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Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging

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Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging

dc.contributor.advisor	Bodí Peris, Vicente
dc.contributor.advisor	Moratal Pérez, David
dc.contributor.author	Larroza Santacruz, Andrés Martín
dc.contributor.other	Departament d'Enginyeria Electrònica	es_ES
dc.date.accessioned	2017-09-07T12:53:36Z
dc.date.available	2017-09-08T04:45:05Z
dc.date.issued	2017	es_ES
dc.date.submitted	11-09-2017	es_ES
dc.identifier.uri	http://hdl.handle.net/10550/60645
dc.description.abstract	Cardiovascular diseases constitute the leading global cause of morbidity and mortality. Magnetic resonance imaging (MRI) has become the gold standard technique for the assessment of patients with myocardial infarction. However, limitations still exist thus new alternatives are open to investigation. Texture analysis is a technique that aims to quantify the texture of the images that are not always perceptible by the human eye. It has been successfully applied in medical imaging but applications to cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply texture analysis in conventional CMR images for the assessment of patients with myocardial infarction, as an alternative to current methods. Three applications of texture analysis and machine learning techniques were studied: i) Detection of infarcted myocardium in late gadolinium enhancement (LGE) CMR. Segmentation of the infarcted myocardium is routinely performed using image intensity thresholds. The inclusion of texture features to aid the segmentation was analyzed obtaining overall good results. The method was developed using 10 LGE CMR datasets and tested on a separate dataset comprising 5 cases that were acquired with a completely different scanner than that used for training. Therefore, this preliminary study showed the transferability of texture analysis which is important for clinical applicability. ii) Differentiation of acute and chronic myocardial infarction using LGE CMR and standard pre-contrast cine CMR. In this study, two different feature selection techniques and six different machine learning classifiers were studied and compared. The best classification was achieved using a polynomial SVM obtaining an overall AUC of 0.87 ± 0.06 in LGE CMR. Interestingly, results on cine CMR in which infarctions are visually imperceptible in most cases were also good (AUC = 0.83 ± 0.08). iii) Detection of infarcted non-viable segments in cine CMR. This study was motivated by the findings of the previous one. It demonstrated that texture analysis can be used to distinguish non-viable, viable and remote segments using standard pre-contrast cine CMR solely. This was the most relevant contribution of this thesis as it can be used as hypothesis for future work aiming to accurately delineate the infarcted myocardium as a gadolinium-free alternative that will have potential advantages. The three proposed applications were successfully performed obtaining promising results. In conclusion, texture analysis can be successfully applied to conventional CMR images and provides a potential quantitative alternative to existing methods.	es_ES
dc.format.extent	166 p.	es_ES
dc.language.iso	en	es_ES
dc.subject	cardiac magnetic resonance	es_ES
dc.subject	myocardial infarction	es_ES
dc.subject	machine learning	es_ES
dc.subject	texture analysis	es_ES
dc.title	Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging	es_ES
dc.type	doctoral thesis	es_ES
dc.subject.unesco	resonancia magnética	es_ES
dc.subject.unesco	análisis de datos	es_ES
dc.subject.unesco	tratamiento digital de imágenes	es_ES
dc.subject.unesco	diagnóstico por imagen	es_ES
dc.embargo.terms	0 days	es_ES