Optimized Optical Coherence Tomography Imaging With Hough Transform-Based Fixed-Pattern Noise Reduction

Self-supervised speckle noise reduction of optical coherence tomography without clean data

Optical coherence tomography (OCT) is widely used in clinical diagnosis due to its non-invasive, real-time, and high-resolution characteristics. However, the inherent speckle noise seriously degrades the image quality, which might damage the fine structures in OCT, thus affecting the diagnosis results. In recent years, supervised deep learning-based denoising methods have shown excellent denoising ability. To train a deep denoiser, a large number of paired noisy-clean images are required, which is difficult to achieve in clinical practice, since acquiring a speckle-free OCT image requires dozens of repeated scans and image registration. In this research, we propose a self-supervised strategy that helps build a despeckling model by training it to map neighboring pixels in a single noisy OCT image. Adjacent pixel patches are randomly selected from the original OCT image to generate two similar undersampled images, which are respectively used as the input and target images for training a deep neural network. To ensure both the despeckling and the structure-preserving effects, a multi-scale pixel patch sampler and corresponding loss functions are adopted in our practice. Through quantitative evaluation and qualitative visual comparison, we found that the proposed method performs better than state-of-the-art methods regarding despeckling effects and structure preservation. Besides, the proposed method is much easier to train and deploy without the need for clean OCT images, which has great significance in clinical practice.

Noise Reduction of OCT Images Based on the External Patch Prior Guided Internal Clustering and Morphological Analysis

Optical coherence tomography (OCT) is widely used in biomedical imaging. However, noise severely affects diagnosing and identifying diseased tissues on OCT images. Here, a noise reduction method based on the external patch prior guided internal clustering and morphological analysis (E2PGICMA) is developed to remove the noise of OCT images. The external patch prior guided internal clustering algorithm is used to reduce speckle noise. The morphological analysis algorithm is employed to the background for contrast enhancement. OCT images of in vivo normal skin tissues were analyzed to remove noise using the proposed method. The estimated standard deviations of the noise were chosen as different values for evaluating the quantitative metrics. The visual quality improvement includes more textures and fine detail preservation. The denoising effects of different methods were compared. Then, quantitative and qualitative evaluations of this proposed method were conducted. The results demonstrated that the SNR, PSNR, and XCOR were higher than those of the other noise-reduction methods, reaching 15.05 dB, 27.48 dB, and 0.9959, respectively. Furthermore, the presented method’s noise reduction ratio (NRR) reached 0.8999. This proposed method can efficiently remove the background and speckle noise. Improving the proposed noise reduction method would outperform existing state-of-the-art OCT despeckling methods.

Evaluation of a 3.8-µm laser-induced skin injury and their repair with in vivo OCT imaging and noninvasive monitoring

To explore a 3.8-µm laser-induced damage and wound healing effect, we propose using optical coherence tomography (OCT) and a noninvasive monitoring-based in vivo evaluation method to quantitatively and qualitatively analyze the time-dependent biological effect of a 3.8-µm laser. The optical attenuation coefficient (OAC) is computed using a Fourier-domain algorithm. Three-dimensional (3-D) visualization of OCT images has been implemented to visualize the burnt spots. Furthermore, the burnt spots from the 3-D volumetric data was segmented and visualized, and the quantitative parameters of the burnt spots, such as the mean OACs, areas, and volumes, were computed. Then, OCT images and histological sections were analyzed to compare the structural changes. Within a certain radiation range, there is a linear relationship between radiation dose and temperature. Dermoscopic images, OCT images, and histological sections showed that, within a certain dose range, as the radiation doses increased, the cutaneous damage became more serious. One hour after laser radiation, the mean OACs increased and then decreased; the areas of burnt spots always increased and were 0.95 ± 0.07, 1.01 ± 0.06, 1.025 ± 0.07, 0.99 ± 0.07, 0.98 ± 0.07, 1.00 ± 0.07, 0.96 ± 0.05, and 0.98 ± 0.06 mm-1, respectively; the areas were 2.10 ± 0.63, 3.75 ± 1.85, 5.95 ± 1.62, 8.35 ± 0.88, 9.44 ± 1.28, 10.29 ± 0.49, 12.27 ± 0.96, and 13.127 ± 1.90 mm2; and the volumes were 1.54 ± 0.41, 2.86 ± 0.09, 3.73 ± 0.49, 4.14 ± 0.80, 7.21 ± 0.52, 6.77 ± 0.45, 8.36 ± 0.25, and 10.65 ± 0.51 mm3; and 21 days after laser radiation, the volumes were 0.67 ± 0.18, 1.64 ± 0.08, 1.87 ± 0.12, 2.57 ± 0.34, 3.43 ± 0.26, 3.64 ± 0.04, 3.84 ± 0.15, and 4.16 ± 0.53 mm3, respectively. We investigated the time-dependent biological effect of 3.8-µm laser-induced cutaneous damage and wound healing using the quantitative parameters of OCT imaging and noninvasive monitoring. The real-time temperature reflects the photothermal effect during laser radiation of mouse skin. OCT images of burnt spots were segmented to compute the mean OACs, burnt area, and quantitative volumes. This study has the potential for in vivo noninvasive and quantitative clinical evaluation in the future.

Quantitative investigation of laser ablation based on real-time temperature variations and OCT images for laser treatment applications

BACKGROUND AND OBJECTIVE

Lasers are widely employed in clinical applications. In vivo monitoring of real-time information about different-wavelength laser surgeries would provide important surgical feedback for surgeons or clinical therapy instruments. However, the quantitative effect of laser ablation or vaporization still needs to be further explored and investigated. Here, we investigate and quantitatively evaluate the ablation variations and morphological changes of two laser ablation models: point- and sweeping-based models.

METHODS

An infrared thermal imager was used to monitor the temperature variations, and curve fitting was used to build the relationship between the laser radiation duration/sweeping speed and quantitative parameters of the ablated areas. Optical coherence tomography (OCT) images were used to visualize the inner structure and evaluate the depth of the ablated craters. Optical attenuation coefficients (OACs) were computed to characterize the normal and ablated tissues.

RESULTS

The results demonstrated that there was a good linear relationship between radiation duration and temperature variation. Similarly, a linear relationship was observed between the sweeping speed and quantitative parameters of craters or scratches (width and depth). The mean OAC of normal tissues was significantly distinguished from the mean OACs of the ablated craters or scratches.

CONCLUSION

Laser ablation was investigated based on a quantitative parameter analysis, thermal detection, and OCT imaging, and the results successfully demonstrated that there is a linear relationship between the laser parameters and quantitative parameters of the ablated tissues under the current settings. Such technology could be used to provide quantitative solutions for exploring the laser-tissue biological effect and improve the performance of medical image-guided laser ablation in the future.