Dynamic Mode Decomposition (DMD) Cardiac Phase Estimation for adult and fetal real-time MRI

Motivation: Cardiac synchronization in adult and fetal imaging requires external devices (electrocardiogram, Doppler-ultrasound), which may compromise image quality and increase scan time. Self-gating with real-time imaging can mitigate this but may be less reliable for irregular motions and limited in fetal applications. Goal(s): To develop a fast image-based cardiac phase estimation method with no assumption on the heart rate and minimal user input. Approach: Dynamic Mode Decomposition is used to estimate cardiac motion signal for retrospective-gating. Results: DMD cardiac phase estimation captures cardiac motion despite the irregularities and other bulk motions, as demonstrated in real-time adult and fetal cardiac imaging, including a twin gestation. Impact: The proposed technique, Dynamic Mode Decomposition cardiac phase estimation, constructs cardiac signal with no assumption on periodicity, no iterations, and only minimal user input. This may be valuable in fetal cardiac imaging, where the cardiac signal is not readily available.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0749
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic Mode Decomposition (DMD) Cardiac Phase Estimation for adult and fetal real-time MRI

Bilal Taşdelen, Ibrahim K. Ozaslan, Ecrin Yağız, Krishna S. Nayak et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

Dynamic Mode Decomposition (DMD) Cardiac Phase Estimation for adult and fetal real-time MRI

Bilal Taşdelen, Ibrahim K. Ozaslan, Ecrin Yağız, Krishna S. Nayak, Mihailo R. Jovanović, Ye Tian
article en

Abstract

Motivation: Cardiac synchronization in adult and fetal imaging requires external devices (electrocardiogram, Doppler-ultrasound), which may compromise image quality and increase scan time. Self-gating with real-time imaging can mitigate this but may be less reliable for irregular motions and limited in fetal applications. Goal(s): To develop a fast image-based cardiac phase estimation method with no assumption on the heart rate and minimal user input. Approach: Dynamic Mode Decomposition is used to estimate cardiac motion signal for retrospective-gating. Results: DMD cardiac phase estimation captures cardiac motion despite the irregularities and other bulk motions, as demonstrated in real-time adult and fetal cardiac imaging, including a twin gestation. Impact: The proposed technique, Dynamic Mode Decomposition cardiac phase estimation, constructs cardiac signal with no assumption on periodicity, no iterations, and only minimal user input. This may be valuable in fetal cardiac imaging, where the cardiac signal is not readily available.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Good health and well-being
Openalex Percentile: Top 63%
Advanced MRI Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.