Improved factor analysis of dynamic PET images to estimate arterial input function and tissue curves

Abstract. Factor analysis of dynamic structures (FADS) is a methodology of extracting time-activity curves (TACs) for corresponding different tissue types from noisy dynamic images. The challenges of FADS include long computation time and sensitivity to the initial guess, resulting in convergence to local minima far from the true solution. We propose a method of accelerating and stabilizing FADS application to sequences of dynamic PET images by adding preliminary cluster analysis of the time activity curves for individual voxels. We treat the temporal variation of individual voxel concentrations as a set of time-series and use a partial clustering analysis to identify the t ypes of voxel TACs that are most functionally distinct from each other. These TACs provide a good initial guess for the temporal factors for subsequent FADS processing. Applying this approach to a set of single slices of dynamic 11 C-PIB images of the brain allows identification of the arterial input function and two di fferent tissue TACs that are likely to correspond to the specific and non-specific tracer binding-tissue types. These results enable us to perform direct classification of tissues based on their pharmacokinetic properties in dynamic PET without relying on a compartment-based kinetic model, without identification of the reference region, or without using any external methods of estimating the arterial i nput function, as needed in some techniques. Key Words: Dynamic image analysis, Factor analysis in dynamic structures, Image derived arterial input function, PET brain imaging, Alzheimers studies 1. DESCRIPTION OF PURPOSEDynamic imaging (DI) is the general term for procedures in different imaging modalities where the information is acquired over time, thus, allowing one to follow the temporal changes in the quantity represented in the images. In emission tomography, DI a llows one to observe the changes in the distribution of the injected radioactive tracer, thus, probing the kinetic parameters of in vivo tissues. DI has been implemented in most popular medical imaging modalities, including single photon emission computed tomography (SPECT), positron emission tomography (PET), x-ray computed tomography (CT), and magnetic resonance imaging (MRI). In particular, dynamic PET and SPECT are two primary molecular and functional imaging methods. In these modalities, DI provi des additional insight into the pharmacokinetics of the radiotracer and, potentially, better dose utilization, since the scanning begins at the moment of injection, and no doze is wasted while waiting for the tracer distribution to stabilize. The expected output of DI is a 4D dataset that describes the concentration of the radionuclide in space and time, both the dimensions appropriately discretized. Direct application of DI in PET is challenging because the time needed to acquire a dataset with sufficient number of counts for adequate image reconstruction is much longer than the desired time wh en the tracer distribution changes significantly. At the required temporal resolution, images of single recons tructed time frames are extremely noisy. In SPECT with a rotating gantry, this issue is further exacerbated by the projection data consistency requirements. In order to enable DI reconstruction, the unknown 4D data are often represented as a temporal expansion, described by a few 3D parametric datasets. In spectral methods the basi s of this expansion consis ts of known functions, such as B-splines or Gaussians [Reutter07]. In factor analysis of dynamic structures (FADS) the expansion functions are reconstructed along with the expansion coefficients [Di Pola82; Sitek00; Klein08; Vaclav11]. We recently proposed a combination of the two methods for dynamic cardiac SPECT applications [Abdalah14]. All of these methodologies are extremely sensitive to the initialization of the