arXiv subjects

Jack Radford

Publications and source records attributed to Jack Radford.

6 recordsLinked to original sources

arXiv2024

Photon transport through the entire adult human head

Optical brain imaging technologies are promising due to their relatively high temporal resolution, portability and cost-effectiveness. However, the highly scattering nature of near-infrared light in human tissue makes it challenging to collect photons emerging from more than 4 cm below the scalp, or with source-detector separation larger than several centimeters. We explore the physical limits of photon transport in the head and show that despite an extreme attenuation of ~10^(18), we can experimentally detect light that is transmitted diametrically through the entire adult human head. Analysis of various photon migration pathways through the head also indicates how the source-detector configuration can be used to isolate photons interacting with deep regions of the brain that are inaccessible with current optical techniques.

physics.optics↗

arXiv2024

Energy transport in diffusive waveguides

The guiding and transport of energy, for example of electromagnetic waves underpins many technologies that have shaped modern society, ranging from long distance optical fibre telecommunications to on-chip optical processors. Traditionally, a mechanism is required that exponentially localises the waves or particles in the confinement region, e.g. total internal reflection at a boundary. We introduce a waveguiding mechanism that relies on a different origin for the exponential confinement and that arises due to the physics of diffusion. We demonstrate this concept using light and show that photon density waves can propagate as a guided mode along a core-structure embedded in a scattering, opaque material, enhancing light transmission by orders of magnitude and along non-trivial, e.g. curved trajectories. This waveguiding mechanism can also occur naturally, for example in the cerebral spinal fluid surrounding the brain, along tendons in the human body and is to be expected in other systems that follow the same physics e.g. neutron diffusion.

physics.optics↗

arXiv2022

Machine learning opens a doorway for microrheology with optical tweezers in living systems

It has been argued [Tassieri, \textit{Soft Matter}, 2015, \textbf{11}, 5792] that linear microrheology with optical tweezers (MOT) of living systems ``\textit{is not an option}'', because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have taken a first step towards a possible solution of this problem by exploiting modern machine learning (ML) methods to reduce the duration of MOT measurements from several tens of minutes down to one second. This has been achieved by focusing on the analysis of computer simulated trajectories of an optically trapped particle suspended in a set of Newtonian fluids having viscosity values spanning three orders of magnitude, i.e. from $10^{-3}$ to $1$ Pa$\cdot$s. When the particle trajectory is analysed by means of conventional statistical mechanics principles, we explicate for the first time in literature the relationship between the required duration of MOT experiments ($T_m$) and the fluids relative viscosity ($η_r$) to achieve an uncertainty as low as $1\%$; i.e., $T_m\cong 17η_r^3$ minutes. This has led to further evidences explaining why conventional MOT measurements commonly underestimate the materials' viscoelastic properties, especially in the case of high viscous fluids or soft-solids such as gels and cells. Finally, we have developed a ML algorithm to determine the viscosity of Newtonian fluids that uses feature extraction on raw trajectories acquired at a kHz and for a duration of only one second, yet capable of returning viscosity values carrying an error as low as $\sim0.3\%$ at best; hence the opening of a doorway for MOT in living systems.

physics.flu-dyn↗

arXiv2021

3D imaging from multipath temporal echoes

Echo-location is a broad approach to imaging and sensing that includes both man-made RADAR, LIDAR, SONAR and also animal navigation. However, full 3D information based on echo-location requires some form of scanning of the scene in order to provide the spatial location of the echo origin-points. Without this spatial information, imaging objects in 3D is a very challenging task as the inverse retrieval problem is strongly ill-posed. Here, we show that the temporal information encoded in the return echoes that are reflected multiple times within a scene is sufficient to faithfully render an image in 3D. Numerical modelling and an information theoretic perspective prove the concept and provide insight into the role of the multipath information. We experimentally demonstrate the concept by using both radio-frequency and acoustic waves for imaging individuals moving in a closed environment.

eess.IV↗

arXiv2020

The role of late photons in diffuse optical imaging

The ability to image through turbid media such as organic tissues, is a highly attractive prospect for biological and medical imaging. This is challenging however, due to the highly scattering properties of tissues which scramble the image information. The earliest photons that arrive at the detector are often associated with ballistic transmission, whilst the later photons are associated with complex paths due to multiple independent scattering events and are therefore typically considered to be detrimental to the final image formation process. In this work we report on the importance of these highly diffuse, "late" photons for computational time-of-flight diffuse optical imaging. In thick scattering materials, >80 transport mean free paths, we provide evidence that including late photons in the inverse retrieval enhances the image reconstruction quality. We also show that the late photons alone have sufficient information to retrieve images of a similar quality to early photon gated data. This result emphasises the importance in the strongly diffusive regime discussed here, of fully time-resolved imaging techniques.

physics.optics↗

arXiv2020

Variational Inference for Computational Imaging Inverse Problems

Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collections with specific imaging instruments. This paper introduces a novel framework to train variational inference for inverse problems exploiting in combination few experimentally collected data, domain expertise and existing image data sets. In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts. Extensive simulated experiments show the advantages of the proposed framework. The approach is then applied to two real experimental optics settings: holographic image reconstruction and imaging through highly scattering media. In both settings, state of the art reconstructions are achieved with little collection of training data.

cs.LG↗