Takuro Ishii | Searcharxiv

arXiv2026

Development of a 4D Cerebral Microvascular Imaging Platform for Mouse Stroke Model

In ischemic stroke, changes in cerebral hemodynamics during both the ischemic and reperfusion phases strongly influence stroke outcomes. However, these hemodynamic changes remain incompletely understood. To address this challenge, we devised an imaging platform that enables time-resolved ultrasound microvascular imaging during the experimental induction of ischemia and reperfusion in a mouse model. The platform leverages our previous ultrasound imaging framework combined with continuous mechanical scanning, which acquires whole-brain blood-flow signals within 5 s. The experiments demonstrated that the proposed platform can visualize both local and whole-brain hemodynamic responses to the induction of ischemia and reperfusion, suggesting its potential for rapid and continuous whole-brain hemodynamic assessment in small-animal models.

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arXiv2025

Implicit Spatiotemporal Bandwidth Enhancement Filter by Sine-activated Deep Learning Model for Fast 3D Photoacoustic Tomography

3D photoacoustic tomography (3D-PAT) using high-frequency hemispherical transducers offers near-omnidirectional reception and enhanced sensitivity to the finer structural details encoded in the high-frequency components of the broadband photoacoustic (PA) signal. However, practical constraints such as limited number of channels with bandlimited sampling rate often result in sparse and bandlimited sensors that degrade image quality. To address this, we revisit the 2D deep learning (DL) approach applied directly to sensor-wise PA radio-frequency (PARF) data. Specifically, we introduce sine activation into the DL model to restore the broadband nature of PARF signals given the observed band-limited and high-frequency PARF data. Given the scarcity of 3D training data, we employ simplified training strategies by simulating random spherical absorbers. This combination of sine-activated model and randomized training is designed to emphasize bandwidth learning over dataset memorization. Our model was evaluated on a leaf skeleton phantom, a micro-CT-verified 3D spiral phantom and in-vivo human palm vasculature. The results showed that the proposed training mechanism on sine-activated model was well-generalized across the different tests by effectively increasing the sensor density and recovering the spatiotemporal bandwidth. Qualitatively, the sine-activated model uniquely enhanced high-frequency content that produces clearer vascular structure with fewer artefacts. Quantitatively, the sine-activated model exhibits full bandwidth at -12 dB spectrum and significantly higher contrast-to-noise ratio with minimal loss of structural similarity index. Lastly, we optimized our approach to enable fast enhanced 3D-PAT at 2 volumes-per-second for better practical imaging of a free-moving targets.

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