Affiliations: Key Laboratory of Particle & Radiation Imaging
(Tsinghua University), Ministry of Education, Department of Engineering
Physics, Tsinghua University, Beijing, P.R. China | Department of Radiology, General Navy Hospital,
Beijing, P.R. China | Applied Science Laboratory, GE Healthcare China,
Beijing, P.R. China
Note: [] Correspondence author: Kui Ying, PhD, Department of Engineering
Physics, Tsinghua University, Haidian District, Beijing, 100084, P.R. China.
Tel.: +86 10 62797228; Fax: +86 10 62797228; E-mail: [email protected]
Abstract: The parallel imaging technique reduces the scan time at the expense
of increased noise, due to its ill-conditioned system matrix. Tikhonov
regularization has been proposed for SENSE to reduce the noise. However,
Tikhonov regularized images suffer from residual aliasing aritfacts or image
blurring when a low resolution prior image is used. This study used
wavelet-based multivariate regularization to overcome this problem, while
maintaining the computational efficiency of Tikhonov regularization. In this
method, SENSE is formularized as a multilevel-structured problem in the wavelet
domain. Regularization is adaptively performed based on the noise behavior for
different levels and orientations throughout the wavelet domain. Both Tikhonov
regularization and the present method are systematically evaluated using in
vivo anatomical brain data and diffusion weighted brain data. Qualitative and
quantitative results demonstrate the advantage of multivariate regularization
over Tikhonov regularization in the presence of low resolution prior images.
This method suits most parallel imaging applications, especially when a
high-quality prior image is unavailable, such as diffusion weighed imaging.
