Deep learned triple-tracer multiplexed PET myocardial image separation.

Introduction: In multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling (MTCM) method requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known. Methods: In this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. A dynamic triple-tracer noisy MLEM reconstruction was used as the network input, and dynamic single-tracer noisy MLEM reconstructions were used as training labels. Results: A simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([ F 18 ]FDG+ Rb 82 +[ Tc 99m ]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and region of interest (ROI) levels. Discussion: As compared to MTCM separation, the proposed method uses spatiotemporal information for separation, which improves the separation performance at both the voxel and ROI levels. The simulation study also demonstrates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies.

Kinetic model-informed deep learning for multiplexed PET image separation.

BACKGROUND: Multiplexed positron emission tomography (mPET) imaging can measure physiological and pathological information from different tracers simultaneously in a single scan. Separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that each tracer gives rise to indistinguishable 511 keV photon pairs, and thus no unique energy information for differentiating the source of each photon pair. METHODS: Recently, many applications of deep learning for mPET image separation have been concentrated on pure data-driven methods, e.g., training a neural network to separate mPET images into single-tracer dynamic/static images. These methods use over-parameterized networks with only a very weak inductive prior. In this work, we improve the inductive prior of the deep network by incorporating a general kinetic model based on spectral analysis. The model is incorporated, along with deep networks, into an unrolled image-space version of an iterative fully 4D PET reconstruction algorithm. RESULTS: The performance of the proposed method was evaluated on a simulated brain image dataset for dual-tracer [ 18 F]FDG+[ 11 C]MET PET image separation. The results demonstrate that the proposed method can achieve separation performance comparable to that obtained with single-tracer imaging. In addition, the proposed method outperformed the model-based separation methods (the conventional voxel-wise multi-tracer compartment modeling method (v-MTCM) and the image-space dual-tracer version of the fully 4D PET image reconstruction algorithm (IS-F4D)), as well as a pure data-driven separation [using a convolutional encoder-decoder (CED)], with fewer training examples. CONCLUSIONS: This work proposes a kinetic model-informed unrolled deep learning method for mPET image separation. In simulation studies, the method proved able to outperform both the conventional v-MTCM method and a pure data-driven CED with less training data.

Productive cough, a forgotten phenotype of refractory chronic cough.

BACKGROUND: Refractory chronic cough (RCC) patients typically complain of a dry cough. Sputum production in these patients has rarely been described in the literature. However, sputum production in RCC may be common, troublesome and particularly challenging to manage. AIMS: We aimed to characterise patients referred to our regional specialist cough clinic who have significant sputum production (≥1 teaspoon of sputum daily) and compare their demographics, symptomatology and investigation results to those presenting with a dry RCC. METHODS: A retrospective case note analysis of all consecutive referrals to a tertiary cough clinic identified as having a productive cough (≥1 teaspoon of sputum daily) was performed over a 12-month period. They were compared to a paired number of patients with dry RCC. RESULTS: Patients with productive cough represented nearly a quarter (98/398, 24.6 %) of new patient referrals to the clinic. Demographic information, symptomatology and investigation results were comparable to those with a dry RCC. Over a third, 35.8 % of patients with a productive cough reported expectorating an egg-cupful of sputum or more a day. Nearly half, 40% of those with high volume sputum production (≥egg-cupful daily) had both a normal CT thorax and normal spirometry. CONCLUSIONS: Patients with RCC can expectorate significant volumes of sputum in the absence of underlying lung disease. This group have similar demographics to those with dry RCC. This is the first piece of work to our knowledge to describe this cohort. Future work needs to acknowledge this forgotten phenotype to ensure that they receive comprehensive evaluation and evidenced based treatment.

In vivo real-time positron emission particle tracking (PEPT) and single particle PET.

Positron emission particle tracking (PEPT) enables 3D localization and tracking of single positron-emitting radiolabelled particles with high spatiotemporal resolution. The translation of PEPT to the biomedical imaging field has been limited due to the lack of methods to radiolabel biocompatible particles with sufficient specific activity and protocols to isolate a single particle in the sub-micrometre size range, below the threshold for capillary embolization. Here we report two key developments: the synthesis and 68Ga-radiolabelling of homogeneous silica particles of 950 nm diameter with unprecedented specific activities (2.1 ± 1.4 kBq per particle), and the isolation and manipulation of a single particle. We have combined these developments to perform in vivo PEPT and dynamic positron emission tomography (PET) imaging of a single radiolabelled sub-micrometre size particle using a pre-clinical positron emission tomography/computed tomography scanner. This work opens possibilities for quantitative assessment of haemodynamics in vivo in real time, at the whole-body level using minimal amounts of injected radioactive dose and material.

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images.

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmaxestimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmeanwithin the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmaxand current practices of manually defining a volume of interest in the organ should be considered instead.

A review of PET attenuation correction methods for PET-MR.

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

Bronchoscopy for refractory/unexplained cough with mucus.

INTRODUCTION: Refractory/unexplained cough (RUCC) is typically associated with throat symptoms and a dry cough. Some patients attending specialist cough clinics however, report sputum production (>1 tablespoon daily) and atypical sensations (urge-to-cough in chest). Bronchoscopy findings in this specific cohort have rarely been described. AIMS: We aimed to evaluate bronchoscopy, bronchoalveolar lavage (BAL) cell differential and microbiology findings in RUCC with mucus production. METHODS: We retrospectively reviewed case notes, procedure results and treatment of patients undergoing bronchoscopy for RUCC with more than a tablespoon of sputum daily. RESULTS: Data were included from 54 patients with RUCC, normal or trivial findings on CT (Computerised Tomography) imaging and no response to guideline-directed treatment of their cough. Most (84%) patients had BAL neutrophilia and excessive dynamic airway collapse (EDAC) was seen in 31%. Treatment strategies in these patients differed to those adopted in typical RUCC associated with a dry cough. Management was influenced or changed in 48/54 (89%) of the patients undergoing bronchoscopy. CONCLUSIONS: Bronchoscopy provides high diagnostic value in RUCC with mucus production (>1 tbsp daily), identifying specific treatable traits including neutrophilic airway inflammation and EDAC.

Chronic cough and inflammatory bowel disease: an under-recognised association?

Respiratory complications of inflammatory bowel disease (IBD) are common and may be under-recognised. Chronic cough may present many years after a colectomy for IBD, is typically productive and can be very responsive to inhaled corticosteroids. https://bit.ly/3DrHNoy.

The P2X3 receptor antagonist filapixant in patients with refractory chronic cough: a randomized controlled trial.

BACKGROUND: P2X3 receptor antagonists seem to have a promising potential for treating patients with refractory chronic cough. In this double-blind, randomized, placebo-controlled study, we investigated the efficacy, safety, and tolerability of the novel selective P2X3 receptor antagonist filapixant (BAY1902607) in patients with refractory chronic cough. METHODS: Following a crossover design, 23 patients with refractory chronic cough (age: 60.4 ± 9.1 years) received ascending doses of filapixant in one period (20, 80, 150, and 250 mg, twice daily, 4-days-on/3-days-off) and placebo in the other. The primary efficacy endpoint was the 24-h cough frequency on Day 4 of each dosing step. Further, subjective cough severity and health-related quality of life were assessed. RESULTS: Filapixant at doses ≥ 80 mg significantly reduced cough frequency and severity and improved cough health-related quality of life. Reductions in 24-h cough frequency over placebo ranged from 17% (80 mg dose) to 37% (250 mg dose), reductions over baseline from 23% (80 mg) to 41% (250 mg) (placebo: 6%). Reductions in cough severity ratings on a 100-mm visual analog scale ranged from 8 mm (80 mg) to 21 mm (250 mg). No serious or severe adverse events or adverse events leading to discontinuation of treatment were reported. Taste-related adverse events occurred in 4%, 13%, 43%, and 57% of patients treated with filapixant 20, 80, 150, and 250 mg, respectively, and in 12% treated with placebo. CONCLUSIONS: Filapixant proved to be efficacious, safe, and-apart from the occurrence of taste disturbances, especially at higher dosages-well tolerated during the short therapeutic intervention. Clinical trial registration EudraCT, eudract.ema.europa.eu, 2018-000129-29; ClinicalTrials.gov, NCT03535168.

Mini-review: Hypertussivity and allotussivity in chronic cough endotypes.

In recent years our understanding of the neurophysiological basis of cough has increased substantially. In conjunction, concepts around the drivers of chronic coughing in patients have also significantly evolved. Increasingly it is recognised that dysregulation of the neuronal pathways mediating cough play an important role in certain phenotypes of chronic cough and therefore pathological processes affecting the nervous system are likely to represent key endotypes in patients. Taking inspiration from the study of neuropathic pain, the term hypertussia has been employed to describe the phenomenon of abnormal excessive coughing in response to airway irritation and allotussia to describe coughing in response to stimuli not normally provoking cough. This review aims to summarise current clinical evidence supporting a role for the hyperexcitability of neuronal pathways contributing to chronic coughing and suggest how these might align with the clinical features observed in patients.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

Objective.In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake.Approach.In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with 'ground truth' radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network.Results.When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities.Significance.Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

Deep Learning Signal Discrimination for Improved Sensitivity at High Specificity for CMOS Intraoperative Probes.

The challenge in delineating the boundary between cancerous and healthy tissue during cancer resection surgeries can be addressed with the use of intraoperative probes to detect cancer cells labeled with radiotracers to facilitate excision. In this study, deep learning algorithms for background gamma ray signal rejection were explored for an intraoperative probe utilizing CMOS monolithic active pixel sensors optimized toward the detection of internal conversion electrons from [Formula: see text]Tc. Two methods utilizing convolutional neural networks (CNNs) were explored for beta-gamma discrimination: 1) classification of event clusters isolated from the sensor array outputs (SAOs) from the probe and 2) semantic segmentation of event clusters within an acquisition frame of an SAO which provides spatial information on the classification. The feasibility of the methods in this study was explored with several radionuclides including 14C, 57Co, and [Formula: see text]Tc. Overall, the classification deep network is able to achieve an improved area under the curve (AUC) of the receiver operating characteristic (ROC), giving 0.93 for 14C beta and [Formula: see text]Tc gamma clusters, compared to 0.88 for a more conventional feature-based discriminator. Further optimization of the lower left region of the ROC by using a customized AUC loss function during training led to an improvement of 31% in sensitivity at low false positive rates compared to the conventional method. The segmentation deep network is able to achieve a mean dice score of 0.93. Through the direct comparison of all methods, the classification method was found to have a better performance in terms of the AUC.

Assessing cough symptom severity in refractory or unexplained chronic cough: findings from patient focus groups and an international expert panel.

Background: Cough symptom severity represents an important subjective end-point to assess the impact of therapies for patients with refractory or unexplained chronic cough (RCC/UCC). As existing instruments assessing the severity of cough are neither widely available nor tested for measurement properties, we aim to develop a new patient-reported outcome measure addressing cough severity. Objective: The aim of this study was to establish items and domains that would inform development of a new cough severity instrument. Methods: Three focus groups involving 16 adult patients with RCC/UCC provided data that we analysed using directed content analysis. Discussions led to consensus among an international panel of 15 experts on candidate items and domains to assess cough severity. Results: The patient focus group provided 48 unique items arranged under broad domains of urge-to-cough sensations and cough symptom. Feedback from expert panel members confirmed the appropriateness of items and domains, and provided an additional subdomain related to cough triggers. The final conceptual framework comprised 51 items in the following domains: urge-to-cough sensations (subdomains: frequency and intensity) and cough symptom (subdomains: triggers, control, frequency, fit/bout duration, intensity, quality and associated features/sequelae). Conclusions: Consensus findings from patients and international experts established domains of urge-to-cough and cough symptom with associated subdomains and relevant items. The results support item generation and content validity for a novel patient-reported outcome measure for use in health research and clinical practice.

Anti-interleukin-13 and anti-interleukin-4 agents versus placebo, anti-interleukin-5 or anti-immunoglobulin-E agents, for people with asthma.

BACKGROUND: Targeting the immunoglobulin E pathway and the interleukin-5 pathway with specific monoclonal antibodies directed against the cytokines or their receptors is effective in patients with severe asthma. However, there are patients who have suboptimal responses to these biologics. Since interleukin-4 and interleukin-13, signalling through the interleukin-4 receptor, have multiple effects on the biology of asthma, therapies targeting interleukin-4 and -13 (both individually and combined) have been developed. OBJECTIVES: To assess the efficacy and safety of anti-interleukin-13 or anti-interleukin-4 agents, compared with placebo, anti-immunoglobulin E agents, or anti-interleukin-5 agents, for the treatment of children, adolescents, or adults with asthma. SEARCH METHODS: We identified studies from the Cochrane Airways Trials Register, which is maintained by the Information Specialist for the Group and through searches of the US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform. The search was carried out on the 16 October 2020. SELECTION CRITERIA: We included parallel-group randomised controlled trials that compared anti-interleukin-13 or -4 agents (or agents that target both interleukin-13 and interleukin-4) with placebo in adolescents and adults (aged 16 years or older) or children (younger than 16 years), with a diagnosis of asthma; participants could receive their usual short- or long-acting medications (e.g. inhaled corticosteroids (ICS), long-acting beta adrenoceptor agonists (LABA), long-acting muscarinic antagonists (LAMA), and/or leukotriene receptor antagonists) provided that they were not part of the randomised treatment. DATA COLLECTION AND ANALYSIS: We used standard methods expected by Cochrane. MAIN RESULTS: We identified and included 41 RCTs. Of these, 29 studies contributed data to the quantitative analyses, randomly assigning 10,604 people with asthma to receive an anti-interleukin-13 (intervention) or anti-interleukin-4 agent (intervention), or placebo (comparator). No relevant studies were identified where the comparator was an anti-immunoglobulin agent or an anti-interleukin-5 agent. Studies had a duration of between 2 and 52 (median 16) weeks. The mean age of participants across the included studies ranged from 22 to 55 years. Only five studies permitted enrolment of children and adolescents, accounting for less than 5% of the total participants contributing data to the present review. The majority of participants had moderate or severe uncontrolled asthma. Concomitant ICS use was permitted or required in the majority (21 of 29) of the included studies. The use of maintenance systemic corticosteroids was not permitted in 19 studies and was permitted or required in five studies (information not reported in five studies). Regarding the most commonly assessed anti-interleukin-13/-4 agents, four studies evaluated dupilumab (300 mg once every week (Q1W), 200 mg once every two weeks (Q2W), 300 mg Q2W, 200 mg once every four weeks (Q4W), 300 mg Q4W, each administered by subcutaneous (SC) injection); eight studies evaluated lebrikizumab (37.5 mg Q4W, 125 mg Q4W, 250 mg Q4W each administered by SC injection); and nine studies (3259 participants) evaluated tralokinumab (75 mg Q1W, 150 mg Q1W, 300 mg Q1W, 150 mg Q2W, 300 mg Q2W, 600 mg Q2W, 300 mg Q4W, each administered by SC injection; 1/5/10 mg/kg administered by intravenous (IV) injection); all anti-interleukin-13 or-4 agents were compared with placebo. The risk of bias was generally considered to be low or unclear (insufficient detail provided); nine studies were considered to be at high risk for attrition bias and three studies were considered to be at high risk for reporting bias. The following results relate to the primary outcomes. The rate of exacerbations requiring hospitalisation or emergency department (ED) visit was probably lower in participants receiving tralokinumab versus placebo (rate ratio 0.68, 95% CI 0.47 to 0.98; moderate-certainty evidence; data available for tralokinumab (anti-interleukin-13) only). In participants receiving an anti-interleukin-13/-4 agent, the mean improvement versus placebo in adjusted asthma quality of life questionnaire score was 0.18 units (95% CI 0.12 to 0.24; high-certainty evidence); however, this finding was deemed not to be a clinically relevant improvement. There was likely little or no difference between groups in the proportion of patients who reported all-cause serious adverse events (anti-interleukin-13/-4 agents versus placebo, OR 0.91, 95% CI 0.76 to 1.09; moderate-certainty evidence). In terms of secondary outcomes, there may be little or no difference between groups in the proportion of patients who experienced exacerbations requiring oral corticosteroids (anti-interleukin-13/-4 agents versus placebo, rate ratio 0.98, 95% CI 0.72 to 1.32; low-certainty evidence). Anti-interleukin-13/-4 agents probably improve asthma control based on asthma control questionnaire score (anti-interleukin-13/-4 agents versus placebo, mean difference -0.19; 95% CI -0.24 to -0.14); however, the magnitude of this result was deemed not to be a clinically relevant improvement. The proportion of patients experiencing any adverse event was greater in those receiving anti-interleukin-13/-4 agents compared with those receiving placebo (OR 1.16, 95% CI 1.04 to 1.30; high-certainty evidence); the most commonly reported adverse events in participants treated with anti-interleukin-13/-4 agents were upper respiratory tract infection, nasopharyngitis, headache and injection site reaction. The pooled results for the exploratory outcome, the rate of exacerbations requiring oral corticosteroids (OCS) or hospitalisation or emergency department visit, may be lower in participants receiving anti-interleukin-13/-4 agents versus placebo (rate ratio 0.71, 95% CI 0.65 to 0.77; low-certainty evidence). Results were generally consistent across subgroups for different classes of agent (anti-interleukin-13 or anti-interleukin-4), durations of study and severity of disease. Subgroup analysis based on category of T helper 2 (TH2) inflammation suggested greater efficacy in patients with higher levels of inflammatory biomarkers (blood eosinophils, exhaled nitric oxide and serum periostin). AUTHORS' CONCLUSIONS: Based on the totality of the evidence, compared with placebo, anti-interleukin-13/-4 agents are probably associated with a reduction in exacerbations requiring hospitalisation or ED visit, at the cost of increased adverse events, in patients with asthma. No clinically relevant improvements in health-related quality of life or asthma control were identified. Therefore, anti-interleukin-13 or anti-interleukin-4 agents may be appropriate for adults with moderate-to-severe uncontrolled asthma who have not responded to other treatments. These conclusions are generally supported by moderate or high-certainty evidence based on studies with an observation period of up to one year.

Production of copper-64 using a hospital cyclotron: targetry, purification and quality analysis.

OBJECTIVES: To construct and evaluate a 64Cu production system that minimises the amount of costly 64Ni, radionuclidic impurities and nonradioactive metal contamination and maximises radiochemical and radionuclidic purity and molar activity; and to report analytical and quality control methods that can be used within typical PET radiochemistry production facilities to measure metal ion concentrations and radiometal molar activities. METHODS: Low volume was ensured by dissolving the irradiated nickel in a low volume of hydrochloric acid (<1 mL) using the concave gold target backing as a reaction vessel in a custom-built target holder. Removal of contaminating 55Co and nonradioactive trace metals was ensured by adding an intermediate hydrochloric acid concentration step during the conventional ion-exchange elution process. The radionuclidic purity of the product was determined by half-life measurements, gamma spectroscopy and ion radiochromatography. Trace metal contamination and molar activity were determined by ion chromatography. RESULTS AND CONCLUSIONS: On a small scale, suitable for preclinical research, the process produced typically 3.2 GBq 64Cu in 2 mL solution from 9.4 ± 2.1 mg nickel-64 electroplated onto a gold target backing. The product had high molar activity (121.5 GBq/µmol), was free of trace metal contamination detectable by ion chromatography and has been used for many preclinical and clinical PET imaging applications.

Standardisation of conventional and advanced iterative reconstruction methods for Gallium-68 multi-centre PET-CT trials.

PURPOSE: To assess the applicability of the Fluorine-18 performance specifications defined by EANM Research Ltd (EARL), in Gallium-68 multi-centre PET-CT trials using conventional (ordered subset expectation maximisation, OSEM) and advanced iterative reconstructions which include the systems' point spread function (PSF) and a Bayesian penalised likelihood algorithm (BPL) commercially known as Q.CLEAR. The possibility of standardising the two advanced reconstruction methods was examined. METHODS: The NEMA image quality phantom was filled with Gallium-68 and scanned on a GE PET-CT system. PSF and BPL with varying post-reconstruction Gaussian filter width (2-6.4 mm) and penalisation factor (200-1200), respectively, were applied. The average peak-to-valley ratio from six profiles across each sphere was estimated to inspect any edge artefacts. Image noise was assessed using background variability and image roughness. Six GE and Siemens PET-CT scanners provided Gallium-68 images of the NEMA phantom using both conventional and advanced reconstructions from which the maximum, mean and peak recoveries were drawn. Fourteen patients underwent 68Ga-PSMA PET-CT imaging. BPL (200-1200) reconstructions of the data were compared against PSF smoothed with a 6.4-mm Gaussian filter. RESULTS: A Gaussian filter width of approximately 6 mm for PSF and a penalisation factor of 800 for BPL were needed to suppress the edge artefacts. In addition, those reconstructions provided the closest agreement between the two advanced iterative reconstructions and low noise levels with the background variability and the image roughness being lower than 7.5% and 11.5%, respectively. The recoveries for all methods generally performed at the lower limits of the EARL specifications, especially for the 13- and 10-mm spheres for which up to 27% (conventional) and 41% (advanced reconstructions) lower limits are suggested. The lesion standardised uptake values from the clinical data were significantly different between BPL and PSF smoothed with a Gaussian filter of 6.4 mm wide for all penalisation factors except for 800 and 1000. CONCLUSION: It is possible to standardise the advanced reconstruction methods with the reconstruction parameters being also sufficient for minimising the edge artefacts and noise in the images. For both conventional and advanced reconstructions, Gallium-68 specific recovery coefficient limits were required, especially for the smallest phantom spheres.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.