Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Natural Infection of Murraya paniculata and Murraya sumatrana with CLas in Java.

The phloem-limited bacterium, 'Candidatus Liberibacter asiaticus' (CLas), is the putative causal pathogen of the severe Asiatic form of huanglongbing (citrus greening) and is most commonly transmitted by the Asiatic citrus psyllid (ACP), Diaphorina citri. CLas severely affects many Citrus species and hybrids and has been recorded in the Citrus relative, orange jasmine, Murraya paniculata (L.) Jack (syn. M. exotica L.). In this study, 13 accessions of three Murraya species (M. paniculata, M. sumatrana Roxb. and M. lucida (G.Forst.) Mabb,) and the Papuan form of a putative hybrid (M. omphalocarpa Hayata) were identified morphologically and molecularly based on sequence identity of the matK-5'trnK region of the chloroplast genome, and infection on these plants under field conditions was determined by PCR and qPCR on 2-4 occasions over 14 months. CLas was repeatedly detected in leaflet midribs by PCR and qPCR on four and three accessions of M. paniculata and M. sumatrana, respectively. It was not detected in leaflet midribs of single accessions of M. lucida and M. omphalocarpa. The species identification of the CLas-positive accessions was further confirmed using all the molecular taxonomic markers consisting of the six fragments of the maternally inherited chloroplast genome and part of the nuclear-encoded ITS region. The results indicated that natural infection of M. paniculata and M. sumatrana with CLas can occur in Java. This is the first demonstration of the natural infection of M. sumatrana with CLas. Further studies are required to determine if infections persist in the absence of D. citri.

Clinical activity and safety of sintilimab, bevacizumab, and TMZ in patients with recurrent glioblastoma.

PURPOSE: There are limited and no standard therapies for recurrent glioblastoma. We herein report the antitumour activity and safety of sintilimab, bevacizumab and temozolomide (TMZ) in recurrent glioblastoma. METHODS: We retrospectively analysed eight patients with recurrent glioblastoma treated with sintilimab (200 mg) every three weeks + bevacizumab (10 mg/kg) every three weeks + TMZ (200 mg/m²orally) (5 days orally every 28 days for a total of four weeks). The primary objective was investigator-assessed median progression-free survival(mPFS). Secondary objectives were to assess the 6-month PFS, objective response rate (ORR) and duration of response (DOR) accroding to RANO criteria. RESULTS: The mPFS time for 8 patients was 3.340 months (95% CI: 2.217-4.463), The longest PFS was close to 9 months. Five patients were assessed to have achieved partial response (PR), with an overall remission rate of 62.5%, Four patients experienced a change in tumour volume at the best response time of greater than 60% shrinkage from baseline, and one patient remained progression free upon review, with a DOR of more than 6.57 months. The 6-month PFS was 25% (95% CI: 5.0-55.0%). Three patients had a treatment-related adverse events, though no grade 4 or 5 adverse events occurred. CONCLUSION: In this small retrospective study, the combination regimen of sintilimab, bevacizumab and TMZ showed promising antitumour activity in treatment of recurrent glioblastoma, with a good objective remission rate.

Axial super-resolution optical coherence tomography via complex-valued network.

Optical coherence tomography (OCT) is a fast and non-invasive optical interferometric imaging technique that can provide high-resolution cross-sectional images of biological tissues. OCT's key strength is its depth resolving capability which remains invariant along the imaging depth and is determined by the axial resolution. The axial resolution is inversely proportional to the bandwidth of the OCT light source. Thus, the use of broadband light sources can effectively improve the axial resolution and however leads to an increased cost. In recent years, real-valued deep learning technique has been introduced to obtain super-resolution optical imaging. In this study, we proposed a complex-valued super-resolution network (CVSR-Net) to achieve an axial super-resolution for OCT by fully utilizing the amplitude and phase of OCT signal. The method was evaluated on three OCT datasets. The results show that the CVSR-Net outperforms its real-valued counterpart with a better depth resolving capability. Furthermore, comparisons were made between our network, six prevailing real-valued networks and their complex-valued counterparts. The results demonstrate that the complex-valued network exhibited a better super-resolution performance than its real-valued counterpart and our proposed CVSR-Net achieved the best performance. In addition, the CVSR-Net was tested on out-of-distribution domain datasets and its super-resolution performance was well maintained as compared to that on source domain datasets, indicating a good generalization capability.

Loss-balanced parallel decoding network for retinal fluid segmentation in OCT.

As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.

Self-supervised Blind2Unblind deep learning scheme for OCT speckle reductions.

As a low-coherence interferometry-based imaging modality, optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiply scattered photons. Speckles hide tissue microstructures and degrade the accuracy of disease diagnoses, which thus hinder OCT clinical applications. Various methods have been proposed to address such an issue, yet they suffer either from the heavy computational load, or the lack of high-quality clean images prior, or both. In this paper, a novel self-supervised deep learning scheme, namely, Blind2Unblind network with refinement strategy (B2Unet), is proposed for OCT speckle reduction with a single noisy image only. Specifically, the overall B2Unet network architecture is presented first, and then, a global-aware mask mapper together with a loss function are devised to improve image perception and optimize sampled mask mapper blind spots, respectively. To make the blind spots visible to B2Unet, a new re-visible loss is also designed, and its convergence is discussed with the speckle properties being considered. Extensive experiments with different OCT image datasets are finally conducted to compare B2Unet with those state-of-the-art existing methods. Both qualitative and quantitative results convincingly demonstrate that B2Unet outperforms the state-of-the-art model-based and fully supervised deep-learning methods, and it is robust and capable of effectively suppressing speckles while preserving the important tissue micro-structures in OCT images in different cases.

Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images.

Purpose: Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images. Approach: We propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN. Results: Experiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods. Conclusions: Results demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.

Optical coherence tomography for in vivo longitudinal monitoring of artificial dermal scaffold.

OBJECTIVES: Artificial dermal scaffold (ADS) has undergone rapid development and been increasingly used for treating skin wound in clinics due to its good biocompatibility, controllable degradation, and low risk of disease infection. To obtain good treatment efficacy, ADS needs to be monitored longitudinally during the treatment process. For example, scaffold-tissue fit, cell in-growth, vascular regeneration, and scaffold degradation are the key properties to be inspected. However, to date, there are no effective, real-time, and noninvasive techniques to meet the requirement of the scaffold monitoring above. MATERIALS AND METHODS: In this study, we propose to use optical coherence tomography (OCT) to monitor ADS in vivo through three-dimensional imaging. A swept source OCT system with a handheld probe was developed for in vivo skin imaging. Moreover, a cell in-growth, vascular regeneration, and scaffold degradation rate (IRDR) was defined with the volume reduction rate of the scaffold's collagen sponge layer. To measure the IRDR, a semiautomatic image segmentation algorithm was designed based on U-Net to segment the collagen sponge layer of the scaffold from OCT images. RESULTS: The results show that the scaffold-tissue fit can be clearly visualized under OCT imaging. The IRDR can be computed based on the volume of the segmented collagen sponge layer. It is observed that the IRDR appeared to a linear function of the time and in addition, the IRDR varied among different skin parts. CONCLUSION: Overall, it can be concluded that OCT has a good potential to monitor ADS in vivo. This can help guide the clinicians to control the treatment with ADS to improve the therapy.

Continent-wide evidence that landscape context can mediate the effects of local habitats on in-field abundance of pests and natural enemies.

Landscape-scale factors known to influence in-field abundance of pest herbivores and their natural enemies, but little is known about effects that operate through the shorter-range influences exerted by habitats immediately adjacent to crop fields.This study first compared the abundance of brassica insect pests and their natural enemy arthropods in 24 spatially independent brassica vegetable fields across southern Australia. An 'edge effect' index was used to compare the abundance of each taxon in the field center with abundance in areas of the crop adjacent to differing habitats. Then, three landscape properties: landscape composition, edge density, and connectivity of diverse crop and non-crop habitats were analyzed at five scales up to 5 km from these focal field centers to assess longer-range influences on arthropod abundances in field centers and on the edge effects.Edge effect of adjacent woody vegetation promoted ladybirds and reduced diamondback moth and whiteflies. Conversely, the presence of crops and pastures immediately adjacent to focal crop fields reduced whiteflies and aphids but with no effect on natural enemies.Effect of landscape composition and connectivity on arthropod abundance at field center found promotion of aphids (cabbage aphid and green peach aphid) by woodland in the landscape.Effect of landscape properties on the edge effects of adjacent habitats was contrasting; strengthened (landscape composition and edge density on edge effect of crops, pasture and woody vegetation in reducing diamondback moth and whiteflies) as well as weakened (edge density and landscape connectivity on edge effect of crops, pasture and woody vegetation in reducing diamondback moth on diamondback moth, whiteflies and aphids, and promoting ladybirds). Synthesis and applications: Findings of this geographically extensive study help define the level of pest risk associated with sites as well as suggest potential interventions such as establishment or restorations of woody vegetation adjacent to crop fields that could reduce risk.

CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography.

Automatic optical coherence tomography angiography (OCTA) vessel segmentation is of great significance to retinal disease diagnoses. Due to the complex vascular structure, however, various existing factors make the segmentation task challenging. This paper reports a novel end-to-end three-stage channel and position attention (CPA) module integrated graph reasoning convolutional neural network (CGNet) for retinal OCTA vessel segmentation. Specifically, in the coarse stage, both CPA and graph reasoning network (GRN) modules are integrated in between a U-shaped neural network encoder and decoder to acquire vessel confidence maps. After being directed into a fine stage, such confidence maps are concatenated with the original image and the generated fine image map as a 3-channel image to refine retinal micro-vasculatures. Finally, both the fine and refined images are fused at the refining stage as the segmentation results. Experiments with different public datasets are conducted to verify the efficacy of the proposed CGNet. Results show that by employing the end-to-end training scheme and the integrated CPA and GRN modules, CGNet achieves 94.29% and 85.62% in area under the ROC curve (AUC) for the two different datasets, outperforming the state-of-the-art existing methods with both improved operability and reduced complexity in different cases. Code is available at https://github.com/GE-123-cpu/CGnet-for-vessel-segmentation.

Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers.

Objective.Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF's centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction.Approach.We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation.Main results.This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients.Significance.This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.

Compressive-sensing swept-source optical coherence tomography angiography with reduced noise.

Optical coherence tomography angiography (OCTA), as a functional extension of optical coherence tomography (OCT), has exhibited a great potential to aid in clinical diagnostics. Currently, OCTA still suffers from motion artifact and noise. Therefore, in this article, we propose to implement compressive sensing (CS) on B-scans to reduce motion artifact by increasing B-scan rate. Meanwhile, a noise reduction filter is specially designed by combining CS, Gaussian filter and median filter. Specially, CS filtering is realized by averaging multiple CS repetitions on en-face OCTA images with varied sampling functions. The method is evaluated on in vivo OCTA images of human skin. The results show that vasculature structures can be reconstructed well through CS on B-scans with a sampling rate of 70%. Moreover, the noise can be significantly eliminated by the developed filter. This implies that our method has a good potential to expedite OCTA imaging and improve the image quality.

Climate change and Australia's primary industries: factors hampering an effective and coordinated response.

Australia's primary production sector operates in one of the world's most variable climates with future climate change posing a challenge to its ongoing sustainability. Recognising this, Australia has invested in understanding climate change risks to primary production with a substantial amount of research produced. Recently, focus on this research space has broadened, with interests from the financial sector and expanded scopes of works from government and industry. These expanded needs require sector- and country-wide assessments to assist with the implementation of climate strategies. We considered the applicability of the current research body for these needs by reviewing 188 peer-reviewed studies that considered the quantitative impacts of climate change on Australia's primary industries. Our broad review includes cropping, livestock, horticulture, forestry and fisheries and biosecurity threats. This is the first such review for Australia, and no other similar country-wide review was found. We reviewed the studies through three lenses, industry diversity, geographic coverage and study comparability. Our results show that all three areas are lacking for sector- and country-wide assessments. Industry diversity was skewed towards cropping and biosecurity threats (64% of all studies) with wheat in particular a major focus (25% of all studies). Geographic coverage at a state level appeared to be evenly distributed across the country; however, when considered in conjunction with industry focus, gaps emerged. Study comparability was found to be very limited due to the use of different historical baseline periods and different impact models. We make several recommendations to assist with future research directions, being (1) co-development of a standard set of method guidelines for impact assessments, (2) filling industry and geographic knowledge gaps, and (3) improving transparency in study method descriptions. Uptake of these recommendations will improve study application and transparency enabling and enhancing responses to climate change in Australia's primary industries.

Topical and dietary toxicity of emamectin benzoate, chlorantraniliprole, cyantraniliprole and indoxacarb to larvae of the common armyworm Mythimna convecta (Lepidoptera: Noctuidae).

BACKGROUND: The common armyworm Mythimna convecta is an important pest of pastures and graminaceous crops in Australia, but materials currently registered for its control are limited to broad-spectrum compounds incompatible with integrated pest management (IPM) systems. In this study we assessed the response of M. convecta larvae to four alternative compounds using topical and dietary bioassays. RESULTS: Emamectin benzoate [LC50 (lethal concentration for 50% of insects tested) values 2.69 µg mL-1 topical, 0.017 µg active ingredient (AI) g-1 dietary] and chlorantraniliprole (LC50 values 4.87 µg mL-1  topical, 0.080 µg AI g-1 dietary) were significantly more active than either indoxacarb or cyantraniliprole. Our results showed strong parallels with data on the more extensively studied Australian strains of Helicoverpa armigera, with the most notable differences being the higher contact toxicity of emamectin benzoate to M. convecta and the lower acute dietary activity of formulated cyantraniliprole to this species, which was linked to feeding deterrence. Cyantraniliprole at dietary concentrations of ≥0.02 µg AI g-1 significantly reduced the weight of surviving larvae and frass production (an indirect measure of food consumption) over the seven-day exposure period. There was also some evidence of chlorantraniliprole deterring larval feeding, although to a much more limited extent. CONCLUSIONS: Both emamectin benzoate and chlorantraniliprole are suitable for use against M. convecta. The decision as to which of these compounds should be prioritized for further development should be based on their potential effects on beneficial species once their optimal field rates have been determined.

Development of a handheld compression optical coherence elastography probe with a disposable stress sensor.

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). OCE measures a sample's deformation under force stimuli. Compression is often used to generate the force stimuli in OCE. In this Letter, we report the development of a handheld quantitative compression OCE probe with a novel stress senor, dedicated to measuring the force. The stress sensor consists of a circular glass window and a metal ring which are connected with polyurethane spokes. This sensor is mounted on the tip of the OCT sample arm as an imaging window, so that the force applied to the sample through the window can be measured by detecting the window displacement from the OCT image. The force-displacement function was first developed through simulation on COMSOL Multiphysics and eventually calibrated experimentally. A phase-sensitive OCT technique was employed to measure both the window displacement and the sample deformation. The performance of an OCE probe with this stress sensor was evaluated on a two-layer phantom. The results show that it is extremely capable of measuring the sample Young's modulus. Finally, we successfully measured the elasticity of the human fingertip, indicating a good potential of this OCE probe for in vivo elastogram measurement on human skin.

Conservation biological control research is strongly uneven across trophic levels and economic measures.

Conservation biological control suppresses pests by promoting established rather than inoculative or mass released natural enemies. Research in this approach has expanded rapidly this century but uptake remains limited. Why? Most of the 150 peer reviewed papers reporting field experiments include results on natural enemies and/or pests. Only a minority report effects on crop damage levels or yield, and very few consider economic consequences. This is despite evidence for potential benefits across this full spectrum of response variables. We argue that the limited scope of work to date constrains the development of a compelling evidence base to demonstrate the field effectiveness of conservation biological control, hampering its uptake so encourage researchers to include the assessment of economic impact in future studies of conservation biological control. © 2020 Society of Chemical Industry.

Risk factors associated with pulmonary hemorrhage and hemoptysis following percutaneous CT-guided transthoracic lung core needle biopsy: a retrospective study of 1,090 cases.

BACKGROUND: Pulmonary hemorrhage and hemoptysis are the second-most common and potentially life-threatening complications after pneumothorax following percutaneous computed tomography-guided transthoracic lung biopsy (PCTLB). Preventing hemorrhagic complications after PCTLB requires an accurate estimation of risk factors. This study investigated the risk factors associated with pulmonary hemorrhage and hemoptysis following PCTLB, and whether the ratio of main pulmonary artery diameter (mPAD) to ascending aorta diameter (mPAD/AAD ratio) is a risk factor. METHODS: We retrospectively analyzed 1,090 cases of PCTLB obtained from 1,050 patients using a core needle. The risk factors for overall pulmonary hemorrhage, higher-grade pulmonary hemorrhage, and hemoptysis were evaluated by multivariate analysis of patient characteristics, computed tomography (CT) imaging data including pulmonary artery diameter (mPAD) to ascending aorta diameter (mPAD/AAD) ratio, technical variables related to the biopsy, and pathologic findings. RESULTS: Pulmonary hemorrhage occurred in 31.38% (342/1,090) of PCTLB cases, including lower-grade (24.4%, 266/1,090) and higher-grade hemorrhage (6.97%, 76/1,090). The incidence of hemoptysis was 3.03% (33/1,090). Multivariate analysis revealed significant associations between overall pulmonary hemorrhage and lesion location in the lower lobe, subsolid and smaller lesions, greater lesion depth, and lung metastases. For higher-grade pulmonary hemorrhage, an mPAD/AAD ratio >1, smaller lesions, greater lesion depth, emphysema, and lung metastases were risk factors. Risk factors for hemoptysis were history of hypertension and lower- and higher-grade pulmonary hemorrhage. CONCLUSIONS: Pulmonary artery enlargement detected by CT (mPAD/AAD ratio >1) is independently associated with higher-grade pulmonary hemorrhage following PCTLB.

Dual-side view optical coherence tomography for thickness measurement on opaque materials.

Optical coherence tomography (OCT), as an optical interferometric imaging technique, has found wide applications in various fields. In principle, OCT is well suited for imaging layered structures, and thus, one of the typical applications is thickness measurement. However, due to the limited imaging depth resulting from light attenuation, thickness measurement by OCT is limited to non-opaque materials. In this study, we developed a novel (to the best of our knowledge) dual-side view OCT (DSV-OCT) system for thickness measurement on opaque materials. The dual-side view was achieved on a conventional swept source OCT platform by creating two symmetrical sampling arms. This allows us to image both sides of the material simultaneously and produce the surface contours of the two sides in a single C scan. Finally, the thickness of the opaque material can be calculated from the two surface contours above. We demonstrated that our DSV-OCT technique can measure the thickness of opaque material with an accuracy of about 3 µm.

Extending axial focus of optical coherence tomography using parallel multiple aperture synthesis.

Compromising the inherent trade-off between transverse resolution and depth of focus (DOF) remains a long-standing issue in optical coherence tomography (OCT). In this work, we report a novel technique-parallel multiple aperture synthesis (pMAS) to simultaneously generate multiple optical apertures in an OCT sample arm by employing a two-surface coated mirror. In the proposed pMAS, the DOF is extended by a factor of 16.49 without sacrificing the transverse resolution for proof-of-concept experiments when multiple distinctive apertures are digitally synthesized. The microparticles and tissue experiments demonstrate the feasibility of pMAS to address the fundamental problem of limited DOF in high-resolution OCT.

Depth-encoded synthetic aperture optical coherence tomography of biological tissues with extended focal depth.

Optical coherence tomography (OCT) has proven to be able to provide three-dimensional (3D) volumetric images of scattering biological tissues for in vivo medical diagnostics. Unlike conventional optical microscopy, its depth-resolving ability (axial resolution) is exclusively determined by the laser source and therefore invariant over the full imaging depth. In contrast, its transverse resolution is determined by the objective's numerical aperture and the wavelength which is only approximately maintained over twice the Rayleigh range. However, the prevailing laser sources for OCT allow image depths of more than 5 mm which is considerably longer than the Rayleigh range. This limits high transverse resolution imaging with OCT. Previously, we reported a novel method to extend the depth-of-focus (DOF) of OCT imaging in Mo et al.Opt. Express 21, 10048 (2013)]. The approach is to create three different optical apertures via pupil segmentation with an annular phase plate. These three optical apertures produce three OCT images from the same sample, which are encoded to different depth positions in a single OCT B-scan. This allows for correcting the defocus-induced curvature of wave front in the pupil so as to improve the focus. As a consequence, the three images originating from those three optical apertures can be used to reconstruct a new image with an extended DOF. In this study, we successfully applied this method for the first time to both an artificial phantom and biological tissues over a four times larger depth range. The results demonstrate a significant DOF improvement, paving the way for 3D high resolution OCT imaging beyond the conventional Rayleigh range.