A hybrid algorithm for PET/CT image merger in hybrid scanners.
PURPOSE: To improve the PET image quality of a hybrid PET/CT scanner by merging CT borders with PET texture. PET/CT scanners provide both high-resolution CT images showing anatomical details and PET images of low-resolution physiological information about radiopharmaceutical uptake. Standard smoothing of noisy PET images may further impair PET resolution, reducing small lesion detectability. METHODS: The CT edge data and the PET texture data were merged using a modified form of an algorithm called HCT (hybrid computed tomography). In merged PET/CT images, each PET pixel value was estimated by iteratively applying a corrected 2D Taylor expansion to each of its eight neighbors. The spatial derivative term was used only near anatomical edges provided by the CT. This counts-preserving algorithm was tested on a special resolution phantom and patient data sets obtained by PET/CT acquisitions. RESULTS: The HCT algorithm provided phantom PET images with sharp borders and improved resolution (< or = 3 mm as compared to > or = 4 mm). HCT increased the signal to background contrast ratios by an average of 61% (40-89%) while maintaining noise reduction similar to the Gaussian filtering standard in PET. In the clinical PET images, HCT allowed for an improved delineation of pulmonary and pelvic lesions and an improved visualization of the brain. CONCLUSION: A new reconstruction algorithm for merging CT anatomical edge data with functional PET data has been introduced. The algorithm smooths noisy PET images while retaining sharper edges at corresponding anatomical borders, resulting in an improvement in resolution and contrast ratio.