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Conference Papers Year : 2021

Model-Assisted Time-Synchronization of Cardiac MR Image and Catheter Pressure Data

Abstract

When combining cardiovascular magnetic resonance imaging (CMR) with pressure catheter measurements, the acquired imageand pressure data need to be synchronized in time. The time offset between the image and pressure data depends on a number of factors,such as the type and settings of the MR sequence, duration and shape of QRS complex or the type of catheter, and cannot be typically estimated beforehand. In the present work we propose using a biophysical heart model to synchronize the left ventricular (LV) pressure and volume (P-V) data. Ten patients, who underwent CMR and LV catheterization, were included. A biophysical model of reduced geometrical complexity with physiologically substantiated timing of each phase of the cardiac cycle was first adjusted to individual patients using basic morphological and functional indicators. The pressure and volume waveforms simulated by the patient-specific models were then used as templates to detect the time offset between the acquired ventricular pressure and volume waveforms. Time-varying ventricular elastance was derived from clinical data both as originally acquired as well as when time-synchronized, and normalized with respect to end-systolic time and maximum elastance value$E^N_\text {orig}(t)$, $E^N_\text {t-syn}(t)$, respectively). $E^N_\text {t-syn}(t)$ was significantly closer to the experimentally obtained $E^N_\text {exp}(t)$ published in the literature (p < 0.05, $L^2$ norm). The work concludes that the model-driven time-synchronization of P-V data obtained by catheter measurement and CMR allows to generate high quality P-V loops, which can then be used for clinical interpretation.
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Dates and versions

hal-03313857 , version 1 (04-08-2021)

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Maria Gusseva, Joshua S Greer, Daniel A Castellanos, Mohamed Abdelghafar Hussein, Gerald Greil, et al.. Model-Assisted Time-Synchronization of Cardiac MR Image and Catheter Pressure Data. FIMH 2021 - Functional Imaging and Modeling of Heart (FIMH 2021), Jun 2021, Stanford, CA, United States. pp.362-372, ⟨10.1007/978-3-030-78710-3_35⟩. ⟨hal-03313857⟩
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