Delaunay triangulation based image enhancement for echocardiography images

A novel image enhancement approach for automatic echocardiography image processing is proposed. The main steps include undecimated wavelet based speckle noise reduction, edge detection, followed by a regional enhancement process that employs Delaunay triangulation based thresholding. The edge detection is performed using a fuzzy logic based center point detection and a subsequent radial search based fuzzy multiscale edge detection. The edges obtained are used as the vertices for Delaunay triangulation for enhancement purposes. This method enhances the heart wall region in the echo image. This technique is applied to both synthetic and real image sets that were obtained from a local hospital

Myocardial strain in healthy adults across a broad age range as revealed by cardiac magnetic resonance imaging at 1.5 and 3.0T: associations of myocardial strain with myocardial region, age, and sex

Purpose: We assessed myocardial strain using cine displacement encoding with stimulated echoes (DENSE) using 1.5T and 3.0T MRI in healthy adults. Materials and Methods: Healthy adults without any history of cardiovascular disease underwent MRI at 1.5T and 3.0T within 2 days. The MRI protocol included b-SSFP, 2D cine-EPI-DENSE, and late gadolinium enhancement in subjects>45 years. Acquisitions were divided into 6 segments, global and segmental peak longitudinal and circumferential strain were derived and analyzed by field strength, age and gender. Results: 89 volunteers (mean age 44.8 ± 18.0 years, range: 18-87 years) underwent MRI at 1.5T, and 88 of these subjects underwent MRI at 3.0T (1.4±1.4 days between the scans). Compared with 3.0T, the magnitudes of global circumferential (-19.5±2.6% vs. -18.47±2.6%; p=0.001) and longitudinal (-12.47±3.2% vs -10.53±3.1%; p=0.004) strain were greater at 1.5T. At 1.5T, longitudinal strain was greater in females than in males: -10.17±3.4% vs. -13.67±2.4%; p=0.001. Similar observations occurred for circumferential strain at 1.5T (-18.72±2.2% vs. -20.10±2.7%; p=0.014) and at 3.0T (-17.92 ± 1.8% vs -19.1 ± 3.1%; p=0.047). At 1.5T, longitudinal and circumferential strain were not associated with age after accounting for sex (longitudinal strain p= 0.178, circumferential strain p= 0.733). At 3.0T, longitudinal and circumferential strain were associated with age. (p<0.05) Longitudinal strain values were greater in the apico-septal, basal-lateral and mid-lateral segments and circumferential strain in the inferior, infero-lateral and antero-lateral LV segments. Conclusion: Myocardial strain parameters as revealed by cine-DENSE at different MRI field strengths were associated with myocardial region, age and sex

A novel method for estimating myocardial strain: assessment of deformation tracking against reference magnetic resonance methods in healthy volunteers

We developed a novel method for tracking myocardial deformation using cardiac magnetic resonance (CMR) cine imaging. We hypothesised that circumferential strain using deformation-tracking has comparable diagnostic performance to a validated method (Displacement Encoding with Stimulated Echoes- DENSE) and potentially diagnostically superior to an established cine-strain method (feature-tracking). 81 healthy adults (44.6 ± 17.7 years old, 47% male), without any history of cardiovascular disease, underwent CMR at 1.5T including cine, DENSE, and late gadolinium enhancement in subjects >45 years. Acquisitions were divided into 6 segments, and global and segmental peak circumferential strain were derived and analysed by age and sex. Peak circumferential strain differed between the 3 groups (DENSE: -19.4 ± 4.8 %; deformation-tracking: -16.8 ± 2.4 %; feature-tracking: -28.7 ± 4.8%) (ANOVA with Tukey post-hoc, F-value 279.93, p<0.01). DENSE and deformation-tracking had better reproducibility than feature-tracking. Intra-class correlation co-efficient was >0.90. Larger magnitudes of strain were detected in women using deformation-tracking and DENSE, but not feature-tracking. Compared with a reference method (DENSE), deformation-tracking using cine imaging has similar diagnostic performance for circumferential strain assessment in healthy individuals. Deformation-tracking could potentially obviate the need for bespoke strain sequences, reducing scanning time and is more reproducible than feature-tracking

Magnetic resonance imaging of myocardial strain after acute ST-segment-elevation myocardial infarction: a systematic review

The purpose of this systematic review is to provide a clinically relevant, disease-based perspective on myocardial strain imaging in patients with acute myocardial infarction or stable ischemic heart disease. Cardiac magnetic resonance imaging uniquely integrates myocardial function with pathology. Therefore, this review focuses on strain imaging with cardiac magnetic resonance. We have specifically considered the relationships between left ventricular (LV) strain, infarct pathologies, and their associations with prognosis. A comprehensive literature review was conducted in accordance with the PRISMA guidelines. Publications were identified that (1) described the relationship between strain and infarct pathologies, (2) assessed the relationship between strain and subsequent LV outcomes, and (3) assessed the relationship between strain and health outcomes. In patients with acute myocardial infarction, circumferential strain predicts the recovery of LV systolic function in the longer term. The prognostic value of longitudinal strain is less certain. Strain differentiates between infarcted versus noninfarcted myocardium, even in patients with stable ischemic heart disease with preserved LV ejection fraction. Strain recovery is impaired in infarcted segments with intramyocardial hemorrhage or microvascular obstruction. There are practical limitations to measuring strain with cardiac magnetic resonance in the acute setting, and knowledge gaps, including the lack of data showing incremental value in clinical practice. Critically, studies of cardiac magnetic resonance strain imaging in patients with ischemic heart disease have been limited by sample size and design. Strain imaging has potential as a tool to assess for early or subclinical changes in LV function, and strain is now being included as a surrogate measure of outcome in therapeutic trials

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council

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 multi-modality cardiac imaging approach to the Athlete's heart: an expert consensus of the European Association of Cardiovascular Imaging

The term 'athlete's heart' refers to a clinical picture characterized by a slow heart rate and enlargement of the heart. A multi-modality imaging approach to the athlete's heart aims to differentiate physiological changes due to intensive training in the athlete's heart from serious cardiac diseases with similar morphological features. Imaging assessment of the athlete's heart should begin with a thorough echocardiographic examination. Left ventricular (LV) wall thickness by echocardiography can contribute to the distinction between athlete's LV hypertrophy and hypertrophic cardiomyopathy (HCM). LV end-diastolic diameter becomes larger (>55 mm) than the normal limits only in end-stage HCM patients when the LV ejection fraction is <50%. Patients with HCM also show early impairment of LV diastolic function, whereas athletes have normal diastolic function. When echocardiography cannot provide a clear differential diagnosis, cardiac magnetic resonance (CMR) imaging should be performed. With CMR, accurate morphological and functional assessment can be made. Tissue characterization by late gadolinium enhancement may show a distinctive, non-ischaemic pattern in HCM and a variety of other myocardial conditions such as idiopathic dilated cardiomyopathy or myocarditis. The work-up of athletes with suspected coronary artery disease should start with an exercise ECG. In athletes with inconclusive exercise ECG results, exercise stress echocardiography should be considered. Nuclear cardiology techniques, coronary cardiac tomography (CCT) and/or CMR may be performed in selected cases. Owing to radiation exposure and the young age of most athletes, the use of CCT and nuclear cardiology techniques should be restricted to athletes with unclear stress echocardiography or CMR

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research