Methodology for quantifying and visualizing cell homing/accumulation by combined positron emission tomography and magnetic resonance imaging

ABSTRACT A method for precise and sensitive organ specific quantification of 124 I ( 124 IdUR) activities in small animals has been developed. The method is based upon high precision co-registration between PET and MR imagery, utilizing an innovative mouse fixation system with external point sources visible in bo th modalities. The methodology is generic, and thus can easily be extended to other types of small animal studies. The methodology comprises PET/MR rigid co-registration util ising external fiducial markers, measurement of activities within small 3D volumes manua lly drawn in MR data and extracted from the co-aligned PET data, and subsequently corrected for Partial Volume Effect. Keywords: Small animal imaging, image registration, PET, MRI, cell labelling 1. INTRODUCTION Today it is widely recognized that a variety of imaging technologies can provide valuable information for biology research and diagnosis of clinical patients. In clinical environments, techniques like x-ray computed tomography (CT), magnetic resonance imaging (MR) and ultrasound are used routinely to generate high resolution (~1 mm) images of human anatomy and pathology. In addition, highly sensitive molecular techniques using radio-labelled tracers are used for imaging specific physiological processes like perfusi on, metabolism, receptor binding, and enzyme activity. The classical technique for in-vivo physiological imaging is single-photon emission computed tomography (SPECT), however, more recently positron emission tomography (PET) has become increasingly popular due to better sensitivity and spatial resolution (~5 mm). Also, PET allows studying the distribution and localization of the tracer over time with time resolution down to a few seconds. In many cases, the radioisotope imaging techniques produce im ages with very specific ac cumulation of the tracer in a few selected organs or targets. The absence of general anatomical information complicates interpretation of radioisotope images, both in terms of precise localization of the signal, and separation of specific and non-specific accumulation. Consequently, the need for combining different imaging techniques with complementary information has been recognized, and algorithms for retrospective image registration have been developed (Hill; 2001). A recent