A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

Asymmetric Image-Template Registration

Authors Manuscript received: 2010 May 4. 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part IA natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates.NAMIC (NIH NIBIB NAMIC U54-EB005149)NAC (NIH NCRR NAC P41- RR13218)mBIRN (NIH NCRR mBIRN U24-RR021382)NIH NINDS (R01-NS051826 Grant)National Science Foundation (U.S.) (CAREER Grant 0642971)NIBIB (R01 EB001550)NIBIB (R01EB006758)NCRR (R01 RR16594-01A1)NCRR (P41-RR14075)NINDS (R01 NS052585-01)Singapore. Agency for Science, Technology and Researc

'Leukodystrophy‐like' phenotype in children with myelin oligodendrocyte glycoprotein antibody‐associated disease

Aim: To review the demographics and clinical and paraclinical parameters of children with myelin oligodendrocyte glycoprotein (MOG) antibody‐associated relapsing disease. / Method: In this UK‐based, multicentre study, 31 children with MOG antibody‐associated relapsing disease were studied retrospectively. / Results: Of the 31 children studied, 14 presented with acute disseminated encephalomyelitis (ADEM); they were younger (mean 4.1y) than the remainder (mean 8.5y) who presented with optic neuritis and/or transverse myelitis (p<0.001). Similarly, children who had an abnormal brain magnetic resonance imaging (MRI) at onset (n=20) were younger than patients with normal MRI at onset (p=0.001) or at follow‐up (p<0.001). ‘Leukodystrophy‐like’ MRI patterns of confluent largely symmetrical lesions was seen during the course of the disease in 7 out of 14 children with a diagnosis of ADEM, and was only seen in children younger than 7 years of age. Their disability after a 3‐year follow‐up was mild to moderate, and most patients continued to relapse, despite disease‐modifying treatments. / Interpretation: MOG antibody should be tested in children presenting with relapsing neurological disorders associated with confluent, bilateral white matter changes, and distinct enhancement pattern. Children with MOG antibody‐associated disease present with age‐related differences in phenotypes, with a severe leukoencephalopathy phenotype in the very young and normal intracranial MRI in the older children. This finding suggests a susceptibility of the very young and myelinating brain to MOG antibody‐mediated mechanisms of damage. / What this paper adds: Myelin oligodendrocyte glycoprotein (MOG) antibody‐associated demyelination manifest with an age‐related phenotype. / Children with MOG antibody and ‘leukodystrophy‐like’ imaging patterns tend to have poor response to second‐line immunotherapy

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring.

Purpose: Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera. Methods: The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in in vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction. Results: Images of the numerical phantom exhibited blurring and edge artifacts on the bone-tissue and air-tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the in vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV max for simulated lesions in the range of 7.17%-12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and -0.21% to +1.81% (lesions). CONCLUSIONS: Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction

Symmetric Log-Domain Diffeomorphic Registration: A Demons-based Approach

pmid 18979814International audienceModern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. In this work, we propose a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms. Our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. This implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. This also means that our output can be directly used for log-Euclidean statistics without relying on the heavy computation of the log of the spatial transformation. As it is often desirable, our algorithm is symmetric with respect to the order of the input images. Furthermore, we use an alternate optimization approach related to Thirion's demons algorithm to provide a fast non-linear registration algorithm. First results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining computationally efficient

Differences between Chronological and Brain Age Are Related to Education and Self-Reported Physical Activity

This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age (CA). Cortical and subcortical gray matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting CA (R(2) = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed (FOSC) were the only 2 significant predictors of decreased BA. Effect sizes demonstrated that BA decreased by 0.95 years for each year of education and by 0.58 years for 1 additional FOSC daily. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by CA which supports the utility of regional gray matter volume as a biomarker of healthy brain aging

Post traumatic brain perfusion SPECT analysis using reconstructed ROI maps of radioactive microsphere derived cerebral blood flow and statistical parametric mapping

<p>Abstract</p> <p>Background</p> <p>Assessment of cerebral blood flow (CBF) by SPECT could be important in the management of patients with severe traumatic brain injury (TBI) because changes in regional CBF can affect outcome by promoting edema formation and intracranial pressure elevation (with cerebral hyperemia), or by causing secondary ischemic injury including post-traumatic stroke. The purpose of this study was to establish an improved method for evaluating regional CBF changes after TBI in piglets.</p> <p>Methods</p> <p>The focal effects of moderate traumatic brain injury (TBI) on cerebral blood flow (CBF) by SPECT cerebral blood perfusion (CBP) imaging in an animal model were investigated by parallelized statistical techniques. Regional CBF was measured by radioactive microspheres and by SPECT 2 hours after injury in sham-operated piglets versus those receiving severe TBI by fluid-percussion injury to the left parietal lobe. Qualitative SPECT CBP accuracy was assessed against reference radioactive microsphere regional CBF measurements by map reconstruction, registration and smoothing. Cerebral hypoperfusion in the test group was identified at the voxel level using statistical parametric mapping (SPM).</p> <p>Results</p> <p>A significant area of hypoperfusion (P < 0.01) was found as a response to the TBI. Statistical mapping of the reference microsphere CBF data confirms a focal decrease found with SPECT and SPM.</p> <p>Conclusion</p> <p>The suitability of SPM for application to the experimental model and ability to provide insight into CBF changes in response to traumatic injury was validated by the SPECT SPM result of a decrease in CBP at the left parietal region injury area of the test group. Further study and correlation of this characteristic lesion with long-term outcomes and auxiliary diagnostic modalities is critical to developing more effective critical care treatment guidelines and automated medical imaging processing techniques.</p

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations

Purpose MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element-based simulations were performed to explore this possibility. Methods The natural frequencies of a series of cranial models were calculated, and MRE-associated vibration was simulated for different wave delivery methods at varying frequency, using simple isotropic viscoelastic material models for the brain. Displacement fields and the corresponding brain constitutive properties estimated by standard inversion techniques were compared across delivery methods and frequencies. Results The delivery methods produced widely different MRE displacement fields and inversions. Furthermore, resonances at natural frequencies influenced the displacement patterns. Consequently, some delivery methods led to lower inversion errors than others, and the error on the storage modulus varied by up to 11% between methods. Conclusion Wave delivery has a considerable impact on brain MRE reliability. Assuming small variations in brain biomechanics, as recently reported to accompany neurodegenerative disease (e.g., 7% for Alzheimer's disease), the effect of wave delivery is important. Hence, a consensus should be established on a consistent methodology to ensure diagnostic and prognostic consistency

Preconditioned ADMM with nonlinear operator constraint

We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisation problems with nonlinear operator constraints. Connections to the recently developed Nonlinear Primal-Dual Hybrid Gradient Method (NL-PDHGM) are presented, and the algorithm is demonstrated to handle the nonlinear inverse problem of parallel Magnetic Resonance Imaging (MRI)