# Detection of the Arterial Input Function Using DSC-MRI Data

arXiv: https://arxiv.org/abs/2303.02516 · HTML: https://mlsanity.com/paper/2303.02516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 4 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning](https://mlsanity.com/paper/2010.01617/) (2020)
- [Impact of perfusion ROI detection to the quality of CBV perfusion map](https://mlsanity.com/paper/1912.05471/) (2019)

## Abstract (arXiv)

Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed, and their advantages and disadvantages are listed.