Mocarts: a lightweight radiation transport simulator for easy handling of complex sensing geometries

In functional neuroimaging (fNIRS), elaborated sensing geometries pairing multiple light sources and detectors arranged over the tissue surface are needed. A variety of software tools for probing forward models of radiation transport in tissue exist, but their handling of sensing geometries and specification of complex tissue architectures is, most times, cumbersome. In this work, we introduce a lightweight simulator, Monte Carlo Radiation Transport Simulator (MOCARTS) that attends these demands for simplifying specification of tissue architectures and complex sensing geometries. An object-oriented architecture facilitates such goal. The simulator core is evolved from the Monte Carlo Multi-Layer (mcml) tool but extended to support multi-channel simulations. Verification against mcml yields negligible error (RMSE~4-10e-9) over a photon trajectory. Full simulations show concurrent validity of the proposed tool. Finally, the ability of the new software to simulate multi-channel sensing geometries and to define biological tissue models in an intuitive nested-hierarchy way are exemplified

Simulation study on acousto-optics sensing of focused ultrasound

Abstract. The acousto-optics (AO) technique can provide a good contrast with high penetration depth (up to 5 cm) and can be potentially utilized in real time monitoring of the focused ultrasound (FUS) therapies. This work presents the AO simulation study on the interaction of light and FUS in the single-layer brain (SLB) medium and four-layer brain (FLB) medium. FUS pressure distribution at 0.5 MHz and 0.9 MHz frequency was simulated on k-Wave toolbox and the AO Monte Carlo (MC) algorithm was developed on MATLAB to simulate the AO effect in both mediums. The result for the SLB for both ultrasound (US) frequencies suggests that the modulation depth (MD) is high in the region of US focus with a magnitude of 2%-3% and <1% at 0.5 MHz and 0.9 MHz, respectively. Moreover, the MD decreases to 5 orders of magnitude at the source region. In the FLB, the MD decreased to 4–4.5 orders at the source and was present in the skull and US focus region with a magnitude of <1% at both US frequencies. These results suggest that AO can be utilized in sensing FUS effects on brain tissue and the AO signal-to-noise ratio (SNR) depends not only on the MD but also on the level of light intensity interacting with the US pressure

Impact of Anatomical Variability on Sensitivity Profile in fNIRS-MRI Integration

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 +/- 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth &lt; 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth &lt; 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source-detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation

Measurement of particle flux in a static matrix with suppressed influence of optical properties, using low coherence interferometry

Perfusion measurements using conventional laser Doppler techniques are affected by the variations in tissue optical properties. Differences in absorption and scattering will induce different path lengths and consequently will alter the probability that a Doppler shift will occur. In this study, the fraction of Doppler shifted photons and the Doppler broadening of a dynamic medium, are measured with a phase modulated low coherence Mach-Zehnder interferometer. Path length-resolved dynamic light scattering measurements are performed in various media having a constant concentration of dynamic particles inside a static matrix with different scattering properties and the results are compared with a conventional laser Doppler technique, with a simple model and with Monte Carlo simulations. We demonstrate that, for larger optical path lengths, the scattering coefficient of the static matrix in which the moving particles are embedded have a small to minimal effect on the measured fraction of Doppler shifted photons and on the measured average Doppler frequency of the Doppler shifted light. This approach has potential applications in measuring perfusion independent of the influence of optical properties in the static tissue matrix

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology

Imaging dynamics beneath turbid media via parallelized single-photon detection

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard methods aim to form images based upon optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such data to demonstrate deep-tissue imaging of decorrelation dynamics. In this work, we take advantage of a single-photon avalanche diode (SPAD) array camera, with over one thousand detectors, to simultaneously detect speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating liquid tissue phantoms. We demonstrate the ability to record video of dynamic events occurring 5-8 mm beneath a decorrelating tissue phantom with mm-scale resolution and at a 2.5-10 Hz frame rate