Patient-Specific Mappings between Myocardial and Coronary Anatomy

The segmentation of the myocardium based on the 17-segment model as recommended by the American Heart Association is widely used in medical practice. The patient-specific coronary anatomy does not play a role in this model. Due to large variations in coronary anatomy among patients, this can result in an inaccurate mapping between myocardial segments and coronary arteries. We present two approaches to include the patient-specific coronary anatomy in this mapping. The first approach adapts the 17-segment model to fit the patient. The second approach generates a less constrained mapping that does not necessarily conform to this model. Both approaches are based on a Voronoi diagram computation of the primary coronary arteries using geodesic distances along the epicardium in three-dimensional space. We demonstrate both our approaches with several patients and show how our first approach can also be used to fit volume data to the 17-segment model. Our technique gives detailed insight into the coronary anatomy in a single diagram. Based on the feedback provided by clinical experts we conclude that it has the potential to provide a more accurate relation between deficiencies in the myocardium and the supplying coronary arteries

Image-guided port placement for minimally invasive cardiac surgery

Minimally invasive surgery is becoming popular for a number of interventions. Use of robotic surgical systems in coronary artery bypass intervention offers many benefits to patients, but is however limited by remaining challenges in port placement. Choosing the entry ports for the robotic tools has a large impact on the outcome of the surgery, and can be assisted by pre-operative planning and intra-operative guidance techniques. In this thesis, pre-operative 3D computed tomography (CT) imaging is used to plan minimally invasive robotic coronary artery bypass (MIRCAB) surgery. From a patient database, port placement optimization routines are implemented and validated. Computed port placement configurations approximated past expert chosen configurations with an error of 13.7 ±5.1 mm. Following optimization, statistical classification was used to assess patient candidacy for MIRCAB. Various pattern recognition techniques were used to predict MIRCAB success, and could be used in the future to reduce conversion rates to conventional open-chest surgery. Gaussian, Parzen window, and nearest neighbour classifiers all proved able to detect ‘candidate’ and ‘non-candidate’ MIRCAB patients. Intra-operative registration and laser projection of port placements was validated on a phantom and then evaluated in four patient cases. An image-guided laser projection system was developed to map port placement plans from pre-operative 3D images. Port placement mappings on the phantom setup were accurate with an error of 2.4 ± 0.4 mm. In the patient cases, projections remained within 1 cm of computed port positions. Misregistered port placement mappings in human trials were due mainly to the rigid-body registration assumption and can be improved by non-rigid techniques. Overall, this work presents an integrated approach for: 1) pre-operative port placement planning and classification of incoming MIRCAB patients; and 2) intra-operative guidance of port placement. Effective translation of these techniques to the clinic will enable MIRCAB as a more efficacious and accessible procedure

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

The Virtual Physiological Human: Ten Years After

Biomedical research and clinical practice are struggling to cope with the growing complexity that the progress of health care involves. The most challenging diseases, those with the largest socioeconomic impact (cardiovascular conditions; musculoskeletal conditions; cancer; metabolic, immunity, and neurodegenerative conditions), are all characterized by a complex genotype–phenotype interaction and by a “systemic” nature that poses a challenge to the traditional reductionist approach. In 2005 a small group of researchers discussed how the vision of computational physiology promoted by the Physiome Project could be translated into clinical practice and formally proposed the term Virtual Physiological Human. Our knowledge about these diseases is fragmentary, as it is associated with molecular and cellular processes on the one hand and with tissue and organ phenotype changes (related to clinical symptoms of disease conditions) on the other. The problem could be solved if we could capture all these fragments of knowledge into predictive models and then compose them into hypermodels that help us tame the complexity that such systemic behavior involves. In 2005 this was simply not possible—the necessary methods and technologies were not available. Now, 10 years later, it seems the right time to reflect on the original vision, the results achieved so far, and what remains to be done