Wavelet denoising during optical coherence tomography of the prostate nerves using the complex wavelet transform

Area of Science:

• Biomedical Engineering
• Surgical Technology
• Medical Imaging

Background:

• Preservation of cavernous nerves during prostate cancer surgery is crucial for maintaining post-operative sexual function.
• Optical coherence tomography (OCT) is an emerging imaging modality for visualizing prostate nerves.
• Reducing noise in OCT images is essential for accurate nerve assessment.

Purpose of the Study:

• To evaluate the effectiveness of wavelet denoising techniques for improving OCT image quality of prostate nerves.
• To compare the performance of discrete wavelet transform (DWT) and complex dual-tree wavelet transform (CDTWT) for speckle noise reduction in rat prostate OCT images.

Main Methods:

• Implementation of DWT and CDTWT for wavelet shrinkage denoising.
• Application of denoising techniques to OCT images of rat prostate nerves.
• Quantitative analysis of image quality metrics and signal-to-noise ratio (SNR) before and after denoising.

Main Results:

• The CDTWT method demonstrated superior performance in reducing speckle noise compared to DWT.
• Significant improvements in image quality metrics for cavernous nerves were observed.
• A notable increase in the signal-to-noise ratio (SNR) was achieved using the CDTWT denoising technique.

Conclusions:

• Complex dual-tree wavelet transform denoising is an effective method for enhancing the quality of OCT images of prostate nerves.
• Improved OCT image clarity can aid in nerve-sparing prostate surgery, potentially improving outcomes for sexual function preservation.