Patient-specific requirements and clinical validation of MRI-based pressure mapping: A two-center study in patients with aortic coarctation.

BACKGROUND: Invasive peak-to-peak pressure gradients are the current clinical reference standard for assessing aortic coarctation. To obtain them, patients need to undergo arterial heart catheterization. Unless an intervention is performed, the procedure remains purely diagnostic, while the concomitant risks remain. PURPOSE: To validate MRI-based pressure mapping against pressure drop derived from heart catheterization and to define minimal clinical requirements. STUDY TYPE: Prospective clinical validation study. POPULATION: Twenty-seven coarctation patients with an indicated heart catheterization were enrolled at two clinical centers. MRI SEQUENCES: 1.5T including 4D velocity-encoded MRI and 3D anatomical imaging of the aorta. ASSESSMENT: Pressure drop across the stenosis was calculated by pressure mapping based on the pressure Poisson equation. Calculated pressure drops were compared with catheter measured data. Spatial and temporal resolution were analyzed using in silico phantom-based data as well as in vivo measurements. STATISTICS: Pressure drop was compared to peak-to-peak measurements. A two-sample paired mean equivalence test was used. RESULTS: In patients without imaging artifacts and a required spatial resolution ≥5 voxel/diameter, significant equivalence of pressure mapping compared to heart catheterization was found (17.5 ± 6.49 vs. 16.6 ± 6.53 mmHg, P < 0.001). DATA CONCLUSION: Pressure mapping provides equivalent accuracy to pressure drop obtained from heart catheterization in patients 1) without previous stenting and 2) with sufficient spatial image resolution (at least 5 voxels/diameter). In these patients the method can reliably be performed prior to the actual procedure, and thus allows safe noninvasive treatment planning based on MRI. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:81-89.

Self-supervised learning for interventional image analytics: toward robust device trackers.

Purpose: The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion. Approach: To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model. Results: Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a 3× faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance. Conclusions: The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.

Visual perceptual salience and novel referent selection in children with and without autism spectrum disorder.

Background & Aims: Many young children with autism spectrum disorder (ASD) demonstrate striking delays in early vocabulary development. Experimental studies that teach the meanings of novel nonwords can determine the effects of linguistic and attentional factors. One factor that may affect novel referent selection in children with ASD is visual perceptual salience-how interesting (i.e., striking) stimuli are on the basis of their visual properties. The goal of the current study was to determine how the perceptual salience of objects affected novel referent selection in children with ASD and children who are typically developing (TD) of similar ages (mean age 3-4 years). Methods: Using a screen-based experimental paradigm, children were taught the names of four unfamiliar objects: two high-salience objects and two low-salience objects. Their comprehension of the novel words was assessed in low-difficulty and high-difficulty trials. Gaze location was determined from video by trained research assistants. Results: Contrary to initial predictions, findings indicated that high perceptual salience disrupted novel referent selection in the children with ASD but facilitated attention to the target object in age-matched TD peers. The children with ASD showed no significant evidence of successful novel referent selection in the high-difficulty trials. Exploratory reaction time analyses suggested that the children with autism showed "stickier" attention-had more difficulty disengaging (i.e., looking away)-from high-salience distracter images than low-salience distracter images, even though the two images were balanced in salience for any given test trial. Conclusions and Clinical Implications: These findings add to growing evidence that high perceptual salience has the potential to disrupt novel referent selection in children with ASD. These results underscore the complexity of novel referent selection and highlight the importance of taking the immediate testing context into account. In particular, it is important to acknowledge that screen-based assessments and screen-based learning activities used with children with ASD are not immune to the effects of lower level visual features, such as perceptual salience.

Automated quantification of airway wall thickness on chest CT using retina U-Nets - Performance evaluation and application to a large cohort of chest CTs of COPD patients.

PURPOSE: Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). MATERIALS AND METHODS: This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWT3-8) across the lungs. Mean AWT3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. RESULTS: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). CONCLUSION: Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. CLINICAL RELEVANCE STATEMENT: Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.

Contrastive self-supervised learning from 100 million medical images with optional supervision.

Purpose: Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g., a radiologist). To counter this limitation, we propose a method to learn from medical images at scale in a self-supervised way. Approach: Our approach, based on contrastive learning and online feature clustering, leverages training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US). We propose to use the learned features to guide model training in supervised and hybrid self-supervised/supervised regime on various downstream tasks. Results: We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT, and MR: (1) significant increase in accuracy compared to the state-of-the-art (e.g., area under the curve boost of 3% to 7% for detection of abnormalities from chest radiography scans and hemorrhage detection on brain CT); (2) acceleration of model convergence during training by up to 85% compared with using no pretraining (e.g., 83% when training a model for detection of brain metastases in MR scans); and (3) increase in robustness to various image augmentations, such as intensity variations, rotations or scaling reflective of data variation seen in the field. Conclusions: The proposed approach enables large gains in accuracy and robustness on challenging image assessment problems. The improvement is significant compared with other state-of-the-art approaches trained on medical or vision images (e.g., ImageNet).

Competing Perceptual Salience in a Visual Word Recognition Task Differentially Affects Children With and Without Autism Spectrum Disorder.

Differences in visual attention have long been recognized as a central characteristic of autism spectrum disorder (ASD). Regardless of social content, children with ASD show a strong preference for perceptual salience-how interesting (i.e., striking) certain stimuli are, based on their visual properties (e.g., color, geometric patterning). However, we do not know the extent to which attentional allocation preferences for perceptual salience persist when they compete with top-down, linguistic information. This study examined the impact of competing perceptual salience on visual word recognition in 17 children with ASD (mean age 31 months) and 17 children with typical development (mean age 20 months) matched on receptive language skills. A word recognition task presented two images on a screen, one of which was named (e.g., Find the bowl!). On Neutral trials, both images had high salience (i.e., were colorful and had geometric patterning). On Competing trials, the distracter image had high salience but the target image had low salience, creating competition between bottom-up (i.e., salience-driven) and top-down (i.e., language-driven) processes. Though both groups of children showed word recognition in an absolute sense, competing perceptual salience significantly decreased attention to the target only in the children with ASD. These findings indicate that perceptual properties of objects can disrupt attention to relevant information in children with ASD, which has implications for supporting their language development. Findings also demonstrate that perceptual salience affects attentional allocation preferences in children with ASD, even in the absence of social stimuli. LAY SUMMARY: This study found that visually striking objects distract young children with autism spectrum disorder (ASD) from looking at relevant (but less striking) objects named by an adult. Language-matched, younger children with typical development were not significantly affected by this visual distraction. Though visual distraction could have cascading negative effects on language development in children with ASD, learning opportunities that build on children's focus of attention are likely to support positive outcomes.

Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers.

OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. METHODS: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. RESULTS: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001). CONCLUSIONS: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data.

We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid-structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.

Personalized mitral valve closure computation and uncertainty analysis from 3D echocardiography.

Intervention planning is essential for successful Mitral Valve (MV) repair procedures. Finite-element models (FEM) of the MV could be used to achieve this goal, but the translation to the clinical domain is challenging. Many input parameters for the FEM models, such as tissue properties, are not known. In addition, only simplified MV geometry models can be extracted from non-invasive modalities such as echocardiography imaging, lacking major anatomical details such as the complex chordae topology. A traditional approach for FEM computation is to use a simplified model (also known as parachute model) of the chordae topology, which connects the papillary muscle tips to the free-edges and select basal points. Building on the existing parachute model a new and comprehensive MV model was developed that utilizes a novel chordae representation capable of approximating regional connectivity. In addition, a fully automated personalization approach was developed for the chordae rest length, removing the need for tedious manual parameter selection. Based on the MV model extracted during mid-diastole (open MV) the MV geometric configuration at peak systole (closed MV) was computed according to the FEM model. In this work the focus was placed on validating MV closure computation. The method is evaluated on ten in vitro ovine cases, where in addition to echocardiography imaging, high-resolution µCT imaging is available for accurate validation.

A self-taught artificial agent for multi-physics computational model personalization.

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

Personalized Computer Simulation of Diastolic Function in Heart Failure.

The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (τ). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated significantly across the patient population with τ. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.

Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery.

Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart.

BACKGROUND: Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. METHODS AND RESULTS: State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. CONCLUSION: This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.

Importance of soil organic matter for the diversity of microorganisms involved in the degradation of organic pollutants.

Many organic pollutants are readily degradable by microorganisms in soil, but the importance of soil organic matter for their transformation by specific microbial taxa is unknown. In this study, sorption and microbial degradation of phenol and 2,4-dichlorophenol (DCP) were characterized in three soil variants, generated by different long-term fertilization regimes. Compared with a non-fertilized control (NIL), a mineral-fertilized NPK variant showed 19% and a farmyard manure treated FYM variant 46% more soil organic carbon (SOC). Phenol sorption declined with overall increasing SOC because of altered affinities to the clay fraction (soil particles <2 mm in diameter). In contrast, DCP sorption correlated positively with particulate soil organic matter (present in the soil particle fractions of 63-2000 µm). Stable isotope probing identified Rhodococcus, Arthrobacter (both Actinobacteria) and Cryptococcus (Basidiomycota) as the main degraders of phenol. Rhodococcus and Cryptococcus were not affected by SOC, but the participation of Arthrobacter declined in NPK and even more in FYM. (14)C-DCP was hardly metabolized in the NIL variant, more efficiently in FYM and most in NPK. In NPK, Burkholderia was the main degrader and in FYM Variovorax. This study demonstrates a strong effect of SOC on the partitioning of organic pollutants to soil particle size fractions and indicates the profound consequences that this process could have for the diversity of bacteria involved in their degradation.

Robust image-based estimation of cardiac tissue parameters and their uncertainty from noisy data.

Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patient's physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.

Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals.

Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.

Response of microbial communities to long-term fertilization depends on their microhabitat.

The objective of this study was to characterize the microbial communities attached to clay (< 2 µm), fine silt (2-20 µm), coarse silt (20-63 µm) and sand-sized fractions [> 63 µm; including particulate organic matter (POM)] of an arable soil and analyse their response to more than 100 years of two different fertilization regimes. Mild ultrasonic dispersal, wet-sieving and centrifugation allowed the separation of soil particles with the majority of bacterial cells and DNA still attached. Fertilizations increased soil organic carbon (SOC), total DNA and the abundance of bacterial, archaeal and fungal rRNA genes more strongly in the larger-sized fractions than in fine silt, and no effect was seen with clay, the latter representing above 70% of the total microbial populations. A highly positive correlation was found between microbial rRNA genes and the surface area provided by the particles, while the correlation with SOC was lower, indicating a particle-size-specific heterogeneous effect of SOC. The prokaryotic diversity responded more strongly to fertilization in the larger particles but not with clay. Overall, these results demonstrate that microbial responsiveness to long-term fertilization declined with smaller particle sizes and that especially clay fractions exhibit a high buffering capacity protecting microbial cells against changes even after 100 years under different agricultural management.

Robust model-based 3d/3D fusion using sparse matching for minimally invasive surgery.

Classical surgery is being disrupted by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm CT and C-arm fluoroscopy are routinely used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel sparse matching approach for fusing high quality pre-operative CT and non-contrasted, non-gated intra-operative C-arm CT by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the pre-operative CT and mapped to the intra-operative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments demonstrate that our model-based fusion approach has an average execution time of 2.9 s, while the accuracy lies within expert user confidence intervals.

Automatic region-of-interest segmentation and pathology detection in magnetically guided capsule endoscopy.

Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operator's ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.

Effects of concentrate proportion in the diet with or without Fusarium toxin-contaminated triticale on ruminal fermentation and the structural diversity of rumen microbial communities in vitro.

The objective of this study was to investigate the effects of the concentrate proportion and Fusarium toxin-contaminated triticale (FCT) in the diet on nutrient degradation, microbial protein synthesis and structure of the microbial community, utilising a rumen simulation technique and single-strand conformation polymorphism (SSCP) profiles based on PCR-amplified small subunit ribosomal RNA genes. Four diets containing 60% or 30% concentrates on a dry matter basis with or without FCT were incubated. The fermentation of nutrients and microbial protein synthesis was measured. On the last day of incubation, microbial mass was obtained from the vessel liquid, DNA was extracted and PCR-primers targeting archaea, fibrobacter, clostridia, bifidobacteria, bacillii, fungi, and bacteria were applied to separately study the individual taxonomic groups with SSCP. The concentrate proportion affected the fermentation and the microbial community, but not the efficiency of microbial protein synthesis. Neither the fermentation of organic matter nor the synthesis and composition of microbial protein was affected by FCT. The fermentation of detergent fibre fractions was lower in diets containing FCT compared to diets with uncontaminated triticale. Except for the clostridia group, none of the microbial groups were affected by presence of FCT. In conclusion, our results give no indication that the supplementation of FCT up to a deoxynivalenol concentration in the diet of 5 mg per kg dry matter affects the fermentation of organic matter and microbial protein synthesis. These findings are independent of the concentrate level in the diets. A change in the microbial community composition of the genus Clostridia may be the reason for a reduction in the cellulolytic activity.