Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks. Issue 9 (18th July 2020)

• Record Type: Journal Article
• Authors: Winder, Anthony; d'Esterre, Christopher D.; Menon, Bijoy K.; Fiehler, Jens; Forkert, Nils D.
• Is Part Of: Medical physics. Volume 47:Issue 9(2020)
• Page Start: 4199
• Page End: 4211
• Publication Date: 2020-07-18
• Subjects: ischemic stroke -- machine learning -- perfusion imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14351
• Languages: English
• ISSNs: 0094-2405

Abstract

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. Abstract :