Abstract
In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods.
Highlights
The problem of combining several images into a single one is an old problem of image fusion [1]
Complementary computed tomography (CT) information is used in positron emission tomography (PET) and single photon emission computed tomography (SPECT) for attenuation correction or in some cases for the partial volume correction (PVC) which leads to improvement in resolution for functional images [3]
Since some features in k are not correlated to features in l can initiate false edges in the recovery of k^, it is essential to restrict the use of supplementary information
Summary
The problem of combining several images into a single one is an old problem of image fusion [1]. The problem of fusing images can arise in many applications where data is acquired from different imaging systems or modalities. Recent advances in medical hybrid scanners have posed new challenges in data fusion between data sets representing different characteristics of the biological materials [2]. Functional imaging modalities, such as positron emission tomography (PET) or single photon emission computed tomography (SPECT) are used for diagnosing and monitoring oncological diseases. The functional modalities are combined with anatomical imaging systems, such as X-ray computed tomography (CT) or magnetic resonance (MRI), to help in identifying the exact location of the decease. The measured data from hybrid scanners can be reconstructed separately and fused [4] or PVC corrected, or alternatively, the information on anatomical features can be embedded directly into reconstruction process by means of a priori information [5]
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