Optimised Motion Tracking for Positron Emission Tomography Studies of Brain Function in Awake Rats

Positron emission tomography (PET) is a non-invasive molecular imaging technique using positron-emitting radioisotopes to study functional processes within the body. High resolution PET scanners designed for imaging rodents and non-human primates are now commonplace in preclinical research. Brain imaging in this context, with motion compensation, can potentially enhance the usefulness of PET by avoiding confounds due to anaesthetic drugs and enabling freely moving animals to be imaged during normal and evoked behaviours. Due to the frequent and rapid motion exhibited by alert, awake animals, optimal motion correction requires frequently sampled pose information and precise synchronisation of these data with events in the PET coincidence data stream. Motion measurements should also be as accurate as possible to avoid degrading the excellent spatial resolution provided by state-of-the-art scanners. Here we describe and validate methods for optimised motion tracking suited to the correction of motion in awake rats. A hardware based synchronisation approach is used to achieve temporal alignment of tracker and scanner data to within 10 ms. We explored the impact of motion tracker synchronisation error, pose sampling rate, rate of motion, and marker size on motion correction accuracy. With accurate synchronisation (<100 ms error), a sampling rate of >20 Hz, and a small head marker suitable for awake animal studies, excellent motion correction results were obtained in phantom studies with a variety of continuous motion patterns, including realistic rat motion (<5% bias in mean concentration). Feasibility of the approach was also demonstrated in an awake rat study. We conclude that motion tracking parameters needed for effective motion correction in preclinical brain imaging of awake rats are achievable in the laboratory setting. This could broaden the scope of animal experiments currently possible with PET

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Data-driven motion correction in single photon emission computed tomography of the brain

Introduction: Patient head motion is a well-recognised problem in single photon emission computed tomography (SPECT) of the brain. Motion occurring between or during the acquisition of projections can lead to reconstruction artifacts that compromise accurate patient diagnosis. Although some form of restraint tends to be used in practice, motion incidence and magnitude is still high enough to warrant frequent repeat studies or the application of motion correction. The motivation for this work was the outstanding need for a high-performance motion correction strategy for brain studies. Such a strategy should accurately correct general rigid-body motion. The optimal strategy would also be non-invasive, have a high degree of automation, and be fast, convenient (requiring little or no calibration, patient cooperation, and extra gadgetry), and robust with respect to noise. We describe and implement a fully 3D, non-invasive, data-driven approach that is suitable for use with clinical data and is potentially automatic. The approach is based on a comparison of measured and estimated projection data. Acquired projections are segregated into groups corresponding to discrete locations held by the brain during scanning and the largest group is reconstructed. The position and orientation of this reconstruction is optimised for each remaining group by comparing the measured projections with those generated from the transformed reconstruction. After each optimisation, the current reconstruction estimate is updated with the relevant projections using the ordered-subsets expectation maximisation (OSEM) algorithm. Methods: Three sets of experiments were carried out on different types of data to validate the motion correction procedure and investigate practical aspects of implementing the approach clinically. In the initial set of experiments, seven noisy motion-corrupted projection sets simulating 2-4 head positions were generated from the digital Hoffman brain phantom. The angular location and extent of movement and the magnitude of rotation and translation with respect to each axis was varied for each set. Motion correction was applied to these data using various regimes: with/without attenuation included in the optimisation; with/without a second iteration. Extracted motion parameters were compared with the applied movements. The error between the extracted and applied parameters was quantified in terms of the mean registration error (MRE), an average displacement of the vertices of a box surrounding the brain. Overall improvement from motion correction was quantified in terms of a mean squared difference improvement ratio (MSDR). Corrected, uncorrected, and motion-free slices were also compared visually. For the second group of experiments, three physical Hoffman phantom studies containing single or double movements were obtained. The Polaris motion tracker was used to provide an independent measurement of motion. Motion parameters were extracted using our approach and compared with those measured by the Polaris. An investigation of cost function behaviour was also carried out by mapping the cost function in the neighbourhood of the Polaris solution. The third group of experiments constituted a preliminary clinical validation. Three volunteers underwent a motion-free scan followed by a scan in which they performed one head movement. A fourth volunteer underwent two scans, holding a single (but different) brain location in each. Again the Polaris was used to measure the motion independent of our technique. Data from the fourth volunteer was used to simulate two single-movement studies, facilitating a rigorous quantification of the improvement obtained from motion correction. Optimisations were performed with and without reduced projections, scatter correction, thresholding of background counts, compensation to avoid biasing from truncated data, and pre-smoothing of the acquired data. Results: In the digital phantom experiments, estimated rotations and translations were mostly within 2(degrees) and 1mm of the applied values. The MRE was less than 1 pixel in most cases. Accurate motion estimates could be obtained at over twice the speed by leaving attenuation out of the optimisation stage. Visually, there was a clear reduction in motion-induced artifacts after correction. Most MSDR values were well in excess of 2, and the MSDR tended to increase with increasing corruption. A second iteration of correction did not provide sufficient improvement to warrant the additional time cost. In the physical phantom experiments there was good agreement between the extracted and Polaris measurements for the x and y-rotation and z-translation parameters. A systematic discrepancy existed for the remaining parameters. The discrepancy was reduced for the third dataset (two movements); in this case the corrected study closely resembled that obtained using the Polaris values. Analysis of the cost function indicated that the MSD was fairly insensitive to large rotations whilst being sensitive to typical translations. Discrepancies appeared to be the result of object symmetry. In all of the volunteer studies, sets of motion parameters were obtained that closely followed the trend of the Polaris. In general, however, there was a systematic discrepancy from the actual Polaris values. Scatter correction had little effect on accuracy. Using reduced projections (greater proportion of the image occupied by brain) tended to provide estimates as good or better than using larger projections. Pre-smoothing generally lead to less accurate estimates. For large movements, tracking the plane of truncation was necessary to obtain sensible estimates. Thresholding was important in removing background counts and confining the solution to a sensible portion of the cost function. For all volunteers there was a clear improvement in image symmetry and contrast after using our approach. In certain cases, correction was better than that obtained from the Polaris. Of particular concern is the method used for attaching the head target. Poor attachment can lead to decoupling of target and head movement. For the two semi-simulated studies, the MSD improved by approximately 4 and 2 respectively, whereas the Polaris provided no improvement. Conclusion: We have demonstrated that complex brain movements in simulated and real data can be accurately estimated and corrected using this data-driven approach

An optical tracking system for motion correction in small animal PET

Imaging conscious animals in small animal positron emission tomography (PET) presents a significant challenge, and some form of motion compensation will normally be necessary. In this work, a commercial optical motion tracker called the Micron Tracker has been adapted to the microPET system for this purpose. We evaluated marker size limits, performed a spatial calibration for the devices, developed a synchronization method, and carried out a phantom study involving multiple, discrete 3D movements to test key components of the motion tracking system. We have demonstrated that small and lightweight markers (approx. 15mm x 18mm) are feasible with this system for 3D motion tracking, Calibration accuracy was 0.46mm RMS.Synchronization of the data streams was achieved with a precision of approximately 20ms. Moreover, a marked reduction in motion artifacts was demonstrated in the phantom study. The techniques and results presented here demonstrate the feasibility of adapting the Micron Tracker to the microPET environment for motion tracking of small laboratory animals. There is scope to improve on limitations in synchronization and further optimize marker design to achieve better pose accuracy and precision

Silhouette-Based Markerless Motion Estimation of Awake Rodents in PET

The ability to image the brain of a freely moving rodent using motion-compensated PET presents many exciting possibilities for exploring the links between brain function and behavior. Markerless optical approaches for pose estimation have several potential advantages over marker-based methods: improved accuracy and increased range of detectable motion; no 'decoupling' of marker and head motion; and no acclimatization of the animals to attached markers. Our aim in this work was to describe and validate a silhouette-based multi-camera method for estimating the pose of a rat. Random-walk and K-means clustering approaches were very adaptable to uneven lighting and generally provided excellent object segmentations. In obtaining a high quality rat model, shape-from-silhouette and laser scanning both resulted in useful models; laser scanning provided sub-millimeter resolution with very few artifacts and was the method of choice. In our experimental validation, the 3D-2D (model-silhouette) optimization clearly converged to sub-degree and sub-millimeter alignment of the measured and estimated silhouettes. The average discrepancy between test points transformed using the estimated versus ground-truth poses was 0.94 mm ± 0.51 mm. This investigation focused on rigid motion of a rat phantom as a proof-of-principle of the technique. Future work will focus on investigating the potential of designing a non-rigid rodent body model in order to apply the method to a freely moving animal during PET imaging

A normalization scheme for LOR-based motion correction in PET

Line of response (LOR) rebinning has been demonstrated to be an effective motion correction method for positron emission tomography (PET) imaging. In LOR rebinning, normalization of each motion-corrected event is needed before it is placed into its new sinogram bin. In general, due to data compression strategies the sinogram bincorresponding to the transformed LOR will receive contributionsfrom multiple LORs. This paper demonstrates that normalization of the corrected event must account for the relative change in its contribution to the corresponding sinogram bins before and after transformation. Failure to account for this factor may cause slice-to-slice count variations of transverse slices and visible horizontal stripe artifacts in the coronal and sagittal slices of the reconstructed images

Real-time 3D motion tracking for small animal brain PET

High-resolution positron emission tomography (PET) imaging of conscious, unrestrained laboratory animals presents many challenges. Some form of motion correction will normally be necessary to avoid motion artefacts in the reconstruction. The aim of the current work was to develop and evaluate a motion tracking system potentially suitable for use in small animal PET. This system is based on the commercially available stereo-optical MicronTracker S60 which we have integrated with a Siemens Focus-220 microPET scanner. We present measured performance limits of the tracker and the technical details of our implementation, including calibration and synchronization of the system. A phantom study demonstrating motion tracking and correction was also performed. The system can be calibrated with sub-millimetre accuracy, and small lightweight markers can be constructed to provide accurate 3D motion data. A marked reduction in motion artefacts was demonstrated in the phantom study. The techniques and results described here represent a step towards a practical method for rigid-body motion correction in small animal PET. There is scope to achieve further improvements in the accuracy of synchronization and pose measurements in future work

Event-based motion correction for PET transmission measurements with a rotating point source

Accurate attenuation correction is important for quantitative positron emission tomography (PET) studies. When performing transmission measurements using an external rotating radioactive source, object motion during the transmission scan can distort the attenuation correction factors computed as the ratio of the blank to transmission counts, and cause errors and artefacts in reconstructed PET images. In this paper we report a compensation method for rigid body motion during PET transmission measurements, in which list mode transmission data are motion corrected event-by-event, based on known motion, to ensure that all events which traverse the same path through the object are recorded on a common line of response (LOR). As a result, the motion-corrected transmission LOR may record a combination of events originally detected on different LORs. To ensure that the corresponding blank LOR records events from the same combination of contributing LORs, the list mode blank data are spatially transformed event-by-event based on the same motion information.The number of counts recorded on the resulting blank LOR is then equivalent to the number of counts that would have been recorded on the corresponding motion-corrected transmission LOR in the absence of any attenuating object. The proposed method has been verified in phantom studies with both stepwise movements and continuous motion. We found that attenuation maps derived from motion-corrected transmission and blank data agree well with those of the stationary phantom and are significantly better than uncorrected attenuation data

An event-driven motion correction method for neurological PET studies of awake laboratory animals

Purpose: The purpose of the study is to investigate the feasibility of an event driven motion correction method for neurological microPET imaging of small laboratory animals in the fully awake state. Procedures: A motion tracking technique was developed using an optical motion tracking system and light (<1g) printed targets. This was interfaced to a microPET scanner. Recorded spatial transformations were applied in software to list mode events to create a motion-corrected sinogram. Motion correction was evaluated in microPET studies, in which a conscious animal was simulated by a phantom that was moved during data acquisition. Results: The motion-affected scan was severely distorted compared with a reference scan of the stationary phantom. Motion correction yielded a nearly distortion-free reconstruction and a marked reduction in mean squared error. Conclusions: This work is an important step towards motion tracking and motion correction in neurological studies of awake animals in the small animal PET imaging environment

Correction for continuous motion in small animal PET

In small animal PET imaging experiments, animals are generally required to be anaesthetized to avoid motion artifacts. However, anaesthesia can alter biochemical pathways within the brain, thus affecting the physiological parameters under investigation. The ability to image conscious animals would overcome this problem andopen up the possibility of entirely new investigational paradigms.We have previously reported a motion-correction approach for small animal PET imaging that employs motion tracking and line of response (LOR) rebinning, and successfully demonstrated its use in phantom scans with step wise motion. In this paper we investigate an improvedsynchronization method in which TTL signals output by the motion tracker are sent to the microPET gate input to trigger the insertion of gate marks in the list mode stream that indicate the times of motion tracker measurements. The method is tested in separate microPET scans of a phantom and an anaesthetized rat which were moved continuously during data acquisition. In both cases, the motion-corrected images corresponded well with the motion-free images.We also tested the effect of pose measurement rate and synchronization error on motion correction accuracy by down-sampling and temporally misaligning list mode and motion data in a phantom study. Motion correction errors were relatively large at frequencies below -10Hz and fell rapidly to a roughly constant level above 20Hz. Motion correction errors also increased rapidly with increasingsynchronization error. In practice the acceptable limits of sampling rate and synchronization error will depend on the velocity of the motion. Using the synchronization technique presented here, and an adequate pose sampling rate, it was possible to correct for continuous motion similar to that we expect to be exhibited by conscious rats during microPET imaging experiments