Histopathological information is critical to identify diseased region in coronary tissues, with great potential to guide the treatment of coronary artery disease. We develop a pathology-aware generative adversarial network (GAN) to generate virtual histology images from coronary optical coherence tomography (OCT) images. The proposed network integrates transformer network structure with a cycleGAN framework. Our algorithm advances existing cycleGAN-based method with a lower value of Frechet Inception distance, as demonstrated by a cross-validation experiment from a human coronary dataset. Our work incorporates histopathological visualization into real-time OCT imaging, holding great potential to assist diagnostic and therapeutic applications of cardiovascular diseases.
Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). Image-guided solutions such as Percutaneous Coronary Intervention (PCI) are extensively used during the treatment of CAD. However, unidentified calcified regions within a narrowed artery could impair the outcome of the PCI. Prior to treatments, object detection of the diseased regions is paramount to automatically procure accurate readings on calcifications within the artery. Though deep learning-based object detection methods have been explored in a variety of applications, the quality of predictions can be negatively impacted by overconfident deep learning models, which is not desirable in safety-critical scenarios. In this work, we adopt an object detection model to rapidly draw the calcified region from coronary OCT images using bounding box. We evaluate the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. The calibrated confidence of prediction results in a confidence error of approximately 0.13, suggesting that the confidence calibration on calcification detection could provide a more trustworthy result, which indicates a great potential to assist clinical evaluation of treating the CAD during the imaging-guided procedure.
In previous Monte Carlo (MC) studies of modeling Fourier-domain optical coherence tomography (FD-OCT), the results obtained at single wavelength are often used to reconstruct the image despite of FD-OCT’s broadband nature. Here, we propose a novel image simulator for full-wavelength MC simulation of FD-OCT based on Mie theory, which combines the inverse discrete Fourier transform (IDFT) with a probability distribution-based signal pre-processing to eliminate the excessive noises in image reconstruction via IDFT caused by missing certain wavelength’s signals in some scattering events. Compared with the conventional method, the proposed simulator is more accurate and could better preserve the wavelength-dependent features.
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