Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

Abstract

Objectives: To investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients.

Methods: The study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores.

Results: Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p < 0.001) and negatively with the ASPECTS (r = - 0.43; p < 0.001). The CNN AIF estimated penumbra and core volume matching the patient symptoms, typically in patients with higher NIHSS (> 20) and lower ASPECT score (< 5). In group analysis, the median CBF < 20%, CBF < 30%, rCBF < 38%, Tmax > 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05).

Conclusions: With inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.

Critical relevance statement: With CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.

Keywords: Arterial input function; Core; Ischemic stroke; Penumbra; Perfusion parameters.

Fig. 1

Workflow to estimate perfusion parameters

Fig. 2

Curve fitted AIF for a single dataset (a) AIF location is marked as a red dot. b AIF curve without CF. c Predicted gamma curve fitted AIF curve by CNN model as an output

Fig. 3

Volumetric agreement of the penumbra (Tmax > 6 s) and infract volume (CBF < 30%) with NIHSS and ASPECTS. a Association of Tmax > 6 s volume with NIHSS. b Association of Tmax > 6 s volume with ASPECTS. c Bland–Altman plot between the penumbra estimated by CNN CF and without CF. d Association of CBF < 30% volume with NIHSS. e Association of CBF < 30% volume with ASPECTS. f Bland–Altman plot between the infract core estimated by CNN CF and without CF

Fig. 4

Comparison of infarct core and penumbra for a patient with severe stroke. a CBF maps derived with CF CNN AIF and without CF. b Infarct core (CBF < 30% mL) estimated with CF CNN AIF and without CF. c Tmax maps derived with CF and without CF. d Penumbra (Tmax > 6.0 s mL) estimated with CF CNN CF and without CF

Fig. 5

Comparison of infarct core and penumbra for another patient with severe stroke. a CBF maps derived with CF CNN AIF and without CF. b Infarct core (CBF < 30% mL) estimated with CF CNN AIF and without CF. c Tmax maps derived with CF and without CF. d Penumbra (Tmax > 6.0 s mL) estimated with CF CNN CF and without CF