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.

POSITRON EMISSION TOMOGRAPHY OF THE EQUINE DISTAL LIMB: EXPLORATORY STUDY.

Positron emission tomography (PET) is a highly sensitive, noninvasive imaging technique for quantifying biological functions of tissues. However, at the time of this study, PET imaging applications had not been reported in the horse. The aim of this exploratory study was to determine whether a portable high-resolution PET scanner could be used to image the equine distal limb. Images of the front feet and fetlocks of three research horses, with known lesions localized to the distal front limbs, were acquired under general anesthesia after administration of 18 F-fluorodeoxyglucose (18 F-FDG), with doses ranging from 1.5 to 2.9 MBq/kg. The radiation exposure measured during imaging was slightly higher than 99m Technetium scintigraphy. However, the use of general anesthesia allowed the proximity and the contact time with the patient to be minimized for the staff involved. 18 F-FDG uptake was evident throughout the soft tissues, with the highest uptake in the coronary band and the lowest uptake in the tendons. Anatomic structures could be discriminated due to the high contrast between soft tissue and bone. Detected lesions included lysis of the flexor cortex of the navicular bone, lesions of flexor tendons and suspensory ligament, and abnormal uptake through the lamina of a laminitic subject. Findings indicated that tomographic molecular imaging is feasible in the equine distal limb and could be useful as a future diagnostic technique for clinical and research studies, especially those involving tendinopathy/desmopathy and laminitis.

Virtual cylindrical PET for efficient DOI image reconstruction with sub-millimetre resolution.

Objective.Image reconstruction in high resolution, narrow bore PET scanners with depth of interaction (DOI) capability presents a substantial computational challenge due to the very high sampling in detector and image space. The aim of this study is to evaluate the use of a virtual cylinder in reducing the number of lines of response (LOR) for DOI-based reconstruction in high resolution PET systems while maintaining uniform sub-millimetre spatial resolution.Approach.Virtual geometry was investigated using the awake animal mousePET as a high resolution test case. Using GEANT4 Application for Tomographic Emission (GATE), we simulated the physical scanner and three virtual cylinder implementations with detector size 0.74 mm, 0.47 mm and 0.36 mm (vPET1, vPET2 and vPET3, respectively). The virtual cylinder condenses physical LORs stemming from various crystal pairs and DOI combinations, and which intersect a single virtual detector pair, into a single virtual LOR. Quantitative comparisons of the point spread function (PSF) at various positions within the field of view (FOV) were compared for reconstructions based on the vPET implementations and the physical scanner. We also assessed the impact of the anisotropic PSFs by reconstructing images of a micro Derenzo phantom.Main results.All virtual cylinder implementations achieved LOR data compression of at least 50% for DOI PET reconstruction. PSF anisotropy in radial and tangential profiles was chiefly influenced by DOI resolution and only marginally by virtual detector size. Spatial degradation introduced by virtual cylinders was most prominent in the axial profile. All virtual cylinders achieved sub-millimetre volumetric resolution across the FOV when 6-bin DOI reconstructions (3.3 mm DOI resolution) were performed. Using vPET2 with 6 DOI bins yielded nearly identical reconstructions to the non-virtual case in the transaxial plane, with an LOR compression factor of 86%. Resolution modelling significantly reduced the effects of the asymmetric PSF arising from the non-cylindrical geometry of mousePET.Significance.Narrow bore and high resolution PET scanners require detectors with DOI capability, leading to computationally demanding reconstructions due to the large number of LORs. In this study, we show that DOI PET reconstruction with 50%-86% LOR compression is possible using virtual cylinders while maintaining sub-millimetre spatial resolution throughout the FOV. The methodology and analysis can be extended to other scanners with DOI capability intended for high resolution PET imaging.

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors: a simulation study.

Objective.High-resolution positron emission tomography (PET) relies on the accurate positioning of annihilation photons impinging the crystal array. However, conventional positioning algorithms in light-sharing PET detectors are often limited due to edge effects and/or the absence of additional information for identifying and correcting scattering within the crystal array (known as inter-crystal scattering). This study explores the feasibility of deep neural network (DNN) techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing.Approach.Initially, a Geant4 Application for Tomographic Emission (GATE) simulation was used to study the spatial and statistical properties of inter-crystal scatter (ICS) events in finely segmented LYSO PET detectors. Next, a DNN for crystal localisation was designed, trained and tested with light distributions of photoelectric (P) and Compton + photoelectric (CP) events simulated using optical GATE and an analytical method to speed up data generation. Using the statistical properties of ICS events, an energy-guided positioning algorithm was then built into the DNN. The positioning algorithm enables selection of the unique or first crystal of interaction in P and CP events, respectively. Performance of the DNN was compared with Anger logic using light distributions from simulated 511 keV point sources placed at different locations around a single PET detector module.Main results. The fraction of events forward and backward scattered in the LYSO detector was 0.54 and 0.46, respectively, whereas naïve application of the Klein-Nishina formulation predicts 70% forward scatter. Despite coarse photodetector data due to signal multiplexing, the DNN demonstrated a crystal classification accuracy of 90% for P events and 82% for CP events. For crystal positioning, the DNN outperformed Anger logic by at least 34% and 14% for P and CP events, respectively. Further improvement is somewhat constrained by the physics-specifically, the ratio of backward to forward scattering of gamma rays within the crystal array being close to 1. This prevents selecting the first crystal of interaction in CP events with a high degree of certainty.Significance.Light sharing and multiplexed PET detectors are common in high-resolution PET, yet their traditional positioning algorithms often underperform due to edge effects and/or the difficulty in correcting ICS events. Our study indicates that DNN-based event positioning has the potential to enhance 2D coincidence event positioning accuracy by nearly a factor of 3 compared to Anger logic. However, further improvements are difficult to foresee without additional information such as event timing.

Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network.

Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time.Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions.Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%-15%, 11%-28% and 7%-22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%-76%, 76%-86% and 83%-92%.Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively.

Continuous non-invasive estimates of cerebral blood flow using electrocardiography signals: a feasibility study.

This paper describes a potential method to detect changes in cerebral blood flow (CBF) using electrocardiography (ECG) signals, measured across scalp electrodes with reference to the same signal across the chest-a metric we term the Electrocardiography Brain Perfusion index (EBPi). We investigated the feasibility of EBPi to monitor CBF changes in response to specific tasks. Twenty healthy volunteers wore a head-mounted device to monitor EBPi and electroencephalography (EEG) during tasks known to alter CBF. Transcranial Doppler (TCD) ultrasound measurements provided ground-truth estimates of CBF. Statistical analyses were applied to EBPi, TCD right middle cerebral artery blood flow velocity (rMCAv) and EEG relative Alpha (rAlpha) data to detect significant task-induced changes and correlations. Breath-holding and aerobic exercise induced highly significant increases in EBPi and TCD rMCAv (p < 0.01). Verbal fluency also increased both measures, however the increase was only significant for EBPi (p < 0.05). Hyperventilation induced a highly significant decrease in TCD rMCAv (p < 0.01) but EBPi was unchanged. Combining all tasks, EBPi exhibited a highly significant, weak positive correlation with TCD rMCAv (r = 0.27, p < 0.01) and the Pearson coefficient between EBPi and rAlpha was r = - 0.09 (p = 0.05). EBPi appears to be responsive to dynamic changes in CBF and, can enable practical, continuous monitoring. CBF is a key parameter of brain health and function but is not easily measured in a practical, continuous, non-invasive fashion. EBPi may have important clinical implications in this context for stroke monitoring and management. Additional studies are required to support this claim. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00265-z.

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 deep learning methods may have a unique role to play in this context.

Head movement during cerebral CT perfusion imaging of acute ischaemic stroke: Characterisation and correlation with patient baseline features.

PURPOSE: To quantitatively characterise head motion prevalence and severity and to identify patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute ischaemic stroke. METHODS: The head motion of 80 stroke patients undergoing CTP imaging was classified retrospectively into four categories of severity. Each motion category was then characterised quantitatively based on the average head movement with respect to the first frame for all studies. Statistical testing and principal component analysis (PCA) were then used to identify and analyse the relationship between motion severity and patient baseline features. RESULTS: 46/80 (58%) of patients showed negligible motion, 19/80 (24%) mild-to-moderate motion, and 15/80 (19%) considerable-to-extreme motion sufficient to affect diagnostic/therapeutic accuracy even with correction. The most prevalent movement was "nodding" with maximal translation/rotation in the sagittal/axial planes. There was a tendency for motion to worsen as scan proceeded and for faster motion to occur in the first 15 s. Statistical analyses showed that greater stroke severity (National Institutes of Health Stroke Scale (NIHSS)), older patient age and shorter time from stroke onset were predictive of increased head movement (p < 0.05 Kruskal-Wallis). Using PCA, the combination of NIHSS and patient age was found to be highly predictive of head movement (p < 0.001). CONCLUSIONS: Quantitative methods were developed to characterise CTP studies impacted by motion and to anticipate patients at-risk of motion. NIHSS, age, and time from stroke onset function as good predictors of motion likelihood and could potentially be used pre-emptively in CTP scanning of acute stroke.

Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: performance and clinical feasibility.

Dose reduction in cerebral CT perfusion (CTP) imaging is desirable but is accompanied by an increase in noise that can compromise the image quality and the accuracy of image-based haemodynamic modelling used for clinical decision support in acute ischaemic stroke. The few reported methods aimed at denoising low-dose CTP images lack practicality by considering only small sections of the brain or being computationally expensive. Moreover, the prediction of infarct and penumbra size and location-the chief means of decision support for treatment options-from denoised data has not been explored using these approaches. In this work, we present the first application of a 3D generative adversarial network (3D GAN) for predicting normal-dose CTP data from low-dose CTP data. Feasibility of the approach was tested using real data from 30 acute ischaemic stroke patients in conjunction with low dose simulation. The 3D GAN model was applied to 643voxel patches extracted from two different configurations of the CTP data-frame-based and stacked. The method led to whole-brain denoised data being generated for haemodynamic modelling within 90 s. Accuracy of the method was evaluated using standard image quality metrics and the extent to which the clinical content and lesion characteristics of the denoised CTP data were preserved. Results showed an average improvement of 5.15-5.32 dB PSNR and 0.025-0.033 structural similarity index (SSIM) for CTP images and 2.66-3.95 dB PSNR and 0.036-0.067 SSIM for functional maps at 50% and 25% of normal dose using GAN model in conjunction with a stacked data regime for image synthesis. Consequently, the average lesion volumetric error reduced significantly (p-value <0.05) by 18%-29% and dice coefficient improved significantly by 15%-22%. We conclude that GAN-based denoising is a promising practical approach for reducing radiation dose in CTP studies and improving lesion characterisation.

Marker-free optical stereo motion tracking for in-bore MRI and PET-MRI application.

PURPOSE: Prospective motion correction is arguably the "silver bullet" solution for magnetic resonance imaging (MRI) studies impacted by motion, applicable to almost any pulse sequence and immune from the spin history artifacts introduced by a moving object. In prospective motion correction, the magnetic field gradients and radio frequency waveforms are adjusted in real time in response to measured head motion so as to maintain the head in a stationary reference frame relative to the scanner. Vital for this approach are accurate and rapidly sampled head pose measurements, which may be obtained optically using cameras. However, most optical methods are limited by the need to attach physical markers to the skin, which leads to decoupling of head and marker motion and reduces the effectiveness of correction. In this work we investigate the feasibility and initial performance of a stereo-optical motion tracking method which does not require any attached markers. METHODS: The method relies on detecting distinctive natural features or amplified features (using skin stamps) directly on the forehead in multiple camera views, and then deriving pose estimates via a 3D-2D registration between the skin features and a database of forehead landmarks. To demonstrate the feasibility and potential accuracy of the marker-free method for discrete (step-wise) head motion, we performed out-of-bore and in-bore experiments using robotically and manually controlled phantoms in addition to in-bore testing on human volunteers. We also developed a convenient out-of-bore test bed to benchmark and optimize the motion tracking performance. RESULTS: For out-of-bore phantom tests, the pose estimation accuracy (compared to robotic ground truth) was 0.14 mm and 0.23 degrees for incremental translation and rotation, respectively. For arbitrary motion, the pose accuracy obtained using the smallest forehead feature patch was equivalent to 0.21 ± 0.11 mm positional accuracy in the striatum. For in-bore phantom experiments, the accuracy of rigid-body motion parameters (compared to wireless MR-sensitive markers) was 0.08-0.41 ± 0.18 mm/0.05-0.3 ± 0.12 deg and 0.14-0.16 ± 0.12 mm/0.08-0.17 ± 0.08 deg for the small and large feature patches, respectively. In vivo results in human volunteers indicated sub-millimeter and sub-degree pose accuracy for all rotations and translations except the depth direction (max error 1.8 mm) when compared to a registration-based approach. CONCLUSIONS: In both bench-top and in vivo experiments we demonstrate the feasibility of using very small feature patches directly on the skin to obtain high accuracy head pose measurements needed for motion-correction in MRI brain studies. The optical technique uses in-bore cameras and is consistent with the limited visibility of the forehead afforded by head coils used in brain imaging. Future work will focus on optimization of the technique and demonstration in prospective motion correction.

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.

Imaging Salt Uptake Dynamics in Plants Using PET.

Soil salinity is a global environmental challenge for crop production. Understanding the uptake and transport properties of salt in plants is crucial to evaluate their potential for growth in high salinity soils and as a basis for engineering varieties with increased salt tolerance. Positron emission tomography (PET), traditionally used in medical and animal imaging applications for assessing and quantifying the dynamic bio-distribution of molecular species, has the potential to provide useful measurements of salt transport dynamics in an intact plant. Here we report on the feasibility of studying the dynamic transport of 22Na in millet using PET. Twenty-four green foxtail (Setaria viridis L. Beauv.) plants, 12 of each of two different accessions, were incubated in a growth solution containing 22Na+ ions and imaged at 5 time points over a 2-week period using a high-resolution small animal PET scanner. The reconstructed PET images showed clear evidence of sodium transport throughout the whole plant over time. Quantitative region-of-interest analysis of the PET data confirmed a strong correlation between total 22Na activity in the plants and time. Our results showed consistent salt transport dynamics within plants of the same variety and important differences between the accessions. These differences were corroborated by independent measurement of Na+ content and expression of the NHX transcript, a gene implicated in sodium transport. Our results demonstrate that PET can be used to quantitatively evaluate the transport of sodium in plants over time and, potentially, to discern differing salt-tolerance properties between plant varieties. In this paper, we also address the practical radiation safety aspects of working with 22Na in the context of plant imaging and describe a robust pipeline for handling and incubating plants. We conclude that PET is a promising and practical candidate technology to complement more traditional salt analysis methods and provide insights into systems-level salt transport mechanisms in intact plants.

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.

Markerless motion estimation for motion-compensated clinical brain imaging.

Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation associated with voluntary and involuntary subject head motion during positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be the lack of a practical motion tracking technology that allows for smooth and reliable integration of motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The method requires no attached markers, relying exclusively on the detection and matching of distinctive facial features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the estimated motion with an accurate marker-based method used in applications such as image guided surgery. A range of techniques to optimize performance of the method were also studied. Our results show that the markerless motion tracking method is highly accurate (<2 mm discrepancy against a benchmarking system) on an ethnically diverse range of subjects and, moreover, exhibits lower jitter and estimation of motion over a greater range than some marker-based methods. Our optimization tests indicate that the basic pose estimation algorithm is very robust but generally benefits from rudimentary background masking. Further marginal gains in accuracy can be achieved by accounting for non-rigid motion of features. Efficiency gains can be achieved by capping the number of features used for pose estimation provided that these features adequately sample the range of head motion encountered in the study. These proof-of-principle data suggest that markerless motion tracking is amenable to motion-compensated brain imaging and holds good promise for a practical implementation in clinical PET, SPECT and CT systems.

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.

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.

Optimising rigid motion compensation for small animal brain PET imaging.

Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal's head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation; improving the temporal synchronisation between the motion tracker measurements and the list-mode stream; post-acquisition smoothing and interpolation of the motion data; and list-mode reconstruction with appropriately selected subsets. These techniques have been tested and verified on measurements of a moving resolution phantom and brain scans of an awake rat. The proposed techniques improved the reconstructed spatial resolution of the phantom by 27% and of the rat brain by 14%. We suggest a set of optimal parameter values to use for awake animal PET studies and discuss the relative significance of each parameter choice.

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

UNLABELLED: 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. METHODS: 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). RESULTS: 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. CONCLUSION: These results provide the basis of an implementation protocol for clinical validation of the technique.

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.