Early white matter alterations in men predisposed to FXTAS revealed by MT imaging.

Introduction: The Fragile X - associated Tremor Ataxia Syndrome (FXTAS) is a recently described, and under-diagnosed, late onset (≈ 60y) neurodegenerative disorder affecting male carriers of a premutation in the Fragile X Mental Retardation 1 (FMR1) gene. The premutation is an CGG (Cytosine-Guanine-Guanine) expansion (55 to 200 CGG repeats) in the proximal region of the FMR1 gene. Patients with FXTAS primarily present with cerebellar ataxia and intention tremor. Neuroradiological features of FXTAS include prominent white matter disease in the periventricular, subcortical, middle cerebellar peduncles and deep white matter of the cerebellum on T2-weighted or FLAIR MR imaging (Jacquemmont 2007, Loesch 2007, Brunberg 2002, Cohen 2006). We hypothesize that a significant white matter alteration is present in younger individuals many years prior to clinical symptoms and/or the presence of visible lesions on conventional MR sequences and might be detectable by magnetization transfer (MT) imaging. Methods: Eleven asymptomatic premutation carriers (mean age = 55 years) and seven intra-familial controls participated to the study. A standardized neurological examination was performed on all participants and a neuropsychological evaluation was carried out before MR scanning performed on a 3T Siemens Trio. The protocol included a sagittal T1-weighted 3D gradient-echo sequence (MPRAGE, 160 slices, 1 mm^3 isotropic voxels) and a gradient-echo MTI (FA 30, TE 15, matrix size 256*256, pixel size 1*1 mm, 36 slices (thickness 2mm), MT pulse duration 7.68 ms, FA 500, frequency offset 1.5 kHz). MTI was performed by acquiring consecutively two set of images; first with and then without the MT saturation pulse. MT images were coregistered to the T1 acquisition. The MTR for every intracranial voxel was calculated as follows: MTR = (M0 - MS)/M0*100%, creating a MTR map for each subject. As first analysis, the whole white matter (WM) was used to mask the MTR image in order to create an histogram of the MTR distribution in the whole tissue class over the two groups examined. Then, for each subject, we performed a segmentation and parcellation of the brain by means of Freesurfer software, starting from the high resolution T1-weighted anatomical acquisition. Cortical parcellations was used to assign a label to the underlying white matter by the construction of a Voronoi diagram in the WM voxels of the MR volume based on distance to the nearest cortical parcellation label. This procedure allowed us to subdivide the cerebral WM in 78 ROIs according to the cortical parcellation (see example in Fig 1). The cerebellum, by the same procedure, was subdivided in 5 ROIs (2 per each hemisphere and one corresponding to the brainstem). For each subject, we calculated the mean value of MTR within each ROI and averaged over controls and patients. Significant differences between the two groups were tested using a two sample T-test (p<0.01). Results: Neurological examination showed that no patient met the clinical criteria of Fragile X Tremor and Ataxia Syndrome yet. Nonetheless, premutation carriers showed some subtle neurological signs of the disorder. In fact, premutation carriers showed a significant increase of tremor (CRST, T-test p=0.007) and increase of ataxia (ICARS, p=0.004) when compared to controls. The neuropsychological evaluation was normal in both groups. To obtain general characterizations of myelination for each subject and premutation carriers, we first computed the distribution of MTR values across the total white matter volume and averaged for each group. We tested the equality of the two distributions with the non parametric Kolmogorov-Smirnov test and we rejected the null-hypothesis at a p=0.03 (fig. 2). As expected, when comparing the asymptomatic permutation carriers with control subjects, the peak value and peak position of the MTR values within the whole WM were decreased and the width of the distribution curve was increased (p<0.01). These three changes point to an alteration of the global myelin status of the premutation carriers. Subsequently, to analyze the regional myelination and white matter integrity of the same group, we performed a ROI analysis of MTR data. The ROI-based analysis showed a decrease of mean MTR value in premutation carriers compared to controls in bilateral orbito-frontal and inferior frontal WM, entorhinal and cingulum regions and cerebellum (Fig 3). The detection of these differences in these regions failed with other conventional MR techniques. Conclusions: These preliminary data confirm that in premutation carriers, there are indeed alterations in "normal appearing white matter" (NAWM) and these alterations are visible with the MT technique. These results indicate that MT imaging may be a relevant approach to detect both global and local alterations within NAWM in "asymptomatic" carriers of premutations in the Fragile X Mental Retardation 1 (FMR1) gene. The sensitivity of MT in the detection of these alterations might point towards a specific physiopathological mechanism linked to an underlying myelin disorder. ROI-based analyses show that the frontal, parahippocampal and cerebellar regions are already significantly affected before the onset of symptoms. A larger sample will allow us to determine the minimum CGG expansion and age associated with these subclinical white matter alterations.

Assessment of myocardial edema and area-at-risk in acute myocardial infarction by CMR: Evaluation of a novel T2-mapping method

Objectives. The goal of this study is to evaluate a T2-mapping sequence by: (i) measuring the reproducibility intra- and inter-observer variability in healthy volunteers in two separate scanning session with a T2 reference phantom; (2) measuring the mean T2 relaxation times by T2-mapping in infarcted myocardium in patients with subacute MI and compare it with patient's the gold standard X-ray coronary angiography and healthy volunteers results. Background. Myocardial edema is a consequence of an inflammation of the tissue, as seen in myocardial infarct (MI). It can be visualized by cardiovascular magnetic resonance (CMR) imaging using the T2 relaxation time. T2-mapping is a quantitative methodology that has the potential to address the limitation of the conventional T2-weighted (T2W) imaging. Methods. The T2-mapping protocol used for all MRI scans consisted in a radial gradient echo acquisition with a lung-liver navigator for free-breathing acquisition and affine image registration. Mid-basal short axis slices were acquired.T2-maps analyses: 2 observers semi- automatically segmented the left ventricle in 6 segments accordingly to the AHA standards. 8 healthy volunteers (age: 27 ± 4 years; 62.5% male) were scanned in 2 separate sessions. 17 patients (age : 61.9 ± 13.9 years; 82.4% male) with subacute STEMI (70.6%) and NSTEMI underwent a T2-mapping scanning session. Results. In healthy volunteers, the mean inter- and intra-observer variability over the entire short axis slice (segment 1 to 6) was 0.1 ms (95% confidence interval (CI): -0.4 to 0.5, p = 0.62) and 0.2 ms (95% CI: -2.8 to 3.2, p = 0.94, respectively. T2 relaxation time measurements with and without the correction of the phantom yielded an average difference of 3.0 ± 1.1 % and 3.1 ± 2.1 % (p = 0.828), respectively. In patients, the inter-observer variability in the entire short axis slice (S1-S6), was 0.3 ms (95% CI: -1.8 to 2.4, p = 0.85). Edema location as determined through the T2-mapping and the coronary artery occlusion as determined on X-ray coronary angiography correlated in 78.6%, but only in 60% in apical infarcts. All except one of the maximal T2 values in infarct patients were greater than the upper limit of the 95% confidence interval for normal myocardium. Conclusions. The T2-mapping methodology is accurate in detecting infarcted, i.e. edematous tissue in patients with subacute infarcts. This study further demonstrated that this T2-mapping technique is reproducible and robust enough to be used on a segmental basis for edema detection without the need of a phantom to yield a T2 correction factor. This new quantitative T2-mapping technique is promising and is likely to allow for serial follow-up studies in patients to improve our knowledge on infarct pathophysiology, on infarct healing, and for the assessment of novel treatment strategies for acute infarctions.

Segmentation and grid generation for numerical simulations of vad connections with patient-specific data

Objectives: We are interested in the numerical simulation of the anastomotic region comprised between outflow canula of LVAD and the aorta. Segmenta¬tion, geometry reconstruction and grid generation from patient-specific data remain an issue because of the variable quality of DICOM images, in particular CT-scan (e.g. metallic noise of the device, non-aortic contrast phase). We pro¬pose a general framework to overcome this problem and create suitable grids for numerical simulations.Methods: Preliminary treatment of images is performed by reducing the level window and enhancing the contrast of the greyscale image using contrast-limited adaptive histogram equalization. A gradient anisotropic diffusion filter is applied to reduce the noise. Then, watershed segmentation algorithms and mathematical morphology filters allow reconstructing the patient geometry. This is done using the InsightToolKit library (www.itk.org). Finally the Vascular Model¬ing ToolKit (www.vmtk.org) and gmsh (www.geuz.org/gmsh) are used to create the meshes for the fluid (blood) and structure (arterial wall, outflow canula) and to a priori identify the boundary layers. The method is tested on five different patients with left ventricular assistance and who underwent a CT-scan exam.Results: This method produced good results in four patients. The anastomosis area is recovered and the generated grids are suitable for numerical simulations. In one patient the method failed to produce a good segmentation because of the small dimension of the aortic arch with respect to the image resolution.Conclusions: The described framework allows the use of data that could not be otherwise segmented by standard automatic segmentation tools. In particular the computational grids that have been generated are suitable for simulations that take into account fluid-structure interactions. Finally the presented method features a good reproducibility and fast application.

An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Aim  Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location  World-wide.Methods  Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results  Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions  By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images.

Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd'Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Three-dimensional magnetic resonance myocardial motion tracking from a single image plane.

Three-dimensional imaging for the quantification of myocardial motion is a key step in the evaluation of cardiac disease. A tagged magnetic resonance imaging method that automatically tracks myocardial displacement in three dimensions is presented. Unlike other techniques, this method tracks both in-plane and through-plane motion from a single image plane without affecting the duration of image acquisition. A small z-encoding gradient is subsequently added to the refocusing lobe of the slice-selection gradient pulse in a slice following CSPAMM acquisition. An opposite polarity z-encoding gradient is added to the orthogonal tag direction. The additional z-gradients encode the instantaneous through plane position of the slice. The vertical and horizontal tags are used to resolve in-plane motion, while the added z-gradients is used to resolve through-plane motion. Postprocessing automatically decodes the acquired data and tracks the three-dimensional displacement of every material point within the image plane for each cine frame. Experiments include both a phantom and in vivo human validation. These studies demonstrate that the simultaneous extraction of both in-plane and through-plane displacements and pathlines from tagged images is achievable. This capability should open up new avenues for the automatic quantification of cardiac motion and strain for scientific and clinical purposes.

Three-dimensional ordered-subset expectation maximization iterative protocol for evaluation of left ventricular volumes and function by quantitative gated SPECT: a dynamic phantom study.

The purposes of this study were to characterize the performance of a 3-dimensional (3D) ordered-subset expectation maximization (OSEM) algorithm in the quantification of left ventricular (LV) function with (99m)Tc-labeled agent gated SPECT (G-SPECT), the QGS program, and a beating-heart phantom and to optimize the reconstruction parameters for clinical applications. METHODS: A G-SPECT image of a dynamic heart phantom simulating the beating left ventricle was acquired. The exact volumes of the phantom were known and were as follows: end-diastolic volume (EDV) of 112 mL, end-systolic volume (ESV) of 37 mL, and stroke volume (SV) of 75 mL; these volumes produced an LV ejection fraction (LVEF) of 67%. Tomographic reconstructions were obtained after 10-20 iterations (I) with 4, 8, and 16 subsets (S) at full width at half maximum (FWHM) gaussian postprocessing filter cutoff values of 8-15 mm. The QGS program was used for quantitative measurements. RESULTS: Measured values ranged from 72 to 92 mL for EDV, from 18 to 32 mL for ESV, and from 54 to 63 mL for SV, and the calculated LVEF ranged from 65% to 76%. Overall, the combination of 10 I, 8 S, and a cutoff filter value of 10 mm produced the most accurate results. The plot of the measures with respect to the expectation maximization-equivalent iterations (I x S product) revealed a bell-shaped curve for the LV volumes and a reverse distribution for the LVEF, with the best results in the intermediate range. In particular, FWHM cutoff values exceeding 10 mm affected the estimation of the LV volumes. CONCLUSION: The QGS program is able to correctly calculate the LVEF when used in association with an optimized 3D OSEM algorithm (8 S, 10 I, and FWHM of 10 mm) but underestimates the LV volumes. However, various combinations of technical parameters, including a limited range of I and S (80-160 expectation maximization-equivalent iterations) and low cutoff values (< or =10 mm) for the gaussian postprocessing filter, produced results with similar accuracies and without clinically relevant differences in the LV volumes and the estimated LVEF.

Cross validation of experts versus registration methods for target localization in deep brain stimulation.

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.