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Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging
[article]
2022
arXiv
pre-print
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
arXiv:2204.11850v1
fatcat:glni23525zfppjzsw6liajwjyi