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Background: Several magnetic resonance imaging (MRI) studies investigated the evolution of multiple sclerosis (MS) lesions to understand the pathophysiological mechanisms leading to blood-brain barrier breakdown and lesion formation. Only a few assessed the early natural history of MS lesions using short-interval longitudinal MRI. Objective: The purpose of this study was to characterize MS lesion occurrence and early evolution on high-resolution MRI acquired at weekly intervals. Methods: Active lesions were characterized on 3D fluid attenuation inversion recovery (FLAIR) and gadolinium-enhanced 3D T1-weighted MRI performed weekly (seven weeks) on five untreated patients with relapsing–remitting MS (RRMS). Results: Active lesions ( n=212) were detected in all patients. All showed contrast-enhancement on at least one time-point. Most new lesions (83.5%) were visible on FLAIR and post-contrast T1-weighted images at first detection; 11.2% showed activity on FLAIR images, one or more weeks before the appearance of contrast-enhancement; 12.5% enhanced before being apparent on FLAIR. Conclusion: Blood brain barrier disruption is a constant step in the natural history of active MS lesions, but does not always constitute the initial event. These findings are consistent with the existence of a subpopulation of lesions with an ‘inside-out’ genesis, where neurodegenerative processes might precede microglial activation, and a subsequent adaptive immune response.

Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework.

White matter hyperintensities and lacunes are among the most frequent abnormalities on brain magnetic resonance imaging. They are commonly related to cerebral small vessel disease and associated with both stroke and dementia. We examined the spatial relationships between incident lacunes and white matter hyperintensities and related these findings to information on vascular anatomy to study possible mechanistic links between the two lesion types. Two hundred and seventy-six patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a genetically defined small vessel disease with mutations in the NOTCH3 gene were followed with magnetic resonance imaging over a total of 633 patient years. Using difference images and Jacobian maps from registered images we identified 104 incident lacunes. The majority (n = 95; 91.3%) of lacunes developed at the edge of a white matter hyperintensity whereas few lacunes were found to develop fully within (n = 6; 5.8%) or outside (n = 3; 2.9%) white matter hyperintensities. Adding information on vascular anatomy revealed that the majority of incident lacunes developed proximal to a white matter hyperintensity along the course of perforating vessels supplying the respective brain region. We further studied the spatial relationship between prevalent lacunes and white matter hyperintensities both in 365 patients with CADASIL and in 588 elderly subjects from the Austrian Stroke Prevention Study. The results were consistent with the results for incident lacunes. Lesion prevalence maps in different disease stages showed a spread of lesions towards subcortical regions in both cohorts. Our findings suggest that the mechanisms of lacunes and white matter hyperintensities are intimately connected and identify the edge of white matter hyperintensities as a predilection site for lacunes. Our observations further support and refine the concept of the white matter hyperintensity penumbra.

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.

We present a generic interpolator for label images. The basic idea is to interpolate each label with an ordinary image interpolator, and return the label with the highest value. This is the idea used by the itk::LabelImageGaussianInterpolateImageFunction interpolator. Unfortunately, this class is currently limited to Gaussian interpolation. Using generic programming, our proposed interpolator extends this idea to any image interpolator. Combined with linear interpolation, this results in similar or better accuracy and much improved computation speeds on a test image.

Automated assessment of hepatic fat fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of liver fat fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to get fine segmentation. Fat fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with an automatic graph cut method. Experimental results demonstrate the promises of our assessment framework.

BACKGROUND AND PURPOSE: Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems. MATERIALS AND METHODS: Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm. RESULTS: Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider. CONCLUSIONS: If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.

Human episodic memory is supported by networks of white matter tracts that connect frontal, temporal, and parietal regions. Degradation of white matter microstructure is increasingly recognized as a general mechanism of cognitive deterioration with aging. However, atrophy of gray matter regions also occurs and, to date, the potential role of specific white matter connections has been largely ignored. Changes to frontotemporal tracts may be important for the decline of episodic memory; while frontotemporal cooperation is known to be critical, the precise pathways of interaction are unknown. Diffusion-weighted MRI tractography was used to reconstruct three candidate fasciculi known to link components of memory networks: the fornix, the parahippocampal cingulum, and the uncinate fasciculus. Age-related changes in the microstructure of these tracts were investigated in 40 healthy older adults between the ages of 53 and 93 years. The relationships between aging, microstructure, and episodic memory were assessed for each individual tract. Age-related reductions of mean fractional anisotropy and/or increased mean diffusivity were found in all three tracts. However, age-related decline in recall was specifically associated with degradation of fornix microstructure, consistent with the view that this tract is important for episodic memory. In contrast, a decline in uncinate fasciculus microstructure was linked to impaired error monitoring in a visual object–location association task, echoing the effects of uncinate transection in monkeys. These results suggest that degradation of microstructure in the fornix and the uncinate fasciculus make critical but differential contributions to the mechanisms underlying age-related cognitive decline and subserve distinct components of memory.

In this paper, we propose an original correction method for susceptibility artifact in spin echo and gradient echo MR images when a field map of the imaged object is known. The susceptibility artifact impact on MR images is modeled by means of a pixel shifting map and an intensity distortion map. An original scheme based on MRI simulation is used to build the intensity distortion map to take into account different complex phenomena such as intravoxel dephasing. The proposed method has been successfully tested on both spin echo and gradient echo MR images giving promising results.

We propose a set of scalable discrepancy measures that may be applied for segmentation evaluation when a reference is known. The proposed measures take into account under and over detected points within an adjustable area. They give the intensity of the discrepancy and its relative position. Furthermore a scale parameter allows the accuracy of the measures to be adjusted.

In this paper, a set of scalable discrepancy measures is applied in the context of computer vision. These measures allow tuning of edge detectors and segmentation evaluation when a reference is known. Thanks to a scale parameter in an adjustable area the proposed measures allows to weight the importance of over-detection as well as under-detection. They give the intensity of the discrepancy and its relative position.

In this paper, we propose new evaluation measures for scene segmentation results, which are based on computing the difference between a region extracted from a segmentation map and the corresponding one on an ideal segmentation. The proposed measures take into account separately both under and over detected pixels. It also associates in its computation the compactness of the region under investigation.