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Spatiotemporal 4D PET Image Reconstruction

Spatiotemporal 4D PET Image Reconstruction. Arman Rahmim, PhD arahmim1@jhmi.edu Division of Nuclear Medicine Department of Radiology Johns Hopkins University. Conventional vs. 4D Reconstruction. Independent frame reconstruction of dynamic frames can result in very noisy images

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Spatiotemporal 4D PET Image Reconstruction

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  1. Spatiotemporal 4D PET Image Reconstruction Arman Rahmim, PhD arahmim1@jhmi.edu Division of Nuclear Medicine Department of Radiology Johns Hopkins University

  2. Conventional vs. 4D Reconstruction • Independent frame reconstruction of dynamic frames can result in very noisy images • Spatiotemporal 4D PET reconstruction moves beyond this conventional scheme* • For a given temporal sampling, improved noise is obtained; and vice versa (improved temporal resolution vs. noise trade-off). • For kinetic parameter estimation, exact modeling of noise distribution is difficult/time-consuming • Direct 4D parameter estimation: directly relate parametric image to measured data (which have simple Poisson distribution) • i.e. No need to model noise in intermediate reconstructed images Estimated parametric DV images in simulated raclopride imaging Noise (NSD) Increasing EM iterations Varying FBP filters Bias (NMSE) Increasing EM iterations (3, 5, 10, 20, 40)

  3. 4D FDG image reconstruction Patients with metastatic renal cell carcinoma SUV PET Standard Patlak Direct 4D Patlak Increasing EM iterations (1, 2, 3, 5, 7, 10; 21 subsets) 7 Patients with metastatic renal cell carcinoma 4D imaging: significant improvements (p<0.005) in Tumor/Background CNR compared to (i) SUV and (ii) standard parametric images, by 67% and 39% respectively.

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