TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Abstract

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

Keywords: CT perfusion; Low radiation dose; Stroke; Tensor non-local deconvolution; Total variation.

Fig. 1.

(a) The illustration of long-range similarity in the brain. The red and yellow boxes show the non-local regions which have similar patterns. (b) Perfusion parameter maps (CBF – cerebral blood flow, CBV – cerebral blood volume, and MTT – mean transit time) of a 22-year old with severe left middle cerebral artery (MCA) stenosis. Arrows indicate the regions with altered hemodynamic function represented as abnormally decreased CBF and prolonged MTT. This pattern is indicative of ischemic tissue at risk of stroke in the left hemisphere (right side of the image). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2.

Illustration of TENDER model in a 2D image. TENDER regularization term for voxel i (red dot) is a weighted summation of the difference between voxel i and the most similar voxels (yellow dots) in the search window with width W (red box). The weight w(i, j) depends on the patches around the voxels. Compared to local-TTV, which only considers the 4-connected local neighborhood, TENDER preserves the accuracy and contrast of the vascular structure with higher fidelity of the reference patch. The actual TENDER regularization is imposed on 4D spatio-temporal flow-scaled residue impulse functions across different slices and time points. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3.

Simulation of low-dose CTP data from high-dose CTP data and the evaluation framework.

Fig. 4.

Results from a normal subject. From left to right shows the reference map (high-dose CTP), the low-dose maps of standard singular value decomposition (sSVD), block-circulant singular value decomposition (bSVD), Tikhonov, TTV, and our proposed TENDER. The first row is the whole brain CBF map and the second row is the closeup view of a selected region.

Fig. 5.

Results from a subject with right frontoparietal craniotomy and ischemia in the right anterior cerebral artery (RACA) and right middle cerebral artery (RMCA) territories. From left to right shows the reference map (high-dose CTP), the low-dose maps of standard singular value decomposition (sSVD), block-circulant singular value decomposition (bSVD), Tikhonov, TTV, and our proposed TENDER. The first row is the whole brain CBF map and the second row is the closeup view of a selected region.

Fig. 6.

Boxplot of PSNR and SSIM for the 10 clinical subjects. The proposed TENDER method significantly outperforms all other comparison methods (p < 0.05).

Fig. 7.

Illustration of the evolution of deconvolution algorithms for CTP, from independent voxel to local neighborhood, and to non-local regions. In the left independent model, every voxel is computed without consideration of spatial and temporal context. In the middle local TTV model, only the local neighborhoods (6 connected in 3D space and 2 connected in temporal domain) are used for robust deconvolution. In the right non-local TENDER model, non-local search of the broader neighborhood regions enable more robust estimation of the perfusion parameters.

Fig. 8.

Convergence curve of the cost function for TENDER algorithm.