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Super-resolution reconstruction for medical image enhancement using uncertainty models

: Herbert, Steffen
: El Hakimi, Wissam

Darmstadt, 2011, 71 pp.
Darmstadt, TU, Master Thesis, 2011
Master Thesis
Fraunhofer IGD ()
3D reconstruction; super resolution; medical image processing; Forschungsgruppe Medical Computing (MECO)

The resolution and quality of magnetic resonance imaging (MRI) and computer tomography (CT) plays an important role in the diagnosis of e.g. heart anomalies and brain tumors. Images with a higher resolution lead thereby to significantly less wrong decisions. However, MR and also CT images often suffer from a highly anisotropic resolution. We propose a super-resolution reconstruction (SRR) approach, based on a 3D space-variant interpolation method, for reconstructing a high resolution (HR) image from different orthogonal low resolution (LR) images. Thereby, uncertainties of voxels, which arise during image acquisition and preprocessing, are considered. We formulate a space-variant interpolator in an efficient way and adapt the algorithm to the structure of the interpolation regions in order to allow an efficient parallel processing. Furthermore, we propose an iterative scheme of the SRR algorithm where small regions are independently processed until they are reconstructed. Experiments with synthetic and real data reveal high contrast and sharp details of the SRR result, especially in regions of small object structures. Compared to the average of the upsampled LR images, we obtain a significant improvement in terms of image quality and contrast. We provide an extensive evaluation of the SRR process in order to optimize the algorithm and to estimate its parameters in an adaptive way. Furthermore, the experiments show that the algorithm is able to handle minor intensity differences of the input images, which arise in real scans.