Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment
In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.