William J. Ryder

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.

Microstructural white matter changes are correlated with the stage of psychiatric illness

Microstructural white matter changes have been reported in the brains of patients across a range of psychiatric disorders. Evidence now demonstrates significant overlap in these regions in patients with affective and psychotic disorders, thus raising the possibility that these conditions share common neurobiological processes. If affective and psychotic disorders share these disruptions, it is unclear whether they occur early in the course or develop gradually with persistence or recurrence of illness. Utilisation of a clinical staging model, as an adjunct to traditional diagnostic practice, is a viable mechanism for measuring illness progression. It is particularly relevant in young people presenting early in their illness course. It also provides a suitable framework for determining the timing of emergent brain alterations, including disruptions of white matter tracts. Using diffusion tensor imaging, we investigated the integrity of white matter tracts in 74 patients with sub-syndromal psychiatric symptoms as well as in 69 patients diagnosed with established psychosis or affective disorder and contrasted these findings with those of 39 healthy controls. A significant disruption in white matter integrity was found in the left anterior corona radiata and in particular the anterior thalamic radiation for both the patients groups when separately contrasted with healthy controls. Our results suggest that patients with sub-syndromal symptoms exhibit discernable early white matter changes when compared with healthy control subjects and more significant disruptions are associated with clinical evidence of illness progression.

Frontal and subcortical grey matter reductions in PTSD

Post-traumatic stress disorder (PTSD) is characterised by a range of debilitating psychological, physical and cognitive symptoms. PTSD has been associated with grey matter atrophy in limbic and frontal cortical brain regions. However, previous studies have reported heterogeneous findings, with grey matter changes observed beyond limbic/frontal areas. Seventy-five adults were recruited from the community, 25 diagnosed with PTSD along with 25 healthy and 25 trauma exposed age and gender matched controls. Participants underwent clinical assessment and magnetic resonance imaging. The data-analyses method Voxel Based Morphometry (VBM) was used to estimate cortical grey matter volumes. When compared to both healthy and trauma exposed controls, PTSD subjects demonstrated decreased grey matter volumes within subcortical brain regions-including the hippocampus and amygdala-along with reductions in the anterior cingulate cortex, frontal medial cortex, middle frontal gyrus, superior frontal gyrus, paracingulate gyrus, and precuneus cortex. Significant negative correlations were found between total CAPS lifetime clinical scores/sub-scores and GM volume of both the PTSD and TC groups. GM volumes of the left rACC and right amygdala showed a significant negative correlation within PTSD diagnosed subjects.

4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging

Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity.

Novel SLAM-based markerless motion tracking of conscious unrestrained rodents in PET

Motion-compensated positron emission tomography (PET) has the potential to improve translational investigations by allowing animals to behave normally and respond to external stimuli. Several groups have demonstrated the feasibility of performing motion-compensated brain PET on rodents, obtaining the necessary head motion data using marker-based techniques. However, markerless motion tracking would simplify animal experiments, be less invasive, and potentially provide more accurate pose estimates over a greater range of motion. We describe a markerless stereo motion tracking system and demonstrate the feasibility of using this system to obtain highly accurate (< 0.2 mm) pose estimates for realistic motion of a taxidermied rat head. The system is based on the simultaneous localization and mapping (SLAM) framework used in mobile robotics and involves building a consistent set of landmarks on the head for pose estimation. Pose measurements using the markerless system were approximately 10 times more accurate than a state-of-the-art marker-based system. Planning of experiments to validate markerless tracking in microPET imaging of awake animals is currently underway.

Calculated attenuation correction for awake small animal brain PET studies

Attenuation correction of small animal PET data is very important when quantitative images are of interest. Attenuation correction coefficients are conventionally obtained via a transmission or a computed tomography scan, which require anaesthetisation of the animal. However, in the context of awake and/or freely moving animals, where animal motion is compensated via appropriate motion tracking and correction techniques, anaesthetisation is no longer required. In this work we investigate the accuracy of a transmission-less attenuation correction approach based on the segmentation of the motion corrected emission image. Results on both phantom and real rat data acquired on the microPET Focus220 scanner, indicate good agreement between the segmentation based and conventional transmission based approach (~ 2% difference). In addition, the segmentation based approach has the potential to eliminate noise propagation from the measured transmission data to the reconstructed attenuation corrected emission images.

FastMIST: A Fast Molecular Imaging SimulaTor

The Fast Molecular Imaging SimulaTor (FastMIST) is a hybrid analytical/Monte Carlo code which can simulate almost any arbitrary time-varying source distribution. Additionally, the position of each event can be spatially transformed according to a predefined temporal motion pattern allowing for the simulation of motion-corrupted data. The core algorithms are implemented in C++ following a modular design that is readily parallelisable using OpenMP. The execution time for a FastMIST simulation is greatly reduced compared to a full Monte Carlo simulation, such as GATE, as FastMIST uses forced detection coupled to random sampling of experimentally measured depth-dependent PSFs for parallel hole collimators. A FastMIST simulation of a parallel-hole collimator gamma camera, acquiring 300k photons per projection (64 projections) with source attenuation modelling disabled, took less than 30 seconds on an 15 Intel processor (2.4Ghz).

iCAP: An Individualized Model Combining Gaze Parameters and Image-Based Features to Predict Radiologists’ Decisions While Reading Mammograms

This study introduces an individualized tool for identifying mammogram interpretation errors, called eye-Computer Assisted Perception (iCAP). iCAP consists of two modules, one which processes areas marked by radiologists as suspicious for cancer and classifies these as False Positive (FP) or True Positive (TP) decisions, while the second module classifies fixated but not marked locations as False Negative (FN) or True-Negative (TN) decisions. iCAP relies on both radiologists’ gaze-related parameters, extracted from eye tracking data, and image-based features. In order to evaluate iCAP, eye tracking data from eight breast radiologists reading 120 two-view digital mammograms were collected. Fifty-nine cases had biopsy proven cancer. For each radiologist, a user-specific support vector machine model was built to classify the radiologist’ s reported areas as TPs or FPs and fixated locations as TNs or FNs. The performances of the classifiers were evaluated by utilizing leave-one-out cross validation. iCAP was tested retrospectively in a simulated scenario in which it was assumed that the radiologists would accept all iCAP decisions. Using iCAP led to an average increase of 12%±6% in the number of correctly localized cancer and an average decrease of 44.5%±22.7% in the number of FPs per image.

Efficient time-weighted sensitivity image calculation for motion compensated list mode reconstruction

Accurate motion compensated image reconstruction of freely moving small animals requires the exact calculation of the time-weighted sensitivity correction factors. Back-projection of all possible lines of response for every recorded pose is a computationally intensive task, which requires impractically long reconstruction times. In this work we investigated an approach to accelerate this task, by randomly sampling the lines of response and the poses that are used to calculate the time-averaged sensitivity image. Two phantom datasets, acquired on the microPET Focus220 scanner, were used to quantify errors introduced in the randomly sampled sensitivity images and propagated to the final reconstructed images. In addition, the qualitative performance of the proposed methodology was assessed by reconstructing a freely moving rat acquisition. Results showed that randomisation can severely amplify the noise in the reconstructed images, especially when few LORs are sampled. However, such errors can be suppressed by post-filtering the randomised sensitivity images prior to reconstruction (e.g. 2 mm FHWM). Such an approach can substantially reduce the computational time involved during the estimation of the time-averaged sensitivity image for motion compensated image reconstruction.

Markerless motion tracking enabling motion-compensated PET in awake rats

Motion-compensated PET of awake animals has the potential to greatly improve translational neurological investigations by enabling brain function to be studied during learning tasks and complex behaviors. Previously we have demonstrated the feasibility of performing motion-compensated brain PET on rodents, obtaining the necessary head motion data using marker-based techniques. However, markerless motion tracking would simplify animal experiments and potentially provide more accurate pose estimates over a greater range of motion. Previously we have described a markerless stereo motion tracking system and associated algorithms and validated the approach in phantoms. In this work we performed a pilot study to demonstrate motion-compensated 18F_FDG brain imaging in an awake, unrestrained rat using head pose measurements obtained from the markerless tracking system. Motion compensation clearly worked, resulting in easily identifiable structures in the head. However, it was also obvious that considerable residual error remained after correction. Post analysis of the motion estimates indicated that the residual error was the result of occasional spurious pose estimates, most likely caused by features on non-rigid parts of the head contributing to the pose estimation. Moreover, the line-of-response rebinning used for motion correction resulted in a large proportion of lost events, leading to noisy and inconsistent projection data. The latter is avoided by using a direct list mode reconstruction. In summary, markerless tracking continues to show promise for motion-compensated imaging of awake animals, but further optimization is required to match the accuracy and consistency of marker-based tracking.