  Photoacoustic imaging (PAI) can image high-resolution structures of clinical
interest such as vascularity in cancerous tumor monitoring. When imaging human
subjects, geometric restrictions force limited-view data retrieval causing
imaging artifacts. Iterative physical model based approaches reduce artifacts
but require prohibitively time consuming PDE solves. Machine learning (ML) has
accelerated PAI by combining physical models and learned networks. However, the
depth and overall power of ML methods is limited by memory intensive training.
We propose using invertible neural networks (INNs) to alleviate memory
pressure. We demonstrate INNs can image 3D photoacoustic volumes in the setting
of limited-view, noisy, and subsampled data. The frugal constant memory usage
of INNs enables us to train an arbitrary depth of learned layers on a consumer
GPU with 16GB RAM.
