Effect of novel amplitude/phase binning algorithm on commercial four-dimensional computed tomography quality.
OBJECTIVE
Respiratory motion is a significant source of anatomic uncertainty in radiotherapy planning and can result in errors of portal size and the subsequent radiation dose. Although four-dimensional computed tomography allows for more accurate analysis of the respiratory cycle, breathing irregularities during data acquisition can cause considerable image distortions. The aim of this study was to examine the effect of respiratory irregularities on four-dimensional computed tomography, and to evaluate a novel image reconstruction algorithm using percentile-based tagging of the respiratory cycle.


METHODS
Respiratory-correlated helical computed tomography scans were acquired for 11 consecutive patients. The inspiration and expiration data sets were reconstructed using the default phase-based method, as well as a novel respiration percentile-based method with patient-specific metrics to define the ranges of the reconstruction. The image output was analyzed in a blinded fashion for the phase- and percentile-based reconstructions to determine the prevalence and severity of the image artifacts.


RESULTS
The percentile-based algorithm resulted in a significant reduction in artifact severity compared with the phase-based algorithm, although the overall artifact prevalence did not differ between the two algorithms. The magnitude of differences in respiratory tag placement between the phase- and percentile-based algorithms correlated with the presence of image artifacts.


CONCLUSIONS
The results of our study have indicated that our novel four-dimensional computed tomography reconstruction method could be useful in detecting clinically relevant image distortions that might otherwise go unnoticed and to reduce the image distortion associated with some respiratory irregularities. Additional work is necessary to assess the clinical impact on areas of possible irregular breathing.
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Jeffrey R
JR
Olsen
Wei
W
Lu
James P
JP
Hubenschmidt
Michelle M
MM
Nystrom
Paul
P
Klahr
Jeffrey D
JD
Bradley
Daniel A
DA
Low
Parag J
PJ
Parikh
D000230:Adenocarcinoma / Q000000981:diagnostic imaging / Q000532:radiotherapy; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D001650:Bile Duct Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D045853:Exhalation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D001239:Inhalation; D008168:Lung / Q000000981:diagnostic imaging* / Q000502:physiology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D008875:Middle Aged; D009068:Movement; D012119:Respiration; D036542:Tomography, Spiral Computed / Q000379:methods*