Movement Correction Method for Human Brain PET Images: Application to Quantitative Analysis of Dynamic ¹⁸F-FDDNP Scans

Abstract

Head movement during a PET scan (especially a dynamic scan) can affect both the qualitative and the quantitative aspects of an image, making it difficult to accurately interpret the results. The primary objective of this study was to develop a retrospective image-based movement correction (MC) method and evaluate its implementation on dynamic 2-(1-f6-[(2-18 F-fluoroethyl)(methyl)amino]-2-naphthylgethylidene)-malononitrile ( 18 F-FDDNP) PET images of cognitively intact controls and patients with Alzheimer's disease (AD). Methods: Dynamic 18 F-FDDNP PET images, used for in vivo imaging of b-amyloid plaques and neurofibrillary tangles, were obtained from 12 AD patients and 9 age-matched controls. For each study, a transmission scan was first acquired for attenuation correction. An accurate retrospective MC method that corrected for transmission-emission and emission-emission misalignments was applied to all studies. No restriction was assumed for zero movement between the transmission scan and the first emission scan. Logan analysis, with the cerebellum as the reference region, was used to estimate various regional distribution volume ratio (DVR) values in the brain before and after MC. Discriminant analysis was used to build a predictive model for group membership, using data with and without MC. Results: MC improved the image quality and quantitative values in 18 F-FDDNP PET images. In this subject population, no significant difference in DVR value was observed in the medial temporal (MTL) region of controls and patients with AD before MC. However, after MC, significant differences in DVR values in the frontal, parietal, posterior cingulate, MTL, lateral temporal (LTL), and global regions were seen between the 2 groups (P , 0.05). In controls and patients with AD, the variability of regional DVR values (as measured by the coefficient of variation) decreased on average by more than 18% after MC. Mean DVR separation between controls and patients with AD was higher in frontal, MTL, LTL, and global regions after MC. Group classification by discriminant analysis based on 18 F-FDDNP DVR values was markedly improved after MC. Conclusion: The streamlined and easy-to-use MC method presented in this work significantly improves the image quality and the measured tracer kinetics of 18 F-FDDNP PET images. The proposed MC method has the potential to be applied to PET studies on patients having other disorders (e.g., Down syndrome and Parkinson's disease) and to brain PET scans with other molecular imaging probes.

Methods for Motion Correction Evaluation Using 18F-FDG Human Brain Scans on a High-Resolution PET Scanner

“…In the third method, aligned transmission MC (AIR aligned transmission [ATX]), we use aligned transmission images for the reconstruction of each emission frame (14)(15)(16). The alignment of the transmission image to the reference frame in each dataset was checked visually by an experienced operator, and no manual adjustments were needed.…”

“…The fourth and final MC method is non-attenuation-corrected MC (AIR NAC), in which a preliminary reconstruction of NAC and non-motion-corrected images was performed, and the 6-mm filtered images were aligned with AIR to obtain transformers (14)(15)(16). The alignment of the transmission image to reference frame was verified visually as was done for AIR ATX.…”

“…New motion-tracking methods using stereo cameras are being developed (12) to avoid the fixation problem. Alternatively, software realignment methods have been proposed (13)(14)(15)(16), but their accuracy may be limited by activity distribution changes and noise in low-statistics images as they rely on the image data, which are tracer-dependent. Because external tracking methods are tracer-independent, they are of the most interest.…”

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

“…A number of strategies have been devised for motion correction (Jin et al 2013). These include online optical motion tracking systems and incorporation of motion information in image reconstruction (Bloomfield et al 2003, Qiao et al 2006, Rahmim et al 2007, Dinelle et al 2011, post-reconstruction frame-by-frame realignment methods using either optical systems or image-based algorithms (Wardak et al 2010, Costes et al 2009, Mourik et al 2009, Ye et al 2014, Jiao et al 2015, as well as more advanced methods leading to simultaneous estimation of motion and kinetic parameters (Jiao et al 2014). However, realignment methods can be tracer/activity dependent and susceptible to noise (especially in early frames) when image-based methods are used for frame-by-frame transformations.…”

Accelerated reconstruction for identifying image regions affected by rigid body movement

“…Rigid body movement is often the cause of image corruption in long duration PET scans [4]. Whilst there have been many proposals of how to correct for motion, a method for identifying corrupted image regions could also be useful for determining when a motion correction strategy is needed.…”