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The Optimization of Reconstruction Method Reducing Partial Volume Effect in PET/CT 3D Image Acquisition Department of Nuclear Medicine, Samsung Medical.

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Presentation on theme: "The Optimization of Reconstruction Method Reducing Partial Volume Effect in PET/CT 3D Image Acquisition Department of Nuclear Medicine, Samsung Medical."— Presentation transcript:

1 The Optimization of Reconstruction Method Reducing Partial Volume Effect in PET/CT 3D Image Acquisition Department of Nuclear Medicine, Samsung Medical Center Department of Nuclear Medicine, Samsung Medical Center Gun Chul HONG

2 The underestimate phenomenon due to limit spatial resolution Introduction Low accuracy for quantitative analysisIncreasing error in the small-sized lesion

3 The evaluation of effect according to sphere size Purpose The variation of SUV the PET image according to the changed reconstruction parameters Selection of parameter value to reduce the PVE effectively

4 Materials and Methods GE-Discovery STE Scanner Specifications Spatial resolution : 5.0 ㎜ Sensitivity : 8.5 cps / KBq DFOV : 50 ㎝ Cristal : BGO

5 Materials and Methods NEMA IEC/2001 Body Phantom Phantom Specifications Dimensions : 24.1 x 30.5 x 24.1 ㎝ Spheres Inner Diameter : 10, 13, 17, 22, 28, 37 ㎜ Cylinder Volume : 9.7 ℓ Shipping Weight : 4.9 ㎏

6 PET Scan 10 min/bed CT scan Sphere 0.06 MBq/ ㎖ Background 0.01 MBq/ ㎖ Materials and Methods

7 Iteration : 2 ~ 50 Matrix size : 128 Ⅹ 128 Z axis Filter : Standard Image Reconstruction Reconstruction method Diameter : 50 Post Filter : 4.29 FWHM Subset : 1 ~ 56

8 Measurement of SUV bw set ROI in the image after reconstruction max - true % Difference = max SNR = ( SUV + background SUV ) ( SUV - background SUV ) Reconstruct to measure data 37mm 28mm 22mm 17mm 13mm 10mm Background SUV bw = ( Total activity / Body weight ) ( Tissue activity / tissue mass ) Image Analysis

9 Reconstruction Image 2 iteration 6 iteration 15 iteration 2 subset8 subset35 subset Results

10 SUV - Iteration 24610131520304050 37 ㎜ 5.665.805.865.945.96 5.986.04 28 ㎜ 5.525.645.685.74 5.78 5.76 22 ㎜ 5.405.505.565.525.545.58 17 ㎜ 4.845.325.385.40 5.44 13 ㎜ 3.704.504.864.804.78 4.804.824.80 10 ㎜ 2.32 3.103.603.803.88 3.90 Results Subset : 20 SUV : g/ml sphere number

11 SUV - Iteration Results 68%

12 % Difference - Iteration Results

13 The sum of SNR - Iteration Results 2.8 2.9 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 24681013153050 # SNR

14 1257810203556 37mm 3.24.85.55.6 5.75.85.95.8 28mm 2.74.05.45.55.6 22mm 2.23.34.95.4 5.55.4 5.5 17mm 1.72.54.34.95.05.25.35.45.3 13mm 1.41.83.13.84.04.24.6 4.7 10mm 1.41.53.1 3.33.53.73.8 SUV - Subset Results Iteration : 20 SUV : g/ml sphere number

15 SUV - Subset Results 170 %

16 % Difference - Subset Results

17 1.9 2.1 2.3 2.5 2.7 2.9 3.1 3.3 3.5 3.7 12578102035 56 # The sum of SNR - Subset Results SNR

18 We could change the PVE using iteration and subset parameters in the reconstruction method Because of time constrains in performing the data reconstruction, increment of iteration and subset must be considered with time required for reconstruction Summery

19 As number of iteration and subset is increased, SUV max is increased up 68.1 percent at the 10 ㎜ sphere at the 10 ㎜ sphere The most effective range to decrease the PVE at the small-sized lesion is 6 iteration and 8~20 subset parameters considering reconstruction time the small-sized lesion is 6 iteration and 8~20 subset parameters considering reconstruction time Conclusion

20 Thank You


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