Iterative reconstruction algorithms that significantly improve image quality over filtered backprojection methods have been developed for emission tomography. However, most current reconstruction algorithms implicitly assume that the system model is exact. The daunting computational challenge associated with the direct use of an exact system model in each forward and back projection has often led people to adopt less accurate models. This results in increased noise and reduced resolution in reconstructed images, because the effect of the modeling error cannot be corrected in the existing methods. The goal of this grant is to develop a new class of iterative reconstruction methods that can compensate the effect of modeling error. The work is based on our thorough analysis of error propagation from each component in the system model into reconstructed images. The innovation of the new method is that it does not require an exact system model in every forward and back projection. The method can obtain high-resolution images when direct use of an accurate system model in the iterative reconstruction is impractical, and it can also reduce reconstruction time by using simplified fast forward and back projectors without sacrificing image quality. We will first develop the theory of high-resolution iterative image reconstruction with error correction capability. Then we will focus on the application and validation of the theory in positron emission tomography (PET). We will implement new reconstruction algorithms on microPET scanners, and will evaluate the lesion detection and quantitation performance using Monte Carlo simulations, physical phantom experiments, and real animal data. We believe that the new algorithms will provide high-resolution images and accurate quantitative information for understanding human diseases in small animal models. Upon success, we will extend the reconstruction algorithm to clinical imaging systems and will also apply the theory to other imaging modalities, such as X-ray CT, SPECT, MRI, and optical tomography. Lay abstract: Positron emission tomography (PET) is a functional imaging modality that is widely used in clinical and biological studies. This project will develop a novel image reconstruction method for PET which will provide high-resolution images and accurate quantitative information for understanding and treating human diseases.
| Zhou, Jian; Qi, Jinyi (2011) Fast and efficient fully 3D PET image reconstruction using sparse system matrix factorization with GPU acceleration. Phys Med Biol 56:6739-57 |
| Zhou, Jian; Qi, Jinyi (2011) Adaptive imaging for lesion detection using a zoom-in PET system. IEEE Trans Med Imaging 30:119-30 |
| Tohme, Michel S; Qi, Jinyi (2010) Iterative reconstruction of Fourier-rebinned PET data using sinogram blurring function estimated from point source scans. Med Phys 37:5530-40 |
| Fu, Lin; Qi, Jinyi (2010) A residual correction method for high-resolution PET reconstruction with application to on-the-fly Monte Carlo based model of positron range. Med Phys 37:704-13 |
| Tohme, Michel S; Qi, Jinyi (2009) Iterative image reconstruction for positron emission tomography based on a detector response function estimated from point source measurements. Phys Med Biol 54:3709-25 |
