Scanning laser ophthalmoscopy (SLO) benefits diagnostic imaging and therapeutic guidance by allowing for high-speed imaging of retinal structures. When combined with optical coherence tomography (OCT), SLO enables real-time aiming and retinal tracking and provides complementary information for post-acquisition volumetric co-registration, bulk motion compensation, and averaging. However, multimodality SLO-OCT systems generally require dedicated light sources, scanners, relay optics, detectors, and additional digitization and synchronization electronics, which increase system complexity. Here, we present a multimodal ophthalmic imaging system using swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography (SS-SESLO-OCT) for human retinal imaging. SESLO reduces the complexity of imaging systems by multiplexing spatial positions as a function of wavelength. SESLO image quality benefited from single-mode illumination and multimode collection through a prototype double-clad fiber coupler, which optimized scattered light throughput and reduce speckle contrast while maintaining lateral resolution. Using a shared 1060 nm swept-source, shared scanner and imaging optics, and a shared dual-channel high-speed digitizer, we acquired inherently co-registered retinal images and OCT cross-sections simultaneously at 200 frames-per-second.
Optical coherence tomography (OCT) is the gold standard for quantitative ophthalmic imaging. The majority of commercial and research systems require patients to fixate and be imaged in a seated upright position, which limits the ability to perform ophthalmic imaging in bedridden or pediatric patients. Handheld OCT devices overcome this limitation, but image quality often suffers due to a lack of real-time aiming and patient eye and photographer motion. We describe a handheld spectrally encoded coherence tomography and reflectometry (SECTR) system that enables simultaneous en face reflectance and cross-sectional OCT imaging. The handheld probe utilizes a custom double-pass scan lens for fully telecentric OCT scanning with a compact optomechanical design and a rapid-prototyped enclosure to reduce the overall system size and weight. We also introduce a variable velocity scan waveform that allows for simultaneous acquisition of densely sampled OCT angiography (OCTA) volumes and widefield reflectance images, which enables highresolution vascular imaging with precision motion-tracking for volumetric motion correction and multivolumetric mosaicking. Finally, we demonstrate in vivo human retinal OCT and OCT angiography (OCTA) imaging using handheld SECTR on a healthy volunteer. Clinical translation of handheld SECTR will allow for high-speed, motion-corrected widefield OCT and OCTA imaging in bedridden and pediatric patients who may benefit ophthalmic disease diagnosis and monitoring.
No abstract
Here we aim to evaluate the pulmonary parenchyma from CT scans of the thorax using textural analysis. For each of 34 patients, 3 axial slices were chosen. We split each of the 102 images into grids with block sizes of 4, 8 and 16 pixels and calculated 18 textural features for each block. Using these features and a training set assembled by a radiologist, we train a Support Vector Machine (SVM) to recognise some typical patterns found on the scans and test the accuracy on the training set using cross-validation. Then, larger areas deemed broadly representative of each of the patterns under consideration were labelled on the 102 images and the classification accuracy for each pattern and each block size is presented. Using the classified images, we segment the lung regions using a variation of the normal method. Finally, we fuse the results from the 3 block sizes to form a single image using Naive Bayes and show this matches or improves on the accuracy using each of the individual block sizes alone.
Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential anatomical structures, including blood vessels and tissue layers. Although averaging repeated B-scan frames can significantly improve the signal-to-noise-ratio (SNR), this requires longer acquisition time, which can introduce motion artifacts and cause discomfort to patients. In this study, we propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is created with the aid of the self-fusion method. The pseudo-modality provides good SNR for layers that are barely perceptible in the noisy B-scan but can over-smooth fine features such as small vessels. By using a fusion network, desired features from each modality can be combined, and the weight of their contribution is adjustable. Evaluated by intensity-based and structural metrics, the result shows that our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved. Compared to the single modality network, our method improves the structural similarity with low noise B-scan from 0.559 ± 0.033 to 0.576 ± 0.031.
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