Both normal aging and neurodegenerative diseases such as Alzheimer’s disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.
Conventionally, a single rank-2 tensor is used to assess the white matter integrity in diffusion imaging of the human brain. However, a single tensor fails to describe the diffusion in fiber crossings. Although a dual tensor model is able to do so, the low signal-to-noise ratio hampers reliable parameter estimation as the number of parameters is doubled.
We present a framework for structure-adaptive tensor field filtering to enhance the statistical analysis in complex fiber structures. In our framework, a tensor model will be fitted based on an automated relevance determination method. Particularly, a single tensor model is applied to voxels in which the data seems to represent a single fiber and a dualtensor model to voxels appearing to contain crossing fibers. To improve the estimation of the model parameters we propose a structure-adaptive tensor filter that is applied to tensors belonging to the same fiber compartment only.
It is demonstrated that the structure-adaptive tensor-field filter improves the continuity and regularity of the estimated tensor field. It outperforms an existing denoising approach called LMMSE, which is applied to the diffusion-weighted images. Track-based spatial statistics analysis of fiber-specific FA maps show that the method sustains the detection of more subtle changes in white matter tracts than the classical single-tensor-based analysis.
Thus, the filter enhances the applicability of the dual-tensor model in diffusion imaging research. Specifically, the reliable estimation of two tensor diffusion properties facilitates fiber-specific extraction of diffusion features.
The apparent diffusion coefficient (ADC) is an imaging biomarker providing quantitative information on the diffusion of water in biological tissues. This measurement could be of relevance in oncology drug development, but it suffers from a lack of reliability. ADC images are computed by applying a voxelwise exponential fitting to multiple diffusion-weighted MR images (DW-MRIs) acquired with different diffusion gradients. In the abdomen, respiratory motion induces misalignments in the datasets, creating visible artefacts and inducing errors in the ADC maps. We propose a multistep post-acquisition motion compensation pipeline based on 3D non-rigid registrations. It corrects for motion within each image and brings all DW-MRIs to a common image space. The method is evaluated on 10 datasets of free-breathing abdominal DW-MRIs acquired from healthy volunteers. Regions of interest (ROIs) are segmented in the right part of the abdomen and measurements are compared in the three following cases: no image processing, Gaussian blurring of the raw DW-MRIs and registration. Results show that both blurring and registration improve the visual quality of ADC images, but compared to blurring, registration yields visually sharper images. Measurement uncertainty is reduced both by registration and blurring. For homogeneous ROIs, blurring and registration result in similar median ADCs, which are lower than without processing. In a ROI at the interface between liver and kidney, registration and blurring yield different median ADCs, suggesting that uncorrected motion introduces a bias. Our work indicates that averaging procedures on the scanner should be avoided, as they remove the opportunity to perform motion correction.
Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several
papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution and that brain tissue classification can be performed robustly for images obtained with different protocols, we propose to derive appearance features from brain-tissue density maps instead of directly from the MR images. We evaluated this approach on hippocampus segmentation in two sets of images acquired with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multi-atlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition-independent features significantly improved (0.783) over atlas-based segmentation alone (0.728).
For quantitative MRI techniques, such as T1, T2 mapping and Diffusion Tensor Imaging (DTI), a model has to be
fit to several MR images that are acquired with suitably chosen different acquisition settings. The most efficient
estimator to retrieve the parameters is the Maximum Likelihood (ML) estimator. However, the standard ML
estimator is biased for finite sample sizes. In this paper we derive a bias correction formula for magnitude MR
images. This correction is applied in two different simulation experiments, a T2 mapping experiment and a DTI
experiment. We show that the correction formula successfully removes the bias. As the correction is performed
as post-processing, it is possible to retrospectively correct the results of previous quantitative experiments. With
this procedure more accurate quantitative values can be obtained from quantitative MR acquisitions.
Improving the resolution in magnetic resonance imaging (MRI) is always done at the expense of either the signal-to-noise
ratio (SNR) or the acquisition time. This study investigates whether so-called super-resolution reconstruction (SRR) is an
advantageous alternative to direct high-resolution (HR) acquisition in terms of the SNR and acquisition time trade-offs.
An experimental framework was designed to accommodate the comparison of SRR images with direct high-resolution
acquisitions with respect to these trade-offs. The framework consisted, on one side, of an image acquisition scheme,
based on theoretical relations between resolution, SNR, and acquisition time, and, on the other side, of a protocol for
reconstructing SRR images from a varying number of acquired low-resolution (LR) images. The quantitative experiments
involved a physical phantom containing structures of known dimensions. Images reconstructed by three SRR methods, one
based on iterative back-projection and two on regularized least squares, were quantitatively and qualitatively compared
with direct HR acquisitions. 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 post-mortem mouse) were acquired with
different MRI scanners. The quantitative results indicate that for long acquisition times, when multiple acquisitions are
averaged to improve SNR, SRR can achieve better resolution at better SNR than direct HR acquisitions.
Diffusion Tensor Magnetic Resonance Imaging (DTI) is a well known technique that can provide information about the neuronal fiber structure of the brain. However, since DTI requires a large amount of data, a high speed MRI acquisition technique is needed to acquire these data within a reasonable time. Echo Planar Imaging (EPI)
is a technique that provides the desired speed. Unfortunately, the advantage of speed is overshadowed by image artifacts, especially at high fields. EPI artifacts originate from susceptibility differences in adjacent tissues and correction techniques are required to obtain reliable images. In this work, the fieldmap method, which tries to
measure distortion effects, is optimized by using a non linear least squares estimator for calculating pixel shifts. This method is tested on simulated data and proves to be more robust against noise compared to previously suggested methods. Another advantage of this new method is that other parameters like relaxation and the odd/even phase difference are estimated. This new way of estimating the field map is demonstrated on a hardware phantom, which consists of parallel bundles made of woven strands of Micro Dyneema fibers. Using a modified EPI-sequence, reference data was measured for the calculation of fieldmaps. This allows one to reposition the pixels in order to obtain images with less distortions. The correction is applied to non-diffusion weighted images as well as diffusion weighted images and fiber tracking is performed on this corrected data.
In this paper, we describe a method to evaluate similarities in estimated temporal noise spectra of functional
Magnetic Resonance Imaging (fMRI) time series. Accurate noise spectra are needed for reliable activation
detection in fMRI. Since these spectra are a-priori unknown, they have to be estimated from the fMRI data. A
noise model can be estimated for each voxel separately, but when noise spectra of neighboring voxels are (almost)
equal, the power of the activation detection test can be improved by estimating the noise model from a set of
neighboring voxels. In this paper, a method is described to evaluate the similarity of noise spectra of neighboring
voxels. Noise spectrum similarities are studied in simulation as well as experimental fMRI datasets.
The parameters of the model describing the voxel time series are estimated by a Maximum Likelihood (ML)
estimator. The similarity of the ML estimated noise processes is assessed by the Model Error (ME), which is
based on the Kullback Leibler divergence. Spatial correlations in the fMRI data reduce the ME between the
noise spectra of (neighboring) voxels. This undesired effect is quantified by simulation experiments where spatial
correlation is introduced. By plotting the ME as a function of the distance between voxels, it is observed that
the ME increases as a function of this distance. Additionally, by using the theoretical distribution of the ME, it
is observed that neighboring voxels indeed have similar noise spectra and these neighbors can be used to improve
the noise model estimate.