Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal-to-noise ratio, longer acquisition time or both. This study investigates whether so-called super-resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high-resolution acquisition in terms of the signal-to-noise ratio and acquisition time trade-offs. The performance of six super-resolution reconstruction methods and direct high-resolution acquisitions was compared with respect to these trade-offs. The methods are based on iterative back-projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low-resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super-resolution reconstruction can indeed improve the resolution, signal-to-noise ratio and acquisition time trade-offs compared with direct high-resolution acquisition.
In MRI, the relatively thick slices of multi-slice acquisitions often hamper visualization and analysis of the underlying anatomy. A group of post-processing techniques referred to as super-resolution reconstruction (SRR) have been developed to address this issue. In this study, we present a novel approach to SRR in MRI, which exploits the highresolution content usually available in the 2D slices of MRI slice stacks to reconstruct isotropic high-resolution 3D images. Relying on the assumption of local self-similarity of anatomical structures, the method can be applied both to a single slice stack and to the combination of multiple slice stacks that differ in the orientation of their field of view. We evaluate the method quantitatively on synthetic brain MRI and qualitatively on MRI of the lungs. The results show that the method outperforms state-of-the-art MRI super-resolution methods.
Super-resolution reconstruction (SRR) is a post-acquisition method for producing a high-resolution (HR) image from a set of lowresolution (LR) images. However, for large volumes of data, this technique is computationally very demanding and time consuming. In this study we focus on the specific case of whole-body mouse data and present a novel, integrated, end-to-end approach to overcome this problem. We combine articulated atlas-based segmentation and planar reformation techniques with state-of-the-art in SRR to produce high resolution, interactively selected, localized isotropic volumes-of-interest in whole-body mouse MRI. With this method we overcome time and memory related limitations when applying the SRR algorithm to the entire dataset, enabling interactive visualization and exploration of anatomical structures of interest in whole-body MRI mouse data on a normal desktop PC.
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