Data-Driven Gross Patient Motion Detection and Compensation: Implications for Coronary 18F-NaF PET Imaging

Abstract Background Patient motion is a common problem during cardiac PET. The purpose of the present study was to investigate to what extent motions influence the quantitative accuracy of cardiac 15O-water PET/CT and to develop a method for automated motion detection. Method Frequency and magnitude of motion was assessed visually using data from 50 clinical 15O-water PET/CT scans. Simulations of 4 types of motions with amplitude of 5 to 20 mm were performed based on data from 10 scans. An automated motion detection algorithm was evaluated on clinical and simulated motion data. MBF and PTF of all simulated scans were compared to the original scan used as reference. Results Patient motion was detected in 68% of clinical cases by visual inspection. All observed motions were small with amplitudes less than half the LV wall thickness. A clear pattern of motion influence was seen in the simulations with a decrease of myocardial blood flow (MBF) in the region of myocardium to where the motion was directed. The perfusable tissue fraction (PTF) trended in the opposite direction. Global absolute average deviation of MBF was 3.1% ± 1.8% and 7.3% ± 6.3% for motions with maximum amplitudes of 5 and 20 mm, respectively. Automated motion detection showed a sensitivity of 90% for simulated motions ≥ 10 mm but struggled with the smaller (≤ 5 mm) simulated (sensitivity 45%) and clinical motions (accuracy 48%). Conclusion Patient motion can impair the quantitative accuracy of MBF. However, at typically occurring levels of patient motion, effects are similar to or only slightly larger than inter-observer variability, and downstream clinical effects are likely negligible.

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A Comparison of Techniques for PET Data-Driven Tracking of Head Motion

10.21203/rs.3.rs-202051/v1 ◽

2021 ◽

Author(s):

Ashley Gillman ◽

Stephen Rose ◽

Jye Smith ◽

Jason A Dowling ◽

Nicholas Dowson

Keyword(s):

Attenuation Correction ◽

Pet Imaging ◽

Motion Correction ◽

Robust Regression ◽

Rigid Motion ◽

Block Matching ◽

Data Driven ◽

Phantom Experiment ◽

Motion Sensitivity ◽

The Mean

Abstract Background / AimsPatient motion during positron emission tomography (PET) imaging can corrupt the image by causing blurring and quantitation error due to misalignment with the attenuation correction image. Data-driven techniques for tracking motion in PET imaging allow for retrospective motion correction, where motion may not have been prospectively anticipated.MethodsA two minute PET acquisition of a Hoffman phantom was acquired on a Bi- ograph mCT Flow, during which the phantom was rocked, simulating periodic motion with varying frequency. Motion was tracked using the sensitivity method, the axial centre-of-mass (COM) method, a novel 3D-COM method, and the principal component analysis (PCA) method. A separate two minute acquisition was acquired with no motion as a gold standard. The tracking signal was discretised into 10 gates using k-means clustering. Motion was modelled and corrected using the reconstruct-transform-add (RTA) technique, leveraging Multimodal Image Registration using Block-matching and Robust Regression (Mirorr) for rigid registration of non- attenuation-corrected 4D PET and Software for Tomographic Image Reconstruction (STIR) for PET reconstructions. Evaluation was performed by segmenting white matter (WM) and grey matter (GM) in the attenuation correction computed tomography (CT). The mean uptake in the region of GM was compared with that in the WM region. Additionally, the difference between the intensity distributions of WM and GM regions was measured with the t-statistic from a Welch's t-test.ResultsDifference in the mean distribution of WM to GM ranked the techniques in order of efficacy: no correction, sensitivity, axial-COM, 3D-COM, PCA, no motion. PCA correction had a great WM/GM separation measured by the t-value than the no motion scan. This was attributed to interpolation blurring during motion correction reducing class variance.ConclusionOf the techniques examined, PCA was found to be most effective for tracking rigid motion. The sensitivity and axial-COM techniques are mostly sensitive to axial motion, and so were ineffective in this phantom experiment. 3D-COM demonstrates improved transaxial motion sensitivity, but not to the level of effectiveness of PCA.

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