No abstract

We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation

During medical investigations of the head, ultrasound measurements can offer information with simple, non-invasive, and real-time procedure. However, for human adult applications, the clinical treatment of transcranial acoustic imaging remains a challenge by the presence of the skull, results in acoustic aberrations caused by two main phenomena, i.e., attenuation and distortion. These aberrations may affect the signal understanding because of the induced artifacts and the inaccuracy of the imaging target structural information. Variations of the physical properties of the skull, its thickness and porosity, will strongly affect the mechanical properties of the medium and thus the acoustic response. We propose a method to understand the influence of these characteristics on the signal degradation. In order to mimic the human adult skull, a large quantity of epoxy resin-based phantoms is created to explore all the possible physical characteristic variation in the bone. Additional components, titanium dioxide and seeds, will be added to the samples to recreate the acoustic scattering effects of a skull bone. Signal features from pulse-echo mode ultrasound, such as signal attenuation or broadening, will be extracted and studied in the time and frequency domain. In this paper, we are looking for relationship between these physical parameters and the signal features, with the objective to determine bone characteristics without any direct access in later experiments; and going a step further into aberration correction during transcranial imaging procedure.