods against 'gold standard' simulation or histological data. Similarly, an architecture for conduction velocity vector measurement has been added. We have refined the ablation lesion quantification tools permitting time-domain analysis of ablation lesion formation. A Python-based graphical interface is now under development, with the beta-testing program for the interface planned. The interface provides the ability to visualize, manipulate and analyze electrophysiology data. To facilitate this development, we have implemented an open-source Python3 version of OpenEP: openep-py.

arXiv (Cornell University), Jan 17, 2023

This work presents an open-source software pipeline to create patient-specific left atrial models... more This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72˘12.25 minutes. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median˘IQR of the absolute difference of the total activation times was 2.02 2.45 ms for inter, and 1.37˘2.45 ms for intra. Also, the average˘sd of the mean CV difference was-0.00404˘0.0155 m{s for inter, and 0.0021˘0.0115 m{s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean˘sd SSIM for inter and intra were 0.648˘0.21 and 0.608˘0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.