Kirsten Miriam Kerkering

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 $T_{1}$ -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. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). Results: Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. Conclusion: The proposed method outperforms the reported methods of comparison and yields accurate $T_{1}$ -maps. Although presented for $T_{1}$ -mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. Significance: From a clinical perspective, the obtained $T_{1}$ -maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.

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 2.3 s after an inversion pulse. In a first step, dynamic images are reconstructed which show both contrast changes due to T1 recovery and anatomical changes due to the heartbeat. An image registration algorithm with a signal model for T1 recovery is applied to estimate non‐rigid cardiac motion. In a second step, estimated motion fields are applied during an iterative model‐based T1 reconstruction. The approach was evaluated in numerical simulations, phantom experiments and in in‐vivo scans in healthy volunteers.Results: The accuracy of cardiac motion estimation was shown in numerical simulations with an average motion field error of 0.7 ± 0.6 mm for a motion amplitude of 5.1 mm. The accuracy of T1 estimation was demonstrated in phantom experiments, with no significant difference (p = 0.13) in T1 estimated by the proposed approach compared to an inversion‐recovery reference method. In vivo, the proposed approach yielded 1.3 × 1.3 mm T1 maps with no significant difference (p = 0.77) in T1 and SDs in comparison to a cardiac‐gated approach requiring 16 s scan time (i.e., seven times longer than the proposed approach). Cardiac motion correction improved the precision of T1 maps, shown by a 40% reduced SD.Conclusion: We have presented an approach that provides T1 maps of the myocardium in 2.3 s by utilizing both cardiac motion correction and model‐based T1 reconstruction.

Objective. T1 mapping of the liver is time consuming and can be challenging due to respiratory motion. Here we present a prospective slice tracking approach, which utilizes an external ultra-wide band radar signal and allows for efficient T1 mapping during free-breathing. Approach. The fast radar signal is calibrated to an MR-based motion signal to create a motion model. This motion model provides motion estimates, which are used to carry out slice tracking for any subsequent clinical scan. This approach was evaluated in simulations, phantom experiments and in vivo scans. Main results. Radar-based slice tracking was implemented on an MR system with a total latency of 77 ms. Moving phantom experiments showed accurate motion prediction with an error of 0.12 mm in anterior-posterior and 0.81 mm in head-feet direction. The model error remained stable for up to two hours. In vivo experiments showed visible image improvement with a motion model error three times smaller than with a respiratory bellow. For T1 mapping during free-breathing the proposed approach provided similar results compared to reference T1 mapping during a breathhold. Significance. The proposed radar-based approach achieves accurate slice tracking and enables efficient T1 mapping of the liver during free-breathing. This motion correction approach is independent from scanning parameters and could also be used for applications like MR guided radiotherapy or MR Elastography.

Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR). Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6–8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6–8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5–2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects. Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%. Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5–2 mm³). The approach led to improvements in the visualization of small structures and precise T1 values.

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. In the following scan through-plane motion is corrected prospectively with slice tracking based on the PT. In-plane motion is corrected for retrospectively. Our method was evaluated on a motion phantom and 11 healthy subjects.Results: Non-motion corrected measurements using a moving phantom showed T1 errors of 14 ± 4% (p < 0.05) compared to a reference measurement. The proposed motion correction approach reduced this error to 3 ± 4% (p < 0.05). In vivo the respiratory motion led to an overestimation of T1 values by 26 ± 31% compared to breathhold T1 maps, which was successfully corrected to an average difference of 3 ± 2% (p < 0.05) between our free-breathing approach and breathhold data.Discussion: Our proposed PT-based motion correction approach allows for T1 mapping during free-breathing with the same accuracy as a corresponding breathhold T1 mapping scan.

Purpose: To improve the efficiency of native and postcontrast high‐resolution cardiac T₁ mapping by utilizing cardiac motion correction.Methods: Common cardiac T₁ mapping techniques only acquire data in a small part of the cardiac cycle, leading to inefficient data sampling. Here, we present an approach in which 80% of each cardiac cycle is used for T₁ mapping by integration of cardiac motion correction. Golden angle radial data was acquired continuously for 8 s with in‐plane resolution of 1.3 × 1.3 mm². Cine images were reconstructed for nonrigid cardiac motion estimation. Images at different TIs were reconstructed from the same data, and motion correction was performed prior to T₁ mapping. Native T₁ mapping was evaluated in healthy subjects. Furthermore, the technique was applied for postcontrast T₁ mapping in 5 patients with suspected fibrosis.Results: Cine images with high contrast were obtained, leading to robust cardiac motion estimation. Motion‐corrected T₁ maps showed myocardial T₁ times similar to cardiac‐triggered T₁ maps obtained from the same data (1288 ± 49 ms and 1259 ± 55 ms, respectively) but with a 34% improved precision (spatial variation: 57.0 ± 12.5 ms and 94.8 ± 15.4 ms, respectively, P < 0.0001) due to the increased amount of data. In postcontrast T₁ maps, focal fibrosis could be confirmed with late contrast‐enhancement images.Conclusion: The proposed approach provides high‐resolution T₁ maps within 8 s. Data acquisition efficiency for T₁ mapping was improved by a factor of 5 by integration of cardiac motion correction, resulting in precise T₁ maps.

Purpose To provide high‐resolution cardiac T₁ mapping of various cardiac phases and cine imaging within a single breath‐hold using continuous golden ratio‐based radial acquisition and model‐based iterative image reconstruction. Methods Data acquisition was performed continuously using golden ratio‐based radial sampling and multiple inversion pulses were applied independent of the heart rate. Native T₁ maps of diastole and systole were reconstructed with in‐plane resolution of 1.3 × 1.3 mm² using model‐based iterative image reconstruction. Cine images with 30 cardiac phases were reconstructed from the same data using kt‐SENSE. The method was evaluated in a commercially available T₁ phantom and 10 healthy subjects. In vivo T₁ assessment was carried out segment‐wise. Results Evaluation in the phantom demonstrated accurate T₁ times (R² > 0.99) and insensitivity to the heart rate. In vivo T₁ values did not differ between systole and diastole, and T₁ times assessed by the proposed approach were longer than measured with a modified Look‐Locker inversion recovery (MOLLI) sequence, except for lateral segments. Cine images had a consistent dark‐blood contrast and functional assessment was in agreement with assessment based on Cartesian cine scans (difference in ejection fraction: 0.26 ± 2.65%, P = 0.65). Conclusion The proposed approach provides native T₁ maps of diastole and systole with high spatial resolution and cine images simultaneously within 16 s, which could strongly improve the scan efficiency.

Quantitative native T1 Mapping of the myocardium without the application of contrast agents can be used to detect fibrosis in the left ventricle. Spatial resolution of standard native T1 mapping is limited by cardiac motion and hence is not sufficient to resolve small myocardial structures, such as the right ventricle and the atria. Here, we present a novel MR approach which provides cardiac motion information and native T1 maps from the same data. Motion information is utilized to optimize data selection for T1 mapping and a model-based iterative reconstruction scheme ensures high-resolution T1 maps for the entire heart. Feasibility of the approach was demonstrated in three healthy volunteers. In the T1 maps, the myocardium of all four chambers can be visualized and T1 values of the left atrium and right chambers were comparable to left ventricular T1 values.