Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC in which we use the images edge features as data fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L1-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data. Results show that the Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC algorithms. In general, the Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications

Confocal Blue Reflectance Imaging in Type 2 Idiopathic Macular Telangiectasia

PURPOSE. To report the characteristics of confocal blue reflectance imaging in type 2 idiopathic macular telangiectasia (type 2 IMT). METHODS. In a prospective observational cross-sectional study, both eyes of 33 patients with type 2 IMT were examined by means of fundus biomicroscopy, fundus photography, fluorescein angiography, and optical coherence tomography (OCT). Confocal blue reflectance (CBR) imaging was performed using a confocal scanning laser ophthalmoscope (HRA2; Heidelberg Engineering, Heidelberg, Germany). To compare the results derived from different imaging modalities, an analysis was performed using image analysis software (Heidelberg Eye Explorer; Heidelberg Engineering). RESULTS. CBR imaging revealed a parafoveal area of increased reflectance that was slightly larger than the area of hyperfluorescence in late-phase fluorescein angiography. The area usually encompassed an oval parafoveal area, but sectors could be spared. A parafoveal area of increased CBR was detected in 98% of eyes that showed angiographic evidence for type 2 IMT. CONCLUSIONS. CBR imaging is a new, noninvasive, and sensitive method that may contribute to differentiate type 2 IMT from other diseases. Abnormalities of macular pigment distribution and Miiller cell pathology may contribute to the phenomenon of increased CBR and thus the pathophysiology of type 2 IMT

Pupil-driven quantitative differential phase contrast imaging

In this research, we reveal the inborn but hitherto ignored properties of quantitative differential phase contrast (qDPC) imaging: the phase transfer function being an edge detection filter. Inspired by this, we highlighted the duality of qDPC between optics and pattern recognition, and propose a simple and effective qDPC reconstruction algorithm, termed Pupil-Driven qDPC (pd-qDPC), to facilitate the phase reconstruction quality for the family of qDPC-based phase reconstruction algorithms. We formed a new cost function in which modified L0-norm was used to represent the pupil-driven edge sparsity, and the qDPC convolution operator is duplicated in the data fidelity term to achieve automatic background removal. Further, we developed the iterative reweighted soft-threshold algorithms based on split Bregman method to solve this modified L0-norm problem. We tested pd-qDPC on both simulated and experimental data and compare against state-of-the-art (SOTA) methods including L2-norm, total variation regularization (TV-qDPC), isotropic-qDPC, and Retinex qDPC algorithms. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, in which it significantly increases the experimental robustness while maintaining the data fidelity. In general, the pd-qDPC enables the high-quality qDPC reconstruction without any modification of the optical system. It simplifies the system complexity and benefits the qDPC community and beyond including but not limited to cell segmentation and PTF learning based on the edge filtering property

Porcine model for gluteal artery perforator flap: Anatomy and technique

Although flap anatomy is well studied on cadavers and microsurgical techniques are well practiced on rats, still there are few training models for learning the techniques of perforator flap harvesting. The cadaver has no bloodstream, so accuracy of dissection cannot be evaluated and flap viability cannot be verified. Training on humans carries a high risk of flap damage. A living model for perforator flap harvest is needed to learn the technique before starting with its clinical application

Porcine model for deep superior epigastric artery perforator flap harvesting: Anatomy and technique

BACKGROUND Microsurgical training on rats before starting with clinical practice is a well-established routine. Animal model training is less widespread for perforator flaps, although these flaps represent a technical challenge. Unlike other flaps, they require specific technical skills that need to be adequately trained on a living model 1 : a cadaver is not enough because no bleeding, vessel damage, or vasospasm can be simulated. 2 The purpose of this study was to assess the suitability of the porcine abdomen as a training model for the deep inferior epigastric artery perforator (DIEAP) flap, commonly used in human breast reconstruction. METHODS A female swine (Sus scrofa domesticus, ssp; weight 25kg) was used. The procedure was performed with the pig under general anesthesia and in the supine position. A deep superior epigastric artery perforator (DSEAP) flap was harvested on the left side of the abdomen, including the 3 cranial nipples and stopping in the midline to spare the contralateral flap for another dissection (as in bilateral breast reconstructions in humans; Fig. 1). All steps of a DIEAP harvest were simulated: superficial vein harvest, suprafascial perforator dissection, intramuscular perforator harvest with preservation of the nerves, and flap isolation. Observation of capillary refill was used to confirm flap viability at the end of the dissection. The procedure was recorded by means of a GoPro camera and simultaneously with a head mounted (4 7 magnification) Loupecam system. Photographs were taken using 2 cameras during surgery at relevant time points. RESULTS At the end of the dissection, the flap was viable. The subcutaneous adipose tissue of the pig is less represented than in human and pigs have an additional muscular layer, the panniculus carnosus, which is the analogue of the human Scarpa's fascia. The rectus fascia is thinner. The perforators are lined in 2 rows: 1 lateral and 1 medial, as in the DIEAP, and the intercostal nerves cross the vessels, as happens in humans. The porcine rectus abdominis muscle is thinner than the human one, but vessels' branching faithfully reproduces the human model. 1 We identified 5 perforating vessels of more than 1mm in diameter (2 lateral and 3 medial). We isolated a lateral perforator first and a medial one last: the latter was eventually used to nourish the flap (Fig. 2). CONCLUSIONS The DSEAP flap allows one to closely reproduce all the steps of DIEAP flap harvesting and also to carry out the intramuscular dissection of 2 perforators for each side (up to 4 for each animal), confirming the adequacy of this pig model for microsurgical training. The deep superior epigastric artery is dominant in pigs. 3 Despite this anatomical difference, the DSEAP allows one to reproduce the main steps of DIEAP flap harvesting, providing an excellent training model. Moreover, the presence of double perforating rows allows simulating the dissection twice on each side

Effect of combined water drinking test and dark room provocative testing in Caucasian eyes with narrow angles

Purpose: To assess the usefulness of water drinking test and dark room provocative testing (WDT + DRPT) in current clinical practice by evaluating input parameters from Swept-source Optical Coherence Tomography (SS-OCT) images, and to determine if clinical factors like axial length, central endothelial cell count (CECC) and retinal nerve fibre layer thickness (RNFL) thickness are associated with a positive WDT + DRPT. Methods: SS-OCT examination was performed in consecutive subjects presenting as new patients in the outpatient clinic aged > 40 years. If at least one eye met the inclusion criteria (anterior chamber angles <20° and anterior chamber depth < 2.5 mm on SS-OCT), subjects were included in this study and WDT + DRPT was carried out. The eye with the smallest angle was analysed. The difference in parameters between eyes with a positive (≥8 mmHg) and negative (<8 mmHg) increase in intraocular pressure (IOP) after WDT + DRPT were statistically analysed. Second, the correlation between IOP increase after WDT + DRPT and anterior chamber angle parameters (RNFL thickness, CECC and axial length) was studied. Results: A total of 95 subjects with a mean age of 64 years were included. There was an association between IOP increase after WDT + DRPT and anterior chamber angle characteristics, however this was not of clinical significance. No positive results after WDT + DRPT were found in patients with anterior chamber angles ≥ 20°. Conclusions: The present findings indicate that this combined provocative test has no definite correlative or predictive value in angle closure disease. Further, the test is not useful in predicting early diagnosis or possible CECC or RNFL loss

Lipoprotein Changes Following Consumption of Lutein-enriched Eggs are Associated with Enhanced Lutein Bioavailability

Abstract Lutein is concentrated in the retina and since it cannot be synthesized by the human body, its uptake depends on nutritional intake. Lutein-enriched eggs are a good lutein source, but whether changes in lipoprotein status following lutein-enriched egg consumption may affect an individual&apos;s lutein response is not yet clear. Data from three intervention trials with lutein-enriched eggs or products made from the enriched egg yolks were combined (n=294) and analyzed to investigate the dynamics of the lutein response in relation to lipoprotein levels. Cross sectional correlation was tested at baseline between lutein and lipoprotein profiles in all participants. Subsequently two groups were selected from the combined database whereby individuals receiving lutein-enriched egg yolks (n=137) were compared with controls not receiving eggs (n=117). Significant correlations between blood lutein concentrations and total cholesterol (r=0.309; p&lt;0.001), HDL-C (r=0.246; p&lt;0.001), LDL-C (r=0.241; p&lt;0.001), ApoA1 (r=0.301; p&lt;0.001), and ApoB100 (r=0.199; p&lt;0.005) concentrations, but not with serum triglycerides were found at baseline. Following a three to twelve month intervention, blood lutein concentrations increased from 238 to 463 ng/ml (p&lt;0.001) in the lutein group, whereas levels in controls remained unchanged. The lutein increase in the lutein-enriched egg group correlated significantly with changes in total cholesterol, HDL-C, LDL-C, ApoA1 and ApoB100 concentrations. To conclude, individuals showing the largest lipoprotein increase following egg consumption were also those with the strongest increase in blood lutein concentration. This indicates that therapies directed at altering lipoprotein levels may indirectly affect lutein bioavailability

Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study

Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [$\pm$0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [$\pm$0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6 pages, 1 figur