Local motion correction for lung tumours in PET/CT—first results

Abstract

Respiratory motion of lung lesions is a limiting factor of quantification of positron emission tomography (PET) data. As some important applications of PET such as therapy monitoring and radiation therapy treatment planning require precise quantification, it is necessary to correct PET data for motion artefacts. The method is based on list-mode data. First, the motion of the lesion was detected by a centre of mass approach. In the second step, data were sorted corresponding to the breathing state. A volume of interest (VOI) around the lesion was defined manually, and the motion of the lesion in this VOI was measured with reference to the end-expiration image. Then, all voxels in the VOI were shifted according to the measured lesion motion. After optimisation of parameters and verification of the method using a computer-controlled motion phantom, it was applied to nine patients with solitary lesions of the lung. Fifty percent difference in measured lesion volume and 26% in mean activity concentration were found comparing PET data before and after applying the correction algorithm when simulating a motion amplitude of 28 mm in phantom studies. For patients, maximum changes of 27% in volume and 13% in mean standardised uptake values (SUV) were found. As respiratory motion is affecting quantification of PET images, correction algorithms are essential for applications that require precise quantification. We described a method which improves the quantification of moving lesions by a local motion correction using list-mode data without increasing acquisition time or reduced signal-to-noise ratio of the images.