Concepts of Registration and Correction of Head Motion in Positron Emission Tomography

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

OBJECTIVE

We demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging.

METHODS

PET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability.

RESULTS

For both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC.

CONCLUSION

The first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity.

SIGNIFICANCE

As simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand.

Brain FDG PET and the diagnosis of dementia

OBJECTIVE

We review the role of brain FDG PET in the diagnosis of Alzheimer disease, frontotemporal dementia, dementia with Lewy bodies, and vascular dementia. Characteristic spatial patterns of brain metabolism on FDG PET can help differentiate various subtypes of dementia.

CONCLUSION

In patients with different subtypes of dementia, FDG PET/CT shows distinct spatial patterns of metabolism in the brain and can help clinicians to make a reasonably accurate and early diagnosis for appropriate management or prognosis.

MR-based motion correction for PET imaging using wired active MR microcoils in simultaneous PET-MR: phantom study

PURPOSE

Artifacts caused by head motion present a major challenge in brain positron emission tomography (PET) imaging. The authors investigated the feasibility of using wired active MR microcoils to track head motion and incorporate the measured rigid motion fields into iterative PET reconstruction.

METHODS

Several wired active MR microcoils and a dedicated MR coil-tracking sequence were developed. The microcoils were attached to the outer surface of an anthropomorphic(18)F-filled Hoffman phantom to mimic a brain PET scan. Complex rotation/translation motion of the phantom was induced by a balloon, which was connected to a ventilator. PET list-mode and MR tracking data were acquired simultaneously on a PET-MR scanner. The acquired dynamic PET data were reconstructed iteratively with and without motion correction. Additionally, static phantom data were acquired and used as the gold standard.

RESULTS

Motion artifacts in PET images were effectively removed by wired active MR microcoil based motion correction. Motion correction yielded an activity concentration bias ranging from -0.6% to 3.4% as compared to a bias ranging from -25.0% to 16.6% if no motion correction was applied. The contrast recovery values were improved by 37%-156% with motion correction as compared to no motion correction. The image correlation (mean ± standard deviation) between the motion corrected (uncorrected) images of 20 independent noise realizations and static reference was R(2) = 0.978 ± 0.007 (0.588 ± 0.010, respectively).

CONCLUSIONS

Wired active MR microcoil based motion correction significantly improves brain PET quantitative accuracy and image contrast.

Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET-MR: phantom and non-human primate studies

Brain PET scanning plays an important role in the diagnosis, prognostication and monitoring of many brain diseases. Motion artifacts from head motion are one of the major hurdles in brain PET. In this work, we propose to use wireless MR active markers to track head motion in real time during a simultaneous PET-MR brain scan and incorporate the motion measured by the markers in the listmode PET reconstruction. Several wireless MR active markers and a dedicated fast MR tracking pulse sequence module were built. Data were acquired on an ACR Flangeless PET phantom with multiple spheres and a non-human primate with and without motion. Motions of the phantom and monkey's head were measured with the wireless markers using a dedicated MR tracking sequence module. The motion PET data were reconstructed using list-mode reconstruction with and without motion correction. Static reference was used as gold standard for quantitative analysis. The motion artifacts, which were prominent on the images without motion correction, were eliminated by the wireless marker based motion correction in both the phantom and monkey experiments. Quantitative analysis was performed on the phantom motion data from 24 independent noise realizations. The reduction of bias of sphere-to-background PET contrast by active marker based motion correction ranges from 26% to 64% and 17% to 25% for hot (i.e., radioactive) and cold (i.e., non-radioactive) spheres, respectively. The motion correction improved the channelized Hotelling observer signal-to-noise ratio of the spheres by 1.2 to 6.9 depending on their locations and sizes. The proposed wireless MR active marker based motion correction technique removes the motion artifacts in the reconstructed PET images and yields accurate quantitative values.

MR-based PET motion correction procedure for simultaneous MR-PET neuroimaging of human brain

Positron Emission Tomography (PET) images are prone to motion artefacts due to the long acquisition time of PET measurements. Recently, simultaneous magnetic resonance imaging (MRI) and PET have become available in the first generation of Hybrid MR-PET scanners. In this work, the elimination of artefacts due to head motion in PET neuroimages is achieved by a new approach utilising MR-based motion tracking in combination with PET list mode data motion correction for simultaneous MR-PET acquisitions. The method comprises accurate MR-based motion measurements, an intra-frame motion minimising and reconstruction time reducing temporal framing algorithm, and a list mode based PET reconstruction which utilises the Ordinary Poisson Algorithm and avoids axial and transaxial compression. Compared to images uncorrected for motion, an increased image quality is shown in phantom as well as in vivo images. In vivo motion corrected images show an evident increase of contrast at the basal ganglia and a good visibility of uptake in tiny structures such as superior colliculi.

Image reconstruction for PET/CT scanners: past achievements and future challenges

PET is a medical imaging modality with proven clinical value for disease diagnosis and treatment monitoring. The integration of PET and CT on modern scanners provides a synergy of the two imaging modalities. Through different mathematical algorithms, PET data can be reconstructed into the spatial distribution of the injected radiotracer. With dynamic imaging, kinetic parameters of specific biological processes can also be determined. Numerous efforts have been devoted to the development of PET image reconstruction methods over the last four decades, encompassing analytic and iterative reconstruction methods. This article provides an overview of the commonly used methods. Current challenges in PET image reconstruction include more accurate quantitation, TOF imaging, system modeling, motion correction and dynamic reconstruction. Advances in these aspects could enhance the use of PET/CT imaging in patient care and in clinical research studies of pathophysiology and therapeutic interventions.

Motion correction of multi-frame PET data in neuroreceptor mapping: simulation based validation

Patient motion during positron emission tomography scanning can affect the accuracy of the data analysis in two ways: 1) movement occurring during emission data acquisition alters the time activity curves (TACs), measured at a voxel or region of interest (ROI), and hence introduces errors in the parameter estimates derived from kinetic modeling; 2) emission-transmission mismatches introduce errors during attenuation and scatter correction, and hence in the radioactivity distribution estimates for each time frame of the scan. With the aim of designing an algorithm-based frame realignment method, we first conducted investigations that aimed at optimizing the parameters of a coregistration method, such as the choice of the target volume and the similarity criterion. Based on these results we designed a novel frame realignment strategy in a multi-step algorithm using uncorrected reconstructed images, cross-correlation similarity criteria for the determination of inter-frame motion parameters and emission-transmission mismatch for each frame. Features and validation results are reported here based on a multi-subject simulated [(11)C]raclopride dynamic PET scan database incorporating intra-frame movements of various magnitudes and with various times of occurrence. Performances of the proposed algorithm were evaluated at regional and voxel-based level for binding potential parametric images.

Strategies for attenuation compensation in neurological PET studies

Molecular brain imaging using positron emission tomography (PET) has evolved into a vigorous academic field and is progressively gaining importance in the clinical arena. Significant progress has been made in the design of high-resolution three-dimensional (3-D) PET units dedicated to brain research and the development of quantitative imaging protocols incorporating accurate image correction techniques and sophisticated image reconstruction algorithms. However, emerging clinical and research applications of molecular brain imaging demand even greater levels of accuracy and precision and therefore impose more constraints with respect to the quantitative capability of PET. It has long been recognized that photon attenuation in tissues is the most important physical factor degrading PET image quality and quantitative accuracy. Quantitative PET image reconstruction requires an accurate attenuation map of the object under study for the purpose of attenuation compensation. Several methods have been devised to correct for photon attenuation in neurological PET studies. Significant attention has been devoted to optimizing computational performance and to balancing conflicting requirements. Approximate methods suitable for clinical routine applications and more complicated approaches for research applications, where there is greater emphasis on accurate quantitative measurements, have been proposed. The number of scientific contributions related to this subject has been increasing steadily, which motivated the writing of this review as a snapshot of the dynamically changing field of attenuation correction in cerebral 3D PET. This paper presents the physical and methodological basis of photon attenuation and summarizes state of the art developments in algorithms used to derive the attenuation map aiming at accurate attenuation compensation of brain PET data. Future prospects, research trends and challenges are identified and directions for future research are discussed.