Improved frame-based estimation of head motion in PET brain imaging

Abstract

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type. C 2016 American Association of Physicists in Medicine. [http://dx

Robustness of post-reconstruction and direct kinetic parameter estimates under rigid head motion in dynamic brain PET imaging

“…The transformation parameters simulated were estimated following frame-by-frame registration using the automated image registration (AIR) software [28]. To improve registration accuracy post-smoothed non-attenuation corrected images were used, thus enabling rigid-body registration between dynamic frames by neglecting motion in the early frames [29]. The accuracy of registration was checked visually and was found to be satisfactory.…”

Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI

“…PET only allows retrospective MC, as the acquisition cannot be dynamically adapted to compensate for motion. However, the MC can take place at different phases of the PET reconstruction, from MC of raw listmode data [6–8] to MC of the reconstructed image frames [9–11]. These MC methods are generally based on the assumption of knowing the precise head pose (position and orientation) during the scanning.…”

“…However, feature extraction from stamps or facial characteristics alone may be computationally expensive or unstable and has been demonstrated only for retrospective correction. Data-driven motion detection in PET shows promising results [11, 20]. However, it may be difficult to distinguish motion-induced changes from functional changes in tracer distribution over time.…”

Body motion detection and correction in cardiac PET: Phantom and human studies

“…One group of methods is to employ external markers, such as optical tracking or wireless or wired magnetic resonance (MR) micro‐coils, to track the head motion. Another group of methods is based on image‐driven motion correction techniques . The first step of this method is to reconstruct multiple short‐frame data for a given scan–typically 10–60 s per frame (depending on the tracer used).…”