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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. 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.

Magnetic resonance imaging (MRI) is a medical imaging technique which is widely used in clinical routine. Standard imaging methods lead to so-called contrast-weighted images. The contrast arises from several tissue-related parameters such as the relaxation times T 1 and T 2. The aim in quantitative MRI (qMRI) is an estimation of these quantitative parameters. Magnetic resonance fingerprinting (MRF) is a promising technique in qMRI that allows for the simultaneous determination of multiple tissue-related parameters within a short acquisition time. The conventional MRF method utilizes an approximate inverse Fourier transformation for the highly undersampled data in the Fourier domain, leading to aliasing errors in the reconstructed magnetization courses. Computationally expensive statistical MRF modeling approaches overcome this issue by modeling the data directly in the Fourier domain. However, this leads to a non-convex and large-scale optimization task that is challenging to solve and requires expensive calculations. We compare two recent approaches, namely the conventional MRF method and a statistical MRF modeling approach based on Bayesian statistics in terms of their accuracy, reliability and computational costs. The comparison is carried out for simulated data with known ground truth for different signal-to-noise ratios, in the presence of errors in the physical model, and for several Fourier domain sampling schemes. It is demonstrated that a residual analysis can help to decide if the conventional MRF method is sufficient or if the complex Bayesian Fourier domain modeling approach can lead to a significant improvement.

In this work, we consider the task of image reconstruction in 2D radial cardiac cine MRI using deep learning (DL)-based regularization. As the regularization is achieved by employing an image-prior predicted by a pre-trained convolutional neural network (CNN), the quality of the image-prior is of essential importance. The achievable performance of any DL-based method is limited by the amount and the quality of the available training data. For fast dynamic processes, obtaining good-quality MR data is challenging because of technical and physiological reasons. In this work, we try to overcome these problems by a transfer-learning approach which is motivated by a previously presented DL-method (XT,YT U-Net). There, instead of training the network on the whole 2D dynamic images, it is trained on 2D spatio-temporal profiles (xt,yt-slices) which show the temporal changes of the imaged object. Therefore, for the training and test data, it is more important that their spatio-temporal profiles share similar local features rather than being images of the same anatomy. This allows us to equip arbitrary data with simulated motion that resembles the cardiac motion and use it as training data. By doing so, it is possible to train a CNN which is applicable to cardiac cine MR data without using ground-truth cine MR images for training. We demonstrate that combining XT,YT U-Net with the proposed transfer-learning strategy delivers comparable performance to CNNs trained on cardiac cine MR images and in some cases even qualitatively surpasses these. Additionally, the transfer-learning strategy was investigated for a 2D and 3D U-Net. The images processed by the the CNNs were used as image-priors in the CNN-regularized iterative reconstruction. The XT,YT U-Net yielded visibly better results than the 2D U-Net and slightly better results than the 3D U-Net when used in combination with the presented transfer learning-strategy.

Magnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as the T 1 and T 2 MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates for T 1 and T 2 has not been achieved so far, which makes it challenging to assess if observed differences in these estimates are significant and may indicate pathological changes of the underlying tissue. We propose a Bayesian approach for the uncertainty quantification of dictionary-based MRF which leads to probability distributions for T 1 and T 2 in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MRF estimates. All uncertainty calculations are based on the pre-computed dictionary and the observed sequence of undersampled MR images, and they can be calculated in short time. The approach is explored by analyzing MRF measurements of a phantom consisting of several tubes across which MR relaxation times are constant. The proposed uncertainty quantification is quantitatively consistent with the observed within-tube variability of estimated relaxation times. Furthermore, calculated uncertainties are shown to characterize well observed differences between the MRF estimates and the results obtained from high-accurate reference measurements. These findings indicate that a reliable uncertainty quantification is achieved. We also present results for simulated MRF data and an uncertainty quantification for an in vivo MRF measurement. MATLAB® source code implementing the proposed approach is made available.

This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines. The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft's model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction. The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).

High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS) using 3D radial phase encoding (RPE). An iterative reweighting scheme was applied during image reconstruction to ensure fast convergence and high image quality. Shearlets are mathematically optimal for a simplified model of natural images and have been proven to be more efficient than classical systems such as wavelets. 3DShearCS was compared to three other commonly used reconstruction approaches. Image quality was assessed quantitatively using general image quality metrics and using clinical diagnostic scores from expert reviewers. The proposed technique had lower relative errors, higher structural similarity and higher diagnostic scores compared to the other reconstruction techniques especially for high undersampling factors, i.e. short scan times. 3DShearCS provided ensured accurate depiction of cardiac anatomy for fast imaging and could help to promote 3D high-resolution CMR in clinical practice.