Evaluation of Numerical Techniques for Solving the Current Injection Problem in Biological Tissues

Accurate computational modeling of electric fields in the human head has become important in clinical research to study or influence brain functionality. While existing numerical approaches have been evaluated against simple geometries with known closed form solutions, the relationship between these approaches in more complex geometries has not been studied. Here, we compare the three most commonly used approaches for bioelectric modeling: the finite element method (FEM), the finite difference method (FDM), and the boundary element method (BEM). Using both isotropic and anisotropic conductivity distributions, we construct and compare bioelectric models for a realistic head geometry. Our results suggest that both FEM and FDM are capable of accurately model voltages in the brain, while computations from BEM result in significantly larger errors, due to the increased simplicity and implicit model assumptions.

Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age

PURPOSE

To evaluate the feasibility of using diffusion-weighted magnetic resonance imaging (DW-MRI) to assess the fetal lung apparent diffusion coefficient (ADC) at 3 Tesla (T).

MATERIALS AND METHODS

Seventy-one pregnant women (32 second trimester, 39 third trimester) were scanned with a twice-refocused Echo-planar diffusion-weighted imaging sequence with 6 different b-values in 3 orthogonal diffusion orientations at 3T. After each scan, a region-of-interest (ROI) mask was drawn to select a region in the fetal lung and an automated robust maximum likelihood estimation algorithm was used to compute the ADC parameter. The amount of motion in each scan was visually rated.

RESULTS

When scans with unacceptable levels of motion were eliminated, the lung ADC values showed a strong association with gestational age (P < 0.01), increasing dramatically between 16 and 27 weeks and then achieving a plateau around 27 weeks.

CONCLUSION

We show that to get reliable estimates of ADC values of fetal lungs, a multiple b-value acquisition, where motion is either corrected or considered, can be performed. J. Magn. Reson. Imaging 2016;44:1650-1655.

Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals

This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain.

Spatially-constrained probability distribution model of incoherent motion (SPIM) for abdominal diffusion-weighted MRI

Quantitative diffusion-weighted MR imaging (DW-MRI) of the body enables characterization of the tissue microenvironment by measuring variations in the mobility of water molecules. The diffusion signal decay model parameters are increasingly used to evaluate various diseases of abdominal organs such as the liver and spleen. However, previous signal decay models (i.e., mono-exponential, bi-exponential intra-voxel incoherent motion (IVIM) and stretched exponential models) only provide insight into the average of the distribution of the signal decay rather than explicitly describe the entire range of diffusion scales. In this work, we propose a probability distribution model of incoherent motion that uses a mixture of Gamma distributions to fully characterize the multi-scale nature of diffusion within a voxel. Further, we improve the robustness of the distribution parameter estimates by integrating spatial homogeneity prior into the probability distribution model of incoherent motion (SPIM) and by using the fusion bootstrap solver (FBM) to estimate the model parameters. We evaluated the improvement in quantitative DW-MRI analysis achieved with the SPIM model in terms of accuracy, precision and reproducibility of parameter estimation in both simulated data and in 68 abdominal in-vivo DW-MRIs. Our results show that the SPIM model not only substantially reduced parameter estimation errors by up to 26%; it also significantly improved the robustness of the parameter estimates (paired Student's t-test, p < 0.0001) by reducing the coefficient of variation (CV) of estimated parameters compared to those produced by previous models. In addition, the SPIM model improves the parameter estimates reproducibility for both intra- (up to 47%) and inter-session (up to 30%) estimates compared to those generated by previous models. Thus, the SPIM model has the potential to improve accuracy, precision and robustness of quantitative abdominal DW-MRI analysis for clinical applications.

3D Super-Resolution Motion-Corrected MRI: Validation of Fetal Posterior Fossa Measurements

PURPOSE

Current diagnosis of fetal posterior fossa anomalies by sonography and conventional MRI is limited by fetal position, motion, and by two-dimensional (2D), rather than three-dimensional (3D), representation. In this study, we aimed to validate the use of a novel magnetic resonance imaging (MRI) technique, 3D super-resolution motion-corrected MRI, to image the fetal posterior fossa.

METHODS

From a database of pregnant women who received fetal MRIs at our institution, images of 49 normal fetal brains were reconstructed. Six measurements of the cerebellum, vermis, and pons were obtained for all cases on 2D conventional and 3D reconstructed MRI, and the agreement between the two methods was determined using concordance correlation coefficients. Concordance of axial and coronal measurements of the transcerebellar diameter was also assessed within each method.

RESULTS

Between the two methods, the concordance of measurements was high for all six structures (P < .001), and was highest for larger structures such as the transcerebellar diameter. Within each method, agreement of axial and coronal measurements of the transcerebellar diameter was superior in 3D reconstructed MRI compared to 2D conventional MRI (P < .001).

CONCLUSIONS

This comparison study validates the use of 3D super-resolution motion-corrected MRI for imaging the fetal posterior fossa, as this technique results in linear measurements that have high concordance with 2D conventional MRI measurements. Lengths of the transcerebellar diameter measured within a 3D reconstruction are more concordant between imaging planes, as they correct for fetal motion and orthogonal slice acquisition. This technique will facilitate further study of fetal abnormalities of the posterior fossa.

Planar dGEMRIC Maps May Aid Imaging Assessment of Cartilage Damage in Femoroacetabular Impingement

BACKGROUND

Three-dimensional (3-D) delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) helps quantify biochemical changes in articular cartilage that correlate with early-stage osteoarthritis. However, dGEMRIC analysis is performed slice by slice, limiting the potential of 3-D data to give an overall impression of cartilage biochemistry. We previously developed a computational algorithm to produce unfolded, or "planar," dGEMRIC maps of acetabular cartilage, but have neither assessed their application nor determined whether MRI-based grading of cartilage damage or dGEMRIC measurements predict intraoperative findings in hips with symptomatic femoroacetabular impingement (FAI).

QUESTIONS/PURPOSES

(1) Does imaging-based assessment of acetabular cartilage damage correlate with intraoperative findings in hips with symptomatic FAI? (2) Does the planar dGEMRIC map improve this correlation? (3) Does the planar map improve the correlation between the dGEMRIC index and MRI-based grading of cartilage damage in hips with symptomatic FAI? (4) Does the planar map improve imaging-based evaluation time for hips with symptomatic FAI?

METHODS

We retrospectively studied 47 hips of 45 patients with symptomatic FAI who underwent hip surgery between 2009 and 2013 and had a 1.5-T 3-D dGEMRIC scan within 6 months preoperatively. Our cohort included 25 males and 20 females with a mean ± SD age at surgery of 29 ± 11 years. Planar dGEMRIC maps were generated from isotropic, sagittal oblique TrueFISP and T1 sequences. A pediatric musculoskeletal radiologist with experience in hip MRI evaluated studies using radially reformatted sequences. For six acetabular subregions (anterior-peripheral [AP]; anterior-central [AC]; superior-peripheral [SP]; superior-central [SC]; posterior-peripheral [PP]; posterior-central [PC]), modified Outerbridge cartilage damage grades were recorded and region-of-interest T1 averages (the dGEMRIC index) were measured. Beck's intraoperative cartilage damage grades were compared with the Outerbridge grades and dGEMRIC indices. For a subset of 26 hips, 13 were reevaluated with the map and 13 without the map, and total evaluation times were recorded.

RESULTS

There were no meaningful differences in the correlations obtained with versus without referencing the planar maps. Planar map-independent Outerbridge grades had a notable (p < 0.05) Spearman's rank correlation (ρ) with Beck's grades that was moderate in AP, SC, and PC (0.3 < ρ < 0.5) and strong in SP (ρ > 0.5). For map-dependent Outerbridge grades, ρ was moderate in AP, AC, and SC and strong in SP. Map-independent dGEMRIC indices had a ρ with Beck's grades that was moderate in AP and SC (-0.3 > ρ > -0.5) and strong in SP (ρ < -0.5). For map-dependent dGEMRIC indices, ρ was moderate in SC and strong in SP. Similarly, there were no meaningful, map-dependent differences in the correlations. When comparing Outerbridge grades and dGEMRIC indices, there were notable correlations across all subregions. Without the planar map, ρ was moderate in AC and PC and strong in AP, SP, SC, and PP. With the map, ρ was strong in all six subregions. In AC, there was a notable map-dependent improvement in this correlation (p < 0.001). Finally, referencing the planar dGEMRIC map during evaluation was associated with a decrease in mean evaluation time, from 207 ± 32 seconds to 152 ± 33 seconds (p = 0.001).

CONCLUSIONS

Our work challenges the weak correlation between dGEMRIC and intraoperative findings of cartilage damage that was previously reported in hips with symptomatic FAI, suggesting that dGEMRIC has potential diagnostic use for this patient population. The planar dGEMRIC maps did not meaningfully alter the correlation of imaging-based evaluation of cartilage damage with intraoperative findings; however, they notably improved the correlation of dGEMRIC and MRI-based grading in AC, and their use incurred no additional time cost to imaging-based evaluation. Therefore, the planar maps may improve dGEMRIC's use as a continuous proxy for an otherwise discrete and simplified MRI-based grade of cartilage damage in hips with symptomatic FAI.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Spatially constrained incoherent motion method improves diffusion-weighted MRI signal decay analysis in the liver and spleen

PURPOSE

To evaluate the effect of the spatially constrained incoherent motion (SCIM) method on improving the precision and robustness of fast and slow diffusion parameter estimates from diffusion-weighted MRI in liver and spleen in comparison to the independent voxel-wise intravoxel incoherent motion (IVIM) model.

METHODS

We collected diffusion-weighted MRI (DW-MRI) data of 29 subjects (5 healthy subjects and 24 patients with Crohn's disease in the ileum). We evaluated parameters estimates' robustness against different combinations of b-values (i.e., 4 b-values and 7 b-values) by comparing the variance of the estimates obtained with the SCIM and the independent voxel-wise IVIM model. We also evaluated the improvement in the precision of parameter estimates by comparing the coefficient of variation (CV) of the SCIM parameter estimates to that of the IVIM.

RESULTS

The SCIM method was more robust compared to IVIM (up to 70% in liver and spleen) for different combinations of b-values. Also, the CV values of the parameter estimations using the SCIM method were significantly lower compared to repeated acquisition and signal averaging estimated using IVIM, especially for the fast diffusion parameter in liver (CVIV IM = 46.61 ± 11.22, CVSCIM = 16.85 ± 2.160, p < 0.001) and spleen (CVIV IM = 95.15 ± 19.82, CVSCIM = 52.55 ± 1.91, p < 0.001).

CONCLUSIONS

The SCIM method characterizes fast and slow diffusion more precisely compared to the independent voxel-wise IVIM model fitting in the liver and spleen.

Fast myelin water fraction estimation using 2D multislice CPMG

PURPOSE

T2 relaxometry based on multiexponential fitting to a single slice multiecho sequence has been the most common MRI technique for myelin water fraction mapping, where the short T2 is associated with myelin water. However, very long acquisition times and physically unrealistic models for T2 distribution are limitations of this approach. We present a novel framework for myelin imaging which substantially increases the imaging speed and myelin water fraction estimation accuracy.

METHOD

We used the 2D multislice Carr-Purcell-Meiboom-Gill sequence to increase the volume coverage. To compensate for nonideal slice profiles, we numerically solved the Bloch equations for a range of T2 and B1 inhomogeneity scales to construct the bases for the estimation of the T2 distribution. We used a finite mixture of continuous parametric distributions to describe the complete T2 spectrum and used the constrained variable projection optimization algorithm to estimate myelin water fraction. To validate our model, synthetic, phantom, and in vivo brain experiments were conducted.

RESULTS

Using the Bloch equations, we can model the slice profile and construct the forward model of the T2 curve. Our method estimated myelin water fraction with smaller error than the nonnegative least squares algorithm.

CONCLUSIONS

The proposed framework can be used for reliable whole brain myelin imaging with a resolution of 2×2×4  mm3 in ≈17  min. Magn Reson Med 76:1301-1313, 2016. © 2015 Wiley Periodicals, Inc.

Tubers are neither static nor discrete: Evidence from serial diffusion tensor imaging

OBJECTIVE

To assess the extent and evolution of tissue abnormality of tubers, perituber tissue, and normal-appearing white matter (NAWM) in patients with tuberous sclerosis complex using serial diffusion tensor imaging.

METHODS

We applied automatic segmentation based on a combined global-local intensity mixture model of 3T structural and 35 direction diffusion tensor MRIs (diffusion tensor imaging) to define 3 regions: tuber tissue, an equal volume perituber rim, and the remaining NAWM. For each patient, scan, lobe, and tissue type, we analyzed the averages of mean diffusivity (MD) and fractional anisotropy (FA) in a generalized additive mixed model.

RESULTS

Twenty-five patients (mean age 5.9 years; range 0.5-24.5 years) underwent 2 to 6 scans each, totaling 70 scans. Average time between scans was 1.2 years (range 0.4-2.9). Patient scans were compared with those of 73 healthy controls. FA values were lowest, and MD values were highest in tubers, next in perituber tissue, then in NAWM. Longitudinal analysis showed a positive (FA) and negative (MD) correlation with age in tubers, perituber tissue, and NAWM. All 3 tissue types followed a biexponential developmental trajectory, similar to the white matter of controls. An additional qualitative analysis showed a gradual transition of diffusion values across the tissue type boundaries.

CONCLUSIONS

Similar to NAWM, tuber and perituber tissues in tuberous sclerosis complex undergo microstructural evolution with age. The extent of diffusion abnormality decreases with distance to the tuber, in line with known extension of histologic, immunohistochemical, and molecular abnormalities beyond tuber pathology.

Brain Perfusion Is Increased at Term in the White Matter of Very Preterm Newborns and Newborns with Congenital Heart Disease: Does this Reflect Activated Angiogenesis?

OBJECTIVE

This study aims to evaluate brain perfusion at term in very preterm newborns and newborns with congenital heart disease before their corrective surgery, and to search for histopathological indicators of whether the brain perfusion abnormalities of these newborns may be related to an activated angiogenesis.

MATERIALS AND METHODS

Using magnetic resonance imaging and arterial spin labeling, regional cerebral blood flow was measured at a term-equivalent age for three very preterm newborns (born at < 32 weeks), one newborn with congenital heart disease before his corrective surgery and three healthy newborns. In addition, a histopathological analysis was performed on a newborn with congenital heart disease.

RESULTS

The very preterm newborns and the newborn with congenital heart disease included in this study all displayed an increased signal in their white matter on T2-weighted imaging. The cerebral blood flow of these newborns was increased in their white matter, compared with the healthy term newborns. The vascular endothelial growth factor was overexpressed in the injured white matter of the newborn with congenital heart disease.

CONCLUSION

Brain perfusion may be increased at term in the white matter, in very preterm newborns, and newborns with congenital heart disease, and it correlates with white matter abnormalities on conventional imaging.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND)

PURPOSE

To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel.

THEORY AND METHODS

In prior work, a statistical model of the apparent diffusion coefficient was shown to well-characterize the diffusivity and heterogeneity of the mono-directional diffusion MRI signal. However, this model was unable to characterize the three-dimensional anisotropic diffusion observed in the brain. We introduce a new model that extends the statistical distribution representation to be fully tridimensional, in which apparent diffusion coefficients are extended to be diffusion tensors. The set of compartments present at a voxel is modeled by a finite sum of unimodal continuous distributions of diffusion tensors. Each distribution provides measures of each compartment microstructural diffusivity and heterogeneity.

RESULTS

The ability to estimate the tridimensional diffusivity and heterogeneity of multiple fascicles and of free diffusion is demonstrated.

CONCLUSION

Our novel tissue model allows for the characterization of the intra-voxel orientational heterogeneity, a prerequisite for accurate tractography while also characterizing the overall tridimensional diffusivity and heterogeneity of each tissue compartment. The model parameters can be estimated from short duration acquisitions. The diffusivity and heterogeneity microstructural parameters may provide novel indicator of the presence of disease or injury. Magn Reson Med 76:963-977, 2016. © 2015 Wiley Periodicals, Inc.

Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices

In magnetic resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3-D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution reconstruction based on sparse representation and overcomplete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the nonlocal means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and postacquisition processing.

A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation

White matter (WM) lesions are thought to play an important role in multiple sclerosis (MS) disease burden. Recent work in the automated segmentation of white matter lesions from magnetic resonance imaging has utilized a model in which lesions are outliers in the distribution of tissue signal intensities across the entire brain of each patient. However, the sensitivity and specificity of lesion detection and segmentation with these approaches have been inadequate. In our analysis, we determined this is due to the substantial overlap between the whole brain signal intensity distribution of lesions and normal tissue. Inspired by the ability of experts to detect lesions based on their local signal intensity characteristics, we propose a new algorithm that achieves lesion and brain tissue segmentation through simultaneous estimation of a spatially global within-the-subject intensity distribution and a spatially local intensity distribution derived from a healthy reference population. We demonstrate that MS lesions can be segmented as outliers from this intensity model of population and subject. We carried out extensive experiments with both synthetic and clinical data, and compared the performance of our new algorithm to those of state-of-the art techniques. We found this new approach leads to a substantial improvement in the sensitivity and specificity of lesion detection and segmentation.

Optimal MAP Parameters Estimation in STAPLE Using Local Intensity Similarity Information

In recent years, fusing segmentation results obtained based on multiple template images has become a standard practice in many medical imaging applications. Such multiple-templates-based methods are found to provide more reliable and accurate segmentations than the single-template-based methods. In this paper, we present a new approach for learning prior knowledge about the performance parameters of template images using the local intensity similarity information; we also propose a methodology to incorporate that prior knowledge through the estimation of the optimal MAP parameters. The proposed method is evaluated in the context of segmentation of structures in the brain magnetic resonance images by comparing our results with some of the state-of-the-art segmentation methods. These experiments have clearly demonstrated the advantages of learning and incorporating prior knowledge about the performance parameters using the proposed method.

Voxel-based dipole orientation constraints for distributed current estimation

Distributed electroencephalography source localization is a highly ill-posed problem. With measurements on the order of 10(2), and unknowns in the range of 10(4)-10(5), the range of feasible solutions is quite large. One approach to reducing ill-posedness is to intelligently reduce the number of unknowns. Restricting solutions to gray matter is one approach. A further step is to use the anatomy of each patient to identify and constrain the orientation of the dipole within each voxel. While dipole orientation constraints for cortical patch-based approaches have been proposed, to our knowledge, no solutions for full volumetric localizations have been presented. Patch techniques account for patch surface area, but place dipoles only on the surface, rather than throughout the cortex. Variability in human cortical thickness means that thicker regions of cortex will potentially contribute more to the EEG signal, and should be accounted for in modeling. Additionally, patch models require cortical surface identification techniques, which can separate them from the extensive literature on voxel-based MR image processing, and require additional adaptation to incorporate more complex information. We present a volumetric approach for computing voxel-based distributed estimates of cortical activity with constrained dipole orientations. Using a tissue thickness estimation approach, we obtain estimates of the cortical surface normal at each voxel. These let us constrain the inverse problem, and yield localizations with reduced spatial blurring and better identification of signal magnitude within the cortex. This is demonstrated for a series of simulated and experimental data using patient-specific bioelectric models.

COMBINED DELAY AND GRAPH EMBEDDING OF EPILEPTIC DISCHARGES IN EEG REVEALS COMPLEX AND RECURRENT NONLINEAR DYNAMICS

The dynamical structure of the brain's electrical signals contains valuable information about its physiology. Here we combine techniques for nonlinear dynamical analysis and manifold identification to reveal complex and recurrent dynamics in interictal epileptiform discharges (IEDs). Our results suggest that recurrent IEDs exhibit some consistent dynamics, which may only last briefly, and so individual IED dynamics may need to be considered in order to understand their genesis. This could potentially serve to constrain the dynamics of the inverse source localization problem.

Altered Structural Brain Networks in Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is characterized by benign hamartomas in multiple organs including the brain and its clinical phenotypes may be associated with abnormal neural connections. We aimed to provide the first detailed findings on disrupted structural brain networks in TSC patients. Structural whole-brain connectivity maps were constructed using structural and diffusion MRI in 20 TSC (age range: 3-24 years) and 20 typically developing (TD; 3-23 years) subjects. We assessed global (short- and long-association and interhemispheric fibers) and regional white matter connectivity, and performed graph theoretical analysis using gyral pattern- and atlas-based node parcellations. Significantly higher mean diffusivity (MD) was shown in TSC patients than in TD controls throughout the whole brain and positively correlated with tuber load severity. A significant increase in MD was mainly influenced by an increase in radial diffusivity. Furthermore, interhemispheric connectivity was particularly reduced in TSC, which leads to increased network segregation within hemispheres. TSC patients with developmental delay (DD) showed significantly higher MD than those without DD primarily in intrahemispheric connections. Our analysis allows non-biased determination of differential white matter involvement, which may provide better measures of "lesion load" and lead to a better understanding of disease mechanisms.

Normative biometrics for fetal ocular growth using volumetric MRI reconstruction

OBJECTIVE

To determine normative ranges for fetal ocular biometrics between 19 and 38 weeks gestational age (GA) using volumetric MRI reconstruction.

METHOD

The 3D images of 114 healthy fetuses between 19 and 38 weeks GA were created using super-resolution volume reconstructions from MRI slice acquisitions. These 3D images were semi-automatically segmented to measure fetal orbit volume, binocular distance (BOD), interocular distance (IOD), and ocular diameter (OD).

RESULTS

All biometry correlated with GA (Volume, Pearson's correlation coefficient (CC) = 0.9680; BOD, CC = 0.9552; OD, CC = 0.9445; and IOD, CC = 0.8429), and growth curves were plotted against linear and quadratic growth models. Regression analysis showed quadratic models to best fit BOD, IOD, and OD and a linear model to best fit volume.

CONCLUSION

Orbital volume had the greatest correlation with GA, although BOD and OD also showed strong correlation. The normative data found in this study may be helpful for the detection of congenital fetal anomalies with more consistent measurements than are currently available. © 2015 John Wiley & Sons, Ltd.

Increased Brain Perfusion Persists over the First Month of Life in Term Asphyxiated Newborns Treated with Hypothermia: Does it Reflect Activated Angiogenesis?

Many asphyxiated newborns still develop brain injury despite hypothermia therapy. The development of brain injury in these newborns has been related partly to brain perfusion abnormalities. The purposes of this study were to assess brain hyperperfusion over the first month of life in term asphyxiated newborns and to search for some histopathological clues indicating whether this hyperperfusion may be related to activated angiogenesis following asphyxia. In this prospective cohort study, regional cerebral blood flow was measured in term asphyxiated newborns treated with hypothermia around day 10 of life and around 1 month of life using magnetic resonance imaging (MRI) and arterial spin labeling. A total of 32 MRI scans were obtained from 24 term newborns. Asphyxiated newborns treated with hypothermia displayed an increased cerebral blood flow in the injured brain areas around day 10 of life and up to 1 month of life. In addition, we looked at the histopathological clues in a human asphyxiated newborn and in a rat model of neonatal encephalopathy. Vascular endothelial growth factor (VEGF) was expressed in the injured brain of an asphyxiated newborn treated with hypothermia in the first days of life and of rat pups 24-48 h after the hypoxic-ischemic event, and the endothelial cell count increased in the injured cortex of the pups 7 and 11 days after hypoxia-ischemia. Our data showed that the hyperperfusion measured by imaging persisted in the injured areas up to 1 month of life and that angiogenesis was activated in the injured brain of asphyxiated newborns.

The anatomy and art of writing a successful grant application: a practical step-by-step approach

Writing a compelling grant application is a skill that is crucial to conducting high-quality and high-impact scientific research. A successful grant proposal provides the resources necessary to foster activity in an important area of investigation. A concise and practical overview of the anatomy and art of grant writing is provided in this article, along with citations to resources that are particularly useful for junior investigators.

Fetal MRI: A Technical Update with Educational Aspirations

Fetal magnetic resonance imaging (MRI) examinations have become well-established procedures at many institutions and can serve as useful adjuncts to ultrasound (US) exams when diagnostic doubts remain after US. Due to fetal motion, however, fetal MRI exams are challenging and require the MR scanner to be used in a somewhat different mode than that employed for more routine clinical studies. Herein we review the techniques most commonly used, and those that are available, for fetal MRI with an emphasis on the physics of the techniques and how to deploy them to improve success rates for fetal MRI exams. By far the most common technique employed is single-shot T2-weighted imaging due to its excellent tissue contrast and relative immunity to fetal motion. Despite the significant challenges involved, however, many of the other techniques commonly employed in conventional neuro- and body MRI such as T1 and T2*-weighted imaging, diffusion and perfusion weighted imaging, as well as spectroscopic methods remain of interest for fetal MR applications. An effort to understand the strengths and limitations of these basic methods within the context of fetal MRI is made in order to optimize their use and facilitate implementation of technical improvements for the further development of fetal MR imaging, both in acquisition and post-processing strategies.

A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment of patients. However, because of the complexity of its structures, manual segmentation of the pelvic floor is challenging and suffers from high inter and intra-rater variability of expert raters. Multiple template fusion algorithms are promising segmentation techniques for these types of applications, but they have been limited by imperfections in the alignment of templates to the target, and by template segmentation errors. A number of algorithms sought to improve segmentation performance by combining image intensities and template labels as two independent sources of information, carrying out fusion through local intensity weighted voting schemes. This class of approach is a form of linear opinion pooling, and achieves unsatisfactory performance for this application. We hypothesized that better decision fusion could be achieved by assessing the contribution of each template in comparison to a reference standard segmentation of the target image and developed a novel segmentation algorithm to enable automatic segmentation of MRI of the female pelvic floor. The algorithm achieves high performance by estimating and compensating for both imperfect registration of the templates to the target image and template segmentation inaccuracies. A local image similarity measure is used to infer a local reliability weight, which contributes to the fusion through a novel logarithmic opinion pooling. We evaluated our new algorithm in comparison to nine state-of-the-art segmentation methods and demonstrated our algorithm achieves the highest performance.

Three-dimensional hip cartilage quality assessment of morphology and dGEMRIC by planar maps and automated segmentation

OBJECTIVE

The quantitative interpretation of hip cartilage magnetic resonance imaging (MRI) has been limited by the difficulty of identifying and delineating the cartilage in a three-dimensional (3D) dataset, thereby reducing its routine usage. In this paper a solution is suggested by unfolding the cartilage to planar two-dimensional (2D) maps on which both morphology and biochemical degeneration patterns can be investigated across the entire hip joint.

DESIGN

Morphological TrueFISP and biochemical delayed gadolinium enhanced MRI of cartilage (dGEMRIC) hip images were acquired isotropically for 15 symptomatic subjects with mild or no radiographic osteoarthritis (OA). A multi-template based label fusion technique was used to automatically segment the cartilage tissue, followed by a geometric projection algorithm to generate the planar maps. The segmentation performance was investigated through a leave-one-out study, for two different fusion methods and as a function of the number of utilized templates.

RESULTS

For each of the generated planar maps, various patterns could be seen, indicating areas of healthy and degenerated cartilage. Dice coefficients for cartilage segmentation varied from 0.76 with four templates to 0.82 with 14 templates. Regional analysis suggests even higher segmentation performance in the superior half of the cartilage.

CONCLUSIONS

The proposed technique is the first of its kind to provide planar maps that enable straightforward quantitative assessment of hip cartilage morphology and dGEMRIC values. This technique may have important clinical applications for patient selection for hip preservation surgery, as well as for epidemiological studies of cartilage degeneration patterns. It is also shown that 10-15 templates are sufficient for accurate segmentation in this application.

Passive fMRI mapping of language function for pediatric epilepsy surgical planning: validation using Wada, ECS, and FMAER

In this study we validate passive language fMRI protocols designed for clinical application in pediatric epilepsy surgical planning as they do not require overt participation from patients. We introduced a set of quality checks that assess reliability of noninvasive fMRI mappings utilized for clinical purposes. We initially compared two fMRI language mapping paradigms, one active in nature (requiring participation from the patient) and the other passive in nature (requiring no participation from the patient). Group-level analysis in a healthy control cohort demonstrated similar activation of the putative language centers of the brain in the inferior frontal (IFG) and temporoparietal (TPG) regions. Additionally, we showed that passive language fMRI produced more left-lateralized activation in TPG (LI=+0.45) compared to the active task; with similarly robust left-lateralized IFG (LI=+0.24) activations using the passive task. We validated our recommended fMRI mapping protocols in a cohort of 15 pediatric epilepsy patients by direct comparison against the invasive clinical gold-standards. We found that language-specific TPG activation by fMRI agreed to within 9.2mm to subdural localizations by invasive functional mapping in the same patients, and language dominance by fMRI agreed with Wada test results at 80% congruency in TPG and 73% congruency in IFG. Lastly, we tested the recommended passive language fMRI protocols in a cohort of very young patients and confirmed reliable language-specific activation patterns in that challenging cohort. We concluded that language activation maps can be reliably achieved using the passive language fMRI protocols we proposed even in very young (average 7.5 years old) or sedated pediatric epilepsy patients.

Localization of the epileptogenic foci in tuberous sclerosis complex: a pediatric case report

Tuberous sclerosis complex (TSC) is a rare disorder of tissue growth and differentiation, characterized by benign hamartomas in the brain and other organs. Up to 90% of TSC patients develop epilepsy and 50% become medically intractable requiring resective surgery. The surgical outcome of TSC patients depends on the accurate identification of the epileptogenic zone consisting of tubers and the surrounding epileptogenic tissue. There is conflicting evidence whether the epileptogenic zone is in the tuber itself or in abnormally developed surrounding cortex. Here, we report the localization of the epileptiform activity among the many cortical tubers in a 4-year-old patient with TSC-related refractory epilepsy undergoing magnetoencephalography (MEG), electroencephalography (EEG), and diffusion tensor imaging (DTI). For MEG, we used a prototype system that offers higher spatial resolution and sensitivity compared to the conventional adult systems. The generators of interictal activity were localized using both EEG and MEG with equivalent current dipole (ECD) and minimum norm estimation (MNE) methods according to the current clinical standards. For DTI, we calculated four diffusion scalar parameters for the fibers passing through four ROIs defined: (i) at a large cortical tuber identified at the right quadrant, (ii) at the normal appearing tissue contralateral to the tuber, (iii) at the cluster formed by ECDs fitted at the peak of interictal spikes, and (iv) at the normal appearing tissue contralateral to the cluster. ECDs were consistently clustered at the vicinity of the large calcified cortical tuber. MNE and ECDs indicated epileptiform activity in the same areas. DTI analysis showed differences between the scalar values of the tracks passing through the tuber and the ECD cluster. In this illustrative case, we provide evidence from different neuroimaging modalities, which support the view that epileptiform activity may derive from abnormally developed tissue surrounding the tuber rather than the tuber itself.