Improved in vivo human carotid artery wall T2⁎ estimation

T2⁎ quantification has been shown to noninvasively and accurately estimate tissue iron content in the liver and heart; applying this to thin-walled carotid arteries introduces a new challenge to the estimation process. With most imaging voxels in a vessel being along its boundaries, errors in parameter estimation may result from partial volume mixing and misregistration due to motion in addition to noise and other common error sources. To minimize these errors, we propose a novel technique to reliably estimate T2⁎ in thin regions of vessel wall. The technique weights data points to reduce the influence of expected error sources. It uses neighborhoods of data to increase the number of points for fitting and to assess lack of fit for automated outlier detection and deletion. The performance of this method was observed in simulations, phantom and in vivo patient studies and compared to results obtained using a pixelwise linear least squares estimation of T2⁎. The new proposed method showed a closer match to the expected results, and a 4.2-fold decrease in interobserver variability for in vivo studies. This increased confidence in estimation should improve the ability to reliably quantify iron noninvasively in the arterial wall.