PET imaging provides information on functional or metabolic characteristics of tumors, whereas CT predominately assesses tumor's anatomical and morphological features. Given the ability of PET to localize malignancies in situations where the same tumors do not have a CT correlate, PET/CT guided biopsy helps to improve the diagnostic yield of liver lesion biopsies. However, the diagnostic benefit of PET images, even from hybrid PET/CT machines, is gravely affected by respiratory motion artifacts in current practice. CT data acquisition is rapid and represents a relatively instantaneous snapshot during the breathing cycle. In contrast, PET data acquisition typically requires at least one minute using the newest scanners, but often requires as long as five minutes per gantry table position. These prolonged acquisitions lead to respiratory motion artifacts in the PET images, which are particularly pronounced in the lower lung fields and in the liver. These artifacts cause discrepancies in the spatial correspondence between the CT and PET data, potentially leading to inaccurate tumor localization and incorrect tumor staging. Hence, the development of an effective PET-CT guided biopsy methodology for liver lesions requires a robust respiratory motion correction technique. The overall goal of this project is to improve the clinical effectiveness of liver lesion biopsy by fusing respiratory- compensated PET/CT with CT images in the interventional CT suite. During Phase I, we developed a motion compensation technique that uses phase-matched respiratory-gated PET and CT data to generate motion-free high quality PET and CT images. The method was shown to generate high-quality, motion-free PET images. It was validated using simulated PET and CT phantom data. The method was integrated into our open source image-guided surgery software toolkit (IGSTK). In addition, we extended the toolkit's ability to handle PET image data and generate clinically effective fused PET/CT visualization. We then demonstrated the feasibility of these methods by integrating them into an application prototype and using it to target 18Fluordeoxyglucose (FDG) filled spherical vials implanted in an anthropomorphic phantom. In this Phase II effort, we propose to demonstrate the clinical utility of these techniques by 1) refining and evaluating our respiratory motion-compensation technique using patient data;and 2) conducting a clinical trial feasibility study.
PET/CT imaging plays a key role in early detection, staging, and evaluation of suspicious lesions. PET imaging provides information on functional or metabolic characteristics of tumors, whereas CT predominately assesses the tumor's anatomical and morphological features. By fusing these two imaging techniques and incorporating advanced image registration and respiratory motion compensation techniques, clinicians can more effectively biopsy liver lesions, which would improve cancer screening and diagnosis.
|Khare, Rahul; Sala, Guillaume; Kinahan, Paul et al. (2013) Experimental Evaluation of a Deformable Registration Algorithm for Motion Correction in PET-CT Guided Biopsy. IEEE Nucl Sci Symp Conf Rec (1997) 2013:|
|Pace, Danielle F; Enquobahrie, Andinet; Yang, Hua et al. (2011) Deformable Image Registration of Sliding Organs Using Anisotropic Diffusive Regularization. Proc IEEE Int Symp Biomed Imaging :407-413|