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In Vivo Ultrasound Dynamic Coronary Blood Flow Imaging Through Adaptive Frame Selection Method
Insights
This study introduces an adaptive frame selection method for improved coronary vasculature imaging using ultrasound. The technique enhances dynamic blood flow visualization without electrocardiographic (ECG) gating, offering potential for human coronary artery imaging.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Cardiovascular Ultrasound
Background:
- Coronary imaging via ultrasound faces challenges due to small vessel size and myocardial motion.
- Existing super-resolution techniques often require electrocardiographic (ECG) gating and have long acquisition times, limiting coronary flow measurement to diastole.
Purpose of the Study:
- To develop and validate an adaptive frame selection approach for dynamic coronary vasculature imaging.
- To overcome limitations of existing methods by eliminating the need for ECG gating and reducing data acquisition time.
Main Methods:
- An adaptive frame selection approach using the sum of absolute difference (SAD) algorithm to identify similar frames within cardiac cycles.
- Application of a singular value decomposition (SVD) filter to extract blood flow signals and generate dynamic images.
- High-frequency ultrafast ultrasound imaging in a mouse heart model.
Main Results:
- The proposed method successfully calculated coronary vasculature blood flow without ECG gating.
- Achieved a signal-to-noise ratio (SNR) of 20.74 ± 1.62 dB under optimal parameter settings.
- Demonstrated accuracy through comparison with Doppler sonograms of the left coronary artery (LCA) and arterioles.
Conclusions:
- The adaptive frame selection approach enhances dynamic coronary vasculature imaging.
- This method offers improved SNR and eliminates the need for ECG gating, addressing key limitations of current techniques.
- The approach shows significant potential for application in human dynamic coronary artery imaging.
Abstract:
Use of ultrasound for coronary imaging in commercial echocardiography remains challenging because of the small nature of coronary vasculature and the myocardium's intricate motion. Several super-resolution imaging techniques have been applied for coronary imaging; however, most of them only measure the coronary flow during the diastolic phase and have a long data acquisition time. To address these problems, this study proposes an adaptive frame selection approach for coronary vasculature imaging. In this approach, similar frames within cardiac cycles are selected using the sum of absolute difference (SAD) algorithm, and the coronary vasculature blood flow is calculated without using electrocardiographic (ECG) gating data. Experiments were performed in mouse hearts through high-frequency ultrafast ultrasound imaging. After similar frames were selected from several cardiac cycles (one-five cycles), a singular value decomposition (SVD) filter was applied to extract blood flow signals and obtain a dynamic coronary vasculature image, the accuracy of which was confirmed by measuring Doppler sonograms from the left coronary artery (LCA) and arterioles. The conventional method (without SAD), in which only blood flow in the diastolic phase is calculated, was also conducted to enable a comparison in terms of measured vessel size and signal-to-noise ratio (SNR). The SNR for the proposed approach was found to be $20.74~\pm ~1.62$ dB, under the best parameter settings. The proposed approach was successfully verified in the small animal model and has potential for use in human dynamic coronary artery imaging.
