Dirk H. J. Poot

Automatic estimation of the noise variance from the histogram of a magnetic resonance image

Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of which however require user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean squared error (RMSE). The results show that the newly proposed method is superior in terms of the RMSE.

Optimal Experimental Design for Diffusion Kurtosis Imaging

Diffusion kurtosis imaging (DKI) is a new magnetic resonance imaging (MRI) model that describes the non-Gaussian diffusion behavior in tissues. It has recently been shown that DKI parameters, such as the radial or axial kurtosis, are more sensitive to brain physiology changes than the well-known diffusion tensor imaging (DTI) parameters in several white and gray matter structures. In order to estimate either DTI or DKI parameters with maximum precision, the diffusion weighting gradient settings that are applied during the acquisition need to be optimized. Indeed, it has been shown previously that optimizing the set of diffusion weighting gradient settings can have a significant effect on the precision with which DTI parameters can be estimated. In this paper, we focus on the optimization of DKI gradients settings. Commonly, DKI data are acquired using a standard set of diffusion weighting gradients with fixed directions and with regularly spaced gradient strengths. In this paper, we show that such gradient settings are suboptimal with respect to the precision with which DKI parameters can be estimated. Furthermore, the gradient directions and the strengths of the diffusion-weighted MR images are optimized by minimizing the Crame¿r-Rao lower bound of DKI parameters. The impact of the optimized gradient settings is evaluated, both on simulated as well as experimentally recorded datasets. It is shown that the precision with which the kurtosis parameters can be estimated, increases substantially by optimizing the gradient settings.

More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion‐weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b‐value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non‐Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b‐value‐independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusion‐weighted rat data, which was acquired with eight different b‐values, uniformly distributed in a range of [0,2800 sec/mm2]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusion‐weighted data will result in an overestimated degree of non‐Gaussian diffusion and a b‐value‐dependent underestimation of diffusivity measures, a Rician noise model was used in this study. Magn Reson Med, 2010.

Magnetic Resonance Imaging and Spectroscopy Reveal Differential Hippocampal Changes in Anhedonic and Resilient Subtypes of the Chronic Mild Stress Rat Model

BACKGROUNDnRepeated exposure to mild stressors induces anhedonia-a core symptom of major depressive disorder-in up to 70% of the stress-exposed rats, whereas the remaining show resilience to stress. This chronic mild stress (CMS) model is well documented as an animal model of major depressive disorder. We examined the morphological, microstructural, and metabolic characteristics of the hippocampus in anhedonic and stress resilient rats that may mark the differential behavioral outcome.nnnMETHODSnAnhedonic (n = 8), resilient (n = 8), and control (n = 8) rats were subjected to in vivo diffusion kurtosis imaging, high-resolution three-dimensional magnetic resonance imaging and proton magnetic resonance spectroscopy.nnnRESULTSnDiffusion kurtosis parameters were decreased in both CMS-exposed groups. A significant inward displacement in the ventral part of the right hippocampus was apparent in the resilient subjects and an increase of the glutamate:total creatine ratio and N-acetylaspartylglutamate:total creatine was observed in the anhedonic subjects.nnnCONCLUSIONSnDiffusion kurtosis imaging discloses subtle substructural changes in the hippocampus of CMS-exposed animals irrespective of their anhedonic or resilient nature. In contrast, proton magnetic resonance spectroscopy and magnetic resonance imaging-based shape change analysis of the hippocampus allowed discrimination of these two subtypes of stress sensitivity. Although the precise mechanism discriminating their behavior is yet to be elucidated, the present study underlines the role of the hippocampus in the etiology of depression and the induction of anhedonia. Our results reflect the potency of noninvasive magnetic resonance methods in preclinical settings with key translational benefit to and from the clinic.

Noise measurement from magnitude MRI using local estimates of variance and skewness

In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.

Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time?

Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal‐to‐noise ratio, longer acquisition time or both. This study investigates whether so‐called super‐resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high‐resolution acquisition in terms of the signal‐to‐noise ratio and acquisition time trade‐offs. The performance of six super‐resolution reconstruction methods and direct high‐resolution acquisitions was compared with respect to these trade‐offs. The methods are based on iterative back‐projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low‐resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super‐resolution reconstruction can indeed improve the resolution, signal‐to‐noise ratio and acquisition time trade‐offs compared with direct high‐resolution acquisition. Magn Reson Med, 2012.

Microstructural changes observed with DKI in a transgenic Huntington rat model: evidence for abnormal neurodevelopment.

Huntington Disease (HD) is a fatal neurodegenerative disorder, caused by a mutation in the Huntington gene. Although HD is most often diagnosed in mid-life, the key to its clinical expression may be found during brain maturation. In the present work, we performed in vivo diffusion kurtosis imaging (DKI) in order to study brain microstructure alterations in developing transgenic HD rat pups. Several developing brain regions, relevant for HD pathology (caudate putamen, cortex, corpus callosum, external capsule and anterior commissure anterior), were examined at postnatal days 15 (P15) and 30 (P30), and DKI results were validated with histology. At P15, we observed higher mean (MD) and radial (RD) diffusivity values in the cortex of transgenic HD rat pups. In addition, at the age of P30, lower axial kurtosis (AK) values in the caudate putamen of transgenic HD pups were found. At the level of the external capsule, higher MD values at P15 but lower MD and AD values at P30 were detected. The observed DKI results have been confirmed by myelin basic protein immunohistochemistry, which revealed a reduced fiber staining as well as less ordered fibers in transgenic HD rat pups. These results indicate that neuronal development in young transgenic HD rat pups occurs differently compared to controls and that the presence of mutant huntingtin has an influence on postnatal brain development. In this context, various diffusivity parameters estimated by the DKI model are a powerful tool to assess changes in tissue microstructure and detect developmental changes in young transgenic HD rat pups.

On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods

Although many studies are starting to use voxel-based analysis (VBA) methods to compare diffusion tensor images between healthy and diseased subjects, it has been demonstrated that VBA results depend heavily on parameter settings and implementation strategies, such as the applied coregistration technique, smoothing kernel width, statistical analysis, etc. In order to investigate the effect of different parameter settings and implementations on the accuracy and precision of the VBA results quantitatively, ground truth knowledge regarding the underlying microstructural alterations is required. To address the lack of such a gold standard, simulated diffusion tensor data sets are developed, which can model an array of anomalies in the diffusion properties of a predefined location. These data sets can be employed to evaluate the numerous parameters that characterize the pipeline of a VBA algorithm and to compare the accuracy, precision, and reproducibility of different post-processing approaches quantitatively. We are convinced that the use of these simulated data sets can improve the understanding of how different diffusion tensor image post-processing techniques affect the outcome of VBA. In turn, this may possibly lead to a more standardized and reliable evaluation of diffusion tensor data sets of large study groups with a wide range of white matter altering pathologies. The simulated DTI data sets will be made available online (http://www.dti.ua.ac.be).

Image registration improves human knee cartilage T1 mapping with delayed gadolinium-enhanced MRI of cartilage (dGEMRIC)

AbstractObjectivesTo evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements.MethodsEleven patients with early-stage knee osteoarthritis and ten healthy controls underwent dGEMRIC twice at 3u2009T. Controls underwent unenhanced imaging. ROIs were manually drawn on the femoral and tibial cartilage. T1 calculation was performed with and without registration of the T1-weighted images. Automated three-dimensional rigid registration was performed on the femur and tibia cartilage separately. Registration quality was evaluated using the square root Cramér–Rao lower bound (CRLBσ). Additionally, the reproducibility of dGEMRIC was assessed by comparing automated registration with manual slice-matching.ResultsAutomated registration of the T1-weighted images improved the T1 maps as the 90% percentile of the CRLBσ was significantly (Pu2009<u20090.05) reduced with a median reduction of 55.8xa0ms (patients) and 112.9xa0ms (controls). Manual matching and automated registration of the re-imaged T1 map gave comparable intraclass correlation coefficients of respectively 0.89/0.90 (patients) and 0.85/0.85 (controls).ConclusionsRegistration in dGEMRIC reduces movement artefacts on T1 maps and provides a good alternative to manual slice-matching in longitudinal studies.Key Points• Quantitative MRI is increasingly used for biomedical assessment of knee articular cartilagen • Image registration leads to more accurate quantification of cartilage quality and damagen • Movement artefacts in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) are reducedn • Automated image registration successfully aligns baseline and follow-up dGEMRIC examinationsn • Reproducibility of dGEMRIC with registration is similar to that using manual slice-matching

PCA-based groupwise image registration for quantitative MRI

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.