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

Development of a non-contrast-enhanced method for spatially resolved lung ventilation and perfusion measurement using Magnetic Resonance Imaging

Assessment of the pulmonary function remains a challenge for the development of suitable MRI techniques due to the unique lung tissue structure and its short effective transverse relaxation time (T2* = 1 ms). In this work, a new method of non-contrast-enhanced lung ventilation and perfusion MRI is presented. A 2D bSSFP pulse sequence (TR/TE/TA = 1.9/0.8/116 ms, 3-7 images/s, FA = 75°, ST = 10 mm, matrix = 128 x 128, GRAPPA 3) was implemented on a 1.5 T MR-scanner. The method uses fast image acquisition and submillisecond echo sampling to enhance the signal intensity in the pulmonary tissue. The proposed technique does not rely on respiratory and ECG-triggering. Application of non-rigid image registration was mandatory to compensate for the breathing motion. The rapid acquisition of time-resolved MR-data allowed observing intensity changes in corresponding lung areas modulated with respiratory and cardiac frequencies. Two different spectral analysis methods, Fourier decomposition (FD) and wavelet analysis (WA) were used to produce ventilation- and perfusion-weighted images by retrieving information associated with both physiological frequencies (FD/WA-MRI). The imaging technique was used in volunteers to test the technical and medical reproducibility. For validation purposes a group of cystic fibrosis patients was examined using FD-MRI and dynamic Contrast-Enhanced MRI. A good correlation between both methods (r = 0.82, P < 0.05) was determined. Animal experiments were conducted for validation of FD-MRI against other imaging modalities (CT and SPECT/CT)

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Improvements in the registration of multimodal medical imaging : application to intensity inhomogeneity and partial volume corrections

Alignment or registration of medical images has a relevant role on clinical diagnostic and treatment decisions as well as in research settings. With the advent of new technologies for multimodal imaging, robust registration of functional and anatomical information is still a challenge, particular in small-animal imaging given the lesser structural content of certain anatomical parts, such as the brain, than in humans. Besides, patient-dependent and acquisition artefacts affecting the images information content further complicate registration, as is the case of intensity inhomogeneities (IIH) showing in MRI and the partial volume effect (PVE) attached to PET imaging. Reference methods exist for accurate image registration but their performance is severely deteriorated in situations involving little images Overlap. While several approaches to IIH and PVE correction exist these methods still do not guarantee or rely on robust registration. This Thesis focuses on overcoming current limitations af registration to enable novel IIH and PVE correction methods.El registre d'imatges mèdiques té un paper rellevant en les decisions de diagnòstic i tractament clíniques així com en la recerca. Amb el desenvolupament de noves tecnologies d'imatge multimodal, el registre robust d'informació funcional i anatòmica és encara avui un repte, en particular, en imatge de petit animal amb un menor contingut estructural que en humans de certes parts anatòmiques com el cervell. A més, els artefactes induïts pel propi pacient i per la tècnica d'adquisició que afecten el contingut d'informació de les imatges complica encara més el procés de registre. És el cas de les inhomogeneïtats d'intensitat (IIH) que apareixen a les RM i de l'efecte de volum parcial (PVE) característic en PET. Tot i que existeixen mètodes de referència pel registre acurat d'imatges la seva eficàcia es veu greument minvada en casos de poc solapament entre les imatges. De la mateixa manera, també existeixen mètodes per la correcció d'IIH i de PVE però que no garanteixen o que requereixen un registre robust. Aquesta tesi es centra en superar aquestes limitacions sobre el registre per habilitar nous mètodes per la correcció d'IIH i de PVE

Comparison of T1-maps and late gadolinium enhancement images in the detection of Myocardial Fibrosis in Hypertrophic Cardiomyopathy

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2021, Universidade de Lisboa, Faculdade de CiênciasHypertrophic Cardiomyopathy (HCM) is characterized as an abnormal and heterogeneous thickening of the Left Ventricle (LV) wall. HCM is the leading cause of sudden cardiac death in children and young people, with an estimated prevalence of 1:500 in the general population. Myocardial fibrosis is the key histopathological hallmark in HCM and is presented in different patterns: interstitial diffuse fibrosis which, if not treated, evolves to replacement fibrosis. Cardiac Magnetic Resonance (CMR) imaging has been used for the detection and quantification of myocardial fibrosis. The Late Gadolinium Enhancement (LGE) technique is the primary tool for non-invasive tissue characterization, particularly for replacement fibrosis. Conversely, T1 mapping is commonly used for the detection of diffuse interstitial fibrosis, frequently missed using LGE. The clear disadvantage of LGE relies on the need to inject contrast agents that, despite being considered safe, may accumulate in the body for years and potentially cause nephrogenic systemic fibrosis in end-stage chronic kidney disease patients. The capability of native T1 mapping identifying not only diffuse interstitial but also replacement fibrosis would play a pivotal role in HCM diagnosis. The potential of native T1 mapping for a cheaper and non-contrast HCM assessment needs to be further studied. A database of 15 HCM patients, without and with fibrosis, was acquired at Hospital da Luz, Lisboa. In this project, (1) an extensive image preprocessing pipeline was applied to aim for the best possible spatial alignment of the myocardium between the two modalities (native T1 mapping and LGE); (2) the mean native T1 values of individuals without and with the presence of scarred tissue were examined; (3) a pixel-by-pixel analysis was performed to investigate if there is a correlation between fibrotic tissue in LGE and hyperintense regions in native T1 mapping; (4) a Texture Analysis (TA) was performed to study if texture information of native T1 mapping could provide differential diagnosis or prognostic information beyond mean T1 values. The first step was the most longstanding and challenging process. The registration of T1 and LGE images is difficult due to the different intensity profiles. The registration of the myocardial masks using a model with rigid, affine, and free-form deformation transformations revealed to be the best methodology. Mean native T1 values were not increased in patients with scarred tissue. Regarding the third aim, no clear intensity correlation between techniques was observed, which suggests the need for the TA. Seven features (in a total of 350) were selected to distinguish between cardiac segments without and with fibrotic tissue using a ML (Machine Learning) algorithm that finds the features that most contribute to distinguish the two groups. Four first-order features distinguish the cohorts due to the presence of scarred tissue - hyperintense zones - and three texture features suggest that the fibrotic remodeling in the myocardium of HCM patients might be associated with a more heterogeneous tissue texture. A Receiver Operating Characteristics (ROC) analysis was performed and revealed that the Cluster Prominence is the feature that best distinguishes sections without and with fibrotic tissue (accuracy of 70%) but with low sensitivity (65%) and low specifity (64%). A model with the 90th Percentile feature revealed an accuracy of 64%, sensitivity of 71% and specificity of 57%. Studying the Variance feature, the achieved accuracy was 63%, with 66% of sensitivity and 60% of specificity. The remaining features yielded lower accuracy values than the ones previously mentioned, but all of them higher than 50%. The low sensitivity and specificity of the best three models suggest that analysing these values considering these features may help cardiologists to identify focal fibrosis regions and avoid contrast injection methods but may not provide an accurate diagnosis of the presence of fibrotic tissue alone. Further research on the correlation of native T1 mapping and LGE cardiac images is highly recommended to develop a contrast-agent-free technology to replace LGE.A Cardiomiopatia Hipertrófica (do inglês, HCM) é descrita por um espessamento anormal e heterogéneo da parede do ventrículo esquerdo (do inglês, LV). A HCM é a principal causa de morte súbita cardíaca em crianças e jovens, com uma prevalência estimada de 1:500 na população em geral. Esta doença é, na sua maioria, hereditária, e causada por variantes nos genes da proteína do sarcómero (predominantemente MYH7 e MYBPC3). A fibrose do miocárdio é a principal marca histopatológica da HCM e apresenta-se em diferentes padrões: fibrose intersticial difusa que, se não tratada, evolui para fibrose focal. A fibrose é caracterizada por um aumento da deposição de colagénio, que afeta a viabilidade do miocárdio. A imagem de Ressonância Magnética Cardíaca (do inglês, CMR) tem sido usada para a deteção e quantificação de fibrose do miocárdio. A técnica de Realce Tardio (do inglês, LGE) é a principal ferramenta para caracterização não invasiva de tecidos, particularmente de fibrose focal. Em contrapartida, o mapeamento T1 é a técnica mais utilizada para deteção de fibrose intersticial difusa, frequentemente não detetada usando LGE. A clara desvantagem do LGE reside na necessidade de injeção de agentes de contraste. Apesar destes agentes serem considerados seguros, frequentemente causam alergias, podem-se acumular no corpo, por anos, e podem causar fibrose sistémica nefrogénica em pacientes com doença renal crónica terminal. A capacidade do mapeamento T1 nativo identificar, não só a fibrose intersticial difusa mas também a fibrose focal, desempenharia um papel fundamental no diagnóstico da HCM. Consequentemente, é de extrema importância estudar o potencial do mapeamento T1 nativo para uma avaliação desta patologia sem contraste e, desta forma, eliminar os riscos associados à injeção de contraste e reduzir os custos e tempo de preparação associados à utilização de gadolínio. Uma base de dados de 15 pacientes com HCM, com e sem fibrose, previamente adquirida no Hospital da Luz, Lisboa, foi analisada. Neste projeto, (1) aplicou-se um extenso conjunto de passos de pré-processamento de imagem para alcançar a melhor técnica possível de alinhamento espacial do miocárdio entre as duas modalidades (mapeamento T1 nativo e Realce Tardio); (2) após a divisão do miocárdio em 6 secções, como sugerido pela American Heart Association, examinaram-se os valores médios de T1, para cada secção, de indivíduos sem e com presença de tecido cicatricial; (3) realizou-se uma análise pixel a pixel para investigar se existe uma correlação entre o tecido fibrótico em LGE e as regiões hiperintensas no mapeamento T1 nativo; (4) realizou-se uma análise de textura para estudar se a informação de textura do mapeamento T1 nativo poderia fornecer um diagnóstico diferencial ou informação prognóstica além dos valores médios de T1 nativo. A primeira etapa revelou ser o processo mais demorado e desafiante. O batimento cardíaco e o ciclo respiratório representam dois desafios no registo de imagens cardíacas. Para além dos comuns desafios em alinhamento de imagens cardíacas da mesma modalidade, alinhar imagens de diferentes modalidades torna-se um processo mais complexo. Em primeiro lugar, o registo de imagens T1 e de LGE é dificultado pelos distintos perfis de intensidade das duas modalidades. Em segundo lugar, a aquisição de imagens de Realce Tardio ocorre cerca de 7 minutos após a aquisição do mapeamento T1, e o movimento dos pacientes durante este intervalo de tempo é uma fonte adicional de erro. Diferentes softwares foram utilizados, e uma imagem sintética ponderada em T1 foi criada, com o intuito de apresentar intensidades mais similares à imagem a ser alinhada (imagem de LGE). O registo das máscaras miocárdicas por meio de um modelo com transformações rígida, afim e deformações livres mostrou ser a melhor metodologia a aplicar. Os valores médios de T1 nativo não aumentaram significativamente em pacientes com tecido cicatricial, apesar de haver um aumento dos valores de T1 nativo em determinadas secções, em cortes basais e intermédios. Relativamente ao terceiro objetivo abordado, não foi observada uma clara correlação de intensidades entre as técnicas, o que reforçou a necessidade de uma análise de textura (do inglês, TA). Esta análise revelou as sete melhores características (num total de 350) que distinguem segmentos cardíacos sem e com tecido fibrótico, aplicando um método de Machine Learning (do inglês, ML) que identificou, sequencialmente, as features que adicionavam mais informação ao modelo que distinguia os dois grupos de segmentos. Quatro características de primeira ordem distinguem os segmentos devido à presença de tecido cicatricial - zonas hiperintensas - e três características de textura sugerem que a remodelação fibrótica no miocárdio de pacientes com HCM pode estar associada a uma textura mais heterogénea. Foi implementada uma análise ao desempenho de modelos com as features selecionadas, que revelou que a Cluster Prominence é a característica que melhor distingue secções sem e com tecido fibrótico, apesar de com baixa sensibilidade (65%) e baixa especificidade (64%). Um modelo que analisa o Percentil 90 revelou uma precisão de 64%, sensibilidade de 71% e especificidade de 57%. No estudo da Variância, a precisão foi de 63%, a sensibilidade 66% e a especificidade 60%. As restantes features apresentaram valores de precisão inferiores aos mencionados mas acima de 50%. Um modelo com a combinação das sete features selecionadas não melhorou a performance do modelo (precisão de 62%, sensibilidade de 75% e 49% de especificidade). A baixa sensibilidade e especificidade sugerem que a análise desses valores nessas características pode ajudar os cardiologistas a identificar regiões focais de fibrose e evitar métodos de injeção de contraste, mas pode não fornecer um diagnóstico preciso da presença de tecido fibrótico por si só. Em futuras aquisições, encontrar valores semelhantes nas features acima mencionadas, principalmente na Cluster Prominence, em novos dados, poderia ajudar os cardiologistas a identificar regiões de fibrose focal. Desta forma, não seria necessário analisar imagens de Realce Tardio, o que se traduziria na eliminação de injeção de agentes de contraste. Pesquisas adicionais focadas na correlação do mapeamento T1 nativo e imagens cardíacas de LGE são de extrema importância para desenvolver uma tecnologia independente da injeção de agentes de contraste, que substitua o Realce Tardio

Algorithmic Analysis Techniques for Molecular Imaging

This study addresses image processing techniques for two medical imaging modalities: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), which can be used in studies of human body functions and anatomy in a non-invasive manner. In PET, the so-called Partial Volume Effect (PVE) is caused by low spatial resolution of the modality. The efficiency of a set of PVE-correction methods is evaluated in the present study. These methods use information about tissue borders which have been acquired with the MRI technique. As another technique, a novel method is proposed for MRI brain image segmen- tation. A standard way of brain MRI is to use spatial prior information in image segmentation. While this works for adults and healthy neonates, the large variations in premature infants preclude its direct application. The proposed technique can be applied to both healthy and non-healthy premature infant brain MR images. Diffusion Weighted Imaging (DWI) is a MRI-based technique that can be used to create images for measuring physiological properties of cells on the structural level. We optimise the scanning parameters of DWI so that the required acquisition time can be reduced while still maintaining good image quality. In the present work, PVE correction methods, and physiological DWI models are evaluated in terms of repeatabilityof the results. This gives in- formation on the reliability of the measures given by the methods. The evaluations are done using physical phantom objects, correlation measure- ments against expert segmentations, computer simulations with realistic noise modelling, and with repeated measurements conducted on real pa- tients. In PET, the applicability and selection of a suitable partial volume correction method was found to depend on the target application. For MRI, the data-driven segmentation offers an alternative when using spatial prior is not feasible. For DWI, the distribution of b-values turns out to be a central factor affecting the time-quality ratio of the DWI acquisition. An optimal b-value distribution was determined. This helps to shorten the imaging time without hampering the diagnostic accuracy.Siirretty Doriast