Time-resolved near infrared light propagation using frequency domain superposition

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Comparison of functional activation responses from the auditory cortex derived using multi-distance frequency domain and continuous wave near-infrared spectroscopy

Significance: Quantitative measurements of cerebral hemodynamic changes due to functional activation are widely accomplished with commercial continuous wave (CW-NIRS) instruments despite the availability of the more rigorous multi-distance frequency domain (FD-NIRS) approach. A direct comparison of the two approaches to functional near-infrared spectroscopy can help in the interpretation of optical data and guide implementations of diffuse optical instruments for measuring functional activation. Aim: We explore the differences between CW-NIRS and multi-distance FD-NIRS by comparing measurements of functional activation in the human auditory cortex. Approach: Functional activation of the human auditory cortex was measured using a commercial frequency domain near-infrared spectroscopy instrument for 70 dB sound pressure level broadband noise and pure tone (1000 Hz) stimuli. Changes in tissue oxygenation were calculated using the modified Beer-Lambert law (CW-NIRS approach) and the photon diffusion equation (FD-NIRS approach). Results: Changes in oxygenated hemoglobin measured with the multi-distance FD-NIRS approach were about twice as large as those measured with the CW-NIRS approach. A finite-element simulation of the functional activation problem was performed to demonstrate that tissue oxygenation changes measured with the CW-NIRS approach is more accurate than that with multi-distance FD-NIRS. Conclusions: Multi-distance FD-NIRS approaches tend to overestimate functional activation effects, in part due to partial volume effects.

The LUCA device: a multi-modal platform combining diffuse optics and ultrasound imaging for thyroid cancer screening

We present the LUCA device, a multi-modal platform combining eight-wavelength near infrared time resolved spectroscopy, sixteen-channel diffuse correlation spectroscopy and a clinical ultrasound in a single device. By simultaneously measuring the tissue hemodynamics and performing ultrasound imaging, this platform aims to tackle the low specificity and sensitivity of the current thyroid cancer diagnosis techniques, improving the screening of thyroid nodules. Here, we show a detailed description of the device, components and modules. Furthermore, we show the device tests performed through well established protocols for phantom validation, and the performance assessment for in vivo. The characterization tests demonstrate that LUCA device is capable of performing high quality measurements, with a precision in determining in vivo tissue optical and dynamic properties of better than 3%, and a reproducibility of better than 10% after ultrasound-guided probe repositioning, even with low photon count-rates, making it suitable for a wide variety of clinical applications.

Cerebral perfusion and blood-brain barrier assessment in brain trauma using contrast-enhanced near-infrared spectroscopy with indocyanine green: A review

Contrast-enhanced near-infrared spectroscopy (NIRS) with indocyanine green (ICG) can be a valid non-invasive, continuous, bedside neuromonitoring tool. However, its usage in moderate and severe traumatic brain injury (TBI) patients can be unprecise due to their clinical status. This review is targeted at researchers and clinicians involved in the development and application of contrast-enhanced NIRS for the care of TBI patients and can be used to design future studies. This review describes the methods developed to monitor the brain perfusion and the blood-brain barrier integrity using the changes of diffuse reflectance during the ICG passage and the results on studies in animals and humans. The limitations in accuracy of these methods when applied on TBI patients and the proposed solutions to overcome them are discussed. Finally, the analysis of relative parameters is proposed as a valid alternative over absolute values to address some current clinical needs in brain trauma care. In conclusion, care should be taken in the translation of the optical signal into absolute physiological parameters of TBI patients, as their clinical status must be taken into consideration. Discussion on where and how future studies should be directed to effectively incorporate contrast-enhanced NIRS into brain trauma care is given.

Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography

Time-resolved diffuse optical tomography is a technique used to recover the optical properties of an unknown diffusive medium by solving an ill-posed inverse problem. In time-domain, reconstructions based on datatypes are used for their computational efficiency. In practice, most used datatypes are temporal windows and Fourier transform. Nevertheless, neither theoretical nor numerical studies assessing different datatypes have been clearly expressed. In this paper, we propose an overview and a new process to compute efficiently a long set of temporal windows in order to perform diffuse optical tomography. We did a theoretical comparison of these large set of temporal windows. We also did simulations in a reflectance geometry with a spherical inclusion at different depths. The results are presented in terms of inclusion localization and its absorption coefficient recovery. We show that (1) the new windows computed with the developed method improve inclusion localization for inclusions at deep layers, (2) inclusion absorption quantification is improved at all depths and, (3) in some cases these windows can be equivalent to frequency based reconstruction at GHz order.

Broadband Time Domain Diffuse Optical Reflectance Spectroscopy: A Review of Systems, Methods, and Applications

This review presents recent developments and a wide overview of broadband time domain diffuse optical spectroscopy (TD-DOS). Various topics including physics of photon migration, advanced instrumentation, methods of analysis, applications covering multiple domains (tissue chromophore, in vivo studies, food, wood, pharmaceutical industry) are elaborated. The key role of standardization and recent studies in that direction are discussed. Towards the end, a brief outlook is presented on the current status and future trends in broadband TD-DOS.

Self-calibrating time-resolved near infrared spectroscopy

Time-resolved near infrared spectroscopy is considered to be a gold standard technique when measuring absolute values of tissue optical properties, as it provides separable and independent information about both tissue absorption and scattering. However, time-resolved instruments require an accurate characterization by measuring the instrument response function in order to decouple the contribution of the instrument itself from the measurement. In this work, a new approach to the methodology of analysing time-resolved data is presented where the influence of instrument response function is eliminated from the data and a self-calibrating analysis is proposed. The proposed methodology requires an instrument to provide at least two wavelengths and allows spectral parameters recovery (optical properties or constituents concentrations and reduced scatter amplitude and power). Phantom and in-vivo data from two different time-resolved systems are used to validate the accuracy of the proposed self-calibrating approach, demonstrating that parameters recovery compared to the conventional curve fitting approach is within 10% and benefits from introducing a spectral constraint to the reconstruction problem. It is shown that a multi-wavelength time-resolved data can be used for parameters recovery directly without prior calibration (instrument response function measurement).