Purpose Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data. Theory and Methods High resolution 3D T 1 maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data. Results Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T 1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions. Conclusions The proposed algorithm is able to recover T 1 maps with an isotropic resolution of 1 mm 3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.

The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The 1822 | MAIER Et Al.

Various medical examinations are seeing a shift to a more patient centric and personalized view, based on quantitative instead of qualitative observations and comparisons. This trend has also affected medical imaging, and particularly quantitative MRI (qMRI) gained importance in recent years. qMRI aims to identify the underlying biophysical and tissue parameters that determine contrast in an MR imaging experiment. In addition to contrast information, qMRI permits insights into diseases by providing biophysical, microstructural, and functional information in absolute quantitative values. For quantification, biophysical models are used, which describe the relationship between image intensity and physical properties of the tissue for certain scanning sequences and sequence parameters. By performing several measurements with different sequence parameters (e.g. flip angle, repetition time, echo time) the related inverse problem of identifying the tissue parameters sought can be solved.Quantitative MR typically suffers from increased measurement time due to repeated imaging experiments. Therefore, methods to reduce scanning time by means of optimal scanning protocols and subsampled data acquisition have been extensively studied. However, these approaches are typically associated with a reduced SNR, and can suffer from subsampling artifacts. To address both aspects, it has been shown that the inclusion of a biophysical model in the reconstruction process leads to much faster data acquisition, while simultaneously improving image quality. The inverse problem associated with this special reconstruction approach requires dedicated numerical solution strategies (

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