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

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Radiological Physics

Background:

• Respiratory motion in lung lesions poses a significant challenge for accurate Positron Emission Tomography (PET) quantification.
• Precise quantification is vital for applications like therapy monitoring and radiation therapy planning.

Purpose of the Study:

• To develop and validate a motion correction algorithm for PET data affected by respiratory motion in lung lesions.
• To improve the accuracy of quantitative measurements in PET imaging of moving lung lesions.

Main Methods:

• A novel method utilizing list-mode PET data to detect and correct for lesion motion.
• Employs a center of mass approach to track lesion movement and sorts data based on breathing state.
• Applies motion correction by shifting voxels within a defined volume of interest (VOI) relative to end-expiration.

Main Results:

• Phantom studies demonstrated significant differences (up to 50% in volume, 26% in activity concentration) before and after correction.
• Patient data showed maximum changes of 27% in volume and 13% in mean standardized uptake values (SUV) after applying the algorithm.
• The method effectively improved quantification of moving lesions without increasing scan time or reducing signal-to-noise ratio.

Conclusions:

• Motion correction algorithms are essential for accurate PET quantification, especially for lung lesions.
• The described local motion correction method using list-mode data enhances the reliability of PET imaging for clinical applications.
• This technique offers a valuable tool for precise quantification in PET, supporting improved patient management.