1.Robust low-dose CT myocardial perfusion deconvolution via high-dimension total variation regularization.
Changfei GONG ; Dong ZENG ; Zhaoying BIAN ; Hua ZHANG ; Zhang ZHANG ; Jing ZHANG ; Jing HUANG ; Jianhua MA
Journal of Southern Medical University 2015;35(11):1579-1585
OBJECTIVETo develop a computed tomography myocardial perfusion (CT-MP) deconvolution algorithm by incorporating high-dimension total variation (HDTV) regularization.
METHODSA perfusion deconvolution model was formulated for the low-dose CT-MPI data, followed by HDTV regularization to regularize the consistency of the solution by fusing the spatial correlation of the vascular structure and the temporal continuation of the blood flow signal.
RESULTSBoth qualitative and quantitative studies were conducted using XCAT and pig myocardial perfusion data to evaluate the present algorithm. The experimental results showed that this algorithm achieved hemodynamic parameter maps with better performances than the existing methods in terms of streak-artifacts suppression, noise-resolution tradeoff, and diagnosis structure preservation.
CONCLUSIONThe proposed algorithm can achieve high-quality hemodynamic parameter maps in low-dose CT-MPI.
Algorithms ; Animals ; Artifacts ; Models, Theoretical ; Phantoms, Imaging ; Swine ; Tomography, X-Ray Computed
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