Biomedical optics express最新文献

Two-photon vision is a new and developing field in vision science. The phenomenon is based on visual perception of pulsed infrared lasers (800-1300 nm) due to the isomerization of visual pigments caused by two-photon absorption, with color perception corresponding to a wavelength about one-half of the stimulating wavelength in the near-infrared spectral range. Future applications of this effect, both in medical diagnostics and in virtual/augmented reality (VR/AR), require the ability to determine the luminance of the two-photon stimuli. However, the luminous efficiency function V(λ) outside of the visible range is unknown, requiring a non-standard approach to quantifying the luminance of two-photon stimuli. This study proposes a brightness adjustment method to determine the subjective luminance of two-photon infrared stimuli using photometric units. The repeatability of the proposed method with the background on was approximately equal to 407 td, more than twice as good as with the background off. In this report, we present the relationship between the luminance of two-photon stimuli and a physical quantity proposed for the first time: two-photon retinal illuminance. This relationship enables the prediction of stimulus luminance that could achieve nearly 670 cd/m² within the safe range of laser power for the eye.

In this work, we developed a novel microfluidic paper-based analytical device to quantify the blood markers of liver function from human fingertips and whole blood samples. The device can quickly acquire information for screening liver injury and supporting clinical decision-making by simultaneously performing quantitative tests for alanine aminotransferase, aspartate aminotransferase, and albumin. We evaluated the detection accuracy and the storage stability of the device using fingertip samples. The yielded results of our device correlated well with those from Mindray BS350s, even under the conditions of 35 °C and 90%RH. Thus, it offers an effective platform for clinical assessment of liver injury particularly in resource-limited areas.

In this work, broadband diffuse reflectance spectroscopy (DRS) and diffuse correlation spectroscopy (DCS) were used to quantify deep tissue hemodynamics in a patient-derived orthotopic xenograft mouse model of clear cell renal cancer undergoing antiangiogenic treatment. A cohort of twenty-two mice were treated with sunitinib and compared to thirteen control untreated mice, and monitored by DRS/DCS. A reduction in total hemoglobin concentration (THC, p = 0.03), oxygen saturation (SO_2, p = 0.03) and blood flow index (BFI, p = 0.02) was observed over the treatment course. Early changes in tumor microvascular blood flow and total hemoglobin concentration were correlated with the final microvessel density (p = 0.014) and tumor weight (p = 0.024), respectively. Higher pre-treatment tumor microvascular blood flow was observed in non-responder mice with respect to responder mice, which was statistically predictive of the tumor intrinsic resistance (p = 0.01). This hybrid diffuse optical technique provides a method for predicting tumor intrinsic resistance to antiangiogenic therapy and could be used as predictive biomarker of response to antiangiogenic therapies in pre-clinical models.

We propose a new simple and cost-effective optical imaging technique, full-field amplitude speckle decorrelation angiography (FASDA), capable of visualizing skin microvasculature with high resolution, and sensitive to small, superficial vessels with slow blood flow and larger, deeper vessels with faster blood flow. FASDA makes use of a laser source with limited temporal coherence, can be implemented with cameras with conventional frame rates, and does not require raster scanning. The proposed imaging technique is based on the simultaneous evaluation of two metrics: the blood flow index, a contrast-based metric used in laser speckle contrast imaging, and the adaptive speckle decorrelation index (ASDI), a new metric that we defined based on the second-order autocorrelation function that considers the limited speckle modulation that occurs in partially-coherent imaging. We demonstrate excellent delineation of small, superficial vessels with slow blood flow in skin nevi using ASDI and larger, deeper vessels with faster blood flow using BFI, providing a powerful new tool for the imaging of microvasculature with significantly lower hardware complexity and cost than other optical imaging techniques.

Optical coherence tomography (OCT) can be an important tool for non-invasive dermatological evaluation, providing useful data on epidermal integrity for diagnosing skin diseases. Despite its benefits, OCT's utility is limited by the challenges of accurate, fast epidermal segmentation due to the skin morphological diversity. To address this, we introduce a lightweight segmentation network (LS-Net), a novel deep learning model that combines the robust local feature extraction abilities of Convolution Neural Network and the long-term information processing capabilities of Vision Transformer. LS-Net has a depth-wise convolutional transformer for enhanced spatial contextualization and a squeeze-and-excitation block for feature recalibration, ensuring precise segmentation while maintaining computational efficiency. Our network outperforms existing methods, demonstrating high segmentation accuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computational demands (floating point operations: 1.131 G). We further validate LS-Net on our acquired dataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinical conditions. This model promises to enhance the diagnostic capabilities of OCT, making it a valuable tool for dermatological practice.

The development of microrobots for biomedical applications has enabled tasks such as targeted drug delivery, minimally invasive surgeries, and precise diagnostics. However, effective in vivo navigation and control remain challenging due to their small size and complex body environment. Photoacoustic (PA) and ultrasound (US) imaging techniques, which offer high contrast, high resolution, and deep tissue penetration, are integrated to enhance microrobot visualization and tracking. Traditional imaging systems have a narrow effective illumination area, suffer from severe reflection artifacts, and are affected by strong electromagnetic fields. To address this, we present an illumination-adjustable PA and harmonic US imaging system with a customized pushrod mechanism for real-time focus adjustment. Experiments demonstrate high-resolution imaging and accurate microrobot positioning, showcasing the potential for biomedical applications, especially in minimally invasive procedures.

Whole slide imaging provides a wide field-of-view (FOV) across cross-sections of biopsy or surgery samples, significantly facilitating pathological analysis and clinical diagnosis. Such high-quality images that enable detailed visualization of cellular and tissue structures are essential for effective patient care and treatment planning. To obtain such high-quality images for pathology applications, there is a need for scanners with high spatial bandwidth products, free from aberrations, and without the requirement for z-scanning. Here we report a whole slide imaging system based on angular ptychographic imaging with a closed-form solution (WSI-APIC), which offers efficient, tens-of-gigapixels, large-FOV, aberration-free imaging. WSI-APIC utilizes oblique incoherent illumination for initial high-level segmentation, thereby bypassing unnecessary scanning of the background regions and enhancing image acquisition efficiency. A GPU-accelerated APIC algorithm analytically reconstructs phase images with effective digital aberration corrections and improved optical resolutions. Moreover, an auto-stitching technique based on scale-invariant feature transform ensures the seamless concatenation of whole slide phase images. In our experiment, WSI-APIC achieved an optical resolution of 772 nm using a 10×/0.25 NA objective lens and captures 80-gigapixel aberration-free phase images for a standard 76.2 mm × 25.4 mm microscopic slide.

We report the design, development, and characterization of a miniaturized version of the photonic resonator absorption microscope (PRAM Mini), whose cost, size, and functionality are compatible with point-of-care (POC) diagnostic assay applications. Compared to previously reported versions of the PRAM instrument, the PRAM Mini components are integrated within an optical framework comprised of an acrylic breadboard and plastic alignment fixtures. The instrument incorporates a Raspberry Pi microprocessor and Bluetooth communication circuit board for wireless control and data connection to a linked smartphone. PRAM takes advantage of enhanced optical absorption of ∼80 nm diameter gold nanoparticles (AuNP) whose localized surface plasmon resonance overlaps with the ∼625 nm resonant reflection wavelength of a photonic crystal (PC) surface. When illuminated with wide-field low-intensity collimated light from a ∼617 nm wavelength red LED, each AuNP linked to the PC surface results in locally reduced reflection intensity, which is visualized by observing dark spots in the PC-reflected image with an inexpensive CMOS image sensor. Each AuNP in the image field of view can be easily counted with digital resolution. We report upon the selection of optical/electronic components, image processing algorithm, and contrast achieved for single AuNP detection. The instrument is operated via a wireless connection to a linked mobile device using a custom-developed software application that runs on an Android smartphone. As a representative POC application, we used the PRAM Mini as the detection instrument for an assay that measures the presence of antibodies against SARS-CoV-2 infection in cat serum samples, where each dark spot in the image represents a complex between one immobilized viral antigen, one antibody molecule, and one AuNP tag. With dimensions of 23 × 21 × 10 cm³, the PRAM Mini offers a compact detection instrument for POC diagnostics.

Early detection of (pre)malignant esophageal lesions is critical to improve esophageal cancer morbidity and mortality rates. In patients with advanced esophageal adenocarcinoma (EAC) who undergo neoadjuvant chemoradiation therapy, the efficacy of therapy could be optimized and unnecessary surgery prevented by the reliable assessment of residual tumors after therapy. Optical coherence tomography (OCT) provides structural images at a (sub)-cellular level and has the potential to visualize morphological changes in tissue. However, OCT lacks molecular imaging contrast, a feature that enables the study of biological processes at a cellular level and can enhance esophageal cancer diagnostic accuracy. We combined OCT with near-infrared fluorescence molecular imaging using fluorescently labelled antibodies (immuno-OCT). The main goal of this proof of principle study is to investigate the feasibility of immuno-OCT for esophageal cancer imaging. We aim to assess whether the sensitivity of our immuno-OCT device is sufficient to detect the tracer uptake using an imaging dose (∼100 times smaller than a dose with therapeutic effects) of a targeted fluorescent agent. The feasibility of immuno-OCT was demonstrated ex-vivo on dysplastic lesions resected from Barrett's patients and on esophageal specimens resected from patients with advanced EAC, who were respectively topically and intravenously administrated with the tracer bevacizumab-800CW. The detection sensitivity of our system (0.3 nM) is sufficient to detect increased tracer uptake with micrometer resolution using an imaging dose of labelled antibodies. Moreover, the absence of layered structures that are typical of normal esophageal tissue observed in OCT images of dysplastic/malignant esophageal lesions may further aid their detection. Based on our preliminary results, immuno-OCT could improve the detection of dysplastic esophageal lesions.