Abstract

Nowadays many MRI scans can give 3D volume data with different contrasts, but the observers may want to view various contrasts in the same 3D volume. The conventional 2D medical fusion methods can only fuse the 3D volume data layer by layer, which may lead to the loss of interframe correlative information. In this paper, a novel 3D medical volume fusion method based on 3D band limited shearlet transform (3D BLST) is proposed. And this method is evaluated upon MRI T2* and quantitative susceptibility mapping data of 4 human brains. Both the perspective impression and the quality indices indicate that the proposed method has a better performance than conventional 2D wavelet, DT CWT, and 3D wavelet, DT CWT based fusion methods.

Highlights

  • Medical image fusion is a special case of image fusion and has been studied for decades

  • It refers to extracting and merging the feasible information from different source images, which were captured by different kinds of sensors, such as CT, MRI, and PET, or different configurations of the same sensor, such as MRI T2∗ and quantitative susceptibility mapping (QSM)

  • CT images provide the details of dense hard tissues, MRI images give information of soft tissues: T2∗ provides the contrast of the tissue relaxation time, and QSM can be provide susceptibility contrast information, which is produced by a range of endogenous magnetic biomarkers and contrast agents such as iron, calcium, and gadolinium (Gd)

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Summary

Introduction

Medical image fusion is a special case of image fusion and has been studied for decades. Let ψ be a band limited shearlet defined as before; the family of functions PPΨ(ψ) forms a tight frame for L2(P) := {f ∈ L2(R3) : supp f ⊂ P}, where PP denotes the orthogonal projection onto L2(P) and Ψ(ψ) = {ψj,k,m : j ≥ 0, |k| ≤ ⌈2j/2⌉, m ∈ (1/8)Z3}. By this theorem and a change of variables, the shearlet tight frames for L2(P), L2(P), and L2(P ) can be constructed, respectively. More details of 3D shearlet theory and the implementation of band limited shearlet transform as well as other implementations can be found in [10,11,12,13,14]

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