TY - GEN
T1 - Model-Assisted Time-Synchronization of Cardiac MR Image and Catheter Pressure Data
AU - Gusseva, Maria
AU - Greer, Joshua S.
AU - Castellanos, Daniel A.
AU - Hussein, Mohamed Abdelghafar
AU - Greil, Gerald
AU - Veeram Reddy, Surendranath R.
AU - Hussain, Tarique
AU - Chapelle, Dominique
AU - Chabiniok, Radomír
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - When combining cardiovascular magnetic resonance imaging (CMR) with pressure catheter measurements, the acquired image and 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 (EorigN(t), Et-synN(t), respectively). Et-synN(t) was significantly closer to the experimentally obtained EexpN(t) published in the literature (p < 0.05, L2 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.
AB - When combining cardiovascular magnetic resonance imaging (CMR) with pressure catheter measurements, the acquired image and 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 (EorigN(t), Et-synN(t), respectively). Et-synN(t) was significantly closer to the experimentally obtained EexpN(t) published in the literature (p < 0.05, L2 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.
KW - Cardiovascular modeling
KW - Interventional cardiovascular magnetic resonance imaging
KW - Personalized medicine
KW - Pressure-volume loops
KW - Time-synchronization of clinical data
KW - Translational research
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U2 - 10.1007/978-3-030-78710-3_35
DO - 10.1007/978-3-030-78710-3_35
M3 - Conference contribution
AN - SCOPUS:85111866964
SN - 9783030787097
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 362
EP - 372
BT - Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
A2 - Ennis, Daniel B.
A2 - Perotti, Luigi E.
A2 - Wang, Vicky Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
Y2 - 21 June 2021 through 25 June 2021
ER -