True Blood Flow Imaging in Tumors Using Spatially Constrained PET/MRI Modeling.

436 Objectives Blood flow imaging in cancer can shed light on important physiological information about the tumor, both in treatment planning and post treatment evaluation. However, in classical blood flow imaging, the parameter of interest, the flow rate, is unidentifiable, and instead a meta-parameter K1 (or Ktrans) is modeled. Here we aim to show a method for accurate and reproducible measurement of the true blood flow rate using simultaneous PET/MR imaging. Instead of using the MR component to provide structural data, we use it to make simultaneous measurements of physiological data. Such an acquisition allows for synergistic joint modeling of the kinetic data acquired from both modalities. This joint modeling has several benefits, including the ability to overcome low signal-to-noise applications, as well as the ability to obtain accurate estimates for previously unidentifiable parameters, such as flow. For this reason we apply this method to tumor imaging of blood flow with 15O-water PET and DCE-MRI, to extract true blood flow values on a voxel-wise basis. Methods A rabbit was implanted in its flank with a VX-2 tumor xenograft, and underwent serial PET/MRI scans (Siemens Biograph). The rabbit was serially imaged with 15O-water (~5 mCi per injection) and simultaneously with DCE-MRI after an injection of Magnevist. Data was modeled in two ways. First, voxel-wise time activity curves (TACs) were modeled with standard techniques, fitting the metaparameter K1, and using the PET and MRI data independently. Second, the same TACs were analyzed using a joint estimation method, which used the previously unidentifiable parameter or blood flow rate which also had a spatial smoothness constraint. This joint estimation method constrains parameters which are shared between modalities, such as the blood flow rate, and the capillary surface area per voxel. Additionally, a single global parameter was fit for the ratio of capillary permeability between water and Magnevist. This optimization was performed using the Alternating Direction Method of Multipliers (ADMM), which only requires a single parameter to be shared between the two modalities. Results We found that although mean parametric images of 15O-Water K1 were similar between the standard and ADMM approach, there was much greater test-retest consistency of the K1 parameter when modeled with the joint estimation approach. Average voxel-wise correlation increased from ~0.34 to ~0.65 across 3 replicates when moving from the standard approach to the spatially constrained joint estimation. Furthermore, the joint estimated method allows for extraction of the true blood flow parameter as opposed to the meta-parameter K1, which is also a function of capillary permeability and surface area (PS product). Figure 1 shows DCE-MRI of the tumor showing a necrotic core (a), as well as the PS product (b) and true blood flow values (c) for the tumor. Additionally, the joint estimation approach allowed for the measurement of the permeability ratio between Magnevist and water, which was highly conserved across the three imaging datasets. Conclusions We present a method for joint estimation of PET and DCE-MRI data for tumor blood flow imaging which allows for the reproducible extraction of the previously unidentifiable true blood flow. This method allows for more accurate measurement of tumor physiology, which can be used in optimizing treatment planning and more accurately evaluating treatment response.