Faculty Bibliography

Magnetic resonance imaging (MRI) has become an unrivalled medical diagnostic technique able to map tissue anatomy and physiology non-invasively. MRI measurements are meticulously engineered to control experimental conditions across the sample. However, residual radiofrequency (RF) field inhomogeneities are often unavoidable, leading to artefacts that degrade the diagnostic and scientific value of the images. Here we show that, paradoxically, these artefacts can be eliminated by deliberately interweaving freely varying heterogeneous RF fields into a magnetic resonance fingerprinting data-acquisition process. Observations made based on simulations are experimentally confirmed at 7 Tesla (T), and the clinical implications of this new paradigm are illustrated with in vivo measurements near an orthopaedic implant at 3T. These results show that it is possible to perform quantitative multiparametric imaging with heterogeneous RF fields, and to liberate MRI from the traditional struggle for control over the RF field uniformity.

PURPOSE: To study and reduce the effect of Gibbs ringing artifact on computed diffusion parameters. METHODS: We reduce the ringing by extrapolating the k-space of each diffusion weighted image beyond the measured part by selecting an adequate regularization term. We evaluate several regularization terms and tune the regularization parameter to find the best compromise between anatomical accuracy of the reconstructed image and suppression of the Gibbs artifact. RESULTS: We demonstrate empirically and analytically that the Gibbs artifact, which is typically observed near sharp edges in magnetic resonance images, has a significant impact on the quantification of diffusion model parameters, even for infinitesimal diffusion weighting. We find the second order total generalized variation to be a good choice for the penalty term to regularize the extrapolation of the k-space, as it provides a parsimonious representation of images, a practically full suppression of Gibbs ringing, and the absence of staircasing artifacts typical for total variation methods. CONCLUSIONS: Regularized extrapolation of the k-space data significantly reduces truncation artifacts without compromising spatial resolution in comparison to the default option of window filtering. In particular, accuracy of estimating diffusion tensor imaging and diffusion kurtosis imaging parameters improves so much that unconstrained fits become possible. Magn Reson Med, 2015. (c) 2015 Wiley Periodicals, Inc.

Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR

This article describes a new acquisition and reconstruction concept for positive contrast imaging of cells labeled with superparamagnetic iron oxides (SPIOs). Overcoming the limitations of a negative contrast representation as gained with gradient echo and fully balanced steady state (bSSFP), the proposed method delivers a spatially localized contrast with high cellular sensitivity not accomplished by other positive contrast methods. Employing a 3D radial bSSFP pulse sequence with half-echo sampling, positive cellular contrast is gained by adding artificial global frequency offsets to each half-echo before image reconstruction. The new contrast regime is highlighted with numerical intravoxel simulations including the point-spread function for 3D half-echo acquisitions. Furthermore, the new method is validated on the basis of in vitro cell phantom measurements on a clinical MRI platform, where the measured contrast-to-noise ratio (CNR) of the new approach exceeds even the negative contrast of bSSFP. Finally, an in vivo proof of principle study based on a mouse model with a clear depiction of labeled cells within a subcutaneous cell islet containing a cell density as low as 7 cells/mm(3) is presented. The resultant isotropic images show robustness to motion and a high CNR, in addition to an enhanced specificity due to the positive contrast of SPIO-labeled cells.

The task of imaging is to gather spatiotemporal information which can be organized into a coherent map. Tomographic imaging in particular involves the use of multiple projections, or other interactions of a probe (light, sound, etc.) with a body, in order to determine cross-sectional information. Though the probes and the corresponding imaging modalities may vary, and though the methodology of particular imaging approaches is in constant ferment, the conceptual underpinnings of tomographic imaging have in many ways remained fixed for many decades. Recent advances in applied mathematics, however, have begun to roil this intellectual landscape. The advent of compressed sensing, anticipated in various algorithms dating back many years but unleashed in full theoretical force in the last decade, has changed the way imagers have begun to think about data acquisition and image reconstruction. The power of incoherent sampling and sparsity-enforcing reconstruction has been demonstrated in various contexts and, when combined with other modern fast imaging techniques, has enabled unprecedented increases in imaging efficiency. Perhaps more importantly, however, such approaches have spurred a shift in perspective, prompting us to focus less on nominal data sufficiency than on information content. Beginning with examples from MRI, then proceeding through selected other modalities such as CT and PET, as well as multimodality combinations, this paper explores the potential of newly evolving acquisition and reconstruction paradigms to change the way we do imaging in the lab and in the clinic.

Purpose: Despite the extensive opportunities offered by current state-of-the-art PET-MR systems [1], their use is still far from routine clinical practice. While it is feasible to acquire PET data from a single bed position in about 5 minutes, collecting the clinically relevant variety of traditional MR contrasts requires substantially more time. This bottleneck formed by the traditional MR paradigm leads to a relatively inefficient use of the PET component and is particularly prohibitive for multiplebed-position PET protocols. This work proposes a one-step procedure that merges the MR fingerprinting (MRF) framework [2] with the PET acquisition, and employs a dedicated multi-modality reconstruction exploiting joint information among multiple contrast weightings to enable a 6 minute comprehensive PET-MR exam, which can provide the majority of clinical MR contrasts alongside quantitative parametric maps of the relaxation parameters (T1, T2) together with improved PET images. Theory & Methods: Although MRF is inherently robust against incoherent undersampling artifacts, there is a limit beyond which the final image quality will suffer. Instead of relaying purely on incoherence between undersampling artifacts and simulated signal evolutions (standard MRF reconstruction), we propose an extension of a recently proposed nonlinear joint multimodality reconstruction [3] to simultaneously reconstruct the series of MRF images and the PET image by enforcing joint sparsity, thereby reducing residual undersampling artifacts in MR while at the same time improving PET reconstruction quality. The joint MRF-PET reconstruction is performed by minimizing the …