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Yann Fischer

Publications and source records attributed to Yann Fischer.

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Low-rank modal endpoints from beat-resolved retinal arterial Doppler holography velocity waveforms characterize the response to flicker provocation

Conventional retinal flicker endpoints quantify changes in diameter, mean velocity, or flow but do not capture the modal concentration of the cardiac velocity waveform during neurovascular stimulation. Here, beat-resolved retinal Doppler holography and low-rank modal decomposition are combined to derive compact arterial endpoints from unfiltered arterial segment-velocity waveforms sampled across beats and vessel locations. For each acquisition, locally centered waveforms are assembled into a common matrix and decomposed by singular-value decomposition (SVD). Robust summaries quantify total pulsatile scale, modal amplitude, residual amplitude, mean-to-pulsatile balance, and singular-spectrum dimensionality; the same construction can also be applied descriptively to individual beats. We demonstrate the framework using 33 temporally ordered Baseline~1/Flicker/Baseline~2 acquisitions from one eye. Relative to the pooled baselines, 13-Hz flicker was associated with lower centered-waveform RMS scale $R_0$ and leading-mode amplitude $A_1$, and with higher robust residual-amplitude ratios $\rho_1$ and $\rho_2$, mean-to-pulsatile ratio MPR, effective rank, and participation ratio; these contrasts showed exploratory within-session separation after Holm adjustment. No evidence of differences in the absolute residual amplitudes $R_1$ and $R_2$ was detected; together, these observations are consistent with reduced concentration in the leading acquisition-specific arterial pulse mode rather than an increase in absolute residual pulsatility.

physics.med-ph

Improving segmentation of retinal arteries and veins using cardiac signal in doppler holograms

Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arteries and veins, but traditional segmentation methods focus solely on spatial information and overlook the temporal richness of holographic data. In this work, we propose a simple yet effective approach for artery-vein segmentation in temporal Doppler holograms using standard segmentation architectures. By incorporating features derived from a dedicated pulse analysis pipeline, our method allows conventional U-Nets to exploit temporal dynamics and achieve performance comparable to more complex attention- or iteration-based models. These findings demonstrate that time-resolved preprocessing can unlock the full potential of deep learning for Doppler holography, opening new perspectives for quantitative exploration of retinal hemodynamics. The dataset is publicly available at https://huggingface.co/datasets/DigitalHolography/

cs.CV

Retinal arterial blood flow measured by real-time Doppler holography at 33,000 frames per second

This study presents a novel quantitative estimation method for total retinal arterial blood flow utilizing real-time Doppler holography at an unprecedented frame rate of 33,000 frames per second. This technique, leveraging high-speed digital holography, enables non-invasive angiographic imaging of the retina, providing detailed blood flow contrasts essential for assessing retinal health. The proposed quantitative analysis method consists of segmenting primary in-plane retinal arteries and calculating local blood velocity using Doppler frequency broadening. The analysis integrates a forward scattering model to achieve blood flow estimation. Our findings highlight the potential of Doppler holography as a powerful tool for diagnosing and monitoring the treatment of retinal vascular conditions, complementary to existing imaging methods.

eess.IV