Respiratory motion-corrected T1 mapping of the abdomen.

Objective: The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency.

Materials And Methods: Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping.

Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice.

Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI).

Methods: Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner.

Motion-corrected model-based reconstruction for 2D myocardial T1 mapping.

Purpose: To allow for T1 mapping of the myocardium within 2.3 s for a 2D slice utilizing cardiac motion-corrected, model-based image reconstruction.

Methods: Golden radial data acquisition is continuously carried out for 2.

3D model-based super-resolution motion-corrected cardiac T1 mapping.

. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR)..

Pilot tone-based prospective correction of respiratory motion for free-breathing myocardial T1 mapping.

Objective: To provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition.

Materials And Methods: During a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior-posterior direction is estimated and two linear models are derived between the PT and heart motion.