Bojeballard3573

Several specialized retinal optical coherence tomography (OCT) acquisition and processing methods have been recently developed to allow in vivo probing of light-evoked photoreceptors function, focusing on measurements in individual photoreceptors (rods and cones). Recent OCT investigations in humans and experimental animals have shown that the outer segments in dark-adapted rods and cones elongate in response to the visible optical stimuli that bleach fractions of their visual photopigment. We have previously successfully contributed to these developments by implementing OCT intensity-based "optoretinograms" (ORG), the paradigm of using near-infrared OCT (NIR OCT) to measure bleaching-induced back-scattering and/or elongation changes of photoreceptors in the eye in vivo. In parallel, several groups have successfully implemented phase-based ORGs, mainly in human studies, exploiting changes in the phases of back-scattered light. This allowed more sensitive observations of tiny alterations of photoreceptors strul recovery by adding decorrelation weights. The phase-sensitive ORG signal analysis developed here for mouse retinal raster scanning OCT systems could be in principle extended to clinical retinal raster scanning OCT systems, potentially opening doors for clinically friendly ORG probing.Photoacoustic (PA) computed tomography (PACT) shows great potential in various preclinical and clinical applications. A great number of measurements are the premise that obtains a high-quality image, which implies a low imaging rate or a high system cost. The artifacts or sidelobes could pollute the image if we decrease the number of measured channels or limit the detected view. In this paper, a novel compressed sensing method for PACT using an untrained neural network is proposed, which decreases a half number of the measured channels and recovers enough details. This method uses a neural network to reconstruct without the requirement for any additional learning based on the deep image prior. The model can reconstruct the image only using a few detections with gradient descent. As an unlearned strategy, our method can cooperate with other existing regularization, and further improve the quality. In addition, we introduce a shape prior to easily converge the model to the image. We verify the feasibility of untrained network-based compressed sensing in PA image reconstruction and compare this method with a conventional method using total variation minimization. The experimental results show that our proposed method outperforms 32.72% (SSIM) with the traditional compressed sensing method in the same regularization. It could dramatically reduce the requirement for the number of transducers, by sparsely sampling the raw PA data, and improve the quality of PA image significantly.Henoch-Schönlein purpura (HSP) is a typical cutaneous immune skin disease, usually diagnosed by invasive biopsy. In this study, we develop a noninvasive optical method by combining in vivo optical clearing, confocal microscopy and immune-staining together to present the real-time in vivo dynamics of blood vessels, IgA molecules, and T cells in a HSP rat model. The small vessels in the skin are found with acute damage and then hyperplasia, which enhances deposition of IgA complexes in blood vessels. The migrating T cells in blood vessels in HSP regions can be detected by setting fast line scanning in this method. Our method provides in vivo vascular, cellular, and molecular dynamics during HSP development and is thus of great potential in research and diagnosis of HSP and other skin diseases.Fluorescent molecular tomography (FMT) is a highly sensitive and noninvasive imaging approach for providing three-dimensional distribution of fluorescent marker probes. However, owing to its light scattering effect and the ill-posedness of inverse problems, it is challenging to develop an efficient reconstruction algorithm that can achieve the exact location and morphology of the fluorescence source. In this study, therefore, in order to satisfy the need for early tumor detection and improve the sparsity of solution, we proposed a novel L 1-L 2 norm regularization via the forward-backward splitting method for enhancing the FMT reconstruction accuracy and the robustness. By fully considering the highly coherent nature of the system matrix of FMT, it operates by splitting the objective to be minimized into simpler functions, which are dealt with individually to obtain a sparser solution. An analytic solution of L 1-L 2 norm proximal operators and a forward-backward splitting algorithm were employed to efficiently solve the nonconvex L 1-L 2 norm minimization problem. Numerical simulations and an in-vivo glioma mouse model experiment were conducted to evaluate the performance of our algorithm. The comparative results of these experiments demonstrated that the proposed algorithm obtained superior reconstruction performance in terms of spatial location, dual-source resolution, and in-vivo practicability. It was believed that this study would promote the preclinical and clinical applications of FMT in early tumor detection.A method for the continuous detection of heart rate (HR) in signals acquired from patients using a sensor mat comprising a nine-element array of fiber Bragg gratings during routine magnetic resonance imaging (MRI) procedures is proposed. The method is based on a deep learning neural network model, which learned from signals acquired from 153 MRI patients. In addition, signals from 343 MRI patients were used for result verification. The proposed method provides automatic continuous extraction of HR with the root mean square error of 2.67 bpm, and the limits of agreement were -4.98-5.45 bpm relative to the reference HR.We present a shot-noise limited SRS implementation providing a >200 mW per excitation wavelength that is optimized for addressing two molecular vibrations simultaneously. As the key to producing a 3 ps laser of different colors out of a single fs-laser (15 nm FWHM), we use ultra-steep angle-tunable optical filters to extract 2 narrow-band Stokes laser beams (1-2 nm & 1-2 ps), which are separated by 100 cm-1. The center part of the fs-laser is frequency doubled to pump an optical parametric oscillator (OPO). The temporal width of the OPO's output (1 ps) is matched to the Stokes beams and can be tuned from 650-980 nm to address simultaneously two Raman shifts separated by 100 cm-1 that are located between 500 cm-1 and 5000 cm-1. We demonstrate background-free SRS imaging of C-D labeled biological samples (bacteria and Drosophila). Furthermore, high quality virtual stimulated Raman histology imaging of a brain adenocarcinoma is shown for pixel dwell times of 16 µs.Otitis media (OM) is one of the most common ear diseases in children and a common reason for outpatient visits to medical doctors in primary care practices. Adhesive OM (AdOM) is recognized as a sequela of OM with effusion (OME) and often requires surgical intervention. OME and AdOM exhibit similar symptoms, and it is difficult to distinguish between them using a conventional otoscope in a primary care unit. The accuracy of the diagnosis is highly dependent on the experience of the examiner. The development of an advanced otoscope with less variation in diagnostic accuracy by the examiner is crucial for a more accurate diagnosis. Thus, we developed an intelligent smartphone-based multimode imaging otoscope for better diagnosis of OM, even in mobile environments. The system offers spectral and autofluorescence imaging of the tympanic membrane using a smartphone attached to the developed multimode imaging module. Moreover, it is capable of intelligent analysis for distinguishing between normal, OME, and AdOM ears using a machine learning algorithm. Using the developed system, we examined the ears of 69 patients to assess their performance for distinguishing between normal, OME, and AdOM ears. find more In the classification of ear diseases, the multimode system based on machine learning analysis performed better in terms of accuracy and F1 scores than single RGB image analysis, RGB/fluorescence image analysis, and the analysis of spectral image cubes only, respectively. These results demonstrate that the intelligent multimode diagnostic capability of an otoscope would be beneficial for better diagnosis and management of OM.Cataract is the most common cause of preventable blindness and vision loss where the only treatment is surgical replacement of the natural lens with an intraocular lens. Computer-generated holography (CGH) enables to control phase, size, and shape of the light beam entering through the eye-pupil. We developed a holographic vision simulator to assess visual acuity for patients to experience the postoperative corrected vision before going through surgery. A holographically shaped light beam is directed onto the retina using small non-cataractous regions of the lens with the help of a pupil tracker. A Snellen chart hologram is shown to subjects at desired depth with myopia and hyperopia correction. Tests with 13 patients demonstrated substantial improvements in visual acuity and the simulator results are consistent with the post-operative vision tests. Holographic simulator overperforms the existing vision simulators, which are limited to static pinhole exit pupils and incapable of correcting aberrations.Blood pressure (BP) responds instantly to the body's conditions, such as movements, diseases or infections, and sudden excitation. Therefore, BP monitoring is a standard clinical measurement and is considered one of the fundamental health signs that assist in predicting and diagnosing several cardiovascular diseases. The traditional BP techniques (i.e. the cuff-based methods) only provide intermittent measurements over a certain period. Additionally, they cause turbulence in the blood flow, impeding the continuous BP monitoring, especially in emergency cases. In this study, an instrumentation system is designed to estimate BP noninvasively by measuring the PPG signal utilizing the optical technique. The photoplethysmogram (PPG) signals were measured and processed for ≈ 450 cases with different clinical conditions and irrespective of their health condition. A total of 13 features of the PPG signal were used to estimate the systolic and diastolic blood pressure (SBP and DBP), utilizing several machine learning achycardia, bradycardia, and atrial fibrillation (as the early detection can be a critical issue).The performance of structured illumination microscopy (SIM) systems depends on the computational method used to process the raw data. In this paper, we present a regularized three-dimensional (3D) model-based (MB) restoration method with positivity constraint (PC) for 3D processing of data from 3D-SIM (or 3-beam interference SIM), in which the structured illumination pattern varies laterally and axially. The proposed 3D-MBPC method introduces positivity in the solution through the reconstruction of an auxiliary function using a conjugate-gradient method that minimizes the mean squared error between the data and the 3D imaging model. The 3D-MBPC method provides axial super resolution, which is not the same as improved optical sectioning demonstrated with model-based approaches based on the 2D-SIM (or 2-beam interference SIM) imaging model, for either 2D or 3D processing of a single plane from a 3D-SIM dataset. Results obtained with our 3D-MBPC method show improved 3D resolution over what is achieved by the standard generalized Wiener filter method, the first known method that performs 3D processing of 3D-SIM data.