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A Method for Composition of Medical Imaging Volumes Using Selective Blending

IP.com Disclosure Number: IPCOM000208568D
Original Publication Date: 2011-Jul-13
Included in the Prior Art Database: 2011-Jul-13
Document File: 5 page(s) / 188K

Publishing Venue

Siemens

Related People

Juergen Carstens: CONTACT

Abstract

In medical imaging it is often necessary to merge images from several smaller volumes into one larger image. The process of fusion of two image volumes along the area of overlapping scanned regions is called composition. An algorithm is used to fill in the voxels of the output larger volume according to the locations computed by alignment. Where only one input volume overlaps an output voxel location, data is copied directly from the input to the output. In places where two or more input volumes overlap at an output voxel, program logic is used to determine how to choose between the input data points or blend them together. Wherever no input volume overlaps the output, black voxels are used to fill. Figure 1 and 2 illustrate this concept. Blending the component volumes aims at limiting artifacts that can occur from image volume distortion or patient moving. It can limit visible seams, contrast variation and ghosting effects due to blending. One reason for image volume distortion is the increase of field strength in newer MR machines which improves image resolution and quality also magnifies B0 effects. B0 distortion can occur anywhere within the volume, but is most pronounced in the periphery of the isocenter of the volume. Since B0 effects are most evident in the leading and trailing edges of a MR volume, having sufficient volume overlap and discarding the named regions is a possible solution. However, this solution will result in the need for more volumes and makes the process less efficient. Also, there is no certainty where B0 effects might occur in a volume. This makes narrowing the field of view of a volume not only an expensive but also an imprecise solution.

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A Method for Composition of Medical Imaging Volumes Using Selective

Blending

Idea: James Reismann, US- Iselin, New Jersey

In medical imaging it is often necessary to merge images from several smaller volumes into one larger image. The process of fusion of two image volumes along the area of overlapping scanned regions is called composition. An algorithm is used to fill in the voxels of the output larger volume according to the locations computed by alignment. Where only one input volume overlaps an output voxel location, data is copied directly from the input to the output. In places where two or more input volumes overlap at an output voxel, program logic is used to determine how to choose between the input data points or blend them together. Wherever no input volume overlaps the output, black voxels are used to fill. Figure 1 and 2 illustrate this concept. Blending the component volumes aims at limiting artifacts that can occur from image volume distortion or patient moving. It can limit visible seams, contrast variation and ghosting effects due to blending. One reason for image volume distortion is the increase of field strength in newer MR machines which improves image resolution and quality also magnifies B0 effects. B0 distortion can occur anywhere within the volume, but is most pronounced in the periphery of the isocenter of the volume. Since B0 effects are most evident in the leading and trailing edges of a MR volume, having sufficient volume overlap and discarding the named regions is a possible solution. However, this solution will result in the need for more volumes and makes the process less efficient. Also, there is no certainty where B0 effects might occur in a volume. This makes narrowing the field of view of a volume not only an expensive but also an imprecise solution.

Currently a method called Elastic Composition is used which determines the distortion field between the overlapping regions of two adjoining volumes. Then a method of gradual displacement morphing and intensity blending is calculated to create a seamless transition between the two volumes. This process is shown in Figure 2. The process begins with identifying the overlap regions of the two adjoining volumes and determining the offset either from machine parameters of the scanner or via an initial alignment stage. Then the correspondence between the overlapping regions is established via elastic registration. The algorithm estimates a deformation that maximizes the local cross-correlation of the two images and arbitrarily defines one as reference. The deformation is represented by a smooth vector field that gives for each pixel on the reference its corresponding location on the second image. Simultaneously the deformation and its inverse are approximated by composition of small displacements, incrementally maximizing the similarity crit...