KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Image resolution, Lawrencium, Super resolution, Image processing, Computer programming, Radiology, Scanners, Transform theory
Improving the resolution in magnetic resonance imaging (MRI) is always done at the expense of either the signal-to-noise
ratio (SNR) or the acquisition time. This study investigates whether so-called super-resolution reconstruction (SRR) is an
advantageous alternative to direct high-resolution (HR) acquisition in terms of the SNR and acquisition time trade-offs.
An experimental framework was designed to accommodate the comparison of SRR images with direct high-resolution
acquisitions with respect to these trade-offs. The framework consisted, on one side, of an image acquisition scheme,
based on theoretical relations between resolution, SNR, and acquisition time, and, on the other side, of a protocol for
reconstructing SRR images from a varying number of acquired low-resolution (LR) images. The quantitative experiments
involved a physical phantom containing structures of known dimensions. Images reconstructed by three SRR methods, one
based on iterative back-projection and two on regularized least squares, were quantitatively and qualitatively compared
with direct HR acquisitions. 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 post-mortem mouse) were acquired with
different MRI scanners. The quantitative results indicate that for long acquisition times, when multiple acquisitions are
averaged to improve SNR, SRR can achieve better resolution at better SNR than direct HR acquisitions.
Recent studies indicate that maximizing the mutual information of the joint histogram of two images is an accurate and robust way to rigidly register two mono- or multimodal images. Using mutual information for registration directly in a local manner is often not admissible owing to the weakened statistical power of the local histogram compared to a global one. We propose to use a global joint histogram based on optimized mutual information combined with a local registration measure to enable local elastic registration.
Conference Committee Involvement (13)
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