Coordinate-based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography

TL;DR

This paper presents a fast, self-supervised method for jointly reconstructing the speed of sound and high-quality images in photoacoustic computed tomography, improving accuracy and speed over existing techniques.

Contribution

It introduces a semi-blind inverse reconstruction approach using neural fields and differentiable models, significantly enhancing SOS correction efficiency in PACT imaging.

Findings

Removes SOS aberrations more accurately

Operates 35 times faster than state-of-the-art methods

Validated on simulation and real in vivo data

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 for ring array PACT systems. To solve this semi-blind inverse problem, we parametrize the SOS using either a pixel grid or a neural field (NF) and update it directly by backpropagating the gradients through a differentiable imaging forward model. 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. Our code and synthetic numerical phantoms are available on our project page: https://lukeli0425.github.io/Coord-SoS-PACT/.