Antonella Zanna Munthe-Kaas

Assessment of Kidney Volumes From MRI: Acquisition and Segmentation Techniques

OBJECTIVE The prevalence of chronic kidney disease (CKD) is increasing worldwide. In Europe alone, at least 8% of the population currently has some degree of CKD. CKD is associated with serious comorbidity, reduced life expectancy, and high economic costs; hence, early detection and adequate treatment of kidney disease are important. CONCLUSION We review state-of-the-art MRI acquisition techniques for CKD, with a special focus on image segmentation methods used for the estimation of kidney volume.

Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging

In this work we describe an integrated and automated workflow for a comprehensive and robust analysis of multimodal MR images from a cohort of more than hundred subjects. Image examinations are done three years apart and consist of 3D high-resolution anatomical images, low resolution tensor-valued DTI recordings and 4D resting state fMRI time series. The integrated analysis of the data requires robust tools for segmentation, registration and fiber tracking, which we combine in an automated manner. Our automated workflow is strongly desired due to the large number of subjects. Especially, we introduce the use of histogram segmentation to processed fMRI data to obtain functionally important seed and target regions for fiber tracking between them. This enables analysis of individually important resting state networks. We also discuss various approaches for the assessment of white matter integrity parameters along tracts, and in particular we introduce the use of functional data analysis (FDA) for this task.

Segmentation-Driven Image Registration-Application to 4D DCE-MRI Recordings of the Moving Kidneys

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

Normalized gradient fields for nonlinear motion correction of DCE-MRI time series.

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 have 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.

Quantitative assessment of kidney function using dynamic contrast enhanced MRI - steps towards an integrated software prototype

Renal diseases, caused by e.g. diabetes mellitus, hypertension or multiple cyst formations, can lead to kidney failure that requires renal replacement therapy (RRT). Early detection and treatment can delay or prevent this progression towards end-stage renal disease (ESRD). Worldwide an increasing number of people will in the near future suffer from ESRD, with dialysis or kidney transplantation as the costly therapeutic alternatives. In a clinical setting, the detection of renal failure (i.e. reduction in glomerular filtration rate, GFR) is a challenge, and todays methods (e.g. elevated serum creatinine and urine analysis) are very crude and cannot even differentiate between left and right kidney function. Magnetic resonance imaging methods such as DCE-MRI have proven to be a very promising tool for (semi)quantitative and localized assessment of renal function, representing a non-invasive procedure that most patients can tolerate. In order to fully realize the potential of data from such methods, a series of image processing and analysis steps are required, including image registration, segmentation, kidney compartment modeling and visualization. We present here methods and results from these steps, and describes how the processing pipeline has been integrated as a software prototype.

Normalized gradient fields and mutual information for motion correction of DCE-MRI images

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidney typically displays spatial motion and undesired artefacts due to unavoidable patient movement and physiological pulsations, with the effect of corrupting voxel-wise signal intensity changes arising from contrast agent wash-in and wash-out. Image registration is a necessary tool to counteract such motion artefacts and to estimate physiological parameters reliably. In this work, we present a fluid-registration-based method for deformable multimodal image registration based on normalized gradients, particularly well suited to handle the motion challenges in DCE-MRI time series. We evaluate and confirm that both normalized gradients and mutual information are high-performing cost functionals for co-registration of DCE-MRI time series. Further, there are indications that normalized gradients have better performance than mutual information on this kind of images. These results promote normalized gradients as a promising tool for proper motion correction of DCE-MRI images applied in the clinic or in biomedical research.

Physical Models for Simulation and Reconstruction of Human Tissue Deformation Fields in Dynamic MRI

Objective: Medical image registration can be formulated as a tissue deformation problem, where parameter estimation methods are used to obtain the inverse deformation. However, there is limited knowledge about the ability to recover an unknown deformation. The main objective of this study is to estimate the quality of a restored deformation field obtained from image registration of dynamic MR sequences. Methods: We investigate the behavior of forward deformation models of various complexities. Further, we study the accuracy of restored inverse deformations generated by image registration. Results: We found that the choice of 1) heterogeneous tissue parameters and 2) a poroelastic (instead of elastic) model had significant impact on the forward deformation. In the image registration problem, both 1) and 2) were found not to be significant. Here, the presence of image features were dominating the performance. We also found that existing algorithms will align images with high precision while at the same time obtain a deformation field with a relative error of

Comparative analysis of various thresholding techniques on TerraSAR-X images in the presence of speckle noise

40\%

In vivo estimation of glomerular filtration in the kidney using DCE-MRI

. Conclusion: Image registration can only moderately well restore the true deformation field. Still, estimation of volume changes instead of deformation fields can be fairly accurate and may represent a proxy for variations in tissue characteristics. Volume changes remain essentially unchanged under choice of discretization and the prevalence of pronounced image features. Significance: We suggest that image registration of high-contrast MR images has potential to be used as a tool to produce imaging biomarkers sensitive to pathology affecting tissue stiffness.

Automated workflow for spatial alignment of multimodal MR image acquisitions in a longitudinal study of cognitive aging

This paper compares several methods of thresholding applied to TerraSAR-X radar images in the presence of speckle noise and a method based on the use of the local extremas of the histogram. The methods used are Otsus, Valley-emphasis, maximum entropy, fuzzy sets, Yagars measure of fuzziness and histogram clustering. The local minimum of the histogram provides a good threshold candidate for global thresholding in ship detection in the case of moderate signal noise but fails for high signal noise and variable illumination. The methods are tested on sample images to evaluate their performance in object detection in real TerraSAR-X datasets.