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Abstract #4197

Concentration time-course Model-based Angiogram SEparation (MASE) for dynamic contrast-enhanced magnetic resonance angiography

Eun Ji Lim1,2 and Jaeseok Park2

1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea, Republic of, 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

Dynamic contrast-enhanced (DCE) 3D MRA has been widely used for diagnostic assessment of vascular morphology and hemodynamics in a clinical routine. It acquires a series of time-resolved images, revealing details on contrast dynamics. To extract angiograms while eliminating unwanted background tissues, subtraction between the reference (pre-contrast) and DCE images in each time frame is typically employed. However, in the presence of non-stationary background signal transition such as subject motion and time-varying magnetic field, subtraction results in incomplete background suppression and noise amplification. Due to the inherent, subtraction sparsity in either between the reference and each dynamic image or between neighboring time frames, compressed sensing (CS) is well suited to DCE MRA to enhance spatial and temporal resolution. Nevertheless, these approaches remain suboptimal due to the inherent limitation of subtraction. In this work, we propose a new, DCE MRA method called “concentration time-course Model-based Angiogram SEparation (MASE)”, in which DCE signals in the temporal direction are directly modeled and reconstructed with sparsity priors while background signals are attenuated.

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