Purpose: Quantitative T₁ mapping is a magnetic resonance imaging technique that estimates the spin-lattice relaxation time of tissues. Even though T₁ mapping has a broad range of potential applications, it is not routinely used in clinical practice as accurate and precise high resolution T₁ mapping requires infeasibly long acquisition times. Method: To improve the trade-off between the acquisition time, signal-to-noise ratio and spatial resolution, we acquire a set of low resolution T₁-weighted images and directly estimate a high resolution T₁ map by means of super-resolution reconstruction. Results: Simulation and in vivo experiments show an increased spatial resolution of the T₁ map, while preserving a high signal-to-noise ratio and short scan time. Moreover, the proposed method outperforms conventional estimation in terms of root-mean-square error. Conclusion: Super resolution T₁ estimation enables resolution enhancement in T₁ mapping with the use of standard (inversion recovery) T₁ acquisition sequences. Magn Reson Med, 2016.

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In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1-weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1-weighted images is crucial. Conventionally, the T1-weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art modelbased approaches, in terms of both motion and T1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1-weighted data and with two in vivo human brain T1-weighted data sets, showing its applicability in real-life scenarios.@en

Quantification of the spin-lattice relaxation time, T1, of tissues is important for characterization of tissues in clinical magnetic resonance imaging (MRI). In T1 mapping, T1 values are estimated from a set of T1-weighted MRI images. Due to the limited spatial resolution of the T1-weighted images, one voxel might consist of two tissues, causing partial volume effects (PVE). In conventional mono-exponential T1 estimation, these PVE result in systematic errors in the T1 map. To account for PVE, single-voxel bi-exponential estimators have been suggested. Unfortunately, in general, they suffer from low accuracy and precision. In this work, we propose a joint multi-voxel bi-exponential T1 estimator (JMBE) and compare its performance to a single-voxel bi-exponential T1 estimator (SBE). Results show that, in contrast to the SBE, and for clinically achievable single-voxel SNRs, the JMBE is accurate and efficient if four or more neighboring voxels are used in the joint estimation framework. This illustrates that, for clinically realistic SNRs, accurate results for quantitative biexponential T1 estimation are only achievable if information of neighboring voxels is incorporated.@en