Biomedical optics express最新文献

Spectral photoacoustic imaging (sPAI) has the potential to detect clinically relevant measurements of blood oxygenation, non-invasively in real time. The unique advantage of sPAI is its ability to probe optical chromophores at extended imaging depths compared to purely optical modalities. PA imaging has previously been demonstrated using benchtop nanosecond pulsed solid-state lasers; however, their size and high cost limit the potential for clinical translation. Microfabricated pulsed laser diodes (PLDs) are miniaturized laser sources that can be fabricated and integrated at relatively low cost; however, microfabricated PLDs have been minimally investigated for the generation of photoacoustic signals due to the significantly lower energies they typically provide. In this work, we integrated an array of PLD diodes with optical excitation in the second near-infrared window, demonstrating deep imaging depths that exceed the imaging depths expected based on fluence alone. Using five PLDs integrated in series at a fluence of 61.5 µJ/cm², a maximum imaging depth of 4.26 cm was achieved with a detectable CNR in a porcine tissue phantom. Additionally, spectrally unmixed hemoglobin oxygenation measurements were accurate up to a depth of 3.11 cm with ∼10% accuracy. We validated device performance using an in vivo rat model, demonstrating a detectable SNR up to a depth of 4.13 cm and accurate tissue oxygenation assessment up to 3.85 cm from the kidney under layers of porcine tissue. Our device demonstrates the feasibility of microfabricated PLDs for clinically relevant photoacoustic imaging depths.

Transcranial photobiomodulation (tPBM) shows promise in delivering beneficial effects to the brain. However, accurately estimating the stimulus energy reaching the targeted brain region remains difficult due to individual differences in anatomy and optical properties. We present a noninvasive method that combines diffuse reflectance spectroscopy with deep learning to predict the fluence rate of the stimulus light. Incorporating tissue layer thicknesses into the model significantly enhances prediction accuracy, reducing errors to approximately 13%, compared to 49% when assuming a constant irradiance at the scalp surface. By eliminating the need for expensive magnetic resonance imaging, our approach offers a scalable solution for optimizing irradiation parameters in future tPBM applications.

The differential diagnosis of uveitis is broad and often challenging. A key indicator of intraocular inflammation is the presence of cells in the aqueous or vitreous humor. We developed a lightweight convolutional neural network (CNN) model capable of classifying intraocular inflammatory cell types using ultrahigh-resolution optical coherence tomography (OCT). The model was trained and optimized using OCT images of known cell types-mononuclear cells and granulocytes-and achieved an accuracy of 88.4 ± 0.6%, with an area under the receiver operating characteristic (ROC) curve of 94.3 ± 0.4%. The inflammatory cell compositions predicted by the model were consistent with clinical diagnoses in uveitis patients. This approach offers a promising tool for the diagnosis and monitoring of intraocular inflammation in uveitis.

Optical monitoring of cardiac pulsations using near-infrared spectroscopy (NIRS), photoplethysmography (PPG), and diffuse correlation spectroscopy (DCS) is often hindered by motion artifacts and noise. We introduce a synthetic-data-driven framework using a long short-term memory (LSTM) network to trace and denoise pulsatile optical waveforms without reliance on annotated clinical datasets. Physiologically realistic pulsatile signals are generated, corrupted with parameterized artifacts, and used to train the LSTM model. Applied to experimental NIRS, PPG, and DCS signals, the model recovered beat-to-beat morphology more effectively than widely used wavelet and temporal derivative distribution repair (TDDR) filters. Heart rate (HR) extraction from LSTM-processed signals closely matched ECG-derived measurements (mean absolute error = 0.59 bpm, root mean square error = 0.74 bpm). This flexible approach shows potential for rapid adaptation across various devices and noise conditions.

Fluorescence lifetime imaging (FLIm) can detect macroscopic tumor tissue in various organs by measuring tissue autofluorescence, making it a compelling tool for surgical guidance. However, the fluorescence lifetime characteristics of tissue autofluorescence are complex due to the unpredictable microenvironment of the biomolecules in tissue, which complicates data interpretation. Nevertheless, the phasor analysis method is computationally fast and easily interpretable, making it appealing for clinical applications of FLIm. While many implementations of the phasor analysis operate only at a single frequency or a few harmonic frequencies, the phasor theory applied to pulse sampling FLIm as presented in this study leverages the maximum amount of frequency information, thereby extending the set of features available for tissue characterization. The clinical effectiveness of utilizing the maximum range of frequencies in phasor theory applied to pulse-sampling FLIm is demonstrated by investigating tumor detection in ex vivo tissue from 12 patients with prostate cancer. By accounting for the zonal anatomy of the prostate, it is shown that the degree of separability between healthy and tumor tissue is a function of frequency, and hence, the ability to access arbitrary frequency content can improve tumor detection in clinical guidance.

Dynamic optical coherence tomography (DOCT) enhances conventional OCT by providing specific information related to flow dynamics, cell motility, and organelle metabolic activity. These biological phenomena can be detected with varying sensitivity depending on the OCT architecture parameters, including wavelength, numerical aperture, and implementation method (time domain or Fourier domain). Despite its potential, the field lacks standardization as various research groups have independently developed algorithms for specific applications. In this paper, we compare four widely used DOCT algorithms, each employing a distinct analytical approach: power spectral density moment analysis, frequency band visualization, logarithmic intensity variation evaluation, and motility-based analysis. These algorithms were originally optimized for different OCT technologies (full-field OCT, microscopic OCT, swept-source OCT, and spectral domain OCT), which vary in temporal and spatial resolution as well as susceptibility to motion artifacts. To conduct a fair evaluation, we perform comprehensive cross-wise comparisons using datasets acquired from each of these setups. Our findings reveal that each method exhibits unique advantages in specific imaging environments, thereby providing valuable guidance for algorithm selection based on particular application requirements.

Adaptive optics (AO) ophthalmoscopes allow high-resolution imaging of retinal structure and function at the cellular level. Due to their high magnification and small field-of-view (FOV), these systems require precise fixation and light delivery to control the retinal region being imaged and stimulated. We present a high-efficiency fixation and stimulus channel for AO ophthalmoscopy, offering an extended working distance, wide steering range, and broad dioptric correction. For stimulation, the channel delivers intense, near-monochromatic light flashes across much of the visible spectrum. Our design uses all stock components, except for a 3D-printed conic mount and a few machined parts. We balance key system trade-offs and demonstrate design performance through several AO optical coherence tomography (AO-OCT) structural and functional imaging examples. Although originally developed for the Indiana AO-OCT system, these design principles can be readily applied to other AO ophthalmoscopic platforms.

Full-field optical coherence tomography (FFOCT) has recently regained attention thanks to the development of high-resolution dynamic OCT and cross-talk-free swept source FFOCT. However, the choice of wavelength and axial resolution is often a limiting factor with few existing commercial solutions. Here, we developed a novel method to provide rapid spectral shaping for FFOCT imaging. Combining a supercontinuum laser, a fast controllable acousto-optic tunable filter (AOTF), and a multimode fiber with passive and active mode mixing, we obtained an extremely flexible light source compatible with FFOCT. By tuning the AOTF frequency and integrating the resulting wavelength over one camera exposure time, it becomes possible to build any spectrum of interest in the 575-1000 nm range in time domain FFOCT. Alternatively, the designed source module enables achieving swept source FFOCT at up to 100 kfps at an unprecedented axial resolution of 1.1 μm.

Surgical resection remains a key curative option for cancer, with minimally invasive approaches increasingly adopted. To enhance intraoperative visualization, we developed a laparoscopic near-infrared hyperspectral imaging (NIR-HSI) system comprising a custom laparoscope, supercontinuum light source, and acousto-optic tunable filter. Ex vivo NIR-HSI of porcine arteries, mesentery, and nerves revealed distinct spectral signatures from 1000-1402 nm. Pixel-based classification via neural networks achieved >99% accuracy, sensitivity, and specificity in most cases. In vivo imaging of a living pig enabled identification of exposed nerves (88.4% accuracy, 68.7% recall) and unexposed arteries (83.2% accuracy, 60.2% recall). These results demonstrate that laparoscopic NIR-HSI can differentiate tissues with similar coloration and detect structures embedded beneath the surface, offering potential for safer minimally invasive surgeries.

Visualizing and monitoring morphological and functional information of microvasculature, including arterioles and venules, plays a crucial role in assessing vascular-related diseases. Clinical angiography methods have limitations in observing small peripheral microvessels down to 100 μm. Here, this study achieved three-dimensional (3D) non-invasive imaging of the subcutaneous microvascular network and monitored hemodynamic change by using an ultrasound (US)/photoacoustic (PA) dual-modality imaging system. Not only were the microvasculature and subcutaneous tissues in the extremities visualized with high resolution, but also the quantitative oxygen saturation (sO₂) of microvessels was measured. To monitor the hemodynamic change in microvasculature, vascular occlusion was performed to simulate vascular-related disease, and successfully measured multiple parameters, such as average PA amplitude, oxygenated hemoglobin (HbO₂), deoxygenated hemoglobin (Hb), and sO₂, during normal perfusion, vascular occlusion, and reperfusion processes, respectively. Based on the preliminary results, the high-frequency 3D US/PA dual-modality imaging shows great potential in early diagnosis and therapeutic monitoring of microvascular-related diseases.