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

Perfluorocarbon Enhanced Glasgow Oxygen Level Dependent (GOLD) magnetic resonance metabolic imaging identifies the penumbra following acute ischemic stroke

The ability to identify metabolically active and potentially salvageable ischaemic penumbra is crucial for improving treatment decisions in acute stroke patients. Our solution involves two complementary novel MRI techniques (Glasgow Oxygen Level Dependant (GOLD) Metabolic Imaging), which when combined with a perfluorocarbon (PFC) based oxygen carrier and hyperoxia can identify penumbra due to dynamic changes related to continued metabolism within this tissue compartment. Our aims were (i) to investigate whether PFC offers similar enhancement of the second technique (Lactate Change) as previously demonstrated for the T2*OC technique (ii) to demonstrate both GOLD metabolic imaging techniques working concurrently to identify penumbra, following administration of Oxycyte® (O-PFC) with hyperoxia. Methods: An established rat stroke model was utilised. Part-1: Following either saline or PFC, magnetic resonance spectroscopy was applied to investigate the effect of hyperoxia on lactate change in presumed penumbra. Part-2; rats received O-PFC prior to T2*OC (technique 1) and MR spectroscopic imaging, which was used to identify regions of tissue lactate change (technique 2) in response to hyperoxia. In order to validate the techniques, imaging was followed by [14C]2-deoxyglucose autoradiography to correlate tissue metabolic status to areas identified as penumbra. Results: Part-1: PFC+hyperoxia resulted in an enhanced reduction of lactate in the penumbra when compared to saline+hyperoxia. Part-2: Regions of brain tissue identified as potential penumbra by both GOLD metabolic imaging techniques utilising O-PFC, demonstrated maintained glucose metabolism as compared to adjacent core tissue. Conclusion: For the first time in vivo, enhancement of both GOLD metabolic imaging techniques has been demonstrated following intravenous O-PFC+hyperoxia to identify ischaemic penumbra. We have also presented preliminary evidence of the potential therapeutic benefit offered by O-PFC. These unique theranostic applications would enable treatment based on metabolic status of the brain tissue, independent of time from stroke onset, leading to increased uptake and safer use of currently available treatment options

Reduction of motion effects in myocardial arterial spin labeling

Purpose To evaluate the accuracy and reproducibility of myocardial blood flow measurements obtained under different breathing strategies and motion correction techniques with arterial spin labeling. Methods A prospective cardiac arterial spin labeling study was performed in 12 volunteers at 3 Tesla. Perfusion images were acquired twice under breath-hold, synchronized-breathing, and free-breathing. Motion detection based on the temporal intensity variation of a myocardial voxel, as well as image registration based on pairwise and groupwise approaches, were applied and evaluated in synthetic and in vivo data. A region of interest was drawn over the mean perfusion-weighted image for quantification. Original breath-hold datasets, analyzed with individual regions of interest for each perfusion-weighted image, were considered as reference values. Results Perfusion measurements in the reference breath-hold datasets were in line with those reported in literature. In original datasets, prior to motion correction, myocardial blood flow quantification was significantly overestimated due to contamination of the myocardial perfusion with the high intensity signal of blood pool. These effects were minimized with motion detection or registration. Synthetic data showed that accuracy of the perfusion measurements was higher with the use of registration, in particular after the pairwise approach, which probed to be more robust to motion. Conclusion Satisfactory results were obtained for the free-breathing strategy after pairwise registration, with higher accuracy and robustness (in synthetic datasets) and higher intrasession reproducibility together with lower myocardial blood flow variability across subjects (in in vivo datasets). Breath-hold and synchronized-breathing after motion correction provided similar results, but these breathing strategies can be difficult to perform by patients

Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension

BACKGROUND Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. PURPOSE To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. STUDY TYPE This is a prospective cohort sub-study. POPULATION Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. FIELD STRENGTH/SEQUENCE 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). ASSESSMENT Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDP$_{PREFUL}$ ) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDP$_{DCE}$ ). Furthermore, QDP$_{PREFUL}$ was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. STATISTICAL TESTS t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. RESULTS Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDP$_{PREFUL}$ was significantly correlated with QDP$_{DCE}$ (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDP$_{PREFUL}$  = 33.9 ± 17.2%). DATA CONCLUSION PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Guidelines for Cardiovascular Magnetic Resonance Imaging from the Korean Society of Cardiovascular Imaging-Part 3: Perfusion, Delayed Enhancement, and T1- and T2 Mapping

This document is the third part of the guidelines for the protocol, the interpretation and post-processing of cardiac magnetic resonance (CMR) studies. These consensus recommendations have been developed by the Consensus Committee of the Korean Society of Cardiovascular Imaging to standardize the requirements for image interpretation and post-processing of CMR. This third part of the recommendations describes tissue characterization modules, including perfusion, late gadolinium enhancement, and T1- and T2 mapping. Additionally, this document provides guidance for visual and quantitative assessment consisting of "What-to-See," "How-To," and common pitfalls for the analysis of each module. The Consensus Committee hopes that this document will contribute to the standardization of image interpretation and post-processing of CMR studies.ope

Automated Method for the Volumetric Evaluation of Myocardial Scar from Cardiac Magnetic Resonance Images

In most western countries cardiovascular diseases are the leading cause of death, and for the survivors of ischemic attack an accurate quantification of the extent of the damage is required to correctly assess its impact and for risk stratification, and to select the best treatment for the patient. Moreover, a fast and reliable tool for the assessment of the cardiac function and the measurement of clinical indexes is highly desirable. The aim of this thesis is to provide computational approaches to better detect and assess the presence of myocardial fibrosis in the heart, particularly but not only in the left ventricle, by performing a fusion of the information from different magnetic resonance imaging sequences. We also developed and provided a semiautomatic tool useful for the fast evaluation and quantification of clinical indexes derived from heart chambers volumes. The thesis is composed by five chapters. The first chapter introduces the most common cardiac diseases such as ischemic cardiomyopathy and describes in detail the cellular and structural remodelling phenomena stemming from heart failure. The second chapter regards the detection of the left ventricle through the development of a semi-automated approach for both endocardial and epicardial surfaces, and myocardial mask extraction. In the third chapter the workflow for scar assessment is presented, in which the previously described approach is used to obtain the 3D left ventricle patient-specific geometry; a registration algorithm is then used to superimpose the fibrosis information derived from the late gadolinium enhancement magnetic resonance imaging to obtain a patientspecific 3D map of fibrosis extension and location on the left ventricle myocardium. Focus of the fourth chapter is on the left atrium, and fibrotic tissue detection for gaining insight on atrial fibrillation. In the fifth chapter some conclusive remarks are presented with possible future developments of the presented work