Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities.

PURPOSE: Accurate segmentation (separating diseased portions of the lung from normal appearing lung) is a challenge in radiomic studies of non-neoplastic diseases, such as pulmonary tuberculosis (PTB). In this study, we developed a segmentation method, applicable to chest X-rays (CXR), that can eliminate the need for precise disease delineation, and that is effective for constructing radiomic models for automatic PTB cavity classification. METHODS: This retrospective study used a dataset of 266 posteroanterior CXR of patients diagnosed with laboratory confirmed PTB. The lungs were segmented using a U-net-based in-house automatic segmentation model. A secondary segmentation was developed using a sliding window, superimposed on the primary lung segmentation. Pyradiomics was used for feature extraction from every window which increased the dimensionality of the data, but this allowed us to accurately capture the spread of the features across the lung. Two separate measures (standard-deviation and variance) were used to consolidate the features. Pearson's correlation analysis (with a 0.8 cut-off value) was then applied for dimensionality reduction followed by the construction of Random Forest radiomic models. RESULTS: Two almost identical radiomic signatures consisting of 10 texture features each (9 were the same plus 1 other feature) were identified using the two separate consolidation measures. Two well performing random forest models were constructed from these signatures. The standard-deviation model (AUC = 0.9444 (95% CI, 0.8762; 0.9814)) performed marginally better than the variance model (AUC = 0.9288 (95% CI, 0.9046; 0.9843)). CONCLUSION: The introduction of the secondary sliding window segmentation on CXR could eliminate the need for disease delineation in pulmonary radiomic studies, and it could improve the accuracy of CXR reporting currently regaining prominence as a high-volume screening tool as the developed radiomic models correctly classify cavities from normal CXR.

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.

Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole-Mount Histopathology.

Rigorous validation with ground truth information such as histology is needed to reliably assess the current and potential value of MRI techniques to characterize tissue and identify disease-related tissue alterations. Commonly used methods that aim to directly correlate histology and MRI data generally fall short of this goal due to spatial errors that preclude direct matching. Errors result from tissue deformation, differences in spatial resolution and slice thickness, non-coplanar and/or nonintersecting plane orientations, and different image contrast mechanisms. Some of these problems arise from limitations in standard protocols for clinical tissue processing and histology-based pathology reporting, and to some extent can be addressed by modifications to standard protocols without compromising the clinical process. Typical modifications include ex vivo specimen MRI, block-face photography, addition of fiducial markers, and 3D printed molds to constrain tissue deformation and guide sectioning. This review summarizes the advantages and limitations of MRI validation techniques based on coregistration of MRI with whole-mount histology of tissue specimens. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

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.

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.

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.

Quantitative PET in the 2020s: a roadmap.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

Experimental evaluation of the dosimetric impact of intrafraction prostate rotation using film measurement with a 6DoF robotic arm.

PURPOSE: Tumor motion during radiotherapy can cause a reduction in target dose coverage and an increase in healthy tissue exposure. Tumor motion is not strictly translational and often exhibits complex six degree-of-freedom (6DoF) translational and rotational motion. Although the dosimetric impact of prostate tumor translational motion is well investigated, the dosimetric impact of 6DoF motion has only been studied with simulations or dose reconstruction. This study aims to experimentally quantify the dose error caused by 6DoF motion. The experiment was designed to test the hypothesis that 6DoF motion would cause larger dose errors than translational motion alone through gamma analyses of two-dimensional film measurements. METHODS: Four patient-measured intrafraction prostate motion traces and four VMAT 7.25 Gy/Fx SBRT treatment plans were selected for the experiment. The traces represented typical motion patterns, including small-angle rotations (<4°), transient movement, persistent excursion, and erratic rotations (>6°). Gafchromic film was placed inside a custom-designed phantom, held by a high-precision 6DoF robotic arm for dose measurements in the coronal plane during treatment delivery. For each combination of the motion trace and treatment plan, two film measurements were made, one with 6DoF motion and the other with the three-dimensional (3D) translation components of the same trace. A gamma pass rate criteria of 2% relative dose/2 mm distance-to-agreement was used in this study and evaluated for each measurement with respect to the static reference film. Two test thresholds, 90% and 50% of the reference dose, were applied to investigate the difference in dose coverage for the PTV region and surrounding areas, respectively. The hypothesis was tested using a Wilcoxon signed-rank test. RESULTS: For each of the 16 plan and motion trace pairs, a reduction in the gamma pass rate was observed for 6DoF motion compared with 3D translational motion. With 90% gamma-test threshold, the reduction was 5.8% ± 7.1% (P < 0.01). With 50% gamma-test threshold, the reduction was 4.1% ± 4.8% (P < 0.01). CONCLUSION: For the first time, the dosimetric impact of intrafraction prostate rotation during SBRT treatment was measured experimentally. The experimental results support the hypothesis that 6DoF tumor motion causes higher dose error than translation motion alone.

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.

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.

A six-degree-of-freedom robotic motion system for quality assurance of real-time image-guided radiotherapy.

In this study we develop and characterise a six degree-of-freedom (6 DoF) robotic motion system for quality assurance of real-time image-guided radiotherapy techniques. The system consists of a commercially available robotic arm, an acrylic phantom with embedded Calypso markers, a custom base plate to mount the robot to the treatment couch, and control software implementing the appropriate sequence of transformations to reproduce measured tumour motion traces. The robotic motion system was evaluated in terms of the set-up and motion trace repeatability, static localization accuracy and dynamic localization accuracy. Four prostate, two liver and three lung motion traces, representing a range of tumor motion trajectories recorded in real patient treatments, were executed using the robotic motion system and compared with motion measurements from the clinical Calypso motion tracking system. System set-up and motion trace repeatability was better than 0.5 deg and 0.3 mm for rotation and translation, respectively. The static localization accuracy of the robotic motion system in the LR, SI and AP directions was 0.09 mm, 0.08 mm and 0.02 mm for translations, respectively, and 0.2°, 0.06° and 0.06° for rotations, respectively. The dynamic localization accuracy of the robotic motion system was <0.2 mm and <0.6° for translations and rotations, respectively. Thus, we have demonstrated the ability to accurately mimic rigid-body tumor motion using a robotically controlled phantom to provide precise geometric QA for advanced radiotherapy delivery approaches.

The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques. METHODS: This was a retrospective analysis of state-wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation-based, information gain and wrapper) were examined. Presenting problem was categorised using broad (n = 20) and specific (n = 100) representations. Models were evaluated based on Area Under the Curve (AUC) and accuracy. The results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features. RESULTS: Sixty admission prediction models were trained and validated using data from 1 721 294 patients. Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065). Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). Eleven of the machine learning models had slightly better AUC than the START model. CONCLUSION: Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. This should be investigated further, especially for larger data sets with more complex clinical information.

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.

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.

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.

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.

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.

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.