Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK.

BACKGROUND: Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS: This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS: Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK.

Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development. Clinical practitioners involved with procuring, deploying, and adopting AI tools in the UK should be well-informed about these AI governance frameworks. This scoping review aimed to map out available literature on AI governance in the UK, focusing on medical imaging and radiotherapy. Searches were performed on Google Scholar, Pubmed, and the Cochrane Library, between June and July 2022. Of 4225 initially identified sources, 35 were finally included in this review. A comprehensive conceptual AI governance framework was proposed, guided by the need for rigorous AI validation and evaluation procedures, the accreditation rules and standards, and the fundamental ethical principles of AI. Fairness, transparency, trustworthiness, and explainability should be drivers of all AI models deployed in clinical practice. Appropriate staff education is also mandatory to ensure AI's safe and responsible use. Multidisciplinary teams under robust leadership will facilitate AI adoption, and it is crucial to involve patients, the public, and practitioners in decision-making. Collaborative research should be encouraged to enhance and promote innovation, while caution should be paid to the ongoing auditing of AI tools to ensure safety and clinical effectiveness.

Operationalizing the centiloid scale for [18F]florbetapir PET studies on PET/MRI.

INTRODUCTION: The Centiloid scale aims to harmonize amyloid beta (Aß) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS: We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aß PET positivity were converted. RESULTS: The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSION: Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS: Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.

PURPOSE: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. MATERIALS AND METHODS: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. RESULTS: The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s). CONCLUSION: ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

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.

Population-based blood screening for preclinical Alzheimer's disease in a British birth cohort at age 70.

Alzheimer's disease has a preclinical stage when cerebral amyloid-ß deposition occurs before symptoms emerge, and when amyloid-ß-targeted therapies may have maximum benefits. Existing amyloid-ß status measurement techniques, including amyloid PET and CSF testing, are difficult to deploy at scale, so blood biomarkers are increasingly considered for screening. We compared three different blood-based techniques-liquid chromatography-mass spectrometry measures of plasma amyloid-ß, and single molecule array (Simoa) measures of plasma amyloid-ß and phospho-tau181-to detect cortical 18F-florbetapir amyloid PET positivity (defined as a standardized uptake value ratio of >0.61 between a predefined cortical region of interest and eroded subcortical white matter) in dementia-free members of Insight 46, a substudy of the population-based British 1946 birth cohort. We used logistic regression models with blood biomarkers as predictors of amyloid PET status, with or without age, sex and APOE ε4 carrier status as covariates. We generated receiver operating characteristics curves and quantified areas under the curves to compare the concordance of the different blood tests with amyloid PET. We determined blood test cut-off points using Youden's index, then estimated numbers needed to screen to obtain 100 amyloid PET-positive individuals. Of the 502 individuals assessed, 441 dementia-free individuals with complete data were included; 82 (18.6%) were amyloid PET-positive. The area under the curve for amyloid PET status using a base model comprising age, sex and APOE ε4 carrier status was 0.695 (95% confidence interval: 0.628-0.762). The two best-performing Simoa plasma biomarkers were amyloid-ß42/40 (0.620; 0.548-0.691) and phospho-tau181 (0.707; 0.646-0.768), but neither outperformed the base model. Mass spectrometry plasma measures performed significantly better than any other measure (amyloid-ß1-42/1-40: 0.817; 0.770-0.864 and amyloid-ß composite: 0.820; 0.775-0.866). At a cut-off point of 0.095, mass spectrometry measures of amyloid-ß1-42/1-40 detected amyloid PET positivity with 86.6% sensitivity and 71.9% specificity. Without screening, to obtain 100 PET-positive individuals from a population with similar amyloid PET positivity prevalence to Insight 46, 543 PET scans would need to be performed. Screening using age, sex and APOE ε4 status would require 940 individuals, of whom 266 would proceed to scan. Using mass spectrometry amyloid-ß1-42/1-40 alone would reduce these numbers to 623 individuals and 243 individuals, respectively. Across a theoretical range of amyloid PET positivity prevalence of 10-50%, mass spectrometry measures of amyloid-ß1-42/1-40 would consistently reduce the numbers proceeding to scans, with greater cost savings demonstrated at lower prevalence.

Noise removal in resting-state and task fMRI: functional connectivity and activation maps.

OBJECTIVE: Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH: Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. MAIN RESULTS: Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE: This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.

Competencies and training of radiographers and technologists for PET/MR imaging - a study from the UK MR-PET network.

BACKGROUND: After the success of PET/CT as a clinical diagnostic tool, the introduction of PET/MRI is a natural development aimed at further improving combined diagnostic imaging and reduced ionising radiation dose for half-body imaging. As with PET and CT, the combination of PET and MRI presents a series of issues that need to be addressed regarding workforce training and education. At present, there is a lack of agreement over the competencies, training requirements and educational pathways needed for PET/MRI operation. In the UK, following the establishment of the MR-PET imaging network, a task force was created to investigate the status of the workforce training, identify gaps and make recommendations regarding staff training. To do this, we ran a national survey on the status of the workforce training and the local practices across the UK's seven PET/MRI sites, reviewed the literature, and convened a panel of experts, to assess all the evidence and make recommendations regarding PET/MRI competencies and training of nuclear medicine technologists and radiographers. RESULTS: There is limited literature available specifically on competencies and training for technologists and radiographers. The recommendations on the topic needed revisiting and adapting to the UK MR-PET network. The online survey confirmed the need for developing PET/MRI competencies and training pathways. Local organisational structures and practices were shared across the seven sites, based on models derived from experience outside the UK. The panel of experts agreed on the need for PET/MRI competencies and training strategies. Professional organisations started collaborative discussions with partners from both Nuclear Medicine and Radiography to set training priorities. Multidisciplinary collaboration and partnership were suggested as a key to a successful implementation of competencies and training. CONCLUSIONS: The report identified the need for establishing competencies for the PET/MRI workforce, particularly for technologists and radiographers. It also helped defining these competencies as well as identifying the demand for bespoke training and the development of local and national courses to be implemented to fulfil this new training need.

First-in-human in vivo non-invasive assessment of intra-tumoral metabolic heterogeneity in renal cell carcinoma.

Intratumoral genetic heterogeneity and the role of metabolic reprogramming in renal cell carcinoma (RCC) have been extensively documented. However, the distribution of these metabolic changes within the tissue has not been explored. We report on the first-in-human in vivo non-invasive metabolic interrogation of RCC using hyperpolarized carbon-13 (13C) magnetic resonance imaging (HP-MRI) and describe the validation of in vivo lactate metabolic heterogeneity against multi-regional ex vivo mass spectrometry. HP-MRI provides an in vivo assessment of metabolism and provides a novel opportunity to safely and non-invasively assess cancer heterogeneity.

Incidental findings on brain imaging and blood tests: results from the first phase of Insight 46, a prospective observational substudy of the 1946 British birth cohort.

OBJECTIVE: To summarise the incidental findings detected on brain imaging and blood tests during the first wave of data collection for the Insight 46 study. DESIGN: Prospective observational sub-study of a birth cohort. SETTING: Single-day assessment at a research centre in London, UK. PARTICIPANTS: 502 individuals were recruited from the MRC National Survey of Health and Development (NSHD), the 1946 British birth cohort, based on pre-specified eligibility criteria; mean age was 70.7 (SD: 0.7) and 49% were female. OUTCOME MEASURES: Data regarding the number and types of incidental findings were summarised as counts and percentages, and 95% confidence intervals were calculated. RESULTS: 93.8% of participants completed a brain scan (n=471); 4.5% of scanned participants had a pre-defined reportable abnormality on brain MRI (n=21); suspected vascular malformations and suspected intracranial mass lesions were present in 1.9% (n=9) and 1.5% (n=7) respectively; suspected cerebral aneurysms were the single most common vascular abnormality, affecting 1.1% of participants (n=5), and suspected meningiomas were the most common intracranial lesion, affecting 0.6% of participants (n=3); 34.6% of participants had at least one abnormality on clinical blood tests (n=169), but few reached the prespecified threshold for urgent action (n=11). CONCLUSIONS: In older adults, aged 69-71 years, potentially serious brain MRI findings were detected in around 5% of participants, and clinical blood test abnormalities were present in around one third of participants. Knowledge of the expected prevalence of incidental findings in the general population at this age is useful in both research and clinical settings.

Associations between blood pressure across adulthood and late-life brain structure and pathology in the neuroscience substudy of the 1946 British birth cohort (Insight 46): an epidemiological study.

BACKGROUND: Midlife hypertension confers increased risk for cognitive impairment in late life. The sensitive period for risk exposure and extent that risk is mediated through amyloid or vascular-related mechanisms are poorly understood. We aimed to identify if, and when, blood pressure or change in blood pressure during adulthood were associated with late-life brain structure, pathology, and cognition. METHODS: Participants were from Insight 46, a neuroscience substudy of the ongoing longitudinal Medical Research Council National Survey of Health and Development, a birth cohort that initially comprised 5362 individuals born throughout mainland Britain in one week in 1946. Participants aged 69-71 years received T1 and FLAIR volumetric MRI, florbetapir amyloid-PET imaging, and cognitive assessment at University College London (London, UK); all participants were dementia-free. Blood pressure measurements had been collected at ages 36, 43, 53, 60-64, and 69 years. We also calculated blood pressure change variables between ages. Primary outcome measures were white matter hyperintensity volume (WMHV) quantified from multimodal MRI using an automated method, amyloid-ß positivity or negativity using a standardised uptake value ratio approach, whole-brain and hippocampal volumes quantified from 3D-T1 MRI, and a composite cognitive score-the Preclinical Alzheimer Cognitive Composite (PACC). We investigated associations between blood pressure and blood pressure changes at and between 36, 43, 53, 60-64, and 69 years of age with WMHV using generalised linear models with a gamma distribution and log link function, amyloid-ß status using logistic regression, whole-brain volume and hippocampal volumes using linear regression, and PACC score using linear regression, with adjustment for potential confounders. FINDINGS: Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. 465 participants (238 [51%] men; mean age 70·7 years [SD 0·7]; 83 [18%] amyloid-ß-positive) were included in imaging analyses. Higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) at age 53 years and greater increases in SBP and DBP between 43 and 53 years were positively associated with WMHV at 69-71 years of age (increase in mean WMHV per 10 mm Hg greater SBP 7%, 95% CI 1-14, p=0·024; increase in mean WMHV per 10 mm Hg greater DBP 15%, 4-27, p=0·0057; increase in mean WMHV per one SD change in SBP 15%, 3-29, p=0·012; increase in mean WMHV per 1 SD change in DBP 15%, 3-30, p=0·017). Higher DBP at 43 years of age was associated with smaller whole-brain volume at 69-71 years of age (-6·9 mL per 10 mm Hg greater DBP, -11·9 to -1·9, p=0·0068), as were greater increases in DBP between 36 and 43 years of age (-6·5 mL per 1 SD change, -11·1 to -1·9, p=0·0054). Greater increases in SBP between 36 and 43 years of age were associated with smaller hippocampal volumes at 69-71 years of age (-0·03 mL per 1 SD change, -0·06 to -0·001, p=0·043). Neither absolute blood pressure nor change in blood pressure predicted amyloid-ß status or PACC score at 69-71 years of age. INTERPRETATION: High and increasing blood pressure from early adulthood into midlife seems to be associated with increased WMHV and smaller brain volumes at 69-71 years of age. We found no evidence that blood pressure affected cognition or cerebral amyloid-ß load at this age. Blood pressure monitoring and interventions might need to start around 40 years of age to maximise late-life brain health. FUNDING: Alzheimer's Research UK, Medical Research Council, Dementias Platform UK, Wellcome Trust, Brain Research UK, Wolfson Foundation, Weston Brain Institute, Avid Radiopharmaceuticals.

Filtration-histogram based magnetic resonance texture analysis (MRTA) for glioma IDH and 1p19q genotyping.

BACKGROUND: To determine if filtration-histogram based texture analysis (MRTA) of clinical MR imaging can non-invasively identify molecular subtypes of untreated gliomas. METHODS: Post Gadolinium T1-weighted (T1+Gad) images, T2-weighted (T2) images and apparent diffusion coefficient (ADC) maps of 97 gliomas (54 = WHO II, 20 = WHO III, 23 = WHO IV) between 2010 and 2016 were studied. Whole-tumor segmentations were performed on a proprietary texture analysis research platform (TexRAD, Cambridge, UK) using the software's freehand drawing tool. MRTA commences with a filtration step, followed by quantification of texture using histogram texture parameters. Results were correlated using non-parametric statistics with a logistic regression model generated. RESULTS: T1+Gad performed best for IDH typing of glioblastoma (sensitivity 91.9%, specificity 100%, AUC 0.945) and ADC for non-Gadolinium-enhancing gliomas (sensitivity 85.7%, specificity 78.4%, AUC 0.877). T2 was moderately precise (sensitivity 83.1%, specificity 78.9%, AUC 0.821). Excellent results for IDH typing were achieved from a combination of the three sequences (sensitivity 90.5%, specificity 94.5%, AUC = 0.98). For discriminating 1p19q genotypes, ADC produced the best results using unfiltered textures (sensitivity 80.6%, specificity 89.3%, AUC 0.811). CONCLUSION: Preoperative glioma genotyping with MRTA appears valuable with potential for clinical translation. The optimal choice of texture parameters is influenced by sequence choice, tumour morphology and segmentation method.

Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Pediatric Patients with Intractable Epilepsy.

18F-FDG PET is an important tool for the presurgical assessment of children with drug-resistant epilepsy. Standard assessment is performed visually and is often subjective and highly user-dependent. Voxelwise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo- or hypermetabolism associated with the epileptogenic area. In the clinical setting, this analysis is performed using commercially available software. These software packages suffer from two main limitations when applied to pediatric PET data: pediatric scans are spatially normalized to an adult standard template, and statistical comparisons use an adult control dataset. The aim of this work was to provide a reliable observer-independent pipeline for the analysis of pediatric 18F-FDG PET scans, as part of presurgical planning in epilepsy. Methods: A pseudocontrol dataset (19 subjects 6-9 y old, and 93 subjects 10-20 y old) was used to create two age-specific 18F-FDG PET pediatric templates in standard pediatric space. The 18F-FDG PET scans of 46 epilepsy patients (16 patients 6-9 y old, and 30 patients 10-17 y old) were retrospectively collated and analyzed using voxelwise statistics. This procedure was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping, version 8 (SPM8), pipeline (including age-specific pediatric templates and reference database). A κ-test was used to assess the level of agreement between the findings of voxelwise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using postsurgical seizure-free patients. Results: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localization, especially for the younger patient group: κ = 0.489 for Scenium versus 0.826 for SPM. The proposed pipeline also showed a sensitivity of about 70% in both age ranges for the localization of hypometabolic areas on pediatric 18F-FDG PET scans in postsurgical seizure-free patients. Conclusion: We showed that by creating age-specific templates and using pediatric control databases, our pipeline provides an accurate and sensitive semiquantitative method for assessing the 18F-FDG PET scans of patients under 18 y old.

Clinical Impact of Respiratory Motion Correction in Simultaneous PET/MR, Using a Joint PET/MR Predictive Motion Model.

In PET imaging, patient motion due to respiration can lead to artifacts and blurring, in addition to quantification errors. The integration of PET imaging with MRI in PET/MRI scanners provides spatially aligned complementary clinical information and allows the use of high-contrast, high-spatial-resolution MR images to monitor and correct motion-corrupted PET data. On a patient cohort, we tested the ability of our joint PET/MRI-based predictive motion model to correct respiratory motion in PET and show it can improve lesion detectability and quantitation and reduce image artifacts. Methods: Using multiple tracers and multiple organ locations, we applied our motion correction method to 42 clinical PET/MRI patient datasets containing 162 PET-avid lesions. Quantitative changes were calculated using SUV changes in avid lesions. Lesion detectability changes were explored with a study in which 2 radiologists identified lesions in uncorrected and motion-corrected images and provided confidence scores. Results: Mean increases of 12.4% for SUVpeak and 17.6% for SUVmax after motion correction were found. In the detectability study, confidence scores for detecting avid lesions increased, with a rise in mean score from 2.67 to 3.01 (of 4) after motion correction and a rise in detection rate from 74% to 84%. Of 162 confirmed lesions, 49 showed an increase in all 3 metrics-SUVpeak, SUVmax, and combined reader confidence score-whereas only 2 lesions showed a decrease. We also present clinical case studies demonstrating the effect that respiratory motion correction of PET data can have on patient management, with increased numbers of detected lesions, improved lesion sharpness and localization, and reduced attenuation-based artifacts. Conclusion: We demonstrated significant improvements in quantification and detection of PET-avid lesions, with specific case study examples showing where motion correction has the potential to affect diagnosis or patient care.

Diagnostic accuracy and prognostic value of simultaneous hybrid 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging in cardiac sarcoidosis.

Aims: Cardiac death is the leading cause of mortality in patients with sarcoidosis, yet cardiac involvement often remains undetected. Cardiovascular magnetic resonance imaging (CMR) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) have been used to diagnose cardiac sarcoidosis (CS) yet never simultaneously in a cohort. This study sought to assess the diagnostic and prognostic utility of simultaneous hybrid cardiac PET/MR. Methods and results: Fifty-one consecutive patients with suspected CS (age 50 ± 13 years, 31 males) underwent simultaneous PET/MR following a high-fat/low-carbohydrate diet and 12-h fast. Blinded image analysis of FDG uptake and late gadolinium enhancement (LGE) was performed using the American Heart Association (AHA) 16-segment model. The sensitivity and specificity of PET/MR for diagnosing CS was estimated using the Japanese Ministry of Health and Welfare guidelines. The primary endpoint was a composite of death, aborted sudden cardiac death, sustained ventricular arrhythmia, complete heart block, and hospital admission with decompensated heart failure. The secondary endpoints were a fall in left ventricular ejection fraction (LVEF) >10%, non-sustained ventricular tachycardia and other cardiac-related hospital admission. The prevalence of CS was 65% (n = 33). The sensitivity of PET and CMR alone for detecting CS was 0.85 and 0.82, respectively. Hybrid PET/MR was superior for detecting CS with sensitivity, specificity, positive, and negative predictive values of 0.94, 0.44, 0.76, and 0.80, respectively. There was poor inter-modality agreement for the location of cardiac abnormalities (k = 0.02). Over the median follow-up of 2.2 years, there were 18 (35%) adverse events. Cardiac RV PET abnormalities and presence of LGE were independent predictors of adverse events. Abnormalities found on both PET and magnetic resonance imaging was the strongest predictor of major adverse cardiac events. Conclusion: Simultaneous PET/MR is an accurate method for diagnosing CS. FDG-PET and CMR combined offers complementary information on disease pathophysiology. The presence of LGE and FDG uptake on PET/MR identifies patients at higher risk of adverse events. PET and CMR should therefore be considered in the assessment of disease presence, stage, and prognosis in CS.

Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy.

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a "network disease" as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions.

UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer.

OBJECTIVE: Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology. METHODS: A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control. RESULTS: The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T. CONCLUSION: This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development.

BACKGROUND: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including ß-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN: This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, ß-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.

Effects of Housing on Methamphetamine-Induced Neurotoxicity and Spatial Learning and Memory.

Severe stress potentiates methamphetamine (MA) neurotoxicity. However, whether moderate stress increases or decreases the neurotoxic effects of MA is unknown. We assessed the effects of MA (4 × 10 mg/kg at 2 h intervals) in combination with prior barren-cage housing in adult male Sprague-Dawley rats on monoamines and glial fibrillary acid protein (GFAP) in one cohort and spatial learning and memory in the Morris water maze in another cohort. MA reduced dopamine (DA) and serotonin (5-HT) in the neostriatum and nucleus accumbens, 5-HT in the hippocampus, and increased GFAP in neostriatum and nucleus accumbens compared with saline controls. In neostriatum, barren-cage housing protected against MA-induced increases in GFAP, but it did not prevent DA and 5-HT reductions, although it did increase hippocampal norepinephrine. MA impaired spatial learning during acquisition, reversal, and shift phases and impaired reference memory on reversal and shift probe trials. Barren-cage housing enhanced performance during acquisition but not during reversal or shift or on probe trials. The data indicate that prior barren-cage housing moderates MA-induced neostriatal astrogliosis and initial spatial learning, but has no protective effect when the platform is smaller and relocated and therefore requires cognitive flexibility in relearning.