Learned iterative methods based on gradient descent, Gauss-Newton, and Quasi-Newton updates are applied to quantitative photoacoustic tomography, showing improved generalization on simulated and digital twin data with scarce training data and modeling errors.
Quantitative photoacoustic blood oxygenation imaging using deep residual and recurrent neural network,
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Towards robust quantitative photoacoustic tomography via learned iterative methods
Learned iterative methods based on gradient descent, Gauss-Newton, and Quasi-Newton updates are applied to quantitative photoacoustic tomography, showing improved generalization on simulated and digital twin data with scarce training data and modeling errors.