MR-based truncation and attenuation correction in integrated PET/MR hybrid imaging using HUGE with continuous table...

The objective of this study was to introduce and evaluate a method for MR-based attenuation and truncation correction in phantom and patient measurements to improve PET quantification in PET/MR hybrid imaging.

The fully MR-based method HUGE (B₀ Homogenization using gradient enhancement) provides field-of-view extension in MR imaging, which can be used for truncation correction and improved PET quantification in PET/MR hybrid imaging. The HUGE method in this recent implementation is combined with continuously moving table data acquisition to provide a seamless nontruncated whole-body data set of the outer patient contours to complete the established standard MR-based Dixon-VIBE data for attenuation correction. The method was systematically evaluated in NEMA standard phantom experiments to investigate the impact of HUGE truncation correction on PET quantification. The method was then applied to 24 oncologic patients in whole-body PET/MR hybrid imaging. The impact of MR-based truncation correction with HUGE on PET data was compared to the impact of the established PET-based MLAA algorithm for contour detection.

In phantom and in all patient measurements, the standard Dixon-VIBE attenuation correction data show geometric distortions and signal truncations at the edges of the MR imaging field-of-view. In contrast, the Dixon-VIBE-based attenuation correction data additionally extended by applying HUGE shows significantly less distortion and truncations and due to the continuously moving table acquisition robustly provides smooth outer contours of the patient arms. In the investigated patient cases, MLAA frequently showed an overestimation of arm volume and associated artifacts limiting contour detection. When applying HUGE, an average relative increase in SUV_mean in patients' lesion of 4.2% and for MLAA of 4.6% were measured, when compared to standard Dixon-VIBE only. In specific lesions maximal differences in SUV_mean up to 13% for HUGE and 14% for MLAA were measured. Quantification in truncated regions showed maximal differences up to 40% for both, MLAA and HUGE. Average differences in those regions in SUV_mean for HUGE are 13.3% and 14.6% for MLAA. In a patient with I-124 radiotracer PET-based MLAA contour detection completely failed in this specific case, whereas HUGE as MR-based approach provided accurate truncation correction.

The HUGE method for truncation correction combined with continuous table movement extends the lateral MR field-of-view and effectively reduces truncations along the outer contours of the patient's arms in whole-body PET/MR imaging. HUGE as a fully MR-based approach is independent of the choice of radiotracer, thus also offering robust truncation correction in patients that are not injected with Fluordesoxyglucose (FDG) as radiotracer. Therefore, this method improves the standard Dixon MR-based attenuation correction and PET image quantification in whole-body PET/MR imaging applications.