Segmentation of 4D MR Renography Images Using Temporal Dynamics in a Level Set Framework

A novel 4D level set framework was developed to segment dynamic MR images into the cortex, medulla and collecting system. The novelty of the method is that it combines information from spatial anatomical structures and temporal dynamics. The accuracy of the fully automatic 4D level set algorithm was found to be comparable to manual segmentation performed by experts on renal anatomy. The algorithm requires less than one minute to automatically segment a single kidney 4D patient data set with more than 40 time points

Four Dimensional MR Image Analysis of Dynamic Renography

A novel four dimensional image analysis approach including registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmentation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts

Integrated Four Dimensional Registration and Segmentation of Dynamic Renal MR Images

In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmentation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts

Motion correction of free-breathing magnetic resonance renography using model-driven registration

Introduction Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). Materials and methods MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). Results DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. Discussion MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods

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

4D Non-rigid registration of renal dynamic contrast enhanced MRI data

Master'sMASTER OF ENGINEERIN

Tracer-Kinetic Model-Driven Motion Correction with Application to Renal DCE-MRI

A major challenge of the image registration in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is related to the image contrast variations caused by the contrast agent passage. Tracer-kinetic model-driven motion correction is an attractive solution for DCE-MRI, but previous studies only use the 3-parameter modified Tofts model. Firstly, a generalisation based on a 4-parameter 2-compartment tracer-kinetic model is presented. A practical limitation of these models is the need for non-linear least-squares (NLLS) fitting. This is prohibitively slow for image-wide parameter estimations, and is biased by the choice of initial values. To overcome this limitation, a fast linear least-squares (LLS) method to fit the two-compartment exchange and -filtration models (2CFM) to the data is introduced. Simulations of normal and pathological data were used to evaluate calculation time, accuracy and precision of the LLS against the NLLS method. Results show that the LLS method leads to a significant reduction in the calculation times. Secondly, a novel tracer-kinetic model-driven motion correction algorithm is introduced which uses a 4-parameter 2-compartment model to tackle the problem of image registration in 2D renal DCE-MRI. The core architecture of the algorithm can briefly described as follows: the 2CFM is linearly fitted pixel-by-pixel and the model fit is used as target for registration; then a free-form deformation model is used for pairwise co-registration of source and target images at the same time point. Another challenge that has been addressed is the computational complexity of non-rigid registration algorithms by precomputing steps to remove redundant calculations. Results in 5 subjects and simulated phantoms show that the algorithm is computationally efficient and improves alignment of the data. The proposed registration algorithm is then translated to 3D renal dynamic MR data. Translation to 3D is however challenging due to ghosting artefacts caused by within-frame breathing motion. Results in 8 patients show that the algorithm effectively removes between-frame breathing motion despite significant within-frame artefacts. Finally, the effect of motion correction on the clinical utility has been examined. Quantitative evaluation of single-kidney glomerular filtration rate derived from DCE-MRI against reference measurements shows a reduction of the bias, but precision is limited by within-frame artefacts. The suggested registration algorithm with a 4-parameter model is shown to be a computational efficient approach which effectively removes between-frame motion in a series of 2D and 3D renal DCE-MRI data

Segmentation of the kidney from the renal perfusion MR image sequences

Diplomová práce se zabývá problematikou segmentace ledvin v perfúzních MR obrazech. Segmentace ledvin se provádí různými metodami. Jedná se o regionově založené metody, deformovatelné modely, metody založené na modelech, hranově založené metody a další. Dosud není znám univerzální algoritmus, který by se dal použít pro segmentaci ledvin různých pacientů. Navrženou metodou této diplomové práce je aktivní kontura Snake, která je vytvořena v programovacím prostředí MatLab. Výsledné kontury jsou kvantitativně a vizuálně porovnány s manuální segmentací.This master’s thesis deals with kidney segmentation in perfusion magnetic resonance image sequences. Kidney segmentation is carry out by a few methods such as regionbased techniques, deformable models, specimen-based methods, edge-oriented methods etc. The universal algorithm for patient kidney segmentation still does not exist. Proposed method is an active contour Snake, which is created in programming environment MatLab. Final contours are quantitatively and visually compared to manual kidney segmentation.