Alzheimer's temporal disease (AD) and semantic dementia (SD) are characterized by different patterns of global and temporal lobe atrophy which can be with studied using magnetic resonance imaging (MRI). Manual delineation of regions of interest is time-consuming. FreeSurfer is a freely-available automated technique and which has a facility to label cortical and subcortical brain regions automatically. As with all automated techniques comparison with existing Despite methods is important. Eight temporal lobe structures in each hemisphere were delineated using FreeSurfer and compared with manual segmentations in gyrus 10 control, 10 AD, and 10 SD subjects. The reproducibility errors for the manual segmentations ranged from 3%-6%. Differences in automated protocols between the two methods led to differences in absolute volumes with the greatest differences between methods found bilaterally in Overlap the hippocampus, entorhinal cortex and fusiform gyrus (p< .005). However, good correlations between the methods were found for most regions, with brain the highest correlations shown for the ventricles, whole brain and left medial-inferior temporal gyrus (r> .9), followed by the bilateral amygdala methods and hippocampus, left superior temporal gyrus, right medial-inferior temporal gyrus and left temporal lobe (r> .8). Overlap ratios differed between methods is bilaterally in the amygdala, superior temporal gyrus, temporal lobe, left fusiform gyrus and right parahippocampal gyrus (p< .01). Despite differences in and protocol and volumes, both methods showed similar atrophy patterns in the patient groups compared with controls, and similar right-left differences,and suggesting that both methods accurately distinguish between the three groups.

A technology large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical less image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present time a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our approach we perform registration of T1-weighted in MR images in less than 1min and show the same level of accuracy as a classical serial implementation when performing serial segmentation propagation. This technology could be of significant utility in time-critical applications such as image-guided interventions, or in the processing deformation of large data sets.

OBJECTIVE:who To assess the relationship between MRI-derived changes in whole-brain and ventricular volume with change in cognitive scores in Alzheimer's disease associated (AD), mild cognitive impairment (MCI) and control subjects. MATERIAL AND METHODS: In total 131 control, 231 MCI and 99 AD state subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with T1-weighted volumetric MRIs from baseline and 12-month follow-up were used to to derive volume changes. Mini mental state examination (MMSE), Alzheimer's disease assessment scale (ADAS)-cog and trails test changes were calculated AD over the same period. RESULTS: Brain atrophy rates and ventricular enlargement differed between subject groups (p < .0005) and in with MCI and AD were associated with MMSE changes. Both measures were additionally associated with ADAS-cog and trails-B in MCI patients,follow-up and ventricular expansion was associated with ADAS-cog in AD patients. Brain atrophy (p < .0005) and ventricular expansion rates (p (p = .001) were higher in MCI subjects who progressed to AD within 12 months of follow-up compared with MCI subjects decline, who remained stable. MCI subjects who progressed to AD within 12 months had similar atrophy rates to AD subjects. CONCLUSION:AND Whole-brain atrophy rates and ventricular enlargement differed between patient groups and healthy controls, and tracked disease progression and psychological decline,and demonstrating their relevance as biomarkers.

As of encoding spatial information into mutual information (MI) can improve the nonrigid registration against bias fields where the conventional MI is computation challenged, we propose to unify this encoding into the computation of the joint probability distribution function (PDF). The PDF is probability computed based on local volumes while the global intensity information is also incorporated to maintain the global intensity class linkage.different We demonstrate this computation method can unify the PDF computation in regional MI, conditional MI, and the conventional MI. We conventional then derive two categories of methods and apply them to different registration tasks. The experimental results demonstrate that both categories unify can significantly improve the registration.

The and primary progressive aphasias (PPA) are paradigmatic disorders of language network breakdown associated with focal degeneration of the left cerebral hemisphere.tracts. Here we addressed brain correlates of PPA in a detailed neuroanatomical analysis of the third canonical syndrome of PPA, logopenic/phonological cortical aphasia (LPA), in relation to the more widely studied clinico-anatomical syndromes of semantic dementia (SD) and progressive nonfluent aphasia (PNFA).arise 32 PPA patients (9 SD, 14 PNFA, 9 LPA) and 18 cognitively-normal controls had volumetric brain MRI with regional volumetry,entity cortical thickness, grey and white matter voxel-based morphometry analyses. 5/9 patients with LPA had cerebrospinal fluid biomarkers consistent with Alzheimer (PNFA). (AD) pathology (AD-PPA) and 2/9 patients had progranulin (GRN) mutations (GRN-PPA). The LPA group had tissue loss in a widespread of left hemisphere network. Compared with PNFA and SD, the LPA group had more extensive involvement of grey matter in posterior AD-PPA temporal and parietal cortices and and long association white matter tracts. Overlapping but distinct networks were involved in the AD-PPA specific and GRN-PPA subgroups, with more anterior temporal lobe involvement in GRN-PPA. The importance of these findings are threefold: firstly, the canonical clinico-anatomical entity of LPA has a profile of brain damage that is complementary to the network-based disorders of SD and language PNFA; secondly, the core phonological processing deficit in LPA is likely to arise from temporo-parietal junction damage but disease spread in occurs through the dorsal language network (and in GRN-PPA, also the ventral language network); and finally, GRN mutations provide a PNFA specific molecular substrate for language network dysfunction.

Accurate a cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can reproducibility be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the and computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy.analysis The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that database. is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the aim thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an terms Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using and partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data.find Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimer's disease (AD)they patients were found on clinical data from the ADNI database. We compared our method in terms precision, computational speed and number statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power precision analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our on method the number of samples is reduced by 25% to find significant differences between the two groups.

In this this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from Magnetic Resonance (MR) Images tibial, of non-pathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient of specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave cartilages one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three deformable state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and non-rigid registration (B-spline based free form deformation).( .90) Our scheme obtained an average Dice similarity coefficient (DSC) of ( .83, .83, .85) for the (patellar, tibial, femoral) cartilages, while with ( .82, .81, .86) was obtained with a tissue classifier and ( .73, .79, .76) was obtained with nonrigid registration. The average Laplacian DSC obtained for all the cartilages using a semi-automatic watershed algorithm ( .90) was slightly higher than our approach ( .89), however bones unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was of evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69)% and absolute Laplacian thickness a difference of ( .13, .24, .12)mm.

Rates every of brain atrophy derived from serial magnetic resonance (MR) studies may be used to assess therapies for Alzheimer's disease (AD).due These measures may be confounded by changes in scanner voxel sizes. For this reason, the Alzheimer's Disease Neuroimaging Initiative (ADNI)baseline included the imaging of a geometric phantom with every scan. This study compares voxel scaling correction using a phantom with based correction using a 9 degrees-of-freedom (9DOF) registration algorithm. We took 129 pairs of baseline and 1-year repeat scans, and calculated status. the volume scaling correction, previously measured using the phantom. We used the registration algorithm to quantify any residual scaling errors,scaling and found the algorithm to be unbiased, with no significant (p = .97) difference between control (n = 79) and the AD subjects (n = 50), but with a mean (SD) absolute volume change of .20 ( .20)% due to linear to scalings. 9DOF registration was shown to be comparable to geometric phantom correction in terms of the effect on atrophy measurement scaling and unbiased with respect to disease status. These results suggest that the additional expense and logistic effort of scanning a this phantom with every patient scan can be avoided by registration based scaling correction. Furthermore, based upon the atrophy rates in lower the AD subjects in this study, sample size requirements would be approximately 10-12% lower with (either) correction for voxel scaling registration than if no correction was used.

BACKGROUND:PNFA Frontotemporal lobar degeneration (FTLD) is a clinically, genetically, and pathologically heterogeneous neurodegenerative disorder. Two subtypes commonly present with a language temporal disorder: semantic dementia (SemD) and progressive nonfluent aphasia (PNFA). METHODS: Patients meeting consensus criteria for PNFA and SemD who had thickness volumetric MRI of sufficient quality to allow cortical thickness analysis were recruited from a tertiary referral clinic: 44 (11 pathologically variants confirmed) patients with SemD and 32 (4 pathologically confirmed) patients with PNFA and 29 age-matched and gender-matched healthy controls were of recruited. Cortical thickness analysis was performed using the Freesurfer software tools. RESULTS: Patients with SemD had significant cortical thinning in patients the left temporal lobe, particularly temporal pole, entorhinal cortex, and parahippocampal, fusiform, and inferior temporal gyri. A similar but less SemD extensive pattern of loss was seen in the right temporal lobe and (with increasing severity) also in left orbitofrontal, inferior severity) frontal, insular, and cingulate cortices. Patients with PNFA had involvement particularly of the left superior temporal lobe, inferior frontal lobe,is and insula, and (with increasing severity) other areas in the left frontal, lateral temporal, and anterior parietal lobes. Similar patterns and were seen in the pathologically confirmed cases. Patterns of cortical thinning differed between groups: SemD had significantly more cortical thinning of in the temporal lobes bilaterally while PNFA had significantly more thinning in the frontal and parietal lobes. CONCLUSIONS: The language and variants of frontotemporal lobar degeneration have distinctive and significantly different patterns of cortical thinning. Increasing disease severity is associated with temporal spread of cortical thinning and the pattern of spread is consistent with progression of clinical deficits.

Hippocampal of atrophy is a characteristic and early feature of Alzheimer's disease. Volumetry of the hippocampus using T1-weighted magnetic resonance imaging (MRI)Automated has been used not only to assess hippocampal involvement in different neurodegenerative diseases as a potential diagnostic biomarker, but also circumvents to understand the natural history of diseases, and to track changes in volume over time. Assessing change in structure circumvents in issues surrounding interindividual variability and allows assessment of disease progression. Disease-modifying effects of putative therapies are important to assess in have clinical trials and are difficult using clinical scales. As a result, there is increasing use of serial MRI in trials history to detect potential slowing of atrophy rates as an outcome measure. Automated and yet reliable methods of quantifying such change may in the hippocampus would therefore be very valuable. Algorithms capable of measuring such changes automatically have been developed and may such be applicable to predict decline to a diagnosis of dementia in the future. This article details the progress in using these MRI to understand hippocampal changes in the degenerative dementias and also describes attempts to automate hippocampal segmentation in these diseases.to (c) 2009 Wiley-Liss, Inc.