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DWI/FLAIR mismatch assessment for ischemic stroke patients shows promising results in determining if patients are eligible for recombinant tissue-type plasminogen activator (r-tPA) treatment. However, the mismatch criteria suffer from two major issues: binary classification of a non-binary problem and the subjectiveness of the assessor. In this article, we present a simple automatic method for segmenting stroke-related parenchymal hyperintensities on FLAIR, allowing for an automatic and continuous DWI/FLAIR mismatch assessment. We further show that our method's segmentations have comparable inter-rater agreement (DICE 0.820, SD 0.12) compared to that of two neuro-radiologists (DICE 0.856, SD 0.07), that our method appears robust to hyper-parameter choices (suggesting good generalizability), and lastly, that our methods continuous DWI/FLAIR mismatch assessment correlates to mismatch assessments made for a cohort of wake-up stroke patients at hospital submission. The proposed method shows promising results in automating the segmentation of parenchymal hyperintensity within ischemic stroke lesions and could help reduce inter-observer variability of DWI/FLAIR mismatch assessment performed in clinical environments as well as offer a continuous assessment instead of the current binary one.

Topological changes like sliding motion, sources and sinks are a significant challenge in image registration. This work proposes the use of the alternating direction method of multipliers as a general framework for constraining the registration of separate objects with individual deformation fields from overlapping in image registration. This constraint is enforced by introducing a collision detection algorithm from the field of computer graphics which results in a robust divide and conquer optimization strategy using Free-Form Deformations. A series of experiments demonstrate that the proposed framework performs superior with regards to the combination of intersection prevention and image registration including synthetic examples containing complex displacement patterns. The results show compliance with the non-intersection constraints while simultaneously preventing a decrease in registration accuracy. Furthermore, the application of the proposed algorithm to the DIR-Lab data set demonstrates that the framework generalizes to real data by validating it on a lung registration problem., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Alscher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters., (© 2023. The Author(s).)

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Coronavirus disease 2019 (COVID-19) has become a severe global pandemic. Accurate pneumonia infection segmentation is important for assisting doctors in diagnosing COVID-19. Deep learning-based methods can be developed for automatic segmentation, but the lack of large-scale well-annotated COVID-19 training datasets may hinder their performance. Semi-supervised segmentation is a promising solution which explores large amounts of unlabelled data, while most existing methods focus on pseudo-label refinement. In this paper, we propose a new perspective on semi-supervised learning for COVID-19 pneumonia infection segmentation, namely pseudo-label guided image synthesis. The main idea is to keep the pseudo-labels and synthesize new images to match them. The synthetic image has the same COVID-19 infected regions as indicated in the pseudo-label, and the reference style extracted from the style code pool is added to make it more realistic. We introduce two representative methods by incorporating the synthetic images into model training, including single-stage Synthesis-Assisted Cross Pseudo Supervision (SA-CPS) and multi-stage Synthesis-Assisted Self-Training (SA-ST), which can work individually as well as cooperatively. Synthesis-assisted methods expand the training data with high-quality synthetic data, thus improving the segmentation performance. Extensive experiments on two COVID-19 CT datasets for segmenting the infections demonstrate our method is superior to existing schemes for semi-supervised segmentation, and achieves the state-of-the-art performance on both datasets. Code is available at: https://github.com/FeiLyu/SASSL.

Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph's kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph's Kappa, 0.40-0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels.

Purpose: Applying machine learning techniques to magnetic resonance diffusion-weighted imaging (DWI) data is challenging due to the size of individual data samples and the lack of labeled data. It is possible, though, to learn general patterns from a very limited amount of training data if we take advantage of the geometry of the DWI data. Therefore, we present a tissue classifier based on a Riemannian deep learning framework for single-shell DWI data., Approach: The framework consists of three layers: a lifting layer that locally represents and convolves data on tangent spaces to produce a family of functions defined on the rotation groups of the tangent spaces, i.e., a (not necessarily continuous) function on a bundle of rotational functions on the manifold; a group convolution layer that convolves this function with rotation kernels to produce a family of local functions over each of the rotation groups; a projection layer using maximization to collapse this local data to form manifold based functions., Results: Experiments show that our method achieves the performance of the same level as state-of-the-art while using way fewer parameters in the model ( < 10 % ). Meanwhile, we conducted a model sensitivity analysis for our method. We ran experiments using a proportion (69.2%, 53.3%, and 29.4%) of the original training set and analyzed how much data the model needs for the task. Results show that this does reduce the overall classification accuracy mildly, but it also boosts the accuracy for minority classes., Conclusions: This work extended convolutional neural networks to Riemannian manifolds, and it shows the potential in understanding structural patterns in the brain, as well as in aiding manual data annotation., (© 2022 The Authors.)

We investigate the accuracy of intensity-based deformable image registration (DIR) for tumor localization in liver stereotactic body radiotherapy (SBRT). We included 4DCT scans to capture the breathing motion of eight patients receiving SBRT for liver metastases within a retrospective clinical study. Each patient had three fiducial markers implanted. The liver and the tumor were delineated in the mid-ventilation phase, and their positions in the other phases were estimated with deformable image registration. We tested referenced and sequential registrations strategies. The fiducial markers were the gold standard to evaluate registration accuracy. The registration errors related to measured versus estimated fiducial markers showed a mean value less than 1.6mm. The positions of some fiducial markers appeared not stable on the 4DCT throughout the respiratory phases. Markers' center of mass tends to be a more reliable measurement. Distance errors of tumor location based on registration versus markers center of mass were less than 2mm. There were no statistically significant differences between the reference and the sequential registration, i.e., consistency and errors were comparable to resolution errors. We demonstrated that intensity-based DIR is accurate up to resolution level for locating the tumor in the liver during breathing motion., Competing Interests: The authors LBS, MJ, SN and IRV received grants and/or educational fees from Varian Medical Systems.

In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms.

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

Studying different types of tooth movements can help us to better understand the force systems used for tooth position correction in orthodontic treatments. This study considers a more realistic force system in tooth movement modeling across different patients and investigates the effect of the couple force direction on the position of the center of rotation (CRot). The finite-element (FE) models of human mandibles from three patients are used to investigate the position of the CRots for different patients' teeth in 3D space. The CRot is considered a single point in a 3D coordinate system and is obtained by choosing the closest point on the axis of rotation to the center of resistance (CRes). A force system, consisting of a constant load and a couple (pair of forces), is applied to each tooth, and the corresponding CRot trajectories are examined across different patients. To perform a consistent inter-patient analysis, different patients' teeth are registered to the corresponding reference teeth using an affine transformation. The selected directions and applied points of force on the reference teeth are then transformed into the registered teeth domains. The effect of the direction of the couple on the location of the CRot is also studied by rotating the couples about the three principal axes of a patient's premolar. Our results indicate that similar patterns can be obtained for the CRot positions of different patients and teeth if the same load conditions are used. Moreover, equally rotating the direction of the couple about the three principal axes results in different patterns for the CRot positions, especially in labiolingual direction. The CRot trajectories follow similar patterns in the corresponding teeth, but any changes in the direction of the force and couple cause misalignment of the CRot trajectories, seen as rotations about the long axis of the tooth., Competing Interests: Authors TG and PS are affiliated with 3Shape A/S, which provided support in the form of salaries and data for this study. The commercial affiliation of the aforementioned authors does not alter our adherence to PLOS ONE policies on sharing data and materials. Additionally, there are no patents, products in development or marketed products associated with this research to declare.

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Aims: Little is known about cardiac structure and function among atrial fibrillation (AF) subtypes; paroxysmal AF vs. persistent AF (PxAF), and across AF burden. We sought to assess differences in left atrial (LA) measures by AF subtype and burden., Methods and Results: This was a cross-sectional echocardiographic substudy of a randomized trial of AF patients scheduled for catheter ablation. Patients had an echocardiogram performed 0-90 days prior to study inclusion. We performed conventional echocardiographic measures, left ventricular (LV) and LA speckle tracking. Measures were compared between AF subtype and burden (0%, 0-99%, and 99-100%) determined by 72-h Holter monitoring. Of 212 patients, 107 had paroxysmal AF and 105 had PxAF. Those with PxAF had significantly reduced systolic function (LV ejection fraction: 48% vs. 53%; P < 0.001), larger end-systolic and end-diastolic LA volumes (LAVi and LAEDVi), reduced LA emptying fraction (LAEF: 29% vs. 36%, P < 0.001), and reduced LA strain (LAs) (LAs: 20% vs. 26%, P < 0.001). LA measures remained significantly lower in PxAF after multivariable adjustments. All LA measures and measures of systolic function were significantly impaired in patients with 99-100% AF burden, whereas all measures were similar between the other groups (LAVi: 40mL/m2 vs. 33mL/m2 vs. 34mL/m2; LAEDVi: 31mL/m2 vs. 21mL/m2 vs. 22mL/m2, LA emptying fraction: 23% vs. 35% vs. 36%, LAs: 16% vs. 25% vs. 25%, for 99-100%, 0-99%, and 0% AF, respectively, P < 0.001 for all). These differences were consistent after multivariable adjustments., Conclusion: LA mechanics differ between AF subtype and burden and these characteristics influence the clinical interpretation of these measures., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)

Imaging ultrastructures in cells using Focused Ion Beam Scanning Electron Microscope (FIB-SEM) yields section-by-section images at nano-resolution. Unfortunately, we observe that FIB-SEM often introduces sub-pixel drifts between sections, in the order of 2.5 nm. The accumulation of these drifts significantly skews distance measures and geometric structures, which standard image registration techniques fail to correct. We demonstrate that registration techniques based on mutual information and sum-of-squared-distances significantly underestimate the drift since they are agnostic to image content. For neuronal data at nano-resolution, we discovered that vesicles serve as a statistically simple geometric structure, making them well-suited for estimating the drift with sub-pixel accuracy. Here, we develop a statistical model of vesicle shapes for drift correction, demonstrate its superiority, and provide a self-contained freely available application for estimating and correcting drifted datasets with vesicles.

Aims: Prolonged corrected QT interval (QTc) might be associated with arrhythmia recurrence after atrial fibrillation (AF) ablation. The effect of short-term amiodarone in this setting remains unknown. This study seeks to quantify short-term amiodarone's impact on QTc, and to investigate QTc and amiodarone treatment as predictors of recurrence of arrhythmia after ablation., Methods and Results: The Short-term AMIOdarone treatment after CATheter ablation for atrial fibrillation (AMIO-CAT) trial randomized patients to 8 weeks of oral amiodarone or placebo following AF ablation. Scheduled and symptom-driven 12-lead electrocardiography and 3-day Holter-monitorings were performed. The endpoint was atrial fibrillation, atrial flutter or atrial tachycardia (AF/AT) lasting >30 s. The cut-off for prolonged QTc was 450 ms for men and 460 ms for women. A total of 212 patients were included, of which 108 were randomized to amiodarone and 104 to placebo. From baseline to 1 month QTc in the amiodarone group increased by 27 (±30) ms, while at 6 months QTc had normalized. After 3-months of blanking, new AF/AT recurrence was detected in 63% of patients with prolonged QTc vs. 41% of patients with normal QTc at baseline, and in multivariate Cox regression, prolonged QTc was associated with AF/AT recurrence [hazard ratio (HR) 2.19, P = 0.023]. Among patients with baseline QTc below median, amiodarone treatment decreased the rate of AF/AT recurrences (HR 0.43, P = 0.008)., Conclusions: Amiodarone increased QTc with 27 ms compared to placebo, and this effect decreased rapidly after drug discontinuation. Prolonged QTc at baseline independently predicted AF/AT recurrence, and baseline QTc identified patients who would possibly benefit from short-term amiodarone following ablation., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.

Aim: To investigate whether chromogranin A (CgA) is secreted from the heart into circulation., Materials & Methods: Porcine cardiac tissue was analyzed for the presence of CgA-derived glycopeptides using a global O-glycoproteomic strategy. Blood was sampled from the femoral vein, right atrium, coronary sinus and the left atrium from patients with predominantly atrial disease. The local concentration of proatrial natriuretic peptide and CgA was measured with immunoassays., Results: We identified CgA-derived glycopeptides exclusively in the atrial tissue. Proatrial natriuretic peptide is secreted from the heart (coronary sinus [795 pmol/l] vs left atrium [678 pmol/l]; p < 0.01) whereas no CgA gradient across the heart could be established (p = 0.6366)., Conclusion: The cardiac atria express but do not secrete CgA into circulation in patients with atrial disease.

Background: Salivary gland hypofunction and xerostomia are major complications following radiotherapy for head and neck cancer and may lead to debilitating oral disorders and impaired quality of life. Currently, only symptomatic treatment is available. However, mesenchymal stem cell (MSC) therapy has shown promising results in preclinical studies. Objectives are to assess safety and efficacy in a first-in-man trial on adipose-derived MSC therapy (ASC) for radiation-induced xerostomia., Methods: This is a single-center, phase I/II, randomized, placebo-controlled, double-blinded clinical trial. A total of 30 patients are randomized in a 1:1 ratio to receive ultrasound-guided, administered ASC or placebo to the submandibular glands. The primary outcome is change in unstimulated whole salivary flow rate. The secondary outcomes are safety, efficacy, change in quality of life, qualitative and quantitative measurements of saliva, as well as submandibular gland size, vascularization, fibrosis, and secretory tissue evaluation based on contrast-induced magnetic resonance imaging (MRI) and core-needle samples. The assessments are performed at baseline (1 month prior to treatment) and 1 and 4 months following investigational intervention., Discussion: The trial is the first attempt to evaluate the safety and efficacy of adipose-derived MSCs (ASCs) in patients with radiation-induced xerostomia. The results may provide evidence for the effectiveness of ASC in patients with salivary gland hypofunction and xerostomia and deliver valuable information for the design of subsequent trials., Trial Registration: EudraCT, Identifier: 2014-004349-29. Registered on 1 April 2015. ClinicalTrials.gov, Identifier: NCT02513238 . First received on 2 July 2015. The trial is prospectively registered.

Differentiation of the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS) is a persistent clinical challenge [1]. Based on positron emission tomography (PET) studies with [(18)F]-fluorodeoxyglucose (FDG) during sleep and anesthesia, the global cerebral metabolic rate of glucose has been proposed as an indicator of consciousness [2, 3]. Likewise, FDG-PET may contribute to the clinical diagnosis of disorders of consciousness (DOCs) [4, 5]. However, current methods are non-quantitative and have important drawbacks deriving from visually guided assessment of relative changes in brain metabolism [4]. We here used FDG-PET to measure resting state brain glucose metabolism in 131 DOC patients to identify objective quantitative metabolic indicators and predictors of awareness. Quantitation of images was performed by normalizing to extracerebral tissue. We show that 42% of normal cortical activity represents the minimal energetic requirement for the presence of conscious awareness. Overall, the cerebral metabolic rate accounted for the current level, or imminent return, of awareness in 94% of the patient population, suggesting a global energetic threshold effect, associated with the reemergence of consciousness after brain injury. Our data further revealed that regional variations relative to the global resting metabolic level reflect preservation of specific cognitive or sensory modules, such as vision and language comprehension. These findings provide a simple and objective metabolic marker of consciousness, which can readily be implemented clinically. The direct correlation between brain metabolism and behavior further suggests that DOCs can fundamentally be understood as pathological neuroenergetic conditions and provide a unifying physiological basis for these syndromes., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter- and intra-subject image registration.

Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP)-a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer's disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer's disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.

A challenge when using current magnetic resonance (MR)-based attenuation correction in positron emission tomography/MR imaging (PET/MRI) is that the MRIs can have a signal void around the dental fillings that is segmented as artificial air-regions in the attenuation map. For artifacts connected to the background, we propose an extension to an existing active contour algorithm to delineate the outer contour using the nonattenuation corrected PET image and the original attenuation map. We propose a combination of two different methods for differentiating the artifacts within the body from the anatomical air-regions by first using a template of artifact regions, and second, representing the artifact regions with a combination of active shape models and k-nearest-neighbors. The accuracy of the combined method has been evaluated using 25 [Formula: see text]-fluorodeoxyglucose PET/MR patients. Results showed that the approach was able to correct an average of [Formula: see text] of the artifact areas.

Several studies have shown an overlap between genes involved in the pathophysiological mechanisms of atrial fibrillation (AF) and Brugada Syndrome (BrS). We investigated whether three single-nucleotide polymorphisms (SNPs) (rs11708996; G>C located intronic to SCN5A, rs10428132; T>G located in SCN10A, and rs9388451; T>C located downstream to HEY2) at loci associated with BrS in a recent genome-wide association study (GWAS) also were associated with AF. A total of 657 patients diagnosed with AF and a control group comprising 741 individuals free of AF were included. The three SNPs were genotyped using TaqMan assays. The frequencies of risk alleles in the AF population and the control population were compared in two-by-two models. One variant, rs10428132 at SCN10A, was associated with a statistically significant decreased risk of AF (odds ratio (OR)=0.77, P=0.001). A meta-analysis was performed by enriching the control population with allele frequencies from controls in the recently published BrS GWAS (2230 alleles). In this meta-analysis, both rs10428132 at SCN10A (OR=0.73, P=5.7 × 10(-6)) and rs11708996 at SCN5A (OR=0.80, P=0.02) showed a statistically significant decreased risk of AF. When assessing the additive effect of the three loci, we found that the risk of AF decreased in a dose-responsive manner with increasing numbers of risk alleles (OR=0.50, P=0.001 for individuals carrying ≥4 risk alleles vs ≤1 allele). In conclusion, the prevalence of three risk alleles previously associated with BrS was lower in AF patients than in patients free of AF, suggesting a protective role of these loci in developing AF.

This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the three inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume.