Compressed sensing techniques for radial Ultra-short Echo Time (UTE) magnetic resonance imaging

This thesis proposes two techniques, namely Compressed Sensing (CS) and self-gating, for pre-clinical (CMRI) to reduce scan time and RF exposure to mouse heart, simply experimental procedures, and improve imaging quality. The proposed CS technique reduces the number of radial trajectories in Ultra-short Echo Time (UTE) CMRI scans on a 7 Tesla MRI machine to acquire 13% to 38% of the fully sampled k-space data. To reconstruct the image, the Non-Uniform Fast Fourier Transform (NUFFT) is utilized in each iteration of the l1-norm optimization algorithm of the CS to reduce error and aliasing. Experimental results with a phantom and a mouse heart samples show that the image quality of the proposed NUFFT-CS reconstructions, measured by the Peak Signal to Noise ratio (PSNR) and structural similarity (SSIM), is obviously better than those of traditional zero-filling method and regridding-CS method. Comparing the images of the CS technique with the reconstructions of fully sampled data, the quality degradation is illegible while the scan time is largely reduced. The proposed self-gating technique extracts the cardiac cycle information directly from the UTE CMRI measurements that are acquired without Electrocardiography (ECG) trigger. The proposed detector filters the k0 lines in the no-trigger UTE MRI scans to extract the cardiac cycle features, and automatically detects the peaks of the filtered signal as the cycle start points. The reconstruct cardiac images based on the self-gating signals reflect the cardiac cycle clearly and the scan time in MRI exams is reduced by 40% to 70%. The proposed self-gating detector differs from existing k0-line detector in the filter design and in the combination with NUFFT image reconstruction. Future research in this direction may include thorough analysis of the detector performance and may combine self-gated MRI with CS reconstruction. --Abstract, page iv

Novel imaging techniques for assessing disease affecting the right heart

Right ventricular (RV) size and function are prognostic in congenital and acquired heart disease. Two-dimensional echocardiography (2DE) is the most readily available modality for RV assessment, but is limited by its complex shape. Furthermore, biventricular function is intimately related through a shared septum and pericardium. The simplest metric of left ventricular (LV) function is ejection fraction (LVEF). However, LVEF is often maintained in pulmonary hypertension (PH), for example. Therefore better indicators of LV function are required to identify patients at risk of deterioration. In this thesis, novel imaging techniques for assessing cardiac function in right heart disease are investigated. The first experiment tested the hypothesis that single-beat threedimensional echocardiography (3DE) accurately and reproducibly quantifies RV volumes. 3DE traditionally acquires sub-volumes over consecutive heartbeats, whereas novel 3DE transducers can acquire datasets in a single cardiac cycle. Single-beat 3DE was compared against CMRI in 100 subjects including patients with PH and carcinoid heart disease. Single-beat 3DE was feasible and accurate for RV volumetric quantification, but with limitations of test-retest reproducibility. The second experiment tested the hypothesis that 2D knowledge-based reconstruction (2DKBR) accurately and reproducibly quantifies RV volumes. 2DKBR involves 2DE-acquired RV coordinates localized in 3D space and connected by reference to a disease-specific RV catalogue. This was validated against CMRI in 28 PH patients, and test-retest reproducibility was assessed. 2DKBR was feasible and accurate for RV volumetric quantification in PH, and more reproducible than conventional 2DE. The final experiment tested the hypothesis that multi-directional myocardial velocities could be assessed in PH by CMRI. A tissue phase mapping sequence was utilized in 40 PH patients and 20 healthy volunteers. Over a median follow-up period of 20 months, LV early diastolic wave velocities were the only independent predictors of functional capacity and clinical worsening in a model that includes conventional metrics of biventricular function

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section

Technological innovations in magnetic resonance for early detection of cardiovascular diseases

Most recent technical innovations in cardiovascular MR imaging (CMRI) are presented in this review. They include hardware and software developments, and novelties in parametric mapping. All these recent improvements lead to high spatial and temporal resolution and quantitative information on the heart structure and function. They make it achievable ambitious goals in the field of mapletic resonance, such as the early detection of cardiovascular pathologies. In this review article, we present recent innovations in CMRI, emphasizing the progresses performed and the solutions proposed to some yet opened technical problems

Myocardial strain analysis with high temporal resolution MRI tagging: extended 3D motion tracking in normal and LBBB hearts

Tese de doutoramento em Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 200