Regularized iterative reconstruction in tensor tomography using gradient constraints

This paper investigates the iterative reconstruction of tensor fields in diffusion tensor magnetic resonance imaging (MRI). The gradient constraints on eigenvalue and tensor component images of the diffusion tensor were exploited. A computer-generated phantom was used in order to simulate the diffusion tensor in a cardiac MRI study with a diffusion model that depends on the fiber structure of the myocardium. Computer simulations verify that the regularized methods provide an improved reconstruction of the tensor principal directions. The reconstruction from experimentally acquired data is also presented.

[1]  C. Vogel,et al.  Analysis of bounded variation penalty methods for ill-posed problems , 1994 .

[2]  M Defrise,et al.  Efficient cardiac diffusion tensor MRI by three-dimensional reconstruction of solenoidal tensor fields. , 2001, Magnetic resonance imaging.

[3]  M. Defrise,et al.  Iterative reconstruction for helical CT: a simulation study. , 1998, Physics in medicine and biology.

[4]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..

[5]  G.T. Gullberg,et al.  Tensor tomography , 1999, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[6]  G. Gullberg,et al.  Regularization parameter selection for Bayesian reconstruction of attenuation maps , 1998 .

[7]  Michel Defrise,et al.  Diffusion tensor MR imaging of principal directions: a tensor tomography approach. , 2002, Physics in medicine and biology.

[8]  P M Jakob,et al.  High-resolution diffusion imaging using a radial turbo-spin-echo sequence: implementation, eddy current compensation, and self-navigation. , 2000, Journal of magnetic resonance.

[9]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[10]  R. Dinsmore,et al.  Imaging myocardial fiber architecture in vivo with magnetic resonance , 1995, Magnetic resonance in medicine.

[11]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[12]  Gengsheng L. Zeng,et al.  Total variation regulated EM algorithm [SPECT reconstruction] , 1999 .

[13]  Benjamin M. W. Tsui,et al.  Simulation evaluation of Gibbs prior distributions for use in maximum a posteriori SPECT reconstructions , 1992, IEEE Trans. Medical Imaging.

[14]  G H Glover,et al.  Projection Reconstruction Techniques for Reduction of Motion Effects in MRI , 1992, Magnetic resonance in medicine.

[15]  Grant T. Gullberg,et al.  Total variation regulated EM algorithm , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[16]  An iterative approach to tensor tomography , 2000, 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149).

[17]  S. H. Manglos,et al.  Constrained IntraSPECT reconstruction from SPECT projections , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.