Abstract A88: Voxel-based tracking of 18F-FDG PET images for evaluation of treatment response for NSCLC patients

Purpose: To improve the accuracy of tumor response assessed using 18F-Fluorodeoxyglucose Position Emission Tomography (18F-FDG PET), a new deformable image registration technique was investigated that has potential to more precisely track changes of the standardized uptake value (SUV) at each image voxel. FDG-PET/CT is a standard clinical tool for cancer detection and the assessment of tumor response to treatment. However, due to the lack of anatomical information in the PET images, a point-by-point assessment of treatment response cannot be derived directly from successive PET images until their associated CT images are appropriately registered. Due to tumor reduction following therapy, the conventional image intensity-based deformable image registration algorithms will shrink the tumor volume to match the residual target, but this could cause the responses evaluated from the two PET images mismatched. We propose an improved deformable image registration technique and demonstrate its potential to evaluate tumor response to radiation in lung cancer patients. Methods: An IMRT treatment plan was developed in a Pinnacle system with contours delineated on a 4DCT image. 18F-FDG PET images were acquired before and after treatment. The 4DCT was rigidly aligned to the pre-treatment PET-CT image. Peak activities normalized to the mean intravascular background in the aortic arch were used to determine relative SUV values. The PET images were refined to the same resolution as the associated CT images using a bilinear interpolation. The two PET-CT images were registered initially with an intensity-based B-spline algorithm implemented in the VelocityAI software, and then with an in-house developed adaptive finite element method (FEM) that used a bounding box of the residual tumor volume defined in the pre-treatment PET-CT image. The bounding box was partitioned into a tetrahedral mesh with the boundary constraints assigned from the VelocityAI generated displacement vector field (DVF). The resultant FEM displacements were used to adjust the VelocityAI registration within the box. The pre-treatment PET image was warped to the post-treatment PET using the two DVFs. The relative SUV differences between the warped and post-treatment PET images were counted as the treatment response. The CT images warped by the two registrations were visually inspected, and the constancy of the warped gross tumor volume (GTVCT) between the pre- and post-treatment CT images was taken as an evaluation criterion. The displacement differences between the two registration methods were calculated and their consequences in the warped PET images were evaluated. Results: With the DVFs generated by the VelocityAI and FEM registrations, the warped GTVCT volumes were reduced 32.8% and 5.7%, respectively. The mean displacement difference between the two registrations is 3.8 mm within the bounding box. The relative SUV was reduced to 12.8% in GTVCT after the completion of the treatment, and the averages of the SUV values mapped by the two DVFs are 3.6 and 4.1. Conclusions: The adaptive image registration method developed was used to correct anatomical deformation. The method can preserve peak activities and relative SUVs after registration of regressed tumors, and therefore could be used for tracking SUV changes in the follow-up PET images. Citation Format: Hualiang Zhong, Stephen Brown, Indrin J. Chetty. Voxel-based tracking of 18F-FDG PET images for evaluation of treatment response for NSCLC patients. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A88.