Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging

Understand

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.