TH-C-201C-07: Reduction of Irregular Breathing Artifacts in Respiration-Correlated CT Images Using a Respiratory Motion Model

Abstract

Purpose: Respiration‐correlated CT (RCCT) images produced with commonly used phase‐based sorting often exhibit discontinuity artifacts between CT slices. Displacement‐based sorting reduces artifacts but missing image data (gaps) may occur. We investigate the application of a respiratory motion model to produce an RCCT image set with reduced artifacts. Method and Materials: Input data consist of CT slices from a cine scan acquired while recording respiration by monitoring abdominal displacement. Model‐based generation of RCCT images consists of 4 processing steps: 1) sorting of CT slices according to respiration signal displacement to form volume images at 10 motion states over the cycle; 2) deformable registration between a reference image at one motion state and each of the remaining images; 3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; 4) application of the motion model to deform the reference image into images at the 9 other motion states. Evaluation is in cine scans of a body phantom programmed to move according to a patient respiratory signal and in patient thoracic scans. Results: Comparison in phantom shows that object distortion caused by variable motion amplitude in phase‐based sorting is visibly reduced with model‐based RCCT. Artifacts in patient images at different motion states are also reduced. Comparison with displacement‐sorted images as a ground truth shows that the model‐based images closely reproduce the ground truth geometry at different motion states. Conclusion: Preliminary results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase‐binned images without the gaps inherent in displacement‐binned images. Further study is needed in phantom and patient cine CT scans including ones with more highly irregular breathing patterns. Research supported by NIH/NCI grant ROI CA126993.