Valentina Carapella1, Henrike Puchta2, Elena Lukaschuk3, Claudia Marini4, Konrad Werys5, Stefan Neubauer6, Vanessa M Ferreira7, Stefan K Piechnik8. 1. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom; Biomedical Engineering Department, King's College London, 5th Floor Becket House, London, United Kingdom. Electronic address: vcarapella@gmail.com. 2. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: henrike.puchta@cardiov.ox.ac.uk. 3. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: elena.lukaschuk@cardiov.ox.ac.uk. 4. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. 5. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: konrad.werys@cardiov.ox.ac.uk. 6. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: stefan.neubauer@cardiov.ox.ac.uk. 7. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: vanessa.ferreira@cardiov.ox.ac.uk. 8. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Radcliffe Department of Medicine, University of Oxford, United Kingdom. Electronic address: stefan.piechnik@cardiov.ox.ac.uk.
Abstract
BACKGROUND: Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. METHODS: We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. RESULTS: The mean absolute difference between reference operators was 8.4 ± 6.3 ms (T1) and 1.2 ± 0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ± 2.4 to 6.7 ± 1.4 ms; p < 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ± 0.4 to 0.7 ± 0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. CONCLUSION: A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33-72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning.
BACKGROUND: Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. METHODS: We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. RESULTS: The mean absolute difference between reference operators was 8.4 ± 6.3 ms (T1) and 1.2 ± 0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ± 2.4 to 6.7 ± 1.4 ms; p < 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ± 0.4 to 0.7 ± 0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. CONCLUSION: A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33-72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning.
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Authors: Qiang Zhang; Evan Hann; Konrad Werys; Cody Wu; Iulia Popescu; Elena Lukaschuk; Ahmet Barutcu; Vanessa M Ferreira; Stefan K Piechnik Journal: Artif Intell Med Date: 2020-09-07 Impact factor: 5.326
Authors: Miroslawa Gorecka; Gerry P McCann; Colin Berry; Vanessa M Ferreira; James C Moon; Christopher A Miller; Amedeo Chiribiri; Sanjay Prasad; Marc R Dweck; Chiara Bucciarelli-Ducci; Dana Dawson; Marianna Fontana; Peter W Macfarlane; Alex McConnachie; Stefan Neubauer; John P Greenwood Journal: J Cardiovasc Magn Reson Date: 2021-06-10 Impact factor: 5.364
Authors: Matthew K Burrage; Mayooran Shanmuganathan; Qiang Zhang; Evan Hann; Iulia A Popescu; Rajkumar Soundarajan; Kelvin Chow; Stefan Neubauer; Vanessa M Ferreira; Stefan K Piechnik Journal: Sci Rep Date: 2021-06-30 Impact factor: 4.379