Magnetic resonance (MR) images (MRI) are routinely acquired with high in-plane resolution and lower through-plane resolution. Improving the resolution of such data can be achieved through post-processing techniques knows as super-resolution (SR), with various frameworks in existence. Many of these approaches rely on external databases from which SR methods infer relationships between low and high resolution data. The concept of self super-resolution (SSR) has been previously reported, wherein there is no external training data with the method only relying on the acquired image. The approach involves extracting image patches from the acquired image constructing new images based on regression and combining the new images by Fourier Burst Accumulation. In this work, we present four improvements to our previously reported SSR approach. We demonstrate these improvements have a significant effect on improving image quality and the measured resolution.
Accurate CT synthesis, sometimes called electron density estimation, from MRI is crucial for successful MRI-based
radiotherapy planning and dose computation. Existing CT synthesis methods are able to synthesize normal tissues but are
unable to accurately synthesize abnormal tissues (i.e., tumor), thus providing a suboptimal solution. We propose a multiatlas-
based hybrid synthesis approach that combines multi-atlas registration and patch-based synthesis to accurately
synthesize both normal and abnormal tissues. Multi-parametric atlas MR images are registered to the target MR images
by multi-channel deformable registration, from which the atlas CT images are deformed and fused by locally-weighted
averaging using a structural similarity measure (SSIM). Synthetic MR images are also computed from the registered
atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing
for the assessment of the CT synthesis fidelity. Poor synthesis regions are automatically detected based on the fidelity
measure and refined by a patch-based synthesis. The proposed approach was tested on brain cancer patient data, and
showed a noticeable improvement for the tumor region.
This paper presents a theoretical analysis of the effect of spatial resolution on image registration. Based on the assumption of additive Gaussian noise on the images, the mean and variance of the distribution of the sum of squared differences (SSD) were estimated. Using these estimates, we evaluate a distance between the SSD distributions of aligned images and non-aligned images. The experimental results show that by matching the resolutions of the moving and fixed images one can get a better image registration result. The results agree with our theoretical analysis of SSD, but also suggest that it may be valid for mutual information as well.