A Unified Variational Approach to Denoising and Bias Correction in MR

Abstract. We propose a novel bias correction method for magnetic resonance (MR) imaging that uses complementary body coil and surface coil images. The former are spatially homogeneous but have low signal intensity; the latter provide excellent signal response but have large bias fields. We present a variational framework where we optimize an energy functional to estimate the bias field and the underlying image using both observed images. The energy functional contains smoothness-enforcing regularization for both the image and the bias field. We present extensions of our basic framework to a variety of imaging protocols. We solve the optimization problem using a computationally efficient numerical algorithm based on coordinate descent, preconditioned conjugate gradient, half-quadratic regularization, and multigrid techniques. We show qualitative and quantitative results demonstrating the effectiveness of the proposed method in producing debiased and denoised MR images.

• Type: Conference
• Year: 2003
• Where: IPMI
• Authors: Ayres C. Fan, William M. Wells III, John W. Fisher III, Müjdat Çetin, Steven Haker, Robert V. Mulkern, Clare M. Tempany, Alan S. Willsky
• Keywords: Bias Correction Method, Bias Field, IPMI 2003, Large Bias Fields, Medical Imaging