Data-driven gating (DDG)-based motion match for improved CTAC registration

Abstract

Background: Respiratory motion artefacts are a pitfall in thoracic PET/CT imaging. A source of these motion artefacts within PET images is the CT used for attenuation correction of the images. The arbitrary respiratory phase in which the helical CT ( ${\text{CT}}_{\text{helical}}$ ) is acquired often causes misregistration between PET and CT images, leading to inaccurate attenuation correction of the PET image. As a result, errors in tumour delineation or lesion uptake values can occur. To minimise the effect of motion in PET/CT imaging, a data-driven gating (DDG)-based motion match (MM) algorithm has been developed that estimates the phase of the ${\text{CT}}_{\text{helical}}$ , and subsequently warps this CT to a given phase of the respiratory cycle, allowing it to be phase-matched to the PET. A set of data was used which had four-dimensional CT (4DCT) acquired alongside PET/CT. The 4DCT allowed ground truth CT phases to be generated and compared to the algorithm-generated motion match CT (MMCT). Measurements of liver and lesion margin positions were taken across CT images to determine any differences and establish how well the algorithm performed concerning warping the ${\text{CT}}_{\text{helical}}$ to a given phase (end-of-expiration, EE).

Results: Whilst there was a minor significance in the liver measurement between the 4DCT and MMCT ( $p=0.045$ ), no significant differences were found between the 4DCT or MMCT for lesion measurements ( $p=1.0$ ). In all instances, the ${\text{CT}}_{\text{helical}}$ was found to be significantly different from the 4DCT ( $p<0.001$ ). Consequently, the 4DCT and MMCT can be considered equivalent with respect to warped CT generation, showing the DDG-based MM algorithm to be successful.

Conclusion: The MM algorithm successfully enables the phase-matching of a ${\text{CT}}_{\text{helical}}$ to the EE of a ground truth 4DCT. This would reduce the motion artefacts caused by PET/CT registration without requiring additional patient dose (required for a 4DCT).

Keywords: 4DCT; Data-driven gating; Respiratory motion.

Fig. 1

Flowchart outlining MMCT algorithm workflow

Fig. 2

Schematic illustrating typical respiratory waveform, with EI and EE segments for the first waveform marked

Fig. 3

CT image illustrating displacement measurement of the superior liver to inferior image boundary, representing measurement (a)

Fig. 4

4DCT (a) and MMCT (b) for an example patient

Fig. 5

Boxplot illustrating differences in percentage change in superior liver margin for ${\text{CT}}_{\text{helical}}$ and MMCT CTAC when compared against the 4DCT group for EE

Fig. 6

Boxplot illustrating differences in percentage change in superior lesion margin for ${\text{CT}}_{\text{helical}}$ and MMCT CTAC when compared against the 4DCT group

Fig. 7

Boxplot illustrating differences in percentage change in inferior lesion margin for ${\text{CT}}_{\text{helical}}$ and MMCT CTAC when compared against the 4DCT group

Fig. 8

a 4DCT image illustrating artefact caused by the gating of the 4DCT file. b MMCT image illustrating the ’bubbled’ artefact across the liver margins