Neural Fields for Adaptive Photoacoustic Computed Tomography

Tianao Li, Manxiu Cui, Cheng Ma, Emma Alexander

Abstract: Photoacoustic computed tomography (PACT) is a non-invasive imaging modality, similar to ultrasound, with wide-ranging medical applications. Conventional PACT images are degraded by wavefront distortion caused by the heterogeneous speed of sound (SOS) in tissue. Accounting for these effects can improve image quality and provide medically useful information, but measuring the SOS directly is burdensome and the existing joint reconstruction method is computationally expensive. Traditional supervised learning techniques are currently inaccessible in this data-starved domain. In this work, we introduce an efficient, self-supervised joint reconstruction method that recovers SOS and high-quality images using a differentiable physics model to solve the semi-blind inverse problem. The SOS, parametrized by either a pixel grid or a neural field (NF), is updated directly by backpropagation. Our method removes SOS aberrations more accurately and 35x faster than the current SOTA. We demonstrate the success of our method quantitatively in simulation and qualitatively on experimentally-collected and in-vivo data.