Evaluation of the accuracy and robustness of a motion correction algorithm for PET using a novel phantom approach

Abstract

We introduce the use of a novel physical phantom to quantify the performance of a motion-correction algorithm. The goal of the study was to assess a PET-PET image registration, the final output of which is a motion-corrected highstatistics PET image volume, a procedure called Reconstruct, Register and Average (RRA). Methods: A phantom was constructed using 5 ~2mL Ge-68 filled spheres suspended in a water-filled tank via lightweight fishing line and driven by a periodic motion. Comparison of maximum and mean concentration and sphere volume was performed. Ground truth data were measured using no-motion. With motion, five replicate datasets of 3-minute phase-gated data for each of 3 different periods of motion were acquired. Gated PET images were registered using a multi-resolution level-sets-based non-rigid registration (NRR). The NRR images were then averaged to form a motion-corrected, high-statistics image volume. Spheres from all images were segmented and compared across the imaging conditions. Results: The average center-of-mass range of motion was 7.35, 5.83 and 2.66 mm for the spheres over the three periods of 8, 6 and 4 seconds. The center-of-mass for all spheres in all conditions was corrected to within 1mm on average using NRR as compared to the gated data. For the RRA data, the sphere maximum activity concentration (MAC) was on average 40.2% higher (-4.0% to 116.7%) and sphere volume was on average 12.0% smaller (-8.2% to 28.1%) as compared to the un-gated data with motion. The RRA results for MAC were on average 70% more accurate and for sphere volume 80% more accurate as compared to the un-gated data. Conclusions: The results show that the novel phantom setup and analysis methods are a promising evaluation technique for the assessment of motion correction algorithms. Benefits include the ability to compare against ground truth data without motion but with control of the statistical data quality and background variability. Use of a nonmoving object adjacent to spheres in motion, the spatial extent of the motion correction algorithm was confirmed to be local to the induced motion and to not affect the stationary object. A further benefit of the assessment technique is the use of ground truth data.

Motion Freeze for Respiration Motion Correction in PET/CT: A Preliminary Investigation with Lung Cancer Patient Data

“…In OFM for deformable image registration, CT as image modality is superior to PET. Although model tissue viscosity and elasticity are used to take into consideration the anatomical environment after OFM registration in GE Q.Freeze technology, the model should not be identical for all patients and usually not feasible for patients with lung injuries [ 24 ].…”

Motion Freeze for Respiration Motion Correction in PET/CT: A Preliminary Investigation With Lung Cancer Patient Data

“…In OFM for deformable image registration, CT as image modality is superior to PET. Although model tissue viscosity and elasticity are used to take into consideration the anatomical environment after OFM registration in GE Q.Freeze technology, the model should not be identical for all patients and usually not feasible for patients with lung injuries [24]. Respiratory motion will blur the activity image and hence reduce the measured activity concentration and corresponding SUV.…”

Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification

“…A level-sets based non-rigid registration algorithm 15 was used, with the implementation described in Ref. 16. Three levels with 10, 10 and 30 iterations per individual level were applied.…”