MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.

Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data.

Multikernel positional embedding convolutional neural network for photoacoustic reconstruction with sparse data.

Photoacoustic imaging (PAI) is an emerging noninvasive imaging modality that merges the high contrast of optical imaging with the high resolution of ultrasonic imaging. Low-quality photoacoustic reconstruction with sparse data due to sparse spatial sampling and limited view detection is a major obstacle to the popularization of PAI for medical applications. Deep learning has been considered as the best solution to this problem in the past decade.

Analytical solutions of coupled-mode equations for multiwaveguide systems, obtained by use of Chebyshev and generalized Chebyshev polynomials.

A novel approach is proposed for obtaining the analytical solutions of the coupled-mode equations (CMEs); the method is applicable for an arbitrary number of coupled waveguides. The mathematical aspects of the CMEs and their solution by use of Chebyshev polynomials are discussed. When mode coupling between only adjacent waveguides is considered (denoted weak coupling), the first and second kinds of the usual Chebyshev polynomials are appropriate for evaluating the CMEs for linearly distributed and circularly distributed multiwaveguide systems, respectively.