Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung, Taiwan, R.O.C.
+Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
[email protected], [email protected]
Abstract
Compared to object-based registration, feature-based registration is much less complex.
However, in order for feature-based registration to work, the two image stacks under consideration must have the same acquisition tilt angle and have the same anatomical location – two requirements that are not always fulfilled. In this paper, we propose a technique that reconstructs two sets of medical images acquired with different acquisition angles and anatomical cross sections into one set of images of identical scanning orientation and positions. The space correlation information among the two image stacks is first extracted and is used to correct the tilt angle and anatomical position differences in the image stacks. Satisfactory reconstruction results were presented to prove our points.
1. Introduction
Compared to objected-based registration[1], feature-based registration [2]is much less complex.
However, in order for these methods to work, the two image stacks under consideration must have the same acquisition tilt angle and have the same anatomical location – two requirements that are not always fulfilled.
The acquisition tilt angle is often different in routine acquisition protocols among different imaging modalities. In addition, if the patient’s scans have already been acquired using a specific acquisition tilt angle, it will be costly and discomfort to rescan with the other acquisition tilt angle just to obtain different orientation image stacks for registration or physician observation.
In this paper, we present a complete solution to convert two sets of medical images acquired with different acquisition angles and anatomical cross sections into one set of images of identical scanning orientation and positions. The sequence of steps about the proposed approach is shown in Fig. 1. First, the original image acquisition parameters (including slice thickness, tile angle, and number of images) are input to simulate (reconstruct) the original image
acquisition circumstance as shown in Figs. 2. The space correlation information among the two image stacks is then extracted. After that, depending on the space correlation information, such as the scan line thicknesses, the scan line tilt angle and orientation of the image, the reconstructed gray level of pixels are computed, based on various intersection/ correlation situations.
2. Method
The purpose of image reconstruction is to convert two sets of medical images acquired with different acquisition angles (orientation) and anatomical positions into image sets of identical orientation and positions. In practical scanning, an acquisition scan plan has two degrees of freedom, a tilt angle (θ ), which denotes the raising angle relative to horizontal plans along the x direction, and an incident angle (δ ) which denotes the raising angle relative to horizontal plans along the z direction. A cross section image is obtained by averaging gray levels in the two sides of the acquisition scan plan within a specific range (thickness). To simplify demonstration, in following figures we will use an
“acquisition scan line” to represent an acquisition scan plan. As illustrated in Fig. 2, a silhouette image shows two sets of acquisition scan lines, one denoting the horizontal axial scans (tilt angle θ =0ο, incident angle δ =0o) and the other the oblique axial scans with tilt angle θ >0οand incident angle δ =0ο.
It is emphasized here that any scan line in the silhouette image represents an image scanned at a certain position of the human body, whereas a point on the scan line corresponds to a row of pixels on that image. For example, let the i-th oblique acquisition scan line, QO in Fig. 3a be the projection of the i oblique-scanned image (Fig. 3b), then the points
t
qoi,1, , qoi,n,ton QOi would be the line projections of the image rows of soi,1, soi,v to the view of sight as shown in Fig. 3b. The one-to-one mapping between a point on the oblique acquisition scan line in Fig. 3a and a row of pixels on the scanned image, e.g., Fig. 3b, allows one to map any image row, soi,k, to the corresponding point, qoi,j,t , on the i-th
oblique acquisition scan line according to the are the nominal pixel rows on the i-th oblique axial scan image that corresponds to the points qoi,j,t and
t n
qoi, , on the i-th oblique acquisition scan line.
Similarly, in the case of axial scan, we can map the pixel rows of SHi,k for the i-th horizontal axial scan i-th horizontal axial image corresponding to the points qhi,l,t and qhi,m,t on the i-th horizontal acquisition scan line.
Without loss of generality, in the following discussion we assume that a set of oblique axial scanned images is used to reconstruct a set of horizontal axial scanned images with θ =0ο, as illustrated in Fig. 4. The finite dimension of an image voxel entails that the gray level at any point along the horizontal or oblique acquisition scan lines is an average of all points that fall within the individual slice thickness. For example, the gray level of point
t contribution of all points within the slice thickness is identical it can therefore be recognized that the gray level of qoc,f,t could substitute for the gray level of Likewise, the gray levels of the points between
1 mappings are established, the gray level for point
t j
qhi,, can be obtained.
Assuming that the calculation of the gray level at point qhi,j,t is dependent on the l-th to the r-th oblique axial scans, and let the influential points at the n-th oblique axial image be qon,e,t ~qon,f,t, then
t j
qhi,, ’s gray level can be determined according to:
N
Equation (7) therefore stands for the summation of the normalized gray levels from the influential pixels over the total involved oblique axial scans.
3. Experimental Results
In the experiments, the images were taken from the radiology departments of the medical school of National Chung-Kung University. The CT scanner is GE 9800Q. The image size is 512 by 512 and the gray level is 256.
In this experiment we set out to test our proposed image transformation technique. We have acquired two sets of CT images of a phantom, one is the horizontal axial scan (33 images) and the other the oblique axial scan with θ=−15o acquisition angle (32 images). According to what was introduced in Section 2, we transform the set of oblique images to horizontal ones and compared the results to the acquired horizontal images (Figs. 5). Figure (5a) is a scanned horizontal CT slices and Fig. (5b) is the transformed ones. A direct subtraction of the corresponding image pairs reveals the difference after applying our transformation technique (Fig. 5c). It can be seen that the difference is small in general and the significant ones are mostly occurred at tissue 58
boundaries. This is because during transformation, we have assumed the gray levels are identical for those pixels within the thickness line of the oblique images. This assumption poses no threats to most regions that are homogeneous in nature but will result larger variations at regions that have large intensity variations, such as those at the boundaries.
Nevertheless, these differences are small in scale compared to the original image intensities.
4. Conclusion
Integration of multi-modality medical images to improve diagnosis accuracy has been a major trend in current clinical diagnosis applications. Prior feature-based registration methods were based on the assumptions that the to-be registered images were captured with the same acquisition tilt angle and at the same body position, but neither assumption can be guaranteed. In this paper, we propose a technique that reconstructs two sets of medical images acquired with different acquisition angles and anatomical cross sections into one set of images of identical scanning orientation and positions. The space correlation information among the two image stacks is first extracted and is used to correct the tilt angle and anatomical position differences found in the image stacks. Satisfactory reconstruction results were presented to prove our method.
Reference
[1] R. P. Woods, S. R. Cherry, and J. C. Mazziotta,
“Rapid automated algorithm for aligning and reslicing PET images,” J. Comput. Assist.
Tomogr., vol. 16, pp. 620-633, 1992.
[2] R. P. Woods, J. C. Mazziotta, and S. R. Cherry,
“MRI-PET registration with automated algorithm,” J. Comput. Assist. Tomogr., vol. 17, pp. 536-546, 1993.
Input the Image Acquisition Parameters
Simulate Image Acquisition
Evaluate Image Intersection
Calculate Pixel Influence
Generate a New Image Input the Image Acquisition Parameters
Simulate Image Acquisition
Evaluate Image Intersection
Calculate Pixel Influence
Generate a New Image
Figure 1 The sequence of steps about the proposed approach
Figure 2 The silhouette image showing horizontal and oblique acquisition scan lines.
QOi Figure 3 (a) Schematic diagram showing the
intersection points of the i-th horizontal acquisition scan line, QH , the i-th oblique acquisition scan i lines, QO , to the skull boundary, i qhi,1,t, qhi,m,t,
t l
qoi,, and qoi,n,t, respectively. Dotted lines above and below QH reveal the thickness of the i horizontal slice. (b) The oblique-scanned image that constituted the QO .i
horizontal slice thickness
oblique slice thickness oblique slice
Horizontal
Figure 4 Schematic diagram showing that a set of oblique axial scanned images with a slant acquisition angle of θ, is used to reconstruct a set of horizontal axial scanned images.
(a) (b) (c)
Figure 5 Comparison of a true horizontal axial scan CT image (a) and a transformed one (b). The difference between the two images is shown in (c).