Autoadaptive motion modelling for MR-based respiratory motion estimation

© 2016 The Authors.Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data.In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible.We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment.We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10 min after a change in breathing pattern. On real data we demonstrated the methods ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12 min of scanning. Furthermore, the method has a prediction latency of 800 ms. These limitations may be overcome in future work by altering the acquisition protocol

Shape-correlated statistical modeling and analysis for respiratory motion estimation

Respiratory motion challenges image-guided radiation therapy (IGRT) with location uncertainties of important anatomical structures in the thorax. Effective and accurate respiration estimation is crucial to account for the motion effects on the radiation dose to tumors and organs at risk. Moreover, serious image artifacts present in treatment-guidance images such 4D cone-beam CT cause difficulties in identifying spatial variations. Commonly used non-linear dense image matching methods easily fail in regions where artifacts interfere. Learning-based linear motion modeling techniques have the advantage of incorporating prior knowledge for robust motion estimation. In this research shape-correlation deformation statistics (SCDS) capture strong correlations between the shape of the lung and the dense deformation field under breathing. Dimension reduction and linear regression techniques are used to extract the correlation statistics. Based on the assumption that the deformation correlations are consistent between planning and treatment time, patient-specific SCDS trained from a 4D planning image sequence is used to predict the respiratory motion in the patient's artifact-laden 4D treatment image sequence. Furthermore, a prediction-driven atlas formation method is developed to weaken the consistency assumption, by integrating intensity information from the target images and the SCDS predictions into a common optimization framework. The strategy of balancing between the prediction constraints and the intensity-matching forces makes the method less sensitive to variation in the correlation and utilizes intensity information besides the lung boundaries. This strategy thus provides improved motion estimation accuracy and robustness. The SCDS-based methods are shown to be effective in modeling and estimating respiratory motion in lung, with evaluations and comparisons carried out on both simulated images and patient images

Fast catheter segmentation and tracking based on x-ray fluoroscopic and echocardiographic modalities for catheter-based cardiac minimally invasive interventions

X-ray fluoroscopy and echocardiography imaging (ultrasound, US) are two imaging modalities that are widely used in cardiac catheterization. For these modalities, a fast, accurate and stable algorithm for the detection and tracking of catheters is required to allow clinicians to observe the catheter location in real-time. Currently X-ray fluoroscopy is routinely used as the standard modality in catheter ablation interventions. However, it lacks the ability to visualize soft tissue and uses harmful radiation. US does not have these limitations but often contains acoustic artifacts and has a small field of view. These make the detection and tracking of the catheter in US very challenging. The first contribution in this thesis is a framework which combines Kalman filter and discrete optimization for multiple catheter segmentation and tracking in X-ray images. Kalman filter is used to identify the whole catheter from a single point detected on the catheter in the first frame of a sequence of x-ray images. An energy-based formulation is developed that can be used to track the catheters in the following frames. We also propose a discrete optimization for minimizing the energy function in each frame of the X-ray image sequence. Our approach is robust to tangential motion of the catheter and combines the tubular and salient feature measurements into a single robust and efficient framework. The second contribution is an algorithm for catheter extraction in 3D ultrasound images based on (a) the registration between the X-ray and ultrasound images and (b) the segmentation of the catheter in X-ray images. The search space for the catheter extraction in the ultrasound images is constrained to lie on or close to a curved surface in the ultrasound volume. The curved surface corresponds to the back-projection of the extracted catheter from the X-ray image to the ultrasound volume. Blob-like features are detected in the US images and organized in a graphical model. The extracted catheter is modelled as the optimal path in this graphical model. Both contributions allow the use of ultrasound imaging for the improved visualization of soft tissue. However, X-ray imaging is still required for each ultrasound frame and the amount of X-ray exposure has not been reduced. The final contribution in this thesis is a system that can track the catheter in ultrasound volumes automatically without the need for X-ray imaging during the tracking. Instead X-ray imaging is only required for the system initialization and for recovery from tracking failures. This allows a significant reduction in the amount of X-ray exposure for patient and clinicians.Open Acces

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Rapid Segmentation Techniques for Cardiac and Neuroimage Analysis

Recent technological advances in medical imaging have allowed for the quick acquisition of highly resolved data to aid in diagnosis and characterization of diseases or to guide interventions. In order to to be integrated into a clinical work flow, accurate and robust methods of analysis must be developed which manage this increase in data. Recent improvements in in- expensive commercially available graphics hardware and General-Purpose Programming on Graphics Processing Units (GPGPU) have allowed for many large scale data analysis problems to be addressed in meaningful time and will continue to as parallel computing technology improves. In this thesis we propose methods to tackle two clinically relevant image segmentation problems: a user-guided segmentation of myocardial scar from Late-Enhancement Magnetic Resonance Images (LE-MRI) and a multi-atlas segmentation pipeline to automatically segment and partition brain tissue from multi-channel MRI. Both methods are based on recent advances in computer vision, in particular max-flow optimization that aims at solving the segmentation problem in continuous space. This allows for (approximately) globally optimal solvers to be employed in multi-region segmentation problems, without the particular drawbacks of their discrete counterparts, graph cuts, which typically present with metrication artefacts. Max-flow solvers are generally able to produce robust results, but are known for being computationally expensive, especially with large datasets, such as volume images. Additionally, we propose two new deformable registration methods based on Gauss-Newton optimization and smooth the resulting deformation fields via total-variation regularization to guarantee the problem is mathematically well-posed. We compare the performance of these two methods against four highly ranked and well-known deformable registration methods on four publicly available databases and are able to demonstrate a highly accurate performance with low run times. The best performing variant is subsequently used in a multi-atlas segmentation pipeline for the segmentation of brain tissue and facilitates fast run times for this computationally expensive approach. All proposed methods are implemented using GPGPU for a substantial increase in computational performance and so facilitate deployment into clinical work flows. We evaluate all proposed algorithms in terms of run times, accuracy, repeatability and errors arising from user interactions and we demonstrate that these methods are able to outperform established methods. The presented approaches demonstrate high performance in comparison with established methods in terms of accuracy and repeatability while largely reducing run times due to the employment of GPU hardware

The anthropometric, environmental and genetic determinants of right ventricular structure and function

BACKGROUND Measures of right ventricular (RV) structure and function have significant prognostic value. The right ventricle is currently assessed by global measures, or point surrogates, which are insensitive to regional and directional changes. We aim to create a high-resolution three-dimensional RV model to improve understanding of its structural and functional determinants. These may be particularly of interest in pulmonary hypertension (PH), a condition in which RV function and outcome are strongly linked. PURPOSE To investigate the feasibility and additional benefit of applying three-dimensional phenotyping and contemporary statistical and genetic approaches to large patient populations. METHODS Healthy subjects and incident PH patients were prospectively recruited. Using a semi-automated atlas-based segmentation algorithm, 3D models characterising RV wall position and displacement were developed, validated and compared with anthropometric, physiological and genetic influences. Statistical techniques were adapted from other high-dimensional approaches to deal with the problems of multiple testing, contiguity, sparsity and computational burden. RESULTS 1527 healthy subjects successfully completed high-resolution 3D CMR and automated segmentation. Of these, 927 subjects underwent next-generation sequencing of the sarcomeric gene titin and 947 subjects completed genotyping of common variants for genome-wide association study. 405 incident PH patients were recruited, of whom 256 completed phenotyping. 3D modelling demonstrated significant reductions in sample size compared to two-dimensional approaches. 3D analysis demonstrated that RV basal-freewall function reflects global functional changes most accurately and that a similar region in PH patients provides stronger survival prediction than all anthropometric, haemodynamic and functional markers. Vascular stiffness, titin truncating variants and common variants may also contribute to changes in RV structure and function. CONCLUSIONS High-resolution phenotyping coupled with computational analysis methods can improve insights into the determinants of RV structure and function in both healthy subjects and PH patients. Large, population-based approaches offer physiological insights relevant to clinical care in selected patient groups.Open Acces

A Database For Exploratory Analysis of Human Sleep

This thesis focuses on the design, development, and exploratory analysis of a human sleep data repository. We have successfully collected comprehensive data for 1,046 sleep disorder patients and created a Terabyte-scale database system to handle it. The data for each patient was collected from the patient\u27s medical records, and from the patient\u27s allnight sleep study (for a total of about 0.6 Gigabytes per patient). Data collected from the patient\u27s medical record contain more than 70 attributes, including demographic data, smoking, drinking, and exercise habits, depression and daytime sleepiness questionnaires, and overall medical history. Data collected from the patient\u27s all-night sleep study consist of 50-55 time-series signals recorded during a period of 6-8 hours at the hospital\u27s sleep clinic. These signals include among others an electroencephalogram, electromyogram, electrooculogram, electrocardiogram, and signals tracking blood oxygen level, body position, limb movements, snoring and blood pressure. 350 additional attributes summarize sleep related events taking place during the night long study, including sleep stages, arousals, and respiratory disturbances. Particular attention during the development of our database system was paid to a database design that effectively handles the data size and complexity, that describes the structure of sleep data in clinically meaningful terms, and that will facilitates the discovery of patterns in sleep data using machine learning algorithms. We have interfaced our database with Weka, a well known data mining system. To the best of our knowledge, our database is one of the world\u27s largest and most comprehensive in the domain of human sleep disorders

Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces