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Blood flow visualization using a ConvLSTM-based deep learning clutter filter
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DOI：10.35848/1347-4065/ae55ae.png)
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Abstract
En 中文
This study investigates the feasibility of applying a convolutional long short-term memory (ConvLSTM) model to high-frame-rate ultrasound data for clutter suppression and blood-flow visualization. The proposed two-layer ConvLSTM model was trained using a Gaussian-masked mean squared error loss to emphasize vascular regions and suppress tissue clutter. The experiments showed that ConvLSTM effectively separated blood-flow and tissue components, achieving higher contrast than conventional singular value decomposition (SVD) filtering. Velocity estimation using block-matching indicated that ConvLSTM closely tracked the temporal velocity trends in the SVD results. A slight increase in the mean velocity during the systolic phase was attributed to delayed temporal accumulation caused by long-term memory. Designed to capture long-term temporal dependencies, the ConvLSTM model can extract hemodynamic features from sequential ultrasound data, enabling effective clutter suppression and stable visualization of blood flow in high-frame-rate imaging.
Keywords:
blood flow imaging
clutter filtering
ConvLSTM
Journal
J
IF:
1.8
Papers:
561
Citations:
0