Dose robustness of deep learning models for anatomic segmentation of computed tomography images.

Purpose: The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations.

Approach: We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient.

Structures and Dynamics of Complex Guest Molecules in Confinement, Revealed by Solid-State NMR, Molecular Dynamics, and Calorimetry.

This review gives an overview of current trends in the investigation of confined molecules such as water, small and higher alcohols, carbonic acids, ethylene glycol, and non-ionic surfactants, such as polyethylene glycol or Triton-X, as guest molecules in neat and functionalized mesoporous silica materials employing solid-state NMR spectroscopy, supported by calorimetry and molecular dynamics simulations. The combination of steric interactions, hydrogen bonds, and hydrophobic and hydrophilic interactions results in a fascinating phase behavior in the confinement. Combining solid-state NMR and relaxometry, DNP hyperpolarization, molecular dynamics simulations, and general physicochemical techniques, it is possible to monitor these confined molecules and gain deep insights into this phase behavior and the underlying molecular arrangements.

Local and diffusive dynamics of LiCl aqueous solutions in pristine and modified silica nanopores.

We use H, H, and Li nuclear magnetic resonance to investigate local and diffusive dynamics of LiCl-7HO and LiCl-7DO solutions in pristine and functionalized silica nanopores in a component-selective manner. Recently, we showed that the solution dynamics become slower when the diameter of the pristine pores is reduced. Here, we determine the effects of (aminopropyl)triethoxysilane and dye surface functionalizations on the motions of the water molecules and lithium ions from ambient temperatures down to the glass transition.

Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks.

Implanting stents to re-open stenotic lesions during percutaneous coronary interventions is considered a standard treatment for acute or chronic coronary syndrome. Intravascular ultrasound (IVUS) can be used to guide and assess the technical success of these interventions. Automatically segmenting stent struts in IVUS sequences improves workflow efficiency but is non-trivial due to a challenging image appearance entailing manifold ambiguities with other structures.

Clinical use of thrombin generation assays.

Determining patient's coagulation profile, i.e. detecting a bleeding tendency or the opposite, a thrombotic risk, is crucial for clinicians in many situations.

Thrombin generation assays are versatile tools in blood coagulation analysis: A review of technical features, and applications from research to laboratory routine.

Thrombin is the pivotal enzyme in the biochemistry of secondary hemostasis crucial to maintaining homeostasis of hemostasis. In contrast to routine coagulation tests (PT or aPTT) or procoagulant or anticoagulant factor assays (e.g.

Trifunctional Silyl Groups as Anchoring Units in the Preparation of Luminescent Phosphole-Silica Hybrids.

A synthetic strategy to β-silylphospholes with three methoxy, ethoxy, chloro, hydrido, or phenyl substituents at silicon has been developed, starting from trimethoxy, triethoxy, or triphenyl silyl substituted phenyl phosphanides and 1,4-diphenyl-1,3-butadiyne. These trifunctional silylphospholes were attached to the surface of uniform spheric silica particles (15 μm) and, for comparison, to a polyhedral silsesquioxane (POSS)-trisilanol as a molecular model to explore their luminescent properties in comparison with the free phospholes. Density functional theory calculations were performed to investigate any electronic perturbation of the phosphole system by the trifunctional silyl anchoring unit.

Solvent-free dynamic nuclear polarization enhancements in organically modified mesoporous silica.

High-field dynamic nuclear polarization is a powerful tool for the structural characterization of species on the surface of porous materials or nanoparticles. For these studies the main source of polarization are radical-containing solutions which are added by post-synthesis impregnation of the sample. Although this strategy is very efficient for a wide variety of materials, the presence of the solvent may influence the chemistry of functional species of interest.

H NMR study on temperature-dependent water dynamics in amino-acid functionalized silica nanopores.

We prepare various amino-acid functionalized silica pores with diameters of ∼6 nm and study the temperature-dependent reorientation dynamics of water in these confinements. Specifically, we link basic Lys, neutral Ala, and acidic Glu to the inner surfaces and combine H nuclear magnetic resonance spin-lattice relaxation and line shape analyses to disentangle the rotational motions of the surfaces groups and the crystalline and liquid water fractions coexisting below partial freezing. Unlike the crystalline phase, the liquid phase shows reorientation dynamics, which strongly depends on the chemistry of the inner surfaces.

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac motion, common metal artifact reduction methods which assume a static object during CT acquisition are not applicable. We propose a fully automatic Dynamic Pacemaker Artifact Reduction (DyPAR+) pipeline which is built of three convolutional neural network (CNN) ensembles.

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks.

Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel motion compensation approach dealing with Coronary Motion estimation by Patch Analysis in CT data (CoMPACT) is presented. First, the required data for supervised learning is generated by the Coronary Motion Forward Artifact model for CT data (CoMoFACT) which introduces simulated motion to 19 artifact-free clinical CT cases with step-and-shoot acquisition protocol.

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images.

Enhanced Optical Head Tracking for Cranial Radiation Therapy: Supporting Surface Registration by Cutaneous Structures.

Purpose: To support surface registration in cranial radiation therapy by structural information. The risk for spatial ambiguities is minimized by using tissue thickness variations predicted from backscattered near-infrared (NIR) light from the forehead.

Methods And Materials: In a pilot study we recorded NIR surface scans by laser triangulation from 30 volunteers of different skin type.

Efficient estimation of tissue thicknesses using sparse approximation for Gaussian processes.

Highly accurate localization of the human skull is vital in cranial radiotherapy. Marker-less optical head tracking provides a fast and accurate way to monitor this motion. Recent research has given evidence that marker-less tracking of the forehead benefits from tissue thickness information in addition to the 3D surface geometry.

An Approach to Improve Accuracy of Optical Tracking Systems in Cranial Radiation Therapy.

This work presents a new method for the accurate estimation of soft tissue thickness based on near infrared (NIR) laser measurements. By using this estimation, our goal is to develop an improved non-invasive marker-less optical tracking system for cranial radiation therapy. Results are presented for three subjects and reveal an RMS error of less than 0.

Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods.

Purpose: Patient immobilization and X-ray-based imaging provide neither a convenient nor a very accurate way to ensure low repositioning errors or to compensate for motion in cranial radiotherapy. We therefore propose an optical tracking device that exploits subcutaneous structures as landmarks in addition to merely spatial registration. To develop such head tracking algorithms, precise and robust computation of these structures is necessary.

Tissue thickness estimation for high precision head-tracking using a galvanometric laser scanner - a case study.

Marker-less optical head-tracking constitutes a comfortable alternative with no exposure to radiation for realtime monitoring in radiation therapy. Supporting information such as tissue thickness has the potential to improve spatial tracking accuracy. Here we study how accurate tissue thickness can be estimated from the near-infrared (NIR) backscatter obtained from laser scans.

Multivariate respiratory motion prediction.

In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor.

Accuracy analysis for triangulation and tracking based on time-multiplexed structured light.

Purpose: The authors' research group is currently developing a new optical head tracking system for intracranial radiosurgery. This tracking system utilizes infrared laser light to measure features of the soft tissue on the patient's forehead. These features are intended to offer highly accurate registration with respect to the rigid skull structure by means of compensating for the soft tissue.

Controlling motion prediction errors in radiotherapy with relevance vector machines.

Purpose: Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution.

Respiratory motion compensation with relevance vector machines.

In modern robotic radiation therapy, tumor movements due to respiration can be compensated. The accuracy of these methods can be increased by time series prediction of external optical surrogates. An algorithm based on relevance vector machines (RVM) is introduced.

Evaluation of the potential of multi-modal sensors for respiratory motion prediction and correlation.

In modern robotic radiotherapy, precise radiation of moving tumors is possible by tracking external optical surrogates. The surrogates are used to compensate for time delays and to predict internal landmarks using a correlation model. The correlation depends significantly on the surrogate position and breathing characteristics of the patient.

Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography.

Objective: Support vector machines (SVM) have developed into a gold standard for accurate classification in brain-computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance.

Estimating soft tissue thickness from light-tissue interactions--a simulation study.

Immobilization and marker-based motion tracking in radiation therapy often cause decreased patient comfort. However, the more comfortable alternative of optical surface tracking is highly inaccurate due to missing point-to-point correspondences between subsequent point clouds as well as elastic deformation of soft tissue. In this study, we present a proof of concept for measuring subcutaneous features with a laser scanner setup focusing on the skin thickness as additional input for high accuracy optical surface tracking.

Optimized order estimation for autoregressive models to predict respiratory motion.

Purpose: To successfully ablate moving tumors in robotic radio-surgery, it is necessary to compensate for motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in the CyberKnife[Formula: see text] Synchrony system. Tracking errors, originating from system immanent time delays, are typically reduced by time series prediction.