PET/CT Quantitative Analysis for Small Animal PET/CT 942001INER017(NL940022) 2005 12 1 1
1.... 3 2.... 4 3.... 5 4.... 6 5.... 7...7...7...8...11 6.... 17 7.... 18 2
1. PET/CT (PET) X (CT) PET CT PET/CT CT PET PET 511 kev (LOR) 3
2. Functional PET imaging using targeted molecular probes has become very important in many drug development using small animal models. However, probe uptakes limited to specific targeted areas greatly increase diagnostic difficulty. However, small animal PET/CT scanners have now been recognized as a powerful diagnostic modality. PET/CT scanner provides hardware image fusion capabilities which will make interpretation of PET images much easier due to the anatomical landmarks offered by CT scans. In addition, the CT data can be used to correct PET scans for photon attenuation. The thesis of two-year project is the development of a quantitative imaging system that provides accurate bio-distribution measurement of positron-labeled molecular images for combined 3D PET/CT scanners. The imaging system consists of two major research tasks: (1) automatic CT image segmentation, and (2) accurate estimation of attenuation correction factors for PET imaging. An automatic segmentation algorithm based of fuzzy clustering will be developed for whole-body CT image. After image segmentation, each region of the partitioned CT image, representing one type of material, is then assigned a unique attenuation coefficient of 512 kev photons. The corresponding ACF can be computed via accurately forward projection the 512keV attenuation image. It is anticipated that the newly developed system can provide some quantitative PET image using the co-registered CT image for small animals. 4
3. (microct) (micropet) 511 kev 5
4. PET/CT (PET) X (CT) PET CT PET/CT CT PET PET PET/CT 6
5. 511 kev 1 (Cluster Analysis) CT (Homogeneity) 7
Centroid Euclidean Distance 2 M Distance x, = x jp µ ip p= 1 1 2 µ µ i x j M N N*M M 1 C-Means Clustering ^ µ C C ( x ) ( x µ ) 2 1 if min j i P ωi j 0 otherwise P( ω ) i x j 1 0 8
1973 Bezdek Fuzzy K-Means Clustering, FCM P( ω ) i x j 0 1 0 1 FCM Cost Function J fuz = c i n j ( x ) b P ωi j x j µ i b b b b 2 P( ω ) i x j 2 µ x x P( ω ) i x j µ P( ω i x j ) P( ω ) i x j µ µ 1 2 J ( x j µ i) fuz = 0 P( ωi xj) = P( ωi xj) c 1 2 i ( x j µ i) 1 b 1 1 b 1 (4) 9
b { P( ωi xj) xj } P( ωi xj) J fuz = 0 µ = µ i n j= 1 i n b j= 1 FCM µ i FCM C 4 P( ω ) N*C P( ω ) i x j i x j N C 5 µ i ( k 1) ( k ) µ µ. = ( k ) µ. FCM k=k+1 10-5 [13] FCM x FCM Homogeneous 10
FCM FCM In-homogeneity FCM FCM CT CT CT Gaussian Distribution f () x = ( x µ ) 1 2 2σ e 2πσ 2 FCM CT Partial Volume Effect CT PET PET CT Forward Projection LOR CT LOR PET 10 PET CT PET FCM 11
Feature Extraction 25 j= 1 ( x j x) 2 S = 2 24 25 Standard Deviation CT CT FCM CT CT FCM FCM CT Table 1 CT CT CT CT Soft Tiisue FCM 3 CT 4 12
FCM 4 10 FCM FCM 6 CT FCM Fig. 2 FCM FCM 13
3*3 FCM 3 Fig. 5 CT FCM CT FCM FCM FCM 3 Hypothesis Testing 3 99.7% 3 Fig. 3(a) CT Fig. 3(b) FCM 14
FCM a b c d Fig. 3(d) CT FCM FCM 15
Fig. 3(c) FCM FCM (a) (b) Figure 4. (a) INER microct images, (b) Segmentation results. Fig 4 INER micropet PET/CT 16
6. CT FCM FCM FCM Hierarchical CT PET 17
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