A Novel Method to Improve Image Quality for 2-D Small Animal PET Reconstruction by Correcting a Monte Carlo-Simulated System Matrix Using an Artificial Neural Network

The aim of this study was to improve image quality of statistical reconstruction by using the 2-D system matrix (SM) trained with an artificial neural network (ANN). For training the ANN SM (SM_ANN), the inputs of ANN, the digital images, were generated by scanning the mini-deluxe cold spot phantom at ten different orientations using an optical scanner (resolution: 0.01 mm/pixel). The desired outputs were generated by acquiring the projection data with the corresponding angles using the micro positron emission tomography (microPET) R4. In the ANN method, the ADALINE network with a bias vector and a momentum term were used. Moreover, a multiline-source phantom and a four-segment phantom were scanned to obtain the spatial resolutions and the quantitative accuracy for comparison, respectively. A rat FDG microPET image was acquired to compare the difference between the results reconstructed by the microPET´s built-in 2-D-ordered subsets expectation maximization algorithm (OSEM), OSEM_microPET, OSEM by Monte Carlo simulated SM, OSEM_SMd, and OSEM by the SM_ANN, OSEM_ANN. In the multiline-source experiment, the resolutions of OSEM_microPET measured at center, 10, and 20 mm from the center were 1.61, 1.78, and 2.30 mm, respectively. The resolutions of OSEM_SMd and OSEM_ANN were 1.24, 1.68, and 1.87, and 1.28, 1.62, and 1.72 mm, respectively. In the results of the four-segment phantom, the sum of absolute error of the truth versus the values reconstructed by OSEM_microPET, OSEM_SMd, and OSEM_ANN were 1085.53, 913.48, and 435.02, respectively. By interpreting the results of the evaluation, the image quality reconstructed by the SM_ANN is better than that reconstructed by the original SM. The results indicated that SM can be updated toward ideal SM using ANN for statistical reconstruction.