Abstract
A major challenge of transcranial human brain photoacoustic computed tomography (PACT) is correcting for the acoustic aberration induced by the skull. Here, we present a modified universal back-projection (UBP) method, termed layered UBP (L-UBP), that can de-aberrate the transcranial PA signals by accommodating the skull heterogeneity into conventional UBP. In L-UBP, the acoustic medium is divided into multiple layers: the acoustic coupling fluid layer between the skull and detectors, the skull layer, and the brain tissue layer, which are assigned different acoustic properties. The transmission coefficients and wave conversion are considered at the fluid–skull and skull–tissue interfaces. Simulations of transcranial PACT using L-UBP were conducted to validate the method. Ex vivo experiments with a newly developed three-dimensional PACT system with 1-MHz center frequency demonstrated that L-UBP can substantially improve the image quality compared to conventional UBP.
Highlights
The brain is central to human characteristics and behaviors such as cognition, motion control, and language [1]
The two-way skull-induced acoustic aberration remains an obstacle for its translation to transcranial imaging of human adults
We present a modified universal back-projection (UBP) algorithm, termed layered UBP (L-UBP), to correct for the skull-induced acoustic aberration
Summary
The brain is central to human characteristics and behaviors such as cognition, motion control, and language [1]. Several attempts towards transcranial PACT or thermoacoustic tomography (TAT) have been reported, where the im ages were either optimized by tuning the speed of sound (SOS) in a simplified homogeneous medium or reconstructed using a heteroge neous model, but without considering the wave conversion at the skull boundaries [18,19,20,21,22]. More computationally inten sive and complex methods based on the elastic wave equations have been investigated [23,24,25] These approaches can solve the wave propagation relatively accurately, the computational cost is generally high due to the requirements of solving the entire wave propagation, posing potential challenges in efficiently processing large image stacks.
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