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In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246°), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles. [ABSTRACT FROM AUTHOR]

In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two 2D and a 3D deep learning-based post processing methods, three iterative reconstruction methods and two recently proposed methods for dynamic cardiac MRI based on 2D and 3D cascaded networks. Our method outperforms the 2D spatially trained U-net and the 2D spatio-temporal U-net. Compared to the 3D spatio-temporal U-net, our method delivers comparable results, but requiring shorter training times and less training data. Compared to the compressed sensing-based methods kt-FOCUSS and a total variation regularized reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to the iterative reconstruction method based on adaptive regularization with dictionary learning and total variation and when compared to the methods based on cascaded networks, while only requiring a small fraction of the computational and training time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the one of the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial 2D U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required. [ABSTRACT FROM AUTHOR]

A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles. This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or k-t SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings. [ABSTRACT FROM AUTHOR]

We demonstrate a new method to recover 4D blood flow over the entire ventricle from partial blood velocity measurements using multiple 3D+t colour Doppler images and ventricular wall motion estimated using 3D+t BMode images. We apply our approach to realistic simulated data to ascertain the ability of the method to deal with incomplete data, as typically happens in clinical practice. Experiments using synthetic data show that the use of wall motion improves velocity reconstruction, shows more accurate flow patterns and improves mean accuracy particularly when coverage of the ventricle is poor. The method was applied to patient data from 6 congenital cases, producing results consistent with the simulations. The use of wall motion produced more plausible flow patterns and reduced the reconstruction error in all patients. [ABSTRACT FROM AUTHOR]

We have carried out a PET performance evaluation a silicon photo-multiplier (SiPM) based PET scanner designed for fully simultaneous pre-clinical PET/MR studies. The PET scanner has an inner diameter of 20 cm with an LYSO crystal size of 1.3 by 1.3 by 10 mm. The axial PET field of view (FOV) is 30.2 mm. The PET detector modules, which incorporate SiPMs, have been designed to be MR-compatible allowing them to be located directly within a Philips Achieva 3T MR scanner. The spatial resolution of the system measured using a point source in a non-active background, is just under 2.3 mm full width at half maximum (FWHM) in the transaxial direction when single slice rebinning (SSRB) and 2D filtered back-projection (FBP) is used for reconstruction, and 1.3 mm FWHM when resolution modeling is employed. The system sensitivity is 0.6% for a point source at the center of the FOV. The true coincidence count rate shows no sign of saturating at 30 MBq, at which point the randoms fraction is 8.2%, and the scatter fraction for a rat sized object is approximately 23%. Artifact-free images of phantoms have been obtained using FBP and iterative reconstructions. The performance is currently limited because only one of three axial ring positions is populated with detectors, and due to limitations of the first-generation detector readout ASIC used in the system. The performance of the system as described is sufficient for simultaneous PET-MR imaging of rat-sized animals and large organs within the mouse. This is demonstrated with dynamic PET and MR data acquired simultaneously from a mouse injected with a dual-labeled PET/MR probe. [ABSTRACT FROM PUBLISHER]

In hybrid PET/MR systems, attenuation maps can be derived from MR to correct for attenuation in PET. However, MR-based attenuation correction (AC) in abdominal applications remains challenging (i) because of poor signal from important tissue types in common MR sequences (e.g., cortical bone) and (ii) because of respiratory motion which results in misalignments between the derived attenuation maps and the PET emissions. Furthermore, respiratory motion also leads to motion-blurring artefacts in the final PET reconstructions. In this paper, we compute an MR-based 4D attenuation map including cortical bone by combining an Ultrashort Echo Time (UTE) acquisition with a subject-specific motion model derived from a second near real-time 3D MR image acquisition. This model allows us to create attenuation maps at any respiratory position which are used for AC in the reconstruction of different respiratory resolved PET images. The inverse of the model is used for motion compensation (MC) of these images. We demonstrate our approach on MR data from 5 healthy volunteers including 3 manually inserted artificial lesions. The impact of bone tissue and respiratory motion on AC is investigated in PET simulations (i) by misclassifying bone to soft tissue in the attenuation maps leading to errors of up to 26.0% in mean uptake for lesions close to bone, and (ii) by using a non-moving attenuation map leading to errors of up to 24.2%. The impact of respiratory motion on MC showed errors of up to 50.7% in areas of strong motion if MC was not performed. The results show that the effect of motion has to be considered both for attenuation correction and for motion-compensating PET emissions. This additive effect of motion is larger than the effect of a wrong AC. [ABSTRACT FROM PUBLISHER]