Ju-Chieh Cheng

Accurate Event-Driven Motion Compensation in High-Resolution PET Incorporating Scattered and Random Events

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the field-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

Investigation of Subject Motion Encountered During a Typical Positron Emission Tomography Scan

Subject motion has a known detrimental effect on brain Positron Emission Tomography image resolution. Numerous motion compensation techniques exist to address this issue, however prior to their application every effort should be made to limit subject motion. Using a Polaris motion tracking system subject motion was observed under typical scanning conditions for both healthy and Parkinsons disease (PD) volunteers. Motions in the range of 0 to 5 mm were observed for healthy subjects, and 0 to 20 mm for PD subjects. The most common source of motion was due to interaction between the subject and the attending nurse/scanning staff, especially during examination of the subjects symptoms (motions up to 8 mm). Less common activities resulting in significant motions were the use of a bedpan (20 mm), the removal of a cushion from under the subjects legs (5 mm) and leg readjustments (3 mm). Awareness of the effect each of these activities had on head motion can be used to motivate further limitations on these motions. Measured motions were also extrapolated to various regions in the brain, specifically the cerebellum, occipital cortex, and striatum. Subject head rotation about the vertical and horizontal axes resulted in the greatest displacement of regions in the cerebellum, while rotations about the subjects long axis primarily impacted the displacement of the occipital cortex region. This measurement provides motion related information about the expected accuracy of time activity curves for different brain regions.

System Matrix Modeling of Externally Tracked Motion

In high resolution emission tomography imaging, even small patient movements can considerably degrade image quality. This work investigates an approach to motion compensated reconstruction of motion-contaminated data, thus applicable to any scanner in the field (e.g. without list-mode acquisition capability), assuming externally-tracked motion information; it involves incorporation of the measured motion information into the system matrix of the EM algorithm. Furthermore, it is shown that the effect of motion-contamination of the attenuation factors should also be modeled and taken into account in the reconstruction task.

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies conducted in this work.

Accurate Event-Driven Motion Compensation Incorporating All Detected Events

This work develops and investigates a formalism for accurate motion-compensated reconstruction, including elaborate consideration of scattered and random coincidences, which at the same time is particularly feasible in the context of high-resolution PET. The method takes into consideration normally-detected projection data which are not detected due to motion. Furthermore, it incorporates information from all detected events, particularly those which, following correction for motion, fall outside the FoV (e.g. axially or through detector gaps), thus satisfying a mathematical requirement, elaborated in the text, that would allow accurate motion averaging of sensitivity factors in image-space (as opposed to projection-space). The proposed method has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work.