Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

Objective: Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-induced thermoacoustic tomography (MITAT) technique holds potential to do brain imaging. However, transcranial brain imaging based on MITAT is still challenging due to the involved huge heterogeneity in speed of sound and acoustic attenuation of human skull.

A Signal Domain Object Segmentation Method for Ultrasound and Photoacoustic Computed Tomography.

Image segmentation is important in improving the diagnostic capability of ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT), as it can be included in the image reconstruction process to improve image quality and quantification abilities. Segmenting the imaged object out of the background using image domain methods is easily complicated by low contrast, noise, and artifacts in the reconstructed image. Here, we introduce a new signal domain object segmentation method for USCT and PACT which does not require image reconstruction beforehand and is automatic, robust, computationally efficient, accurate, and straightforward.