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

arXiv: https://arxiv.org/abs/2409.10876 · HTML: https://mlsanity.com/paper/2409.10876/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction](https://mlsanity.com/paper/2108.10991/) (2021)
- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields](https://mlsanity.com/paper/2104.11745/) (2021)
- [Coordinate-based neural representations for computational adaptive optics in widefield microscopy](https://mlsanity.com/paper/2307.03812/) (2023)
- [Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach](https://mlsanity.com/paper/1907.09951/) (2019)
- [Implicit Neural Representation for Sparse-view Photoacoustic Computed Tomography](https://mlsanity.com/paper/2409.13696/) (2024)

## Built on by (most cited first, 2 of 2)

- [ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging](https://mlsanity.com/paper/2606.21099/) (2026)
- [PAGS: Autofocusing Photoacoustic Tomography via Speed-of-Sound-Adaptive Gaussian Splatting](https://mlsanity.com/paper/2608.25472/) (2026)

## Abstract (arXiv)

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/.