Summary
Objectives:
Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal
tumors. Methods for compensation require comprehensive knowledge of underlying dynamics.
Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan
a large region of interest simultaneously. So patients have to be scanned in segments.
Commonly used approaches for reconstructing the data segments into 4D CT images cause
motion artifacts. In orderto reduce the artifacts, a new method for 4D CT reconstruction
is presented. The resulting data sets are used to analyze respiratory motion.
Methods:
Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice
CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow
based temporal interpolation. The resulting 4D image data were compared with data
generated bythe commonly used nearest neighbor reconstruction. Subsequent motion analysis
is mainly concerned with tumor mobility.
Results:
The presented optical flow-based method enables the reconstruction of 3D CT images
at arbitrarily chosen points of the patient’s breathing cycle. A considerable reduction
of motion artifacts has been proven in eight patient data sets. Motion analysis showed
that tumor mobility differs strongly between the patients.
Conclusions:
Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction
offers the possibility of high-quality motion analysis. Because the method is based
on an interpolation scheme, it additionally has the potential to enable the reconstruction
of 4D CT data from a lesser number of scans.
Keywords
Computed x-ray tomography - 4D CT - computer-assisted image analysis - respiratory
motion - lung cancer
