Statistical evaluation of PET motion correction methods using MR derived motion fields

Abstract

Although there have been various proposed methods for motion correction in PET, there is not sufficient evidence to answer which method is better in terms of image quality and quantification. This study aims to characterize the behavior of the two main motion correction methods in terms of convergence and image properties. During the first method, reconstruct transform-average (RT A), independent reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), all data are used with the motion information within a single reconstruction. The two methods are studied based on the OS EM algorithm. In this investigation motion fields were obtained from real dynamic MR acquisitions and concurrent PET data were simulated from the dynamic MR images. The two motion correction approaches were assessed statistically. Results indicate that MCIR is successful in the recovery of the true values of all regions, whereas RT A has high bias due to the non negativity constraints and interpolation errors during transformation. It has been demonstrated that whilst at low iterations (i.e. 46 sub-iterations) the two methods have similar noise characteristics; at high iterations MCIR has increased noise. Finally, for low iterations the two methods have marginally similar coefficient of variation (CoV) whereas for large number of iterations RTA has clearly better CoY than MCIR. This studyindicates that MCIR may provide superior performance overall to RT A providing the noise can be minimized.

MR‐assisted PET respiratory motion correction using deep‐learning based short‐scan motion fields

“…This result is consistent with previous findings that the MCIR method amplifies noise compared to the non-MoCo. [27][28][29][30][31] However, no significant difference was found between the three PET reconstructions (non-MoCo, MoCo 2000, and MoCo P2P200 ) in Dunn's post hoc pairwise comparison, perhaps due to the small sample size. Despite the higher noise levels, both MoCo 2000 and MoCo P2P200 PET images provided significantly superior lesion conspicuity.…”

“…Compared to MoCo 2000 , MoCo P2P200 has larger variations in TBR and CNR in the 5.75 mm at >90 min and 4.75 mm sphere at >70 min. This may be explained by the small size of this spheres, the low activity (<0.062 mCi at >90 min or < 0.071 mCi at >70 min) due to tracer activity decay, and the amplified noise introduced by a short MR scan duration (17 s) and MCIR PET MoCo [27][28][29][30][31]. Of note, the nominal spatial resolution of FDG PET isF I G U R E 8 Representative liver cancer patient 2.…”

High-resolution dynamic MR imaging of the thorax for respiratory motion correction of PET using groupwise manifold alignment

“…It has been shown by Polycarpou et al (2011) that under some circumstances MCIR may provide superior PET motion correction than RTA, but comes at greater computational cost. Our technique can also be used to provide motion estimates for MCIR-based reconstructions, and in future work we plan to investigate this.…”

MR-Based Cardiac and Respiratory Motion-Compensation Techniques for PET-MR Imaging

“…6 For PET-MR imaging, precalibrated motion models are based on near real-time MR images acquired before the simultaneous PET-MR acquisition. This approach has been applied in MR-based PET simulation studies [24][25][26][27][28][29] with healthy volunteer MR data. 3D T1-weighted turbo field echo (TFE) MR images were acquired using parallel imaging (SENSE) with an acceleration factor of 8, so that each whole-thorax volume is acquired in 0.7 seconds.…”

“…Polycarpou and colleagues 26,27 and Tsoumpas and colleagues 29 also performed a comparison between the 2 approaches using OSEM reconstruction in an MR-based PET simulation study. They found that MCIR achieves better contrast and smaller bias in low-activity regions compared with PRR, but has lower SNR.…”