Model-based image reconstruction for dynamic cardiac perfusion MRI from sparse data

Journal ArticleThe paper presents a novel approach for dynamic magnetic resonance imaging (MRI) cardiac perfusion image reconstruction from sparse k-space data. It formulates the reconstruction problem in an inverse-methods setting. Relevant prior information is incorporated via a parametric model for the perfusion process. This wealth of prior information empowers the proposed method to give high-quality reconstructions from very sparse k-space data. The paper presents reconstruction results using both Cartesian and radial sampling strategies using data simulated from a real acquisition. The proposed method produces high-quality reconstructions using 14% of the k-space data. The model-based approach can potentially greatly benefit cardiac myocardial perfusion studies as well as other dynamic contrast-enhanced MRI applications including tumor imaging

Reordering for Improved Constrained Reconstruction from Undersampled k-Space Data

Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented

Effects of attenuation and blurring in cardiac SPECT and compensations using parallel computers

Ph.D.Ronald W. Schafe

Data reordering for improved constrained reconstruction from undersampled k-space data,”

Recommended by Habib Zaidi Recently, there has been a significant interest in applying reconstruction techniques, like constrained reconstruction or compressed sampling methods, to undersampled k-space data in MRI. Here, we propose a novel reordering technique to improve these types of reconstruction methods. In this technique, the intensities of the signal estimate are reordered according to a preprocessing step when applying the constraints on the estimated solution within the iterative reconstruction. The ordering of the intensities is such that it makes the original artifact-free signal monotonic and thus minimizes the finite differences norm if the correct image is estimated; this ordering can be estimated based on the undersampled measured data. Theory and example applications of the method for accelerating myocardial perfusion imaging with respiratory motion and brain diffusion tensor imaging are presented