Generation of 4D CT image data and analysis of lung tumour mobility during the breathing cycle.
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Generation of 4D CT image data and analysis of lung tumour mobility during the breathing cycle. / Handels, Heinz; Werner, Rene; Frenzel, Thorsten; Säring, Dennis; Lu, Wei; Low, Daniel; Ehrhardt, Jan.
In: Stud Health Technol Inform, Vol. 124, 2006, p. 977-982.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Generation of 4D CT image data and analysis of lung tumour mobility during the breathing cycle.
AU - Handels, Heinz
AU - Werner, Rene
AU - Frenzel, Thorsten
AU - Säring, Dennis
AU - Lu, Wei
AU - Low, Daniel
AU - Ehrhardt, Jan
PY - 2006
Y1 - 2006
N2 - The mobility of lung tumours during the breathing cycle is a source of error in radiotherapy treatment planning. Spatio-temporal CT data sets can be used to measure the movement of lung tumours caused by breathing. Because modern CT scanners can only scan a limited region of the body simultaneously at different times, patients have to be scanned in segments consisting of multiple slices. For studying free breathing motion multislice CT scans can be collected simultaneously with digital spirometry over several breathing cycles. The image data set is assembled by sorting the free breathing multislice CT scans according to the couch position and the tidal volume. But artefacts can occur because there are no data segments for exactly the same tidal volume and all couch positions. In this paper, a non-linear registration method is used to interpolate and reconstruct 4D CT data sets from multislice CT scans in high quality. The non-linear registration estimates a velocity field between successive scans, which is used to reconstruct a 4D CT data set by interpolating data at user-defined tidal volumes. By this technique, artefacts can be reduced significantly. Furthermore, the reconstructed 4D CT data sets are used for studying the motion of lung tumours during the respiratory cycle. The reconstructed 4D data sets of 4 patients were used to quantify the individual lung tumour motion as well as to estimate the tumour's appearance probability during a breathing cycle.
AB - The mobility of lung tumours during the breathing cycle is a source of error in radiotherapy treatment planning. Spatio-temporal CT data sets can be used to measure the movement of lung tumours caused by breathing. Because modern CT scanners can only scan a limited region of the body simultaneously at different times, patients have to be scanned in segments consisting of multiple slices. For studying free breathing motion multislice CT scans can be collected simultaneously with digital spirometry over several breathing cycles. The image data set is assembled by sorting the free breathing multislice CT scans according to the couch position and the tidal volume. But artefacts can occur because there are no data segments for exactly the same tidal volume and all couch positions. In this paper, a non-linear registration method is used to interpolate and reconstruct 4D CT data sets from multislice CT scans in high quality. The non-linear registration estimates a velocity field between successive scans, which is used to reconstruct a 4D CT data set by interpolating data at user-defined tidal volumes. By this technique, artefacts can be reduced significantly. Furthermore, the reconstructed 4D CT data sets are used for studying the motion of lung tumours during the respiratory cycle. The reconstructed 4D data sets of 4 patients were used to quantify the individual lung tumour motion as well as to estimate the tumour's appearance probability during a breathing cycle.
M3 - SCORING: Zeitschriftenaufsatz
VL - 124
SP - 977
EP - 982
ER -
