An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN - PubMed (original) (raw)

An Automatic Estimation of Arterial Input Function Based on Multi-Stream 3D CNN

Shengyu Fan et al. Front Neuroinform. 2019.

Abstract

Arterial input function (AIF) is estimated from perfusion images as a basic curve for the following deconvolution process to calculate hemodynamic variables to evaluate vascular status of tissues. However, estimation of AIF is currently based on manual annotations with prior knowledge. We propose an automatic estimation of AIF in perfusion images based on a multi-stream 3D CNN, which combined spatial and temporal features together to estimate the AIF ROI. The model is trained by manual annotations. The proposed method was trained and tested with 100 cases of perfusion-weighted imaging. The result was evaluated by dice similarity coefficient, which reached 0.79. The trained model had a better performance than the traditional method. After segmentation of the AIF ROI, the AIF was calculated by the average of all voxels in the ROI. We compared the AIF result with the manual and traditional methods, and the parameters of further processing of AIF, such as time to the maximum of the tissue residue function (Tmax), relative cerebral blood flow, and mismatch volume, which are calculated in the Section Results. The result had a better performance, the average mismatch volume reached 93.32% of the manual method, while the other methods reached 85.04 and 83.04%. We have applied the method on the cloud platform, Estroke, and the local version of its software, NeuBrainCare, which can evaluate the volume of the ischemic penumbra, the volume of the infarct core, and the ratio of mismatch between perfusion and diffusion images to help make treatment decisions, when the mismatch ratio is abnormal.

Keywords: 3D CNN; AIF; MRI; multi-stream; perfusion.

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Figures

FIGURE 1

FIGURE 1

Single 3D CNN network architecture. The 3D CNN network architecture includes eight convolutional layers, five pooling layers, and two full connected layers.

FIGURE 2

FIGURE 2

Multi-stream 3D CNN network. Each stream is a 3D CNN network, and the streams are combined by a fusion layer using linear SVM.

FIGURE 3

FIGURE 3

Data sequence order. The sequence is arranged slice by slice in each frame and then arranged frame by frame.

FIGURE 4

FIGURE 4

Arterial input function (AIF) curve extracted from time series. The red point shows the location where AIF is extracted, the value of PWI decreases first and then increases with time.

FIGURE 5

FIGURE 5

AIF ROI on each AIF masked on MIP in a single slice. (A) Manual, (B) MS3DCNN, (C) Fuzzy c-means, and (D) U-Net3D + fuzzy c-means, from left to right.

FIGURE 6

FIGURE 6

AIF estimated by manual method, multi-stream 3DCNN, Unet3D + fuzzy c-means, and fuzzy c-means. AIF obtained by multi-stream 3D CNN is closest to the manual AIF.

FIGURE 7

FIGURE 7

rCBF calculated by AIF from (A) Manual, (B) MS3DCNN, (C) fuzzy c-means, and (D) U-Net3D + fuzzy c-means in each column from left to right.

FIGURE 8

FIGURE 8

Tmax calculated by AIF from (A) Manual, (B) MS3DCNN, (C) fuzzy c-means, and (D) U-Net3D + fuzzy c-means in each column from left to right.

FIGURE 9

FIGURE 9

Stroke analysis results of the ischemic region, the infarct core, the mismatch volume, and the mismatch ratio. The infarct core was marked in magenta while the ischemic region was marked in green.

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