Automatic perfusion analysis using phase-based registration and object-based image analysis

MRI perfusion imaging enables the non-invasive assessment of myocardial blood supply. The purpose of the presented work is to enable a quantitative assessment of the image sequences for clinical application. To this end an automatic preprocessing including ROI detection and outlier removal has been combined with a phase-based registration approach and an object-based myocardium segmentation. The suggested processing pipeline has been tested with 21 image sequences provided by the STACOM motion correction challenge. The corrected image sequences have been assessed by comparison with gamma variate curves fitted to the voxels intensity curves. The automatic segmentation could be compared with expert segmentations provided by the challenge organizers. The results indicate an improvement through the motion correction and a good agreement with the reference segmentation in most cases

Object-based boundary properties

While object-based image analysis specializes in using region features for object detection, it lacks the possibility to use border strength and local geometry, common in edge detection. We propose to enhance common object-based image representation with boundary features that measure strength and continuity. Using these we formulate strategies for merging regions in a partitioned image to identify potentially regular shapes. To illustrate the capacity of this approach, we apply the proposed concepts to CT bone segmentation

Automatic classification of salient boundaries in object-based image segmentation

We present a supervised classification approach for image segmentation that operates in an object-based image representation and combines object features with boundary features. While classical algorithms focus on either regions (i.e. objects) or edges (i.e. boundaries), we offer a hybrid solution that takes both aspects into consideration. To illustrate the capacity of this approach, we apply the proposed classification to CT bone segmentation

Real-time myocardium segmentation for the assessment of cardiac function variation

Recent developments in MRI enable the acquisition of image sequences with high spatio-temporal resolution. Cardiac motion can be captured without gating and triggering. Image size and contrast relations differ from conventional cardiac MRI cine sequences requiring new adapted analysis methods. We suggest a novel segmentation approach utilizing contrast invariant polar scanning techniques. It has been tested with 20 datasets of arrhythmia patients. The results do not differ significantly more between automatic and manual segmentations than between observers. This indicates that the presented solution could enable clinical applications of real-time MRI for the examination of arrhythmic cardiac motion in the future

Context-based segmentation and analysis of multi-cycle real-time cardiac MRI

The recent development of a real-time magnetic resonance imaging (MRI) technique with 20 to 30 ms temporal resolution allows for imaging multiple consecutive heart cycles, without the need for breath holding or ECG synchronization. Manual analysis of the resulting image series is no longer feasible because of their length. We propose a region-based algorithm for automatically segmenting the myocardium in consecutive heart cycles based on local context and prior knowledge. The method was evaluated on ten real-time MRI series and compared to segmentations by two observers, with promising results. We show that our approach enables a multicycle analysis of the heart function robust to breathing and arrhythmia

Improving robustness of automatic cardiac function quantification from cine magnetic resonance imaging using synthetic image data

Although having been the subject of intense research over the years, cardiac function quantification from MRI is still not a fully automatic process in the clinical practice. This is partly due to the shortage of training data covering all relevant cardiovascular disease phenotypes. We propose to synthetically generate short axis CINE MRI using a generative adversarial model to expand the available data sets that consist of predominantly healthy subjects to include more cases with reduced ejection fraction. We introduce a deep learning convolutional neural network (CNN) to predict the end-diastolic volume, end-systolic volume, and implicitly the ejection fraction from cardiac MRI without explicit segmentation. The left ventricle volume predictions were compared to the ground truth values, showing superior accuracy compared to state-of-the-art segmentation methods. We show that using synthetic data generated for pre-training a CNN significantly improves the prediction compared to only using the limited amount of available data, when the training set is imbalanced

Clinical cardiovascular imaging

Medical imaging has revolutionised the practice of diagnostic medicine since Wilhelm Röntgen’s discovery of X-rays in 1895. The profound impact of conventional medical imaging on diagnostic medicine is currently being replicated in the domain of clinical research, where quantitative imaging methods provide a powerful and versatile tool for the investigation of disease aetiology, measurement of disease progression and assessment of response to therapy/intervention. Quantitative imaging also holds immense promise as a method for risk assessment, prognosis and treatment stratification. Cardiovascular imaging in particular is a vast and still rapidly evolving field, with a seemingly endless number of methods and applications. Imaging has become central not only to the diagnosis, management and monitoring of cardiovascular disease but is also the basis of many life-saving interventions. Cardiovascular diseases affect all parts of the body, and their evaluation can necessitate imaging of large organs such as the heart and aorta down to the microcirculation where imaging of the microvasculature in the form of tissue perfusion can be crucial. Specialist imaging techniques aimed at investigating every part of the cardiovascular system have been developed and refined, but there is no single modality that comprehensively evaluates all aspects of vascular disease in all body regions. The aim of this chapter is to present the reader with a general introduction to imaging and a roadmap that can be used to explore this field more fully, guided by particular clinical and research interests. For a comprehensive overview of cardiovascular imaging techniques and applications, the reader is directed to The European Society of Cardiology Textbook of Cardiovascular Imaging (Zamorano et al. The ESC textbook of cardiovascular imaging. Oxford University Press; 2015.). A brief overview of selected cardiovascular imaging techniques is presented to illustrate the range of available methods and applications and is not intended to be exhaustive or comprehensive. A section on cardiovascular applications of magnetic resonance imaging (MRI) is preceded by a short primer on the basic technical and practical aspects of MRI