Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.
Kuang Gong, Guobao Wang, Kevin T. Chen, Ciprian Catana, and Jinyi Qi, "Nonlinear PET parametric image reconstruction with MRI information using kernel method," Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101321G (Presented at SPIE Medical Imaging: February 15, 2017; Published: 9 March 2017); https://doi.org/10.1117/12.2254273.
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