Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Sep;47(9):4199-4211.
doi: 10.1002/mp.14351. Epub 2020 Jul 18.

Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks

Anthony Winder  1   2 , Christopher D d'Esterre  2   3 , Bijoy K Menon  1   2   3 , Jens Fiehler  4 , Nils D Forkert  1   2   3   5
Affiliations

Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks

Anthony Winder et al. Med Phys. 2020 Sep.

Abstract

Purpose: The computation of perfusion parameter images requires knowledge of the arterial blood flow in the form of an arterial input function (AIF). This work proposes a novel method to automatically identify AIFs in computed tomography perfusion (CTP) and dynamic susceptibility contrast perfusion-weighted MRI (PWI) datasets using a deep convolutional neural network (CNN).

Methods: One-hundred CTP and 100 PWI datasets of acute ischemic stroke patients were available for model development and evaluation. For each modality, 50 datasets were used for CNN training and 20 for validation using manually selected AIFs and non-arterial tissue concentration time curves. Model evaluation was performed using the remaining 30 independent validation datasets from each modality with manual AIF selections provided by two experts as ground truth. Additionally, AIFs were also extracted using an established automatic shape-based algorithm for comparison purposes. The extracted AIFs were compared using normalized cross-correlation and shape features as well as using the Dice similarity metric and volume of the corresponding hypoperfusion (Tmax > 6 s) lesions.

Results: The cross-correlation values comparing the manual AIFs and those extracted by the proposed CNN method were significantly greater than those comparing the manual AIFs to the shape-based comparison method. Likewise, hypoperfusion lesions generated using the manually selected AIFs and CNN-based AIFs showed higher Dice values compared to hypoperfusion lesions generated using the comparison AIF extraction method. Shape features for AIFs generated by the proposed method did not differ significantly from the manual AIFs, with the exception that the CNN-derived AIFs for the PWI datasets showed marginally greater peak heights.

Conclusion: Deep convolutional neural network models are viable for the automatic extraction of the AIF from CTP and PWI datasets.

Keywords: ischemic stroke; machine learning; perfusion imaging.

PubMed Disclaimer

References

REFERENCES

    1. Goyal M, Yu AY, Menon BK, et al Endovascular therapy in acute ischemic stroke: challenges and transition from trials to bedside. Stroke. 2016;47:548-553.
    1. Boulanger J, Lindsay M, Gubitz G, et al Canadian stroke best practice recommendations for acute stroke management: prehospital, emergency department, and acute inpatient stroke care, 6th edition, update 2018. Int J Stroke. 2018;13:949-984.
    1. Nogueira RG, Jadhav AP, Haussen DC, et al Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378:11-21.
    1. Albers GW, Marks MP, Kemp S, et al Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378:708-718.
    1. Powers WJ, Rabinstein AA, Ackerson T, et al 2018 guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2018;49:e46-e99.

LinkOut - more resources