Tracking and characterizing the head motion of unanaesthetized rats in positron emission tomography

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Small animal PET: a review of what we have done and where we are going

Small animal research is an essential tool in studying both pharmaceutical biodistributions and disease progression over time. Furthermore, through the rapid development of in vivo imaging technology over the last few decades, small animal imaging (also referred to as preclinical imaging) has become a mainstay for all fields of biologic research and a center point for most preclinical cancer research. Preclinical imaging modalities include optical, MRI and MRS, microCT, small animal PET, ultrasound, and photoacoustic, each with their individual strengths. The strong points of small animal PET are its translatability to the clinic; its quantitative imaging capabilities; its whole-body imaging ability to dynamically trace functional/biochemical processes; its ability to provide useful images with only nano- to pico‑ molar concentrations of administered compounds; and its ability to study animals serially over time. This review paper gives an overview of the development and evolution of small animal PET imaging. It provides an overview of detector designs; system configurations; multimodality PET imaging systems; image reconstruction and analysis tools; and an overview of research and commercially available small animal PET systems. It concludes with a look toward developing technologies/methodologies that will further enhance the impact of small animal PET imaging on medical research in the future.

Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; (i) it is time-consuming, (ii) it is prone to human errors, and (iii) no two human annotators will 100% agree on annotation, therefore, it is not reproducible. Consequently, automated annotation for such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine learning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of specific laboratory animals, i.e. rodents. We have divided these papers into categories based upon the hardware they use and the software approach they take. We have also summarized their strengths and weaknesses.

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals

A comprehensive understanding of how the brain responds to a changing environment requires techniques capable of recording functional outputs at the whole-brain level in response to external stimuli. Positron emission tomography (PET) is an exquisitely sensitive technique for imaging brain function but the need for anaesthesia to avoid motion artefacts precludes concurrent behavioural response studies. Here, we report a technique that combines motion-compensated PET with a robotically-controlled animal enclosure to enable simultaneous brain imaging and behavioural recordings in unrestrained small animals. The technique was used to measure in vivo displacement of [¹¹C]raclopride from dopamine D2 receptors (D2R) concurrently with changes in the behaviour of awake, freely moving rats following administration of unlabelled raclopride or amphetamine. The timing and magnitude of [¹¹C]raclopride displacement from D2R were reliably estimated and, in the case of amphetamine, these changes coincided with a marked increase in stereotyped behaviours and hyper-locomotion. The technique, therefore, allows simultaneous measurement of changes in brain function and behavioural responses to external stimuli in conscious unrestrained animals, giving rise to important applications in behavioural neuroscience.

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions.

Open-field mouse brain PET: design optimisation and detector characterisation

'Open-field' PET, in which an animal is free to move within an enclosed space during imaging, is a very promising advance for neuroscientific research. It provides a key advantage over conventional imaging under anesthesia by enabling functional changes in the brain to be correlated with an animal's behavioural response to environmental or pharmacologic stimuli. Previously we have demonstrated the feasibility of open-field imaging of rats using motion compensation techniques applied to a commercially available PET scanner. However, this approach of 'retro-fitting' motion compensation techniques to an existing system is limited by the inherent geometric and performance constraints of the system. The goal of this project is to develop a purpose-built PET scanner with geometry, motion tracking and imaging performance tailored and optimised for open-field imaging of the mouse brain. The design concept is a rail-based sliding tomograph which moves according to the animal's motion. Our specific aim in this work was to evaluate candidate scanner designs and characterise the performance of a depth-of-interaction detector module for the open-field system. We performed Monte Carlo simulations to estimate and compare the sensitivity and spatial resolution performance of four scanner geometries: a ring, parallel plate, and two box variants. Each system was based on a detector block consisting of a 23  ×  23 array of 0.785  ×  0.785  ×  20 mm³ LSO crystals (overall dim. 19.6  ×  19.6  ×  20 mm). We found that a DoI resolution capability of 3 mm was necessary to achieve approximately uniform sub-millimetre spatial resolution throughout the FoV for all scanners except the parallel-plate geometry. With this DoI performance, the sensitivity advantage afforded by the box geometry with overlapping panels (16% peak absolute sensitivity, a 36% improvement over the ring design) suggests this unconventional design is best suited for imaging the mouse brain. We also built and characterised the block detector modelled in the simulations, including a dual-ended readout based on 6  ×  6 arrays of through-silicon-via silicon photomultipliers (active area 84%) for DoI estimation. Identification of individual crystals in the flood map was excellent, energy resolution varied from 12.4%  ±  0.6% near the centre to 24.4%  ±  3.4% at the ends of the crystal, and the average DoI resolution was 2.8 mm  ±  0.35 mm near the central depth (10 mm) and 3.5 mm  ±  1.0 mm near the ends. Timing resolution was 1.4  ±  0.14 ns. Therefore, the DoI detector module meets the target specifications for the application and will be used as the basis for a prototype open-field mouse PET scanner.

Motion compensation using origin ensembles in awake small animal positron emission tomography

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood-expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured 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 animal's 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.

An investigation of the challenges in reconstructing PET images of a freely moving animal

Imaging the brain of a freely moving small animal using positron emission tomography (PET) while simultaneously observing its behaviour is an important goal for neuroscience. While we have successfully demonstrated the use of line-of-response (LOR) rebinning to correct the head motion of confined animals, a large proportion of events may need to be discarded because they either 'miss' the detector array after transformation or fall out of the acceptance range of a sinogram. The proportion of events that would have been measured had motion not occurred, so-called 'lost events', is expected to be even larger for freely moving animals. Moreover, the data acquisition in the case of a freely moving animal is further complicated by a complex attenuation field. The aims of this study were (a) to characterise the severity of the 'lostevents' problem for the freely moving animal scenario, and(b) to investigate the relative impact of attenuation correction errors on quantitative accuracy of reconstructed images. A phantom study was performed to simulate the uncorrelated motion of a target and non-target sourcevolume. A small animal PET scanner was used to acquirelist-mode data for different sets of phantom positions. The list-mode data were processed using the standard LOR rebinning approach, and multiple frame variants of this designed to reduce discarded events. We found that LOR rebinning caused up to 86 % 'lost events', and artifacts that we attribute to incomplete projections, when applied to a freely moving target. This fraction was reduced by up to 18 % using the variant approaches, resulting in slightly reduced image artifacts. The effect of the non-target compartment on attenuation correction of the target volume was surprisingly small. However, for certain poses where the target and non-target volumes are aligned transaxially in the field-of-view, the attenuation problem becomes more complex and sophisticated correction methods will be required. We conclude that there are limitations with the LOR rebinning approach and simplified attenuation correction for freely moving animals requiring the development and validation of more sophisticated approaches.