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Progress In Electromagnetics Research Symposium Proceedings, Suzhou, China, Sept. 12–16, 2011
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Fusion of the T1, T2 Weighted and Perfusion Weighted Images for
Peritumoral Region Evaluation
M. Cap1 , E. Gescheidtova1 , P. Marcon1 , K. Bartusek1, 2 , and A. Sprlakova3
1
Department of Theoretical and Experimental Electrical Engineering
Brno University of Technology, Kolejni 2906/4, 612 00 Brno, Czech Republic
2
Institute of Scientific Instruments, Academy of Sciences of the Czech Republic
Kralovopolska 147, 612 64 Brno, Czech Republic
3
Faculty Hospital Brno, Department of Radiology, Jihlavska 20, 625 00 Brno, Czech Republic
Abstract— MRI is a constantly developing region of medicine, which is suitable for the study
of soft tissues. The current methodologies for obtaining images weighted by relaxation times give
only an idea of the distribution of soft tissues. Differential diagnosis of a high-grade glioms and
solitary metastases is in some cases inconclusive. Investigators in several studies have demonstrated that in perfusion MRI (magnetic resonance imaging) of high-grade glioms and solitary
metastases are differences. Analysis of the peritumoral region could be more useful than the
analysis of the tumor itself. Precise evaluation of mentioned differences in peritumoral region
gives a hopeful chance for tumor diagnosis.
This article describes image processing and fusion of the MR images. T1 weighted (T1W) and
T2 weighted (T2W) images are processed for tumor and peritumoral region detection. By the
fusion of the T1W and T2W images with perfusion weighted images (PWI) is possible to image
perfusion in the peritumoral region in the T1W or T2W image.
1. INTRODUCTION
This article deals with fusion of the perfusion weighted images with T1 , T2 weighted image. The
aim of this work is to create system for tumor and peritumoral region detection, image registration
and fusion of the T2 and perfusion weighted images. The importance of the perfusion imaging
method lies in its ability to describe anatomy and physiology of the tumor and peritumoral region
microvasculature [1]. Several studies have demonstrated that in perfusion MRI of high-grade glioms
and solitary metastases are differences [2–4]. In this article, we are focused on the differences in
perfusion weighted images in the peritumoral region.
Three sets of images were processed. T1 and T2 weighted images are used for detection of
the tumor and peritumoral region size and position. These two image sets contain 22 images.
Resolution of the T1 weighted images is 256 × 256 px (0.9 mm × 0.9 mm pixel size), resolution of
the T2 weighted images is 512 × 512 px (0.45 mm × 0.45 mm pixel size). Fig. 1 gives as examples of
the processed images. Perfusion images are acquired each 1.22 s with space resolution 64 × 64 px
(pixel size 3.4 mm × 3.4 mm).
Image fusion requires precise image registration. In an effort to obtain the most accurate diagnosis is the image registration the key part of the image fusion algorithm. Hence, the PWI images
are processed wit accuracy of ±1 pixel. With the aim of image fusion the structural image is
processed for tumor and peritumoral region detection. Selection of the peritumoral region area in
PWI image and its imaging in structural image will be the aim of this work.
Figure 1: T1 weighted image, T2 weighted image and perfusion weighted image of the same slice.
PIERS Proceedings, Suzhou, China, September 12–16, 2011
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2. METHODS
2.1. Image Registration
The first step for image fusion is the image registration. Precise position detection in both images is
necessary for right perfusion evaluation in peritumoral region. Detection of the size and position of
the patient’s brain is based on the interhemispheric fissure position as can be seen in Fig. 2. Position
of the interhemispheric fissure is detected from the local minimum in the frontal and occipital lobe.
Each value is calculated as an average of 10 positions of the local minimum in surrounded rows. PW
images are acquired with low resolution, only 64 × 64 px. For size adjustment the bilinear method
is used. Pixel is calculated as a weighted average of pixels in the nearest 2-by-2 neighborhood.
2.2. Segmentation
Most important part of the work is the segmentation of the regions of interest. Both structural
images are suitable for tumor detection. T2W image was chosen for higher contrast in peritumoral
region as you can see in Fig. 1. The goal of segmentation is to find position of the tumor and
peritumoral region. As an input for image fusion are used. Segmentation method is based on the
detection of the high intensity of the tumor region [6].
Used method has two steps. At first, the high contrast area of the tumor is detected. Tumor
area intensity exceeds a threshold which is adjusted according to whole image intensity. Rest of
the tumor is detected by the fine detection on the boundaries of previously detected area. The
threshold is now lower then before for detection of the parts of the tumor area with smaller image
intensity. In the second step the incorrectly detected areas are removed or added. Results of the
tumor and peritumoral region detection are in Fig. 3.
Figure 2: Results of the interhemispheric fissure detection. T2W image left, PWI image right.
Figure 3: On the top the detected tumor, on bottom the detected peritumoral region in T2W image.
Figure 4: Results of T2W and PWI images fusion.
Progress In Electromagnetics Research Symposium Proceedings, Suzhou, China, Sept. 12–16, 2011
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Signal intensity in peritumoral region is not as significantly higher as is in tumor area. Algorithm for peritumoral region detection uses a similar method to method used for tumor detection.
Algorithm detects higher signal intensity close to the tumor and signal loss on the peritumoral
region boundaries. By use of a threshold dependent on the signal intensity it was possible to detect
the peritumoral region with sufficient accuracy.
2.3. Image Fusion
Detection of the tumor and peritumoral region gave us knowledge about the position of these
objects. These positions are used for a mask creating. By multiplying the mask with PWI image
the perfusion in peritumoral region is obtained. Results of the image fusion can be seen in Fig. 4.
3. RESULTS
Results of the tumor and peritumoral region detection algorithms are presented in Fig. 4. Result
of both algorithms is the mask with the same size as the T2W image. Resulting mask is obtained
as the peritumoral region mask reduced about the tumor mask.
Fusion is provided for all 60 PWI images. Four images in Fig. 4 were scanned in time: top left
image 3.66 s, top right image 21.96 s, bottom left image 34.16 s and bottom right image 48.80 s.
PWI images were processed for size and position correction and fit to T2W image.
4. CONCLUSION
The paper presents registration, segmentation and fusion of the T2W and PWI images. Interhemispheric fissure were localized and used for image registration. Detection of the tumor and
peritumoral region was the key part of this work. Precise evaluation of the peritumoral region
requires providing all of these detections with high accuracy. Aim of the presented work was the
research into methods, which will enable distinguishing between individual types of tumor. Presented result holds possibility to be useful for more precise tumor diagnosis. In the further work
the considerable attention will be devoted to the peritumoral region evaluation in all slices as well
as a calculation of perfusion time characteristics in selected regions.
ACKNOWLEDGMENT
This work was supported within the framework of projects No. 102/11/0318 of the Grant Agency
of the Czech Republic and the research plan MSM 0021630513.
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