# Perfusion parameter estimation using neural networks and data augmentation

arXiv: https://arxiv.org/abs/1810.04898 · HTML: https://mlsanity.com/paper/1810.04898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

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

- [Differentiable Deconvolution for Improved Stroke Perfusion Analysis](https://mlsanity.com/paper/2103.17111/) (2021)
- [Final infarct prediction in acute ischemic stroke](https://mlsanity.com/paper/2211.04850/) (2022)

## Abstract (arXiv)

Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making. Current perfusion analysis relies on deconvolution of the measured signals, an operation that is mathematically ill-conditioned and requires strong regularization. We propose a neural network and a data augmentation approach to predict perfusion parameters directly from the native measurements. A comparison on simulated CT Perfusion data shows that the neural network provides better estimations for both CBF and Tmax than a state of the art deconvolution method, and this over a wide range of noise levels. The proposed data augmentation enables to achieve these results with less than 100 datasets.