Andre Kyme

Practical aspects of a data-driven motion correction approach for brain SPECT

Patient motion can cause image artifacts in single photon emission computed tomography despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward projections. This enables the brain locations to be estimated and data to be correctly incorporated in a three-dimensional (3-D) reconstruction algorithm. Digital and physical phantom experiments were performed to explore practical aspects of this approach. Noisy simulation data modeling multiple 3-D patient head movements were constructed by projecting the digital Hoffman brain phantom at various orientations. Hoffman physical phantom data incorporating deliberate movements were also gathered. Motion correction was applied to these data using various regimes to determine the importance of attenuation and successive iterations. Studies were assessed visually for artifact reduction, and analyzed quantitatively via a mean registration error (MRE) and mean square difference measure (MSD). Artifacts and distortion in the motion corrupted data were reduced to a large extent by application of this algorithm. MRE values were mostly well within 1 pixel (4.4 mm) for the simulated data. Significant MSD improvements (>2) were common. Inclusion of attenuation was unnecessary to accurately estimate motion, doubling the efficiency and simplifying implementation. Moreover, most motion-related errors were removed using a single iteration. The improvement for the physical phantom data was smaller, though this may be due to object symmetry. In conclusion, these results provide the basis of an implementation protocol for clinical validation of the technique.

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.

A hybrid 3-D reconstruction/registration algorithm for correction of head motion in emission tomography

Even with head restraint, small head movements can occur during data acquisition in emission tomography that are sufficiently large to result in detectable artifacts in the final reconstruction. Direct measurement of motion can be cumbersome and difficult to implement, whereas previous attempts to use the measured projection data for correction have been limited to simple translation orthogonal to the projection. A fully three-dimensional (3-D) algorithm is proposed that estimates the patient orientation based on the projection of motion-corrupted data, with incorporation of motion information within subsequent ordered-subset expectation-maximization subiterations. Preliminary studies have been performed using a digital version of the Hoffman brain phantom. Movement was simulated by constructing a mixed set of projections in discrete positions of the phantom. The algorithm determined the phantom orientation that best matched each constructed projection with its corresponding measured projection. In the case of a simulated single movement in 24 of 64 projections, all misaligned projections were correctly identified. Incorporating data at the determined object orientation resulted in a reduction of mean square difference (MSD) between motion-corrected and motion-free reconstructions, compared to the MSD between uncorrected and motion-free reconstructions, by a factor of 1.9.

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.

An Event-Driven Motion Correction Method for Neurological PET Studies of Awake Laboratory Animals

PurposeThe 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.ProceduresA 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.ResultsThe 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.ConclusionsThis work is an important step towards motion tracking and motion correction in neurological studies of awake animals in the small animal PET imaging environment.

A rigid motion correction method for helical computed tomography (CT)

We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker sampling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For example it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data.

Markerless Motion Tracking of Awake Animals in Positron Emission Tomography

Noninvasive functional imaging of awake, unrestrained small animals using motion-compensation removes the need for anesthetics and enables an animals behavioral response to stimuli or administered drugs to be studied concurrently with imaging. While the feasibility of motion-compensated radiotracer imaging of awake rodents using marker-based optical motion tracking has been shown, markerless motion tracking would avoid the risk of marker detachment, streamline the experimental workflow, and potentially provide more accurate pose estimates over a greater range of motion. We have developed a stereoscopic tracking system which relies on native features on the head to estimate motion. Features are detected and matched across multiple camera views to accumulate a database of head landmarks and pose is estimated based on 3D-2D registration of the landmarks to features in each image. Pose estimates of a taxidermal rat head phantom undergoing realistic rat head motion via robot control had a root mean square error of 0.15 and 1.8 mm using markerless and marker-based motion tracking, respectively. Markerless motion tracking also led to an appreciable reduction in motion artifacts in motion-compensated positron emission tomography imaging of a live, unanesthetized rat. The results suggest that further improvements in live subjects are likely if nonrigid features are discriminated robustly and excluded from the pose estimation process.

A motion adaptive animal chamber for PET imaging of freely moving animals

Small animal positron emission tomography (PET) is a potentially powerful tool for understanding the molecular origins of debilitating brain disease such as dementia, depression and schizophrenia. However, its full potential in such investigations has not yet been realized due to the use of anaesthesia to avoid motion artifacts. Anaesthesia alters biochemical pathways within the brain and precludes the study of animal behavior during the imaging study. Previously we have reported a motion correction approach for conscious animal PET imaging that employs motion tracking and line of response (LOR) rebinning. We are currently extending this technique to allow PET imaging of freely moving animals, enabling the non-invasive measurement of biochemical processes in the brain of a fully conscious rat while simultaneously observing its behavior. As a first step we report a robot-controlled motion adaptive animal chamber which translates in the horizontal plane based on the head position reported by a motion tracking system to compensate for gross animal movement and keep the head within the field of view (FOV) as long as possible during the scan. In a pilot animal study within a simulated microPET environment, the control algorithm increased the time the head spent centrally in the FOV from 38% to 83% without any apparent disturbance to the animals behaviour. We conclude that a robot-controlled motion adaptive chamber is a feasible approach and an important step towards imaging freely moving animals.

Motion tracking of fully conscious small animals in PET

Pre-clinical positron emission tomography (PET) is becoming increasingly important in understanding brain physiology using animal models. One of the major challenges at present is being able to perform brain PET studies without the use of anesthesia. In most cases where the animal is minimally restrained this will require some form of motion tracking to provide the necessary temporal pose information for compensation before or during image reconstruction (eg. [1, 2]). In previous work we have demonstrated successful tracking of continuous movement in phantom studies and an anesthetized rat study in which the animal was moved manually [2]. Here we report on our first trials tracking the head of fully conscious rats moving continuously during emission and transmission PET acquisitions performed on a microPET Focus 220 scanner (Siemens Preclinical Solutions, Knoxville, USA). The motion tracking is based on a commercial stereo-optical motion tracking device called the MicronTracker Sx60 (Claron Tech. Inc., Toronto, Canada). We have previously reported in detail on this device and its suitability for small-scale motion tracking [3]. In this paper we (i) describe the motion tracking setup and marker considerations used for conscious animal head tracking, (ii) describe our animal handling methods, (iii) present data on motion tracker performance in the conscious animal trials, (iv) present data and observations on the character of rat movements in the imaging environment, and (v) show examples of the correction that can be obtained using these data for motion compensation.

A scheme for PET data normalization in event-based motion correction

Line of response (LOR) rebinning is an event-based motion-correction technique for positron emission tomography (PET) imaging that has been shown to compensate effectively for rigid motion. It involves the spatial transformation of LORs to compensate for motion during the scan, as measured by a motion tracking system. Each motion-corrected event is then recorded in the sinogram bin corresponding to the transformed LOR. It has been shown previously that the corrected event must be normalized using a normalization factor derived from the original LOR, that is, based on the pair of detectors involved in the original coincidence event. In general, due to data compression strategies (mashing), sinogram bins record events detected on multiple LORs. The number of LORs associated with a sinogram bin determines the relative contribution of each LOR. This paper provides a thorough treatment of event-based normalization during motion correction of PET data using LOR rebinning. We demonstrate theoretically and experimentally that normalization of the corrected event during LOR rebinning should account for the number of LORs contributing to the sinogram bin into which the motion-corrected event is binned. Failure to account for this factor may cause artifactual slice-to-slice count variations in the transverse slices and visible horizontal stripe artifacts in the coronal and sagittal slices of the reconstructed images. The theory and implementation of normalization in conjunction with the LOR rebinning technique is described in detail, and experimental verification of the proposed normalization method in phantom studies is presented.