Graph-based rotational nonuniformity correction for localized compliance measurement in the human nasopharynx

Learning-based distortion correction enables proximal-scanning endoscopic OCT elastography

Proximal rotary scanning is predominantly used in the clinical practice of endoscopic and intravascular OCT, mainly because of the much lower manufacturing cost of the probe compared to distal scanning. However, proximal scanning causes severe beam stability issues (also known as non-uniform rotational distortion, NURD), which hinders the extension of its applications to functional imaging, such as OCT elastography (OCE). In this work, we demonstrate the abilities of learning-based NURD correction methods to enable the imaging stability required for intensity-based OCE. Compared with the previous learning-based NURD correction methods that use pseudo distortion vectors for model training, we propose a method to extract real distortion vectors from a specific endoscopic OCT system, and validate its superiority in accuracy under both convolutional-neural-network- and transformer-based learning architectures. We further verify its effectiveness in elastography calculations (digital image correlation and optical flow) and the advantages of our method over other NURD correction methods. Using the air pressure of a balloon catheter as a mechanical stimulus, our proximal-scanning endoscopic OCE could effectively differentiate between areas of varying stiffness of atherosclerotic vascular phantoms. Compared with the existing endoscopic OCE methods that measure only in the radial direction, our method could achieve 2D displacement/strain distribution in both radial and circumferential directions.

Cross-attention learning enables real-time nonuniform rotational distortion correction in OCT

Nonuniform rotational distortion (NURD) correction is vital for endoscopic optical coherence tomography (OCT) imaging and its functional extensions, such as angiography and elastography. Current NURD correction methods require time-consuming feature tracking/registration or cross-correlation calculations and thus sacrifice temporal resolution. Here we propose a cross-attention learning method for the NURD correction in OCT. Our method is inspired by the recent success of the self-attention mechanism in natural language processing and computer vision. By leveraging its ability to model long-range dependencies, we can directly obtain the spatial correlation between OCT A-lines at any distance, thus accelerating the NURD correction. We develop an end-to-end stacked cross-attention network and design three types of optimization constraints. We compare our method with two traditional feature-based methods and a CNN-based method on two publicly-available endoscopic OCT datasets. We further verify the NURD correction performance of our method on 3D stent reconstruction using a home-built endoscopic OCT system. Our method achieves a ∼3 × speedup to real time (26 ± 3 fps), and superior correction performance.

Optical coherence tomography evaluation of vaginal epithelial thickness during CO2 laser treatment: A pilot study

Genitourinary syndrome of menopause (GSM) negatively affects more than half of postmenopausal women. Energy-based therapy has been explored as a minimally invasive treatment for GSM; however, its mechanism of action and efficacy is controversial. Here, we report on a pilot imaging study conducted on a small group of menopause patients undergoing laser treatment. Intravaginal optical coherence tomography (OCT) endoscope was used to quantitatively monitor the changes in the vaginal epithelial thickness (VET) during fractional-pixel CO2 laser treatment. Eleven patients with natural menopause and one surgically induced menopause patient were recruited in this clinical study. Following the laser treatment, 6 out of 11 natural menopause patient showed increase in both proximal and distal VET, while two natural menopause patient showed increase in VET in only one side of vaginal tract. Furthermore, the patient group that showed increased VET had thinner baseline VET compared to the patients that showed decrease in VET after laser treatment. These results demonstrate the potential utility of intravaginal OCT endoscope in evaluating the vaginal tissue integrity and tailoring vaginal laser treatment on a per-person basis, with the potential to monitor other treatment procedures.

Multi-scale thermal radiation effects correction via a fast surface fitting with Chebyshev polynomials

In an uncooled infrared imaging system, thermal radiation effects are caused by the heat source from the target or the detection window, which affects the ability of target detection, tracking, and recognition seriously. To address this problem, a multi-scale correction method via a fast surface fitting with Chebyshev polynomials is proposed. A high-precision Chebyshev polynomial surface fitting is introduced into thermal radiation bias field estimation for the first time, to the best of our knowledge. The surface fitting in the gradient domain is added to the thermal radiation effects correction model as a regularization term, which overcomes the ill-posed matrix problem of high-order bivariate polynomials surface fitting, and achieves higher accuracy under the same order. Additionally, a multi-scale iterative strategy and vector representation are adopted to speed up the iterative optimization and surface fitting, respectively. Vector representation greatly reduces the number of basis function calls and achieves fast surface fitting. In addition, split Bregman optimization is used to solve the minimization problem of the correction model, which decomposes the multivariable optimization problem into multiple univariate optimization sub-problems. The experimental results of simulated and real degraded images demonstrate that our proposed method performs favorably against the state of the art in thermal radiation effects correction.