Yue Cao

Publications (2)5.53 Total impact

• Article: Spatially regularized T(1) estimation from variable flip angles MRI.

  Hesheng Wang, Yue Cao
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  ABSTRACT: To develop efficient algorithms for fast voxel-by-voxel quantification of tissue longitudinal relaxation time (T(1)) from variable flip angles magnetic resonance images (MRI) to reduce voxel-level noise without blurring tissue edges. T(1) estimations regularized by total variation (TV) and quadratic penalty are developed to measure T(1) from fast variable flip angles MRI and to reduce voxel-level noise without decreasing the accuracy of the estimates. First, a quadratic surrogate for a log likelihood cost function of T(1) estimation is derived based upon the majorization principle, and then the TV-regularized surrogate function is optimized by the fast iterative shrinkage thresholding algorithm. A fast optimization algorithm for the quadratically regularized T(1) estimation is also presented. The proposed methods are evaluated by the simulated and experimental MR data. The means of the T(1) values in the simulated brain data estimated by the conventional, TV-regularized, and quadratically regularized methods have less than 3% error from the true T(1) in both GM and WM tissues with image noise up to 9%. The relative standard deviations (SDs) of the T(1) values estimated by the conventional method are more than 12% and 15% when the images have 7% and 9% noise, respectively. In comparison, the TV-regularized and quadratically regularized methods are able to suppress the relative SDs of the estimated T(1) to be less than 2% and 3%, respectively, regardless of the image noise level. However, the quadratically regularized method tends to overblur the edges compared to the TV-regularized method. The spatially regularized methods improve quality of T(1) estimation from multiflip angles MRI. Quantification of dynamic contrast-enhanced MRI can benefit from the high quality measurement of native T(1).
  Medical Physics 07/2012; 39(7):4139-48. · 2.83 Impact Factor
• Article: Correction of arterial input function in dynamic contrast-enhanced MRI of the liver.

  Hesheng Wang, Yue Cao
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  ABSTRACT: To develop a postprocessing method to correct saturation of arterial input function (AIF) in T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantification of hepatic perfusion. The saturated AIF is corrected by parameterizing the first pass of the AIF as a smooth function with a single peak and minimizing a least-squares error in fitting the liver DCE-MRI data to a dual-input single-compartment model. Sensitivities of the method to the degree of saturation in the AIF first-pass peak and the image contrast-to-noise ratio were assessed. The method was also evaluated by correlating portal venous perfusion with an independent overall liver function measurement. The proposed method corrects the distorted AIF with a saturation ratio up to 0.45. The corrected AIF improved hepatic arterial perfusion by -23.4% and portal venous perfusion by 26.9% in a study of 12 patients with liver cancers. The correlation between the mean voxelwise portal venous perfusion and overall liver function measurement was improved by using the corrected AIFs (R(2) = 0.67) compared with the saturated AIFs (R(2) = 0.39). The method is robust for correcting AIF distortion and has the potential to improve quantification of hepatic perfusion for assessment of liver tissue response to treatment in patients with hepatic cancers.
  Journal of Magnetic Resonance Imaging 03/2012; 36(2):411-21. · 2.70 Impact Factor