Image Analysis of Renal DCE MRI for the Detection of Acute Renal Rejection.
ABSTRACT Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper we introduce a new approach for the automatic classification of normal and acute rejection transplants from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures. In the second step, a novel nonrigid-registration algorithm is employed to account for the motion of the kidney due to patient breathing, and finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results
Full-textDOI: · Available from: Tarek A El-Diasty, Dec 12, 2013
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ABSTRACT: Dynamic MR image recordings (DCE-MRI) of moving organs using bolus injections create two different types of dynamics in the images: (i) spatial motion artifacts due to patient movements, breathing and physiological pulsations that we want to counteract, and (ii) signal intensity changes during contrast agent wash-in and wash-out that we want to preserve. Proper image registration is needed to counteract the motion artifacts and for a reliable assessment of physiological parameters. In this work we present a partial differential equation-based method for deformable multimodal image registration using normalized gradients and the Fourier transform to solve the Euler-Lagrange equations in a multilevel hierarchy. This approach is particularly well suited to handle the motion challenges in DCE-MRI time series, being validated on ten DCE-MRI datasets from the moving kidney. We found that both normalized gradients and mutual information work as high-performing cost functionals for motion correction of this type of data. Furthermore, we demonstrated that normalized gradients has improved performance compared to mutual information as assessed by several performance measures. We conclude that normalized gradients can be a viable alternative to mutual information regarding registration accuracy, and with promising clinical applications to DCE-MRI recordings from moving organs.Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 01/2013; 38(3). DOI:10.1016/j.compmedimag.2013.12.007 · 1.50 Impact Factor
- 12/2013, Degree: PhD, Supervisor: Ayman El-Baz
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ABSTRACT: 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.03/2014, Degree: PhD, Supervisor: Ayman S. El-Baz and Karla Conn Welch