Jorge Ripoll

SignificanceFluorescent imaging (FI) using indocyanine green (ICG) is a powerful tool in medical diagnostics and surgery. Although numerous studies have focused on optimizing injection protocols and suppressing excitation light leakage, tissue autofluorescence has not been widely recognized as a fundamental factor limiting sensitivity.AimWe aim to quantitatively determine the sensitivity limit for ICG detection in biological tissues, accounting for background signals from both scattered excitation light and tissue autofluorescence.ApproachWe combine experiments on tissue phantoms with varying ICG concentrations and Monte Carlo numerical simulation of light transport in media with different optical properties. Human skin autofluorescence was quantified in vivo using a nonfluorescent reference and a model medium with a known ICG concentration.ResultsIt was established that skin autofluorescence is the dominant source of background, exceeding the scattered light by 4 to 25 times in the imaging system used. The determined ultimate sensitivity for ICG detection in biological tissue is 8×10−12 to 3×10−11 M when accounting for the autofluorescence signal.ConclusionsTissue autofluorescence is a fundamental factor limiting the sensitivity of ICG FI in the near-infrared range. The developed approach will allow for future optimization of imaging equipment and protocols for ICG and other contrast agents.

Optoacoustic signals behave nonlinearly at light fluences above a few mJ/cm², which may affect the interpretation and quantification of measurements. It has been proposed that optoacoustic nonlinearity arises from the heat-induced formation of nanobubbles or changes in local thermo-physical parameters. However, such explanations are only valid at much higher fluences than typically used in biomedical optoacoustic imaging (> 20 mJ/cm²) or in the presence of materials with high absorption coefficients such as gold nanoparticles. We propose herein that electromagnetic permittivity changes in response to photon absorption are major source of optoacoustic signal nonlinearity at low fluences. We provide theoretical and experimental evidence that supports this postulation and show that optoacoustic pressure responses due to permittivity changes, which are function of thermally excited third-order nonlinear susceptibility, can explain the nonlinear behavior of the optoacoustic signal. Since different materials exhibit different thermally excited third-order nonlinear susceptibility, this property could function as a new contrast mechanism that can identify the sensitivity of a substance’s dielectric constant to photon-induced temperature changes. Consequently, we propose an imaging method based on nonlinear optoacoustic signals that exploits this newly identified contrast mechanism. These findings may have far-reaching implications for improving the accuracy of optoacoustics and utilizing the proposed new contrast mechanism would advance our understanding of cellular and tissue functionality.

: Optical tissue clearing techniques can render large-scale tissue samples and whole organs transparent to apply cell-resolution 3D imaging techniques, revolutionizing the fields of biological research and pathological diagnosis, and providing never-before seen structural information. There are several widely used protocols, but they have shortcomings, both in complexity of use and in suboptimal results in certain tissues and organs. Here, we describe a new method dubbed “White and Clear” (WaC) using 2,2’-thiodiethanol (TDE) as a dynamic Refractive Index (RI)-matching reagent alongside a lipid-extraction and a pigment-bleaching step using dilute hydrogen peroxide (H2O2), to provide ease of use and enhanced clearing effect on all kinds of samples and allow for the clearing parameters to be finely adjusted in order to obtain the best results in each situation.

Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.

Selective Plane Illumination Microscopy (SPIM) has become an emerging technology since its first application for 3D in-vivo imaging of the development of a living organism. An extensive number of works have been published, improving both the speed of acquisition and the resolution of the systems. Furthermore, multispectral imaging allows the effective separation of overlapping signals associated with different fluorophores from the spectrum over the whole field-of-view of the analyzed sample. To eliminate the need of using fluorescent dyes, this technique can also be applied to autofluorescence imaging. However, the effective separation of the overlapped spectra in autofluorescence imaging necessitates the use of mathematical tools. In this work, we explore the application of a method based on Principal Component Analysis (PCA) that enables tissue characterization upon spectral autofluorescence data without the use of fluorophores. Thus, enabling the separation of different tissue types in fixed and living samples with no need of staining techniques. Two procedures are described for acquiring spectral data, including a single excitation based method and a multi-excitation scanning approach. In both cases, we demonstrate the effective separation of various tissue types based on their unique autofluorescence spectra.

Optoacoustic imaging is an emerging technology that holds great promise for improving general clinical practice since it offers a unique combination of optical absorption contrast, high resolution, and high penetration depth. This combination of factors provides a distinct advantage over alternative imaging modalities in oncology, cardiology, dermatology, or endocrinology among other medical fields. Optoacoustic technology affords the medical practitioner the possibility of visualizing light-absorbing biomolecules like water, lipids, oxy/deoxy hemoglobin, or melanin in deep tissue with relatively cheap and small form factor instruments, without the need of contrast agents or ionizing radiation. Moreover, exogenous fluorescent contrast agents like indocyanine green or methylene blue can also be observed. Driven by its potential for new applications in the clinic, the field has experienced intense changes in the last 20 years. Continuous technological advancements in instrumentation, image formation methods, or acquisition geometries, have led to the development of numerous clinical devices also motivating the creation of several startup companies that aim to translate the technology from the lab to the clinic. Thus, the question arises: how far are we from clinical adoption? In this article, we aim to shed light on such question by reviewing the clinical studies that have been performed in the last 10 years. Our intention is to go beyond the classical review format by providing a brief general overview of the steps that new imaging methods undergo on their journey from the lab to clinical adoption. Then we examine the progress that optoacoustic technology has made on such a journey.

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer’s disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load have been predominantly based on macroscopic fluorescence reflectance imaging (FRI) and confocal or two-photon microscopy using Aβ-specific imaging agents. However, the former method lacks depth resolution, whereas the latter is restricted by the limited field of view preventing a full coverage of the large brain region. Here, we utilized a fluorescence molecular tomography (FMT)-magnetic resonance imaging (MRI) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition, whereas a customized software platform enabled the integration of MRI-derived anatomical information as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to those from conventional planar FRI recorded under similar physiological conditions, yielding comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region-of-interest comprising the brain. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI strategy. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in arcAβ mouse models, which can also facilitate monitoring the efficacy of putative treatments targeting Aβ.

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer’s disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load in mouse models of cerebral amyloidosis have been predominantly based on two-dimensional diffuse fluorescence reflectance imaging (2D-FRI) and two-photon microscopy (2PM) using Aβ-specific imaging agents. However, 2D-FRI lacks depth resolution, whereas 2PM is restricted by the limited field of view preventing coverage of large brain regions. Here, we utilized a magnetic resonance imaging (MRI) and fluorescence molecular tomography (FMT) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition in combination with a customized software platform enabling the integration of anatomical information derived from MRI as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to data obtained from conventional 2D-FRI recorded under similar physiological conditions. The two methods yielded comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region of interest comprising the mouse brain. The depth resolution inherent to FMT allowed separation of signal contributions from the scalp and different brain regions, indicating preferential accumulation of the fluorescent tracer in the cerebral cortex, a region characterized by significant plaque deposition in arc Aβ mice. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI method. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in mouse models of cerebral amyloidosis, which is also important for monitoring the efficacy of putative treatments targeting Aβ.

In the processes related to the development of cancer, there are different genetic and epigenetic events involved that result in structural changes of the affected cells. In the early stages of the disease, these changes occur at the nanoscale, remaining undetectable by conventional light microscopy, due to diffraction-limited resolution (∼250 - 550 nm). In this sense, a technique termed partial wave spectroscopy (PWS) allows the detection of these nanostructural changes by measuring a statistical parameter called disorder strength (L _d ). PWS uses a combination of a tunable filter and a camera to acquire the backscattering spectra for each pixel on the image. In this paper, we study and validate the possibility of obtaining a qualitative measurement of the disorder using the spectrum of the averaged spatial information. Instead of using spatial information and measuring sequentially spectral ranges, we measure the backscattered signal gathered by an optical fiber by means of a spectrograph. This will allow this method to be applied in systems where it is not possible to acquire a complete high resolution image for many spectral bands, while significantly enhancing speed.