arXiv subjects

Alistair Gorman

Publications and source records attributed to Alistair Gorman.

3 recordsLinked to original sources

arXiv2026

Real-time diffuse correlation spectroscopy with a chip-based correlator for measuring human cerebral blood flow and brain function

Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique that probes microvascular blood flow in deep tissues. Here, we present and validate a new on-chip hardware correlator for high-speed DCS measurements. The correlator is embedded in a custom-built 512 x 512 single-photon avalanche diode (SPAD) array named ATLAS, which computes intensity autocorrelation functions directly on-chip at a sampling rate of 116 Hz - the fastest DCS acquisition reported to date. Unlike conventional DCS systems that suffer from low light throughput and therefore cannot resolve cardiac pulsations at source-detector separations (rho) beyond 30 mm, our massively parallel on-chip architecture computes autocorrelations within each macropixel, eliminating the data-throughput bottleneck. This enables high-SNR, real-time detection of pulsatile blood flow even at rho = 50 mm on the human forehead. In phantom experiments at rho = 25 mm, ATLAS-DCS achieves a 12-fold improvement in signal-to-noise ratio over a conventional single-channel DCS instrument while operating at 116 Hz. In human subjects, we resolve functional hyperemia during a mental arithmetic task at rho = 30 mm. Furthermore, we integrate ATLAS DCS with a frequency-domain near-infrared spectroscopy (FD-NIRS) module, enabling simultaneous monitoring of blood flow and tissue oxygenation. With this combined system, we can concurrently resolve core hemodynamic parameters. The on-chip parallelized DCS design substantially improves detection speed, depth sensitivity, and real-time capability, paving the way for wearable, high-speed cerebral blood flow monitoring in both clinical and research settings.

physics.ins-det↗

arXiv2025

An on-chip Pixel Processing Approach with 2.4μs latency for Asynchronous Read-out of SPAD-based dToF Flash LiDARs

We propose a fully asynchronous peak detection approach for SPAD-based direct time-of-flight (dToF) flash LiDAR, enabling pixel-wise event-driven depth acquisition without global synchronization. By allowing pixels to independently report depth once a sufficient signal-to-noise ratio is achieved, the method reduces latency, mitigates motion blur, and increases effective frame rate compared to frame-based systems. The framework is validated under two hardware implementations: an offline 256$\times$128 SPAD array with PC based processing and a real-time FPGA proof-of-concept prototype with 2.4$\upmu$s latency for on-chip integration. Experiments demonstrate robust depth estimation, reflectivity reconstruction, and dynamic event-based representation under both static and dynamic conditions. The results confirm that asynchronous operation reduces redundant background data and computational load, while remaining tunable via simple hyperparameters. These findings establish a foundation for compact, low-latency, event-driven LiDAR architectures suited to robotics, autonomous driving, and consumer applications. In addition, we have derived a semi-closed-form solution for the detection probability of the raw-peak finding based LiDAR systems that could benefit both conventional frame-based and proposed asynchronous LiDAR systems.

eess.IV↗

arXiv2022

A direct time-of-flight image sensor with in-pixel surface detection and dynamic vision

3D flash LIDAR is an alternative to the traditional scanning LIDAR systems, promising precise depth imaging in a compact form factor, and free of moving parts, for applications such as self-driving cars, robotics and augmented reality (AR). Typically implemented using single-photon, direct time-of-flight (dToF) receivers in image sensor format, the operation of the devices can be hindered by the large number of photon events needing to be processed and compressed in outdoor scenarios, limiting frame rates and scalability to larger arrays. We here present a 64x32 pixel (256x128 SPAD) dToF imager that overcomes these limitations by using pixels with embedded histogramming, which lock onto and track the return signal. This reduces the size of output data frames considerably, enabling maximum frame rates in the 10 kFPS range or 100 kFPS for direct depth readings. The sensor offers selective readout of pixels detecting surfaces, or those sensing motion, leading to reduced power consumption and off-chip processing requirements. We demonstrate the application of the sensor in mid-range LIDAR.

cs.CV↗