Contrast echocardiography for cardiac quantifications

The indicator-dilution-theory for cardiac quantifications has always been limited in practice by the invasiveness of the available techniques. However, the recent introduction of stable ultrasound contrast agents opens new possibilities for indicator dilution measurements. This study describes a new and successful approach to overcome this invasiveness issue. We show a novel approach for minimally invasive quantification of several cardiac parameters based on the dilution of ultrasound contrast agents. A single peripheral injection of an ultrasound contrast agent bolus can result in the simultaneous assessment of cardiac output, pulmonary blood volume, and left and right ventricular ejection fraction. The bolus passage in different sites of the central circulation is detected by an ultrasound transducer. The detected acoustic (or video) intensities are processed and several indicator dilution curves are measured simultaneously. To this end, we exploit that for low concentrations the relation between contrast concentration and acoustic backscatter is approximately linear. The Local Density Random Walk Model is used to fit and interpret the indicator dilution curves for cardiac output, pulmonary blood volume, and ejection fraction measurements. Two fitting algorithms based either on a multiple linear regression in the logarithmic domain or on the solution of the moment equations are developed. The indicator dilution system can be also interpreted as a linear system and, therefore, characterized by an impulse response function. An adaptive Wiener deconvolution filter is implemented for robust dilution system identification. For ejection fraction measurements, the atrial and ventricular indicator dilution curves are measured and processed by the deconvolution filter, resulting in the estimate of the left ventricle dilution-system impulse response. This curve can be fitted and interpreted by a mono-compartment exponential model for the ejection fraction assessment. The proposed deconvolution filter is also used for the identification of the dilution system between right ventricle and left atrium. The Local Density Random Walk Model fit of the estimated impulse response allows the pulmonary blood volume assessment. Both cardiac output and pulmonary blood volume measurements are validated in vitro with accurate results (correlation coefficients larger than 0.99). The Pulmonary blood volume measurement feasibility is also tested in humans with promising results. The ejection fraction measurement is validated in-vivo. The impulse response approach allows accurate left ventricle ejection fraction estimates. Comparison with echocardiographic bi-plane measurements shows a correlation coefficient equal to 0.93. A dedicated image segmentation algorithm for videodensitometry has also been developed for automating the determination of regions of interest. The resulting algorithm has been integrated with the indicator dilution analysis system. The automatic determination of the measurement region results in improved dilution-curve signal-to-noise ratios. In conclusion, this study proves that quantification of cardiac output, pulmonary blood volume, and left and right ventricular ejection fraction by dilution of ultrasound contrast agents is feasible and accurate. Moreover, the proposed methods are applicable in different contexts (e.g., magnetic resonance imaging) and for different types of measurements, leading to a broad range of applications

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

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

Development of novel magnetic resonance methods for advanced parametric mapping of the right ventricle

The detection of diffuse fibrosis is of particular interest in congenital heart disease patients, including repaired Tetralogy of Fallot (rTOF), as clinical outcome is linked to the accurate identification of diffuse fibrosis. In the Left Ventricular (LV) myocardium native T1 mapping and Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) are promising approaches for detection of diffuse fibrosis. In the Right Ventricle (RV) current techniques are limited due to the thinner, mobile and complex shaped compact myocardium. This thesis describes technical development of RV tissue characterisation methods. An interleaved variable density spiral DT-CMR method was implemented on a clinical 3T scanner allowing both ex and in vivo imaging. A range of artefact corrections were implemented and tested (gradient timing delays, off-resonance and T2* corrections). The off- resonance and T2* corrections were evaluated using computational simulation demonstrating that for in vivo acquisitions, off-resonance correction is essential. For the first-time high-resolution Stimulated Echo Acquisition Mode (STEAM) DT-CMR data was acquired in both healthy and rTOF ex-vivo hearts using an interleaved spiral trajectory and was shown to outperform single-shot EPI methods. In vivo the first DT-CMR data was shown from the RV using both an EPI and an interleaved spiral sequence. Both sequences provided were reproducible in healthy volunteers. Results suggest that the RV conformation of cardiomyocytes differs from the known structure in the LV. A novel STEAM-SAturation-recovery Single-sHot Acquisition (SASHA) sequence allowed the acquisition of native T1 data in the RV. The excellent blood and fat suppression provided by STEAM is leveraged to eliminate partial fat and blood signal more effectively than Modified Look-Locker Imaging (MOLLI) sequences. STEAM-SASHA T1 was validated in a phantom showing more accurate results in the native myocardial T1 range than MOLLI. STEAM-SASHA demonstrated good reproducibility in healthy volunteers and initial promising results in a single rTOF patient.Open Acces