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The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect., Three partial‐volume‐based and three probabilistic‐based tissue segmentation approaches were employed to assess thalamic gray matter integrity in patients at different stages of psychosis (early and chronic). This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.

Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines., Highlights • An image-by-image noise-driven methodology for phase correction is proposed. • The approach limits the occurrence of anomalies in the phase-corrected images. • Adaptive phase correction allows the unbiasing of the diffusion signal and indices.

Prematurity disrupts brain maturation by exposing the developing brain to different noxious stimuli present in the neonatal intensive care unit (NICU) and depriving it from meaningful sensory inputs during a critical period of brain development, leading to later neurodevelopmental impairments. Musicotherapy in the NICU environment has been proposed to promote sensory stimulation, relevant for activity-dependent brain plasticity, but its impact on brain structural maturation is unknown. Neuroimaging studies have demonstrated that music listening triggers neural substrates implied in socio-emotional processing and, thus, it might influence networks formed early in development and known to be affected by prematurity. Using multi-modal MRI, we aimed to evaluate the impact of a specially composed music intervention during NICU stay on preterm infant's brain structure maturation. 30 preterm newborns (out of which 15 were exposed to music during NICU stay and 15 without music intervention) and 15 full-term newborns underwent an MRI examination at term-equivalent age, comprising diffusion tensor imaging (DTI), used to evaluate white matter maturation using both region-of-interest and seed-based tractography approaches, as well as a T2-weighted image, used to perform amygdala volumetric analysis. Overall, WM microstructural maturity measured through DTI metrics was reduced in preterm infants receiving the standard-of-care in comparison to full-term newborns, whereas preterm infants exposed to the music intervention demonstrated significantly improved white matter maturation in acoustic radiations, external capsule/ claustrum/extreme capsule and uncinate fasciculus, as well as larger amygdala volumes, in comparison to preterm infants with standard-of-care. These results suggest a structural maturational effect of the proposed music intervention on premature infants' auditory and emotional processing neural pathways during a key period of brain development.

Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.

BACKGROUND AND OBJECTIVE: In patients treated with hip arthroplasty, the muscular condition and presence of inflammatory reactions are assessed using magnetic resonance imaging (MRI). As MRI lacks contrast for bony structures, computed tomography (CT) is preferred for clinical evaluation of bone tissue and orthopaedic surgical planning. Combining the complementary information of MRI and CT could improve current clinical practice for diagnosis, monitoring and treatment planning. In particular, the different contrast of these modalities could help better quantify the presence of fatty infiltration to characterise muscular condition and assess implant failure. In this work, we combine CT and MRI for joint bone and muscle segmentation and we propose a novel Intramuscular Fat Fraction estimation method for the quantification of muscle atrophy. METHODS: Our multimodal framework is able to segment healthy and pathological musculoskeletal structures as well as implants, and develops into three steps. First, input images are pre-processed to improve the low quality of clinically acquired images and to reduce the noise associated with metal artefact. Subsequently, CT and MRI are non-linearly aligned using a novel approach which imposes rigidity constraints on bony structures to ensure realistic deformation. Finally, taking advantage of a multimodal atlas we created for this task, a multi-atlas based segmentation delineates pelvic bones, abductor muscles and implants on both modalities jointly. From the obtained segmentation, a multimodal estimation of the Intramuscular Fat Fraction can be automatically derived. RESULTS: Evaluation of the segmentation in a leave-one-out cross-validation study on 22 hip sides resulted in an average Dice score of 0.90 for skeletal and 0.84 for muscular structures. Our multimodal Intramuscular Fat Fraction was benchmarked on 27 different cases against a standard radiological score, showing stronger association than a single modality approach in a one-way ANOVA F-test analysis. CONCLUSIONS: The proposed framework represents a promising tool to support image analysis in hip arthroplasty, being robust to the presence of implants and associated image artefacts. By allowing for the automated extraction of a muscle atrophy imaging biomarker, it could quantitatively inform the decision-making process about patient's management. ispartof: Computer Methods and Programs in Biomedicine vol:183 ispartof: location:Ireland status: published

Background A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia. Aim We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index. Materials and methods We computed brain health index in N=288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N=157; lupus, N=51) and against measures of brain atrophy in healthy participants (N=80) using multiple regression. We evaluated associations with Addenbrooke's Cognitive Exam Revised in patients and with reaction time in healthy participants. Results The brain health index (standard beta=0.20-0.59, P0.05) and small vessel disease score (standard beta=0.02-0.27, P>0.05) alone in both patient groups. Further, the brain health index (standard beta=0.57-0.59, P0.05) in healthy participants. Conclusions The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease.

Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p

International audience; Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.

Objective We aimed to develop a vendor-neutral and interaction-free quality assurance protocol for measuring geometric accuracy of head and brain magnetic resonance (MR) images. We investigated the usability of nonrigid image registration in the analysis and looked for the optimal registration parameters. Materials and methods We constructed a 3D-printed phantom and imaged it with 12 MR scanners using clinical sequences. We registered a geometric-ground-truth computed tomography (CT) acquisition to the MR images using an open-source nonrigid-registration-toolbox with varying parameters. We applied the transforms to a set of control points in the CT image and compared their locations to the corresponding visually verified reference points in the MR images. Results With optimized registration parameters, the mean difference (and standard deviation) of control point locations when compared to the reference method was (0.17 ± 0.02) mm for the 12 studied scanners. The maximum displacements varied from 0.50 to 1.35 mm or 0.89 to 2.30 mm, with vendors’ distortion correction on or off, respectively. Discussion Using nonrigid CT–MR registration can provide a robust and relatively test-object-agnostic method for estimating the intra- and inter-scanner variations of the geometric distortions.

Several studies have shown that electroconvulsive therapy (ECT) results in increased hippocampal volume. It is likely that a multitude of mechanisms including neurogenesis, gliogenesis, synaptogenesis, angiogenesis, and vasculogenesis contribute to this volume increase. Neurotrophins, like vascular endothelial growth factor (VEGF) and brain-derived neurotrophic factor (BDNF) seem to play a crucial mediating role in several of these mechanisms. We hypothesized that two regulatory SNPs in the VEGF and BDNF gene influence the changes in hippocampal volume following ECT. We combined genotyping and brain MRI assessment in a sample of older adults suffering from major depressive disorder to test this hypothesis. Our results show an effect of rs699947 (in the promotor region of VEGF) on hippocampal volume changes following ECT. However, we did not find a clear effect of rs6265 (in BDNF). To the best of our knowledge, this is the first study investigating possible genetic mechanisms involved in hippocampal volume change during ECT treatment.

Quantitative whole-body PET/MR imaging is challenged by the lack of accurate and robust strategies for attenuation correction. In this work, a new pseudo-CT generation approach, referred to as sorted atlas pseudo-CT (SAP), is proposed for accurate extraction of bones and estimation of lung attenuation properties. This approach improves the Gaussian process regression (GPR) kernel proposed by Hofmann et al, which relies on the information provided by a co-registered atlas (CT and MRI) using a GPR kernel to predict the distribution of attenuation coefficients. Our approach uses two separate GPR kernels for lung and non-lung tissues. For non-lung tissues, the co-registered atlas dataset was sorted on the basis of local normalized cross-correlation similarity to the target MR image to select the most similar image in the atlas for each voxel. For lung tissue, the lung volume was incorporated in the GPR kernel taking advantage of the correlation between lung volume and corresponding attenuation properties to predict the attenuation coefficients of the lung. In the presence of pathological tissues in the lungs, the lesions are segmented on PET images corrected for attenuation using MRI-derived three-class attenuation map followed by assignment of soft-tissue attenuation coefficient. The proposed algorithm was compared to other techniques reported in the literature including Hofmann's approach and the three-class attenuation correction technique implemented on the Philips Ingenuity TF PET/MR where CT-based attenuation correction served as reference. Fourteen patients with head and neck cancer undergoing PET/CT and PET/MR examinations were used for quantitative analysis. SUV measurements were performed on 12 normal uptake regions as well as high uptake malignant regions. Moreover, a number of similarity measures were used to evaluate the accuracy of extracted bones. The Dice similarity metric revealed that the extracted bone improved from 0.58 +/- 0.09 to 0.65 +/- 0.07 when using the SAP technique compared to Hofmann's approach. This enabled to reduce the SUVmean bias in bony structures for the SAP approach to -1.7 +/- 4.8% as compared to -7.3 +/- 6.0% and -27.4 +/- 10.1% when using Hofmann's approach and the three-class attenuation map, respectively. Likewise, the three-class attenuation map produces a relative absolute error of 21.7 +/- 11.8% in the lungs. This was reduced on average to 15.8 +/- 8.6% and 8.0 +/- 3.8% when using Hofmann's and SAP techniques, respectively. The SAP technique resulted in better overall PET quantification accuracy than both Hofmann's and the three-class approaches owing to the more accurate extraction of bones and better prediction of lung attenuation coefficients. Further improvement of the technique and reduction of the computational time are still required. (C) 2016 Elsevier B.V. All rights reserved.

Background:\ud \ud A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia.\ud Aim:\ud \ud We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index.\ud Materials and methods:\ud \ud We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke’s Cognitive Exam Revised in patients and with reaction time in healthy participants.\ud Results:\ud \ud The brain health index (standard beta = 0.20–0.59, P 0.05) and small vessel disease score (standard beta = 0.02–0.27, P > 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57–0.59, P 0.05) in healthy participants.\ud Conclusions:\ud \ud The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease.

Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.

Post-mortem (PM) magnetic resonance imaging (MRI) is increasingly used as an alternative to conventional autopsy in babies dying from neonatal encephalopathy. However, the confounding effect of post-mortem changes on the detection of ante-mortem ischemic injury is unclear. We examined whether quantitative MR measurements can accurately distinguish ante-mortem ischemic brain injury from artifacts using post-mortem MRI. Methods We compared PM brain MRI (1.5 T Siemens, Avanto) in 7 infants who died with neonatal encephalopathy (NE) of presumed hypoxic-ischemic origin with 7 newborn infants who had sudden unexplained neonatal death (SUND controls) without evidence of hypoxic-ischemic brain injury at autopsy. We measured apparent diffusion coefficients (ADCs), T1-weighted signal intensity ratios (SIRs) compared to vitreous humor and T2 relaxation times from 19 predefined brain areas typically involved in neonatal encephalopathy. Results There were no differences in mean ADC values, SIRs on T1-weighted images or T2 relaxation times in any of the 19 predefined brain areas between NE and SUND infants. All MRI images showed loss of cortical gray/white matter differentiation, loss of the normal high signal intensity (SI) in the posterior limb of the internal capsule on T1-weighted images, and high white matter SI on T2-weighted images. Conclusion Normal post-mortem changes may be easily mistaken for ante-mortem ischemic injury, and current PM MRI quantitative assessment cannot reliably distinguish these. These findings may have important implications for appropriate interpretation of PM imaging findings, especially in medico-legal practice.

A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40. weeks corrected age, (ii) coronal scans acquired at 30. weeks corrected age and (iii) coronal scans acquired at 40. weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumour classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbour (t-SNE) approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest (STF) for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Palmar and finger dermatoglyphics are formed between the 10th and the 17th weeks of gestation and their morphology can be influenced by genetic or environmental factors, interfering with normal intrauterine development. As both the skin and the brain develop from the same embryonal ectoderm, dermatoglyphic alterations may be informative for early abnormal neurodevelopmental processes in the brain. We investigated whether dermatoglyphic alterations are related to structural brain abnormalities in bipolar disorder and to what extent they are of a genetic and of an environmental origin. Dermatoglyphics and volumetric data from structural MRI were obtained in 53 twin pairs concordant or discordant for bipolar disorder and 51 healthy matched control twin pairs. Structural equation modeling was used. Bipolar disorder was significantly positively associated with palmar a-b ridge count (ABRC), indicating higher ABRC in bipolar patients (rph=.17 (Cl.04-.30)). Common genes appear to be involved because the genetic correlation with ABRC was significant (rph-A=.21 (CI .05-.36). Irrespective of disease, ABRC showed a genetically mediated association with brain volume, indicated by a significant genetic correlation rph-A of respectively .36 (CI .52 to .22) for total brain, -.34 (CI -.51 to -.16) total cortical volume, -.27 (CI -.43 to -.08) cortical gray matter and -.23 (CI -.41 to -.04) cortical white matter. In conclusion, a genetically determined abnormal development of the foetal ectoderm between the 10th and 15th week of gestation appears related to smaller brain volumes in (subjects at risk for) bipolar disorder. (C) 2014 Elsevier B.V. and ECNP. All rights reserved.

Highlights • The first application of voxel lesion symptom mapping (VLSM) in rodents. • A comparison of VLSM with tensor based morphometry (TBM) methods in a stroke model. • Comparison of automated techniques with manual measurements and model power calculations. • Analysis of both local and non-local lesion effects. • Correlation of structural change with behaviour., Neurological damage, due to conditions such as stroke, results in a complex pattern of structural changes and significant behavioural dysfunctions; the automated analysis of magnetic resonance imaging (MRI) and discovery of structural–behavioural correlates associated with these disorders remains challenging. Voxel lesion symptom mapping (VLSM) has been used to associate behaviour with lesion location in MRI, but this analysis requires the definition of lesion masks on each subject and does not exploit the rich structural information in the images. Tensor-based morphometry (TBM) has been used to perform voxel-wise structural analyses over the entire brain; however, a combination of lesion hyper-intensities and subtle structural remodelling away from the lesion might confound the interpretation of TBM. In this study, we compared and contrasted these techniques in a rodent model of stroke (n = 58) to assess the efficacy of these techniques in a challenging pre-clinical application. The results from the automated techniques were compared using manually derived region-of-interest measures of the lesion, cortex, striatum, ventricle and hippocampus, and considered against model power calculations. The automated TBM techniques successfully detect both lesion and non-lesion effects, consistent with manual measurements. These techniques do not require manual segmentation to the same extent as VLSM and should be considered part of the toolkit for the unbiased analysis of pre-clinical imaging-based studies.

Fabry disease is a rare lysosomal storage disorder leading to cellular accumulation of globotriaosylceramide, especially in blood vessels. It is associated with severe early onset cerebrovascular disease and kidney and heart failure. The purpose of this study was to reveal possible disturbances in white matter integrity in Fabry disease patients using voxelwise diffusion-tensor imaging (DTI) analysis.Twelve Fabry disease patients, along with 13 healthy controls, underwent DTI and structural MRI. Voxel-based analysis of the DTI data was performed to assess possible differences in DTI parameters between Fabry disease patients and healthy controls. A selective region of interest analysis was performed for healthy volunteers and Fabry disease patients having a mild burden of T2-hyperintense lesions. We also measured normalised brain tissue volumes and performed a voxel-based volume analysis for grey matter.Voxel-based analysis of DTI data showed areas of significantly reduced fractional anisotropy and increased mean diffusivity in patients with Fabry disease. Eight patients had a mild burden of white matter lesions on their T2 scans. Region of interest analysis on areas showing reduced fractional anisotropy in voxelwise analysis also revealed reduced fractional anisotropy values in this patient group compared to eight healthy volunteers. The brain volume analyses did not reveal significant differences between the Fabry disease patients and the controls.These findings suggest a microstructural damage in brain white matter of Fabry disease patients, which can be revealed before excessive white matter lesions load is visible on conventional MR scans.

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.

Context: The nosologic dichotomy between schizophrenia and bipolar disorder (BD) as formulated by Kraepelin is currently being questioned, stimulated by the finding that schizophrenia and BD partly share a common genetic origin. Although both disorders are characterized by changes in brain structure, family studies suggest more segregating than overlapping neuroanatomical abnormalities in both disorders.Objectives: To investigate whether patients with schizophrenia and patients with BD display overlapping abnormalities in brain volumes and cortical thickness and whether these are caused by shared genetic or environmental influences.Design: Magnetic resonance imaging findings of monozygotic (MZ) and dizygotic (DZ) twin pairs discordant for schizophrenia, twin pairs concordant and discordant for BD, and healthy twin pairs were compared using structural equation modeling.Setting: The Netherlands Twin Register and University Medical Center Utrecht.Participants: A total of 310 individuals from 158 (152 complete and 6 incomplete) twin pairs were included: 26 pairs discordant for schizophrenia (13 MZ and 13 DZ), 49 pairs with BD (9 MZ and 4 DZ concordant; 14 MZ and 22 DZ discordant), and 83 healthy twin pairs (44 MZ and 39 DZ).Main Outcome Measures: Estimates of additive genetic and unique environmental associations between schizophrenia and BD with overlapping and nonoverlapping volumes and cortical thickness.Results: Higher genetic liabilities for schizophrenia and BD were associated with smaller white matter volume, thinner right (and left) parahippocampus, thinner right orbitofrontal cortex, and thicker temporoparietal and left superior motor cortices; higher environmental liabilities were associated with thinner right medial occipital cortex. Genetic liability for schizophrenia was associated with thicker right parietal cortex; for BD, with larger intracranial volume.Conclusions: Brain structures reflect overlapping and segregating genetic liabilities for schizophrenia and BD. The overlapping smaller white matter volume and common areas of thinner cortex suggest that both disorders share genetic (neurodevelopmental) roots.

A number of MRI studies have shown focal or diffuse cortical gray matter (GM) abnormalities in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are unclear regarding the cortical regions involved in this condition, perhaps due to the heterogeneity of the PTSD population included or to the differences in the methodology used for the quantification of the brain structures. In this study, we assessed differences in cortical GM volumes between a selected group of 25 drug-naive PTSD patients with history of adulthood trauma and 25 matched non-traumatized controls. Analyses were performed by using two different automated methods: the structural image evaluation using normalization of atrophy (SIENAX) and the voxel-based morphometry (VBM), as we trusted that if these complementary techniques provided similar results, it would increase the confidence in the validity of the assessment. Results of SIENAX and VBM analyses similarly showed that cortical GM volume decreases in PTSD patients when compared to healthy controls, particularly in the frontal and occipital lobes. These decreases seem to correlate with clinical measures. Our findings suggest that in drug-naïve PTSD patients with a history of adulthood trauma, brain structural damage is diffuse, with a particular prevalence for the frontal and occipital lobes, and is clinically relevant.

Summary Purpose: The cognitive consequences of hippocampal malrotation (HIMAL) were investigated in a matched control study of children with epilepsy. Methods: Seven children with HIMAL were compared on a range of memory and attention tasks with 21 control children with epilepsy without temporal role pathology and 7 children with epilepsy and magnetic resonance imaging (MRI)–documented hippocampal sclerosis. In addition, in a statistical morphometric analysis, MRI studies from four children with HIMAL were compared to similar images of 20 age-matched typically developing control children. Results: Although the task battery was sensitive to the memory deficit of the children with hippocampal sclerosis, it did not reveal memory impairment in the patients with HIMAL. In contrast, the patients with HIMAL were impaired on the attentionally more demanding dual tasks, compared to both the control and the hippocampal sclerosis group. The structural MRI analysis revealed morphometric abnormalities in the tail of the affected hippocampus, the adjacent neocortex, and the ipsilateral medial thalamus. The basal forebrain was bilaterally affected. Abnormalities in remote cortex were found in the ipsilateral temporal lobe, the contralateral anterior cingulate gyrus, and bilateral in the dorsolateral and lateral-orbitofrontal prefrontal cortex. Discussion: Because the prefrontal cortical regions have been shown to be active during dual-task performance, the MRI results converge with the neuropsychological findings of impairment on these tasks. We conclude that HIMAL had no direct memory repercussions, but was secondary to subtle but widespread neurologic abnormalities that also affected morphology and functioning of the prefrontal cortex.

Context: Structural neuroimaging studies suggest the presence of subtle abnormalities in the brains of patients with bipolar disorder. The influence of genetic and/or environmental factors on these brain abnormalities is unknown. Objective: To investigate the contribution of genetic and environmental factorsonbrain volume in bipolar disorder. Design: Magnetic resonance imaging (1.5 T) brain scans of monozygotic (MZ) or dizygotic (DZ) twins concordant and discordant for bipolar disorder were compared with healthy twin pairs. Setting: Subjects were recruited from the population, the Netherlands Twin Register, and the twin pair cohort at the University Medical Center Utrecht, Utrecht, The Netherlands. Participants: A total of 234 subjects including 50 affected twin pairs (9 MZ concordant; 15 MZ discordant; 4 DZ concordant; 22 DZ discordant) and 67 healthy twin pairs (39 MZ and 28 DZ) were included. Main Outcome Measures: Volumes of the intracranium, cerebrum, cerebellum, lateral and third ventricle, and gray and white matter from the cerebrum and frontal, parietal, temporal, and occipital lobes, both with and without correction for lithium use. To estimate the influence of additive genetic, common, and unique environmental factors, structural equation modeling was applied. Results: Bipolar disorder was associated with a decrease in total cortical volume. Decreases in white matter were related to the genetic risk of developing bipolar disorder (bivariate heritability, 77%; 95% confidence interval, 38% to 100%). Significant environmental correlations were found for cortical gray matter. These relationships all became more pronounced when data were corrected for lithium use. Conclusions: Focusing on genes controlling white matter integrity may be a fruitful strategy in the quest to discover genes implicated in bipolar disorder. Elucidating the mechanism by which lithium attenuates brain matter loss may lead to the development of neuroprotective drugs.

Context: Structural neuroimaging studies suggest the presence of subtle abnormalities in the brains of patients with bipolar disorder. The influence of genetic and/or environmental factors on these brain abnormalities is unknown. Objective: To investigate the contribution of genetic and environmental factors on brain volume in bipolar disorder. Design: Magnetic resonance imaging (1.5 T) brain scans of monozygotic (MZ) or dizygotic (DZ) twins concordant and discordant for bipolar disorder were compared with healthy twin pairs. Setting: Subjects were recruited from the population, the Netherlands Twin Register, and the twin pair cohort at the University Medical Center Utrecht, Utrecht, The Netherlands. Participants: A total of 234 subjects including 50 affected twin pairs (9 MZ concordant; 15 MZ discordant; 4 DZ concordant; 22 DZ discordant) and 67 healthy twin pairs (39 MZ and 28 DZ) were included. Main Outcome Measures: Volumes of the intracranium, cerebrum, cerebellum, lateral and third ventricle, and gray and white matter from the cerebrum and frontal, parietal, temporal, and occipital lobes, both with and without correction for lithium use. To estimate the influence of additive genetic, common, and unique environmental factors, structural equation modeling was applied. Results: Bipolar disorder was associated with a decrease in total cortical volume. Decreases in white matter were related to the genetic risk of developing bipolar disorder (bivariate heritability, 77%; 95% confidence interval, 38% to 100%). Significant environmental correlations were found for cortical gray matter. These relationships all became more pronounced when data were corrected for lithium use. Conclusions: Focusing on genes controlling white matter integrity may be a fruitful strategy in the quest to discover genes implicated in bipolar disorder. Elucidating the mechanism by which lithium attenuates brain matter loss may lead to the development of neuroprotective drugs. © 2009 American Medical Association. All rights reserved.

Accurate quantification and timing of regional myocardial function allows early identification of dysfunction, and therefore becomes increasingly important for clinical risk assessment, patient management, and evaluation of therapeutic efficacy. For this purpose, the application of tissue Doppler echocardiography has rapidly increased. However, echocardiography has some major inherent limitations. Cardiovascular magnetic resonance imaging with tissue tagging provides highly reproducible data on myocardial function, not only in longitudinal and radial directions, but also in the circumferential direction. Because of the development of faster imaging protocols, improved temporal resolution, less time-consuming postprocessing procedures, and the potential of quantifying myocardial deformation in 3 dimensions at any point in the heart, this technique may serve as an alternative for tissue Doppler echocardiography and is now ready for more widespread clinical use. This review discusses the clinical use of cardiovascular magnetic resonance tissue tagging for quantitative assessment of regional myocardial function, thereby underlining the specific features and emerging role of this technique.

Objective: To study the influence of increased familial risk for attention-deficit/hyperactivity disorder (ADHD) on brain morphology. Method: Volumetric cerebral measures based on whole brain magnetic resonance imaging scans from 30 boys with ADHD, 30 of their unaffected siblings, and 30 matched controls were compared. Results: Both subjects with ADHD and their unaffected siblings displayed reductions in right prefrontal gray matter and left occipital gray and white matter of up to 9.1% (p

Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T(1)w, T(2)w, PDW), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and on eight real patient data sets are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multi spectrality in classifying brain tissues is discussed. (C) 2003 Elsevier B.V. All rights reserved.

Magnetic resonance imaging (MRI) can be used in vivo in combination with computational fluid dynamics (CFD) to derive velocity profiles in space and time and accordingly, pressure drop and wall shear stress distribution in natural or artificial vessel segments. These hemodynamic data are difficult or impossible to acquire directly in vivo. Therefore, research has been performed combining MRI and CFD for flow simulations in flow phantoms, such as bends or anastomoses, and even in human vessels such as the aorta, the carotid, and the abdominal bifurcation. There is, however, no unanimity concerning the use of MRI velocity measurements as input for the inflow boundary condition of a CFD simulation. In this study, different input possibilities for the inflow boundary conditions are compared. MRI measurements of steady and pulsatile flow were performed on a U bend phantom, representing the aorta geometry. PAMFLOW (ESI Software, Krimpen aan den Ussel, The Netherlands), an industrial CFD software package, was used to solve the Navier-Stokes equations for incompressible flow. Three main parameters were found to influence the choice of an inflow boundary condition type. First, the flow rate through a vessel should be exact, since it proves to be a determining factor for the accuracy of the velocity profile. The other decisive parameters are the physiology of the flow profile and the required computer processing unit time. Our comparative study indicates that the best way to handle an inflow boundary condition is to use the velocities measured by MRI at the inflow plane as being fixed velocities. However, before using these MRI velocity data, they first should be corrected for the partial volume effect by filtering and second scaled in order to obtain the correct flow rate. This implies that a reliable flow rate measurement absolutely is needed for CFD calculations based on MRI velocity measurements.

Spinocerebellar ataxia type 2 (SCA2) is the second most frequent autosomal dominant inherited ataxia worldwide. We investigated the capability of magnetic resonance imaging (MRI) to track in vivo progression of brain atrophy in SCA2 by examining twice 10 SCA2 patients (mean interval 3.6 years) and 16 age- and gender-matched healthy controls (mean interval 3.3 years) on the same 1.5 T MRI scanner. We used T1-weighted images and tensor-based morphometry (TBM) to investigate volume changes and the Inherited Ataxia Clinical Rating Scale to assess the clinical deficit. With respect to controls, SCA2 patients showed significant higher atrophy rates in the midbrain, including substantia nigra, basis pontis, middle cerebellar peduncles and posterior medulla corresponding to the gracilis and cuneatus tracts and nuclei, cerebellar white matter (WM) and cortical gray matter (GM) in the inferior portions of the cerebellar hemisphers. No differences in WM or GM volume loss were observed in the supratentorial compartment. TBM findings did not correlate with modifications of the neurological deficit. In conclusion, MRI volumetry using TBM is capable of demonstrating the progression of pontocerebellar atrophy in SCA2, supporting a possible role of MRI as biomarker in future trials.

In this paper, we develop data driven registration algorithms, relying on pixel similarity metrics, that enable an accurate (subpixel) rigid registration of dissimilar single or multimodal 2D/3D images. Gross dissimilarities are handled by considering similarity measures related to robust M-estimators. In particular, a novel (robust) similarity metric is proposed for comparing multimodal images. The proposed robust similarity metrics are compared to the most popular standard similarity metrics, on synthetic as well as on real-world image pairs showing gross dissimilarities. Three case studies are considered: the registration of single modal and multimodal 3D medical images, the matching of multispectral remotely sensed images, and the registration of intensity and range images. The proposed robust similarity measures compare favourably with the standard (non-robust) techniques. (C) 1994 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. Pattern Recognition

This paper describes a robust, fully automated algorithm to register intrasubject 3D single and multimodal images of the human brain. The proposed technique accounts for the major limitations of the existing voxel similarity-based methods: sensitivity of the registration to local minima of the similarity function and inability to cope with gross dissimilarities in the two images to be registered. Local minima are avoided by the implementation of a stochastic iterative optimization technique (fast simulated annealing). In addition, robust estimation is applied to reject outliers in case the images show significant differences (due to lesion evolution, incomplete acquisition, non-Gaussian noise, etc.). In order to evaluate the performance of this technique, 2D and 3D MR and SPECT human brain images were artificially rotated, translated, and corrupted by noise. A test object was acquired under different angles and positions for evaluating the accuracy of the registration. The approach has also been validated on real multiple sclerosis MR images of the same patient taken at different times. Furthermore, robust MR/SPECT image registration has permitted the representation of functional features for patients with partially complex seizures. The fast simulated annealing algorithm combined with robust estimation yields registration errors that are less than 1 degrees in rotation and less than 1 voxel in translation (image dimensions of 128(3)). It compares favorably with other standard voxel similarity-based approaches. (C) 1998 Academic Press. Neuroimage

Background Progressive heart failure due to remodeling is a major cause of morbidity and mortality following myocardial infarction. Conventional clinical imaging measures global volume changes, and currently there is no means of assessing regional myocardial dilatation in relation to ischemic burden. Here we use 3D co-registration of Cardiovascular Magnetic Resonance (CMR) images to assess the long-term effects of ischemia-reperfusion injury on left ventricular structure after acute ST-elevation myocardial infarction (STEMI). Methods Forty six patients (age range 33–77 years) underwent CMR imaging within 7 days following primary percutaneous coronary intervention (PPCI) for acute STEMI with follow-up at one year. Functional cine imaging and Late Gadolinium Enhancement (LGE) were segmented and co-registered. Local left ventricular wall dilatation was assessed by using intensity-based similarities to track the structural changes in the heart between baseline and follow-up. Results are expressed as means, standard errors and 95% confidence interval (CI) of the difference. Results Local left ventricular remodeling within infarcted myocardium was greater than in non-infarcted myocardium (1.6% ± 1.0 vs 0.3% ± 0.9, 95% CI: -2.4% – -0.2%, P = 0.02). One-way ANOVA revealed that transmural infarct thickness had a significant effect on the degree of local remodeling at one year (P 20% (4.8% ± 1.4 vs −0.15% ± 1.2, 95% CI: -8.9% – -0.9%, P = 0.017). Conclusions The severity of ischemic injury has a significant effect on local ventricular wall remodeling with only modest dilatation observed within non-ischemic myocardium. Limitation of chronic remodeling may therefore depend on therapies directed at modulating ischemia-reperfusion injury. CMR co-registration has potential for assessing dynamic changes in ventricular structure in relation to therapeutic interventions.

We examined the effect of the catechol-O-methyltransferase (COMT) Val158Met polymorphism (rs4680), on brain structure in a subset (N = 82) of general population members of the Northern Finland 1966 Birth Cohort, selected through a randomization procedure, aged 33–35. Optimised voxel-based morphometry was used to produce grey matter maps from each subject's high resolution T1 weighted brain magnetic resonance images, which were subsequently entered into a general linear model with COMT genotype as defined by Met allele loading, gender and genotype by gender interaction as independent variables. Additional analyses were carried out on grey matter volumes within the dorsal lateral pre-frontal cortex (DLPFC) to examine effects on overall DLPFC volume and also using the DLPFC as a mask for voxelwise analyses, as this is an area previously reported as associated with Met allele loading. We failed to find any statistically significant association with grey matter volume and Met allele loading in the COMT gene or interaction affects between COMT and gender in either the whole brain voxel-wise analysis or in the area of the DLPFC.

An unresolved issue in patients with diastolic dysfunction is that the estimation of myocardial stiffness cannot be decoupled from diastolic residual active tension (AT) because of the impaired ventricular relaxation during diastole. To address this problem, this paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastolic residual AT. Dynamic C(1)-continuous meshes are automatically built from the anatomy and deformation captured from dynamic MRI sequences. Diastolic deformation is simulated using a mechanical model that combines passive and active material properties. The problem of non-uniqueness of constitutive parameter estimation using the well known Guccione law is characterized by reformulation of this law. Using this reformulated form, and by constraining the constitutive parameters to be constant across time points during diastole, we separate the effects of passive constitutive properties and the residual AT during diastolic relaxation. Finally, the method is applied to two clinical cases and one control, demonstrating that increased residual AT during diastole provides a potential novel index for delineating healthy and pathological cases.

International audience; PURPOSE: Four-dimensional computed tomography (4D CT) can provide patient-specific motion information for radiotherapy planning and delivery. Motion estimation in 4D CT is challenging due to the reduced image quality and the presence of artifacts. We aim to improve the robustness of deformable registration applied to respiratory-correlated imaging of the lungs, by using a global problem formulation and pursuing a restrictive parametrization for the spatiotemporal deformation model. METHODS: A spatial transformation based on free-form deformations was extended to the temporal domain, by explicitly modeling the trajectory using a cyclic temporal model based on B-splines. A global registration criterion allowed to consider the entire image sequence simultaneously and enforce the temporal coherence of the deformation throughout the respiratory cycle. To ensure a parametrization capable of capturing the dynamics of respiratory motion, a prestudy was performed on the temporal dimension separately. The temporal parameters were tuned by fitting them to diaphragm motion data acquired for a large patient group. Suitable properties were retained and applied to spatiotemporal registration of 4D CT data. Registration results were validated using large sets of landmarks and compared to consecutive spatial registrations. To illustrate the benefit of the spatiotemporal approach, we also assessed the performance in the presence of motion-induced artifacts. RESULTS: Cubic B-splines gave better or similar fitting results as lower orders and were selected because of their inherently stronger regularization. The fitting and registration errors increased gradually with the temporal control point spacing, representing a trade-off between achievable accuracy and sensitivity to noise and artifacts. A piecewise smooth trajectory model, allowing for a discontinuous change of speed at end-inhale, was found most suitable to account for the sudden changes of motion at this breathing phase. The spatiotemporal modeling allowed a reduction of the number of parameters of 45%, while maintaining registration accuracy within 0.1 mm. The approach reduced the sensitivity to artifacts. CONCLUSIONS: Spatiotemporal registration can provide accurate motion estimation for 4D CT and improves the robustness to artifacts.

Purpose: To compare left ventricular (LV) function assessment using five different software tools on the same dual source computed tomography (DSCT) datasets with the results of MRI.Materials and methods: Twenty-six patients, undergoing cardiac contrast-enhanced DSCT were included (20 men, mean age 59 +/- 12 years). Reconstructions were made at every 10% of the RR-interval. Function analysis was performed with five different, commercially available workstations. In all software tools, semi-automatic LV function measurements were performed, with manual corrections if necessary. Within 0-22 days, all 26 patients were scanned on a 1.5T MRI-system. Bland-Altman analysis was performed to calculate limits of agreement between DSCT and MRI. Pearson's correlation coefficient was calculated to assess the correlation between the different DSCT software tools and MRI. Repeated measurements were performed to determine intraobserver and interobserver variability.Results: For all five DSCT workstations, mean LV functional parameters correlated well with measurements on MRI. Bland-Altman analysis of the comparison of DSCT and MRI showed acceptable limits of agreement. Best correlation and limits of agreement were obtained by DSCT software tools with software algorithms comparable to MRI software.Conclusion: The five different DSCT software tools we examined have interchangeable results of LV functional parameters compared to regularly analysed results by MRI.The best correlation and the narrowest limits of agreement were found when the same software algorithm was used for both DSCT and MRI examinations, therefore our advice for clinical practice is to always evaluate images with the same type of post-processing tools in follow-up. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Postmortem histological studies have demonstrated that myelination in human brain white matter (WM) continues throughout adolescence and well into adulthood. We used in vivo diffusion-weighted magnetic resonance imaging to test for age-related WM changes in 42 adolescents and 20 young adults. Tract-Based Spatial Statistics (TBSS) analysis of the adolescent data identified widespread age-related increases in fractional anisotropy (FA) that were most significant in clusters including the body of the corpus callosum and right superior corona radiata. These changes were driven by changes in perpendicular, rather than parallel, diffusivity. These WM clusters were used as seeds for probabilistic tractography, allowing us to identify the regions as belonging to callosal, corticospinal, and prefrontal tracts. We also performed voxel-based morphometry-style analysis of conventional T1-weighted images to test for age-related changes in grey matter (GM). We identified a cluster including right middle frontal and precentral gyri that showed an age-related decrease in GM density through adolescence and connected with the tracts showing age-related WM FA increases. The GM density decrease was highly significantly correlated with the WM FA increase in the connected cluster. Age-related changes in FA were much less prominent in the young adult group, but we did find a significant age-related increase in FA in the right superior longitudinal fascicle, suggesting that structural development of this pathway continues into adulthood. Our results suggest that significant microstructural changes in WM continue throughout adolescence and are associated with corresponding age-related changes in cortical GM regions.

Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimer's disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.

We address the problem of label fusion in multi-atlas based image segmentation.A modification of a previously published label fusion method is proposed.Our method uses estimates of local segmentation accuracy to discard outliers.The method combines advantages of atlas selection and those of performance estimation. In multi-atlas based segmentation, a target image is segmented by registering multiple atlas images to this target image and propagating the corresponding atlas segmentations. These propagated segmentations are then combined into a single segmentation in a process called label fusion.Multi-atlas based segmentation is a segmentation method that allows fully automatic segmentation of image populations that exhibit a large variability in shape and image quality. Fusing the results of multiple atlases makes this technique robust and reliable. Previously, we have presented the SIMPLE method for label fusion and have shown that it outperforms existing methods. However, the downside of this method is its computation time and the fact that it requires a large atlas set. This is not always a problem, but in some cases segmentation may be time-critical or large atlas sets are not available.This paper presents a new label fusion method which is a local version of the SIMPLE method that has two advantages: when a large atlas set is available it improves the accuracy of label fusion and when this is not the case it gives the same accuracy as the original SIMPLE method, but with considerably fewer atlases. This is made possible by better utilizing the local information contained in propagated segmentations that would otherwise be discarded. Our method (semi-)automatically divides the propagated segmentations in multiple regions. A label fusion process can then be applied to each of these regions separately and the end result can be reconstructed out of multiple partial results. We demonstrate that the number of atlases needed can be reduced to 20 atlases without compromising segmentation quality. Our method is validated in an application to segmentation of the prostate, using an atlas set of 125 manually segmented images.

Accurate quantification and timing of regional myocardial function allows early identification of dysfunction, and therefore becomes increasingly important for clinical risk assessment, patient management, and evaluation of therapeutic efficacy. For this purpose, the application of tissue Doppler echocardiography has rapidly increased. However, echocardiography has some major inherent limitations. Cardiovascular magnetic resonance imaging with tissue tagging provides highly reproducible data on myocardial function, not only in longitudinal and radial directions, but also in the circumferential direction. Because of the development of faster imaging protocols, improved temporal resolution, less time-consuming postprocessing procedures, and the potential of quantifying myocardial deformation in 3 dimensions at any point in the heart, this technique may serve as an alternative for tissue Doppler echocardiography and is now ready for more widespread clinical use. This review discusses the clinical use of cardiovascular magnetic resonance tissue tagging for quantitative assessment of regional myocardial function, thereby underlining the specific features and emerging role of this technique.

Contains fulltext : 57110.pdf (Publisher’s version ) (Closed access) OBJECTIVE: To study the influence of increased familial risk for attention-deficit/hyperactivity disorder (ADHD) on brain morphology. METHOD: Volumetric cerebral measures based on whole brain magnetic resonance imaging scans from 30 boys with ADHD, 30 of their unaffected siblings, and 30 matched controls were compared. RESULTS: Both subjects with ADHD and their unaffected siblings displayed reductions in right prefrontal gray matter and left occipital gray and white matter of up to 9.1% (p < 0.05). Right cerebellar volume was reduced by 4.9% in subjects with ADHD (p = 0.026) but not in their unaffected siblings (p = 0.308). A 4.0% reduction in intracranial volume was found in subjects with ADHD (p = 0.031), while a trend was observed in their unaffected siblings (p = 0.068). CONCLUSIONS: The volumetric reductions in cortical gray and white matter in subjects with ADHD are also present in their unaffected siblings, suggesting that they are related to an increased familial risk for the disorder. In contrast, the cerebellum is unaffected in siblings, suggesting that the reduction in volume observed in subjects with ADHD may be more directly related to the pathophysiology of this disorder.

This paper proposes a new data-driven segmentation technique of 3D T1-weighted magnetic resonance scans of human head. This technique serves to the construction of individual head models. Several structures of the head are extracted. The morphology-oriented approach combined with an extensive use of topological constraints provides a robust and automatic method requiring minimum user intervention. This new approach is suitable to applications where the topology is one of the main constraints. The originality of the approach lies in the satisfaction of such constraints and in an effort towards robustness. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. Dokladal P., Bloch I., Couprie M., Ruijters D., Urtasun R., Garnero L., ''Topologically controlled segmentation of 3D magnetic resonance images of the head by using morphological operators'', Pattern recognition, vol. 36, no. 10, pp. 2463-2478, October 2003. ispartof: Pattern recognition vol:36 issue:10 pages:2463-2478 status: published

Magnetic resonance imaging (MRI) can be used in vivo in combination with computational fluid dynamics (CFD) to derive velocity profiles in space and time and accordingly, pressure drop and wall shear stress distribution in natural or artificial vessel segments. These hemodynamic data are difficult or impossible to acquire directly in vivo. Therefore, research has been performed combining MRI and CFD for flow simulations in flow phantoms, such as bends or anastomoses, and even in human vessels such as the aorta, the carotid, and the abdominal bifurcation. There is, however, no unanimity concerning the use of MRI velocity measurements as input for the inflow boundary condition of a CFD simulation. In this study, different input possibilities for the inflow boundary conditions are compared. MRI measurements of steady and pulsatile flow were performed on a U bend phantom, representing the aorta geometry. PAMFLOW (ESI Software, Krimpen aan den Ussel, The Netherlands), an industrial CFD software package, was used to solve the Navier-Stokes equations for incompressible flow. Three main parameters were found to influence the choice of an inflow boundary condition type. First, the flow rate through a vessel should be exact, since it proves to be a determining factor for the accuracy of the velocity profile. The other decisive parameters are the physiology of the flow profile and the required computer processing unit time. Our comparative study indicates that the best way to handle an inflow boundary condition is to use the velocities measured by MRI at the inflow plane as being fixed velocities. However, before using these MRI velocity data, they first should be corrected for the partial volume effect by filtering and second scaled in order to obtain the correct flow rate. This implies that a reliable flow rate measurement absolutely is needed for CFD calculations based on MRI velocity measurements.

A probabilistic deformable, model for the representation of multiple brain structures is described. The statistically learned deformable model! represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loeve expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model; and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multiobject deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain. IEEE Trans Med Imaging