# Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning

arXiv: https://arxiv.org/abs/1806.03848 · HTML: https://mlsanity.com/paper/1806.03848/

## Key facts

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

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

- [Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke](https://mlsanity.com/paper/2101.05992/) (2021)
- [Final infarct prediction in acute ischemic stroke](https://mlsanity.com/paper/2211.04850/) (2022)

## Abstract (arXiv)

In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The proposed architecture is trained end-to-end and solely relies on raw perfusion data for inference. We used a dataset of 151 acute ischemic stroke cases for evaluation. Our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy.