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Generation of Parametric Image of Regional Myocardial Blood Flow Using H215O Dynamic PET and a Linear Least-Squares Method

Jae Sung Lee, PhD1,2, Dong Soo Lee, MD, PhD1, Ji Young Ahn, PhD1, Jeong Seok Yeo, MD1, Gi Jeong Cheon, MD1, Seok-Ki Kim, MD1, Kwang Suk Park, PhD1,2, June-Key Chung, MD1 and Myung Chul Lee, MD1

1 Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea
2 Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea



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  		FIGURE 1. (A) CV, (B) bias, and (C) error in estimation of MBFp (f) calculated by computer simulation.

 


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  		FIGURE 2. Parametric images generated using LLS method from dynamic H215O canine PET study at rest. (A) MBFt ({alpha}·f). (B) PTF ({alpha}). (C) Arterial blood volume fraction (Va).

 


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  		FIGURE 3. Parametric images generated by LLS method from dynamic H215O canine PET study after dipyridamole stress. (A) MBFt ({alpha}·f). (B) PTF ({alpha}). (C) Arterial blood volume fraction (Va).

 


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  		FIGURE 4. Correlation between MBF (f) values. (A) Parametric image vs. ROI method (y = 0.84x + 0.40; r = 0.99). (B) Parametric image vs. microsphere technique (y = 0.95x + 0.29; r = 0.96). (C) ROI method vs. microsphere technique (y = 1.12x – 0.13; r = 0.96).

 


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  		FIGURE 5. Parametric image of MBFt generated from rest H215O PET data of healthy volunteer. (A) Static image. (B) MBFt ({alpha}·f) parametric image superimposed on static image. (C) MBFt parametric image.

 




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