Voxel Scale Research Articles

Graph Theoretical Approaches in Brain Networks

In the last decade network theory has proved an effective tool for modeling and describing the complex topology emerging either from anatomical or functional brain connectivity patterns. From a graph theoretical perspective, the brain can be conceived as a networked system composed of nodes coincident with different brain sites and links which in the current view can either represent anatomical tracts between brain regions or measures of statistical dependencies between their electrical activity. One of the most intriguing, and now well known, examples of the application of network theory to neuroimaging data unveiled that the way brain regions are connected is typically neither regular nor random. Instead brain networks, like other real networked systems, tend to exhibit a complex structure theoretically consistent with the capability of processing information within regional clusters and avoiding excessive connections between clusters. While the simplest instantiation of this configuration showed similar characteristics to mathematically defined “small-world” networks, it has become clear that additional topological structures including hierarchical modularity can nuance our expectations of the brain's underlying architecture and dynamics. An important goal in these research endeavors is to identify how brain network organization can inform our understanding of the brain's intuitive need to balance the two competing principles of integration and segregation and how alterations in brain structure and dynamics can lead to alterations in human behavior and cognitive function. The present special issue collects a series of selected contributions related to the methodological and practical applications of network theory to anatomical and functional brain connectivity patterns. The contribution entitled “Voxel scale complex networks of functional connectivity in the rat brain: neurochemical state dependence of global and local topological properties” addresses the dependence of the functional connectivity estimated from the fMRI signals of the rat brain in response to alterations of the neurotransmitter system as induced by the administration of specific pharmaceutical drugs such as d-amphetamine, fluoxetine, and nicotine. The contribution entitled “A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification” proposes a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed method is evaluated on event-related potential (ERP) data, which demonstrates the dynamic nature of functional connectivity. The contribution entitled “how the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network” addresses important methodological choices that are often made in the construction of functional brain network from EEG data, including the choice of statistical thresholds to determine the presence or absence of network links and the role of spatial correlations in determining graph properties. The contribution entitled “weighted phase lag index and graph analysis: preliminary investigation of functional connectivity during resting state in children” presents original results concerning the application of small-world parameters and betweenness centrality measures to characterize the topological structure of the functional network in the children's brain from noninvasive MEG recordings. The contribution entitled “source space analysis of event-related dynamic reorganization of brain networks” conducts a quantitative study of the dynamic reconfiguration of connectivity for event-related experiments at source space level, which provides a global and complete view of the stages of processing associated with the regional changes in activity. The contribution entitled “Redundancy as a Graph-Based Index of Frequency Specific MEG Functional Connectivity” focuses on quantifying the differential role that paths of different lengths potentially play in brain connectivity and demonstrates that a redundancy-based measure captures unique information not accessible via approaches that only examine the shortest paths through a network. The contribution entitled “A computationally efficient, exploratory approach to brain connectivity incorporating false discovery rate control, a priori knowledge, and group inference” proposes a multisubject, exploratory brain connectivity modeling approach. The proposed method allows for the incorporation of prior knowledge of connectivity and the determination of the dominant brain connectivity pattern among a group of subjects. Fabrizio De Vico Fallani Danielle Bassett Tianzi Jiang

Voxel Scale Complex Networks of Functional Connectivity in the Rat Brain: Neurochemical State Dependence of Global and Local Topological Properties

Network analysis of functional imaging data reveals emergent features of the brain as a function of its topological properties. However, the brain is not a homogeneous network, and the dependence of functional connectivity parameters on neuroanatomical substrate and parcellation scale is a key issue. Moreover, the extent to which these topological properties depend on underlying neurochemical changes remains unclear. In the present study, we investigated both global statistical properties and the local, voxel-scale distribution of connectivity parameters of the rat brain. Different neurotransmitter systems were stimulated by pharmacological challenge (d-amphetamine, fluoxetine, and nicotine) to discriminate between stimulus-specific functional connectivity and more general features of the rat brain architecture. Although global connectivity parameters were similar, mapping of local connectivity parameters at high spatial resolution revealed strong neuroanatomical dependence of functional connectivity in the rat brain, with clear differentiation between the neocortex and older brain regions. Localized foci of high functional connectivity independent of drug challenge were found in the sensorimotor cortices, consistent with the high neuronal connectivity in these regions. Conversely, the topological properties and node roles in subcortical regions varied with neurochemical state and were dependent on the specific dynamics of the different functional processes elicited.

4D Cone‐beam CT reconstruction using a motion model based on principal component analysis

To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine.

Improving measurement of functional connectivity through decreasing partial volume effects at 7T

Several applications of fMRI at high field have taken advantage of the increased BOLD contrast to increase spatial resolution, but the potential benefits of higher fields for detecting and analyzing functional connectivity have largely been unexplored. We measured the influence of spatial resolution at 7 T on estimates of functional connectivity through decreased partial volume averaging. Ten subjects were imaged at 7 T with a range of spatial resolutions (1×1×2 mm to 3×3×2 mm) during performance of a finger tapping task and in the resting state. We found that resting state correlations within the sensory-motor system increase as voxel dimensions decreased from 3×3×2 mm to 1×1×2 mm, whereas connectivity to other brain regions was unaffected. This improvement occurred even as overall signal to noise ratios decrease. Our data suggest that this increase may be due to decreased partial volume averaging, and that functional connectivity within the primary seed region is heterogeneous on the scale of single voxels.

A whole brain fMRI atlas generated via spatially constrained spectral clustering

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.

Toward Truly Optimal IMRT Dose Distribution: Inverse Planning with Voxel-specific Penalty

To establish an inverse planning framework with adjustable voxel penalty for more conformal IMRT dose distribution as well as improved interactive controllability over the regional dose distribution of the resultant plan. In the proposed coarse-to-fine planning scheme, a conventional inverse planning with organ specific parameters is first performed. The voxel penalty scheme is then "switched on" by allowing the prescription dose to change on an individual voxel scale according to the deviation of the actual voxel dose from the ideally desired dose. The rationale here is intuitive: when the dose at a voxel does not meet its ideal dose, it simply implies that this voxel is not competitive enough when compared with the ones that have met their planning goal. In this case, increasing the penalty of the voxel by varying the prescription can boost its competitiveness and thus improve its dose. After the prescription adjustment, the plan is re-optimized. The dose adjustment/re-optimization procedure is repeated until the resultant dose distribution cannot be improved anymore. The prescription adjustment on a finer scale can be accomplished either automatically or manually. In the latter case, the regions/voxels where a dose improvement is needed are selected visually, unlike in the automatic case where the selection is done purely based on the difference of the actual dose at a given voxel and its ideal prescription. The performance of the proposed method is evaluated using a head and neck and a prostate case. An inverse planning framework with the voxel-specific penalty is established. By adjusting voxel prescriptions iteratively to boost the region where large mismatch between the actual calculated and desired doses occurs, substantial improvements can be achieved in the final dose distribution. The proposed method is applied to a head and neck case and a prostate case. For the former case, a significant reduction in the maximum dose to the brainstem is achieved while the PTV dose coverage is greatly improved. The doses to other organs at risk are also reduced, ranging from 10% to 30%. For the prostate case, the use of the voxel penalty scheme also results in vast improvements to the final dose distribution. The PTV experiences improved dose uniformity and the mean dose to the rectum and bladder is reduced by as much as 15%. Introduction of the spatially non-uniform and adjustable prescription provides room for further improvements of currently achievable dose distributions and equips the planner with an effective tool to modify IMRT dose distributions interactively. The technique is easily implementable in any existing inverse planning platform, which should facilitate clinical IMRT planning process and, in future, off-line/on-line adaptive IMRT.

Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: Flow variations induced by global or localized modifications of arteriolar diameters

In a companion paper (Lorthois et al., Neuroimage, in press), we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking into account the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, volumes of functional vascular territories, regional flow at voxel and network scales, etc. Using the same approach, we study flow reorganizations induced by global arteriolar vasodilations (an isometabolic global increase in cerebral blood flow). For small to moderate global vasodilations, the relationship between changes in volume and changes in flow is in close agreement with Grubb's law, providing a quantitative tool for studying the variations of its exponent with underlying vascular architecture. A significant correlation between blood flow and vascular structure at the voxel scale, practically unchanged with respect to baseline, is demonstrated. Furthermore, the effects of localized arteriolar vasodilations, representative of a local increase in metabolic demand, are analyzed. In particular, localized vasodilations induce flow changes, including vascular steal, in the neighboring arteriolar trunks at small distances (<300μm), while their influence in the neighboring veins is much larger (about 1mm), which provides an estimate of the vascular point spread function. More generally, for the first time, the hemodynamic component of various functional neuroimaging techniques has been isolated from metabolic and neuronal components, and a direct relationship with several known characteristics of the BOLD signal has been demonstrated.

Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network: Part I: Methodology and baseline flow

Hemodynamically based functional neuroimaging techniques, such as BOLD fMRI and PET, provide indirect measures of neuronal activity. The quantitative relationship between neuronal activity and the measured signals is not yet precisely known, with uncertainties remaining about the relative contribution by their metabolic and hemodynamic components. Empirical observations have demonstrated the importance of the latter component and suggested that micro-vascular anatomy has a potential influence. The recent development of a 3D computer-assisted method for micro-vascular cerebral network analysis has produced a large quantitative library on the microcirculation of the human cerebral cortex (Cassot et al., 2006), which can be used to investigate the hemodynamic component of brain activation through fluid dynamic modeling. For this purpose, we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, as well as volumes of functional vascular territories, and regional flow at voxel and network scales. First, the influence of the prescribed boundary conditions (BCs) on the baseline flow structure is investigated, highlighting relevant lower- and upper-bound BCs. Independent of these BCs, large heterogeneities of baseline flow from vessel to vessel and from voxel to voxel, are demonstrated. These heterogeneities are controlled by the architecture of the intra-cortical vascular network. In particular, a correlation between the blood flow and the proportion of vascular volume occupied by arterioles or venules, at voxel scale, is highlighted. Then, the extent of venous contamination downstream to the sites of neuronal activation is investigated, demonstrating a linear relationship between the catchment surface of the activated area and the diameter of the intra-cortical draining vein.

Voxel-Scale Digital Volume Correlation

Among various correlation techniques to find the displacement field of a volume imaged by X-ray tomography at several deformation states, a new approach is proposed where the displacement is measured down to the voxel scale and determined from a mechanically regularized system using the equilibrium gap method, and an additional boundary regularization. It is shown that even if the underlying material behavior is not very well known, this approach leads to extremely small correlation residuals. An excellent stability of the estimated displacement field for noisy (reconstructed) volumes is also observed.

Ventilation from four-dimensional computed tomography: density versus Jacobian methods

Two calculation methods to produce ventilation images from four-dimensional computed tomography (4DCT) acquired without added contrast have been reported. We reported a method to obtain ventilation images using deformable image registration (DIR) and the underlying CT density information. A second method performs the ventilation image calculation from the DIR result alone, using the Jacobian determinant of the deformation field to estimate the local volume changes resulting from ventilation. For each of these two approaches, there are variations on their implementation. In this study, two implementations of the Jacobian-based methodology are evaluated, as well as a single density change-based model for calculating the physiologic specific ventilation from 4DCT. In clinical practice, 99mTc-labeled aerosol single photon emission computed tomography (SPECT) is the standard method used to obtain ventilation images in patients. In this study, the distributions of ventilation obtained from the CT-based ventilation image calculation methods are compared with those obtained from the clinical standard SPECT ventilation imaging. Seven patients with 4DCT imaging and standard 99mTc-labeled aerosol SPECT/CT ventilation imaging obtained on the same day as part of a prospective validation study were selected. The results of this work demonstrate the equivalence of the Jacobian-based methodologies for quantifying the specific ventilation on a voxel scale. Additionally, we found that both Jacobian- and density-change-based methods correlate well with global measurements of the resting tidal volume. Finally, correlation with the clinical SPECT was assessed using the Dice similarity coefficient, which showed statistically higher (p-value < 10−4) correlation between density-change-based specific ventilation and the clinical reference than did either Jacobian-based implementation.

TU‐B‐204B‐04: Ventilation from Four Dimensional Computed Tomography: Density versus Jacobian Methods

Purpose: Two calculation methods to produce ventilation images from four‐dimensional computed tomography (4D CT) acquired without added contrast have been reported in the literature. In previous work, we demonstrated a method to obtain ventilation images using deformable image registration and the underlying CT density information. A second method performs the ventilation image calculation from the DIR result alone, using the Jacobian determinant of the deformation field to estimate local volume changes resulting from ventilation. Two implementations of the Jacobian‐based methodology are evaluated, as well as a density change‐based model for calculating the physiologic specific ventilation from 4D CT. Materials and Methods: 99mTc‐labeled aerosol single photon emission computed tomography (SPECT) is the standard clinical method used to obtain ventilation images in patients. The distributions of ventilation obtained from the CT‐based ventilation image calculations are compared with those obtained from the clinical SPECT ventilation. The standard method is prone to airway deposition artifacts, which are further characterized. Seven patients with 4D CT imaging and standard 99mTc‐labeled aerosol SPECT/CT ventilation imaging obtained as part of a prospective validation study were selected. Results: Results demonstrate the equivalence of the Jacobian‐based implementations for quantifying the specific ventilation on a voxel scale. Additionally, both Jacobian‐ and density change‐based methods correlated well with global measurements of the resting tidal volume. Finally, correlation with the clinical SPECT was assessed using the Dice Similarity Coefficient, which showed significantly higher (p‐value &lt; 10−4) correlation with density change‐based specific ventilation than with either Jacobian‐based implementation. Conclusion: Both 4D CT ventilation methods performed well globally, showing good correlation with image segmentation‐based measures of the resting tidal volume. However, CT density change‐based calculations achieved better spatial correlation with clinically acquired SPECT images. Future work will directly address the impact of spatial registration errors on degradation of both density change‐and Jacobian‐based ventilation methods.

Noninvasive 3‐D Transport Characterization in a Sandy Soil Using ERT: 2. Transport Process Inference

In this study, we examined four tracer displacement experiments in a large undisturbed soil column under unsaturated hydraulic steady‐state conditions. Using electrical resistivity tomography (ERT), we were able to investigate the corresponding solute transport processes in unprecedented three‐dimensional (3‐D) spatiotemporally resolved detail. In addition, time‐domain reflectometry (TDR) probes and tensiometers provided hydrologic state variables relevant to solute transport. One important result from the study is that despite considerable differences between soil hydrologic state variables of the low‐ and high‐flux experiments, the 3‐D–resolved transport patterns remained approximately stable and were aligned to a structural feature in the topsoil. Furthermore, the ERT image data indicated that the solute front evolutions were influenced by spatiotemporal heterogeneities of the irrigation system. Another result is that the column‐scale mixing regime was found to be convective–dispersive, although the transport process exhibited considerable heterogeneities at smaller scales. We introduced an upscaling approach that allowed us to predict the column‐scale apparent dispersivity on the basis of the convection–dispersion equation parameters at the scale of the ERT voxels. The approach proved to be useful to evaluate at which scale the apparent dispersivity was generated. We consider the investigation of the relationship between apparent dispersivity and lateral scale as a promising tool for characterizing solute transport in future studies.

Noninvasive 3‐D Transport Characterization in a Sandy Soil Using ERT: 1. Investigating the Validity of ERT‐derived Transport Parameters

We used time‐lapse electrical resistivity tomography (ERT) and time‐domain reflectometry (TDR) probes to noninvasively capture three‐dimensional solute transport during four tracer experiments under different steady‐state irrigation rates in a large unsaturated undisturbed soil column (140 cm length and 116 cm inner diameter). The transport was characterized by means of apparent convection–dispersion parameters that were derived from breakthrough curves (BTCs) at different lateral scales: the ERT voxel scale, the sampling volume of TDR, and the cross‐section of the column. We validated the ERT‐derived data by means of mass balance, TDR probes, and the effluent BTC. We observed an excellent mass recovery by ERT. The ERT‐derived transport velocities exhibited minimal bias and high precision at the scale of the TDR measurements. On average the ERT‐derived column‐scale transport velocities were also not biased; however, the spatial variability of the voxel‐scale velocities within the column's cross‐sections underestimated the true velocity variability. In contrast to the transport velocities, the ERT‐derived dispersivities exhibited a large bias and low precision and were sensitive to temporal smoothing. Unlike previous studies, we did not find evidence that the ERT‐derived voxel‐scale dispersivities increase with decreasing ERT sensitivity. Although ERT provided unprecedented information about transport processes, resolution and uncertainty analyses remain important issues requiring further investigation.

Numerical simulation of water flow in three dimensional heterogeneous porous media observed in a magnetic resonance imaging experiment

Magnetic resonance imaging (MRI) was used to obtain sequential images of water (i.e., 1H) doped with a paramagnetic tracer as it flowed through a three‐dimensional (3D) flowcell packed with a spatially correlated heterogeneous distribution of 1 cm3 blocks, each containing one of five different sand fractions. Tracer concentration breakthrough curves (BTCs) were obtained from MRI signal intensity profiles at each voxel (0.1875 × 0.1875 × 0.225 cm3). Voxel scale BTCs were averaged over 0.25 × 0.25 cm2, 1 × 1 cm2, and entire flowcell cross‐sections, all at 0.25 cm increments along the main flow direction, and compared with numerical simulations. Hydraulic conductivity (K) and dispersivity values for each of the five sand types were varied, and root‐mean squared error (RMSE) values for mean arrival times and second central moments, and RMSE values between simulated and measured BTCs, at each of the three averaging scales were calculated. Measured values of K with a porosity 5% higher than measured values, and longitudinal dispersivity values on the order of the grain size, yielded simulated BTCs that adequately captured experimental BTCs averaged over the entire flowcell cross‐section, and BTCs at smaller scales (i.e., 1 × 1 cm2 and 0.25 × 0.25 cm2) in regions of the flow cell characterized by more uniform K fields. However, model simulations deviated more from BTCs at smaller scales in regions of the flowcell characterized by greater K contrast, where flow‐bypassing occurred. This may be due to small discrepancies between the experimental and numerical conductivity fields. Comparison of mechanical dispersion and diffusion in low K zones indicates that molecular diffusion is equally important to mechanical dispersion in these zones and transverse dispersion did not have much effect on improving prediction. These results illustrate that predicting water flow at fine scales (relative to permeability variations) is very challenging, even under the most controlled conditions. This may have large implications for modeling reactive transport, where reactant residence time and mixing can be greatly impacted by water flowpaths.

Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography

Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the organization of the human brain in the normal state and in clinical disorders.

Information Theoretic Deformable Registration Using Local Image Information

We present a deformable registration algorithm for multi-modality images based on information theoretic similarity measures at the scale of individual image voxels. We derive analytical expressions for the mutual information, the joint entropy, and the sum of marginal entropies of two images over a small neighborhood in terms of image gradients. Using these expressions, we formulate image registration algorithms maximizing local similarity over the whole image domain in an energy minimization framework. This strategy produces highly elastic image alignment as the registration is driven by voxel similarities between the images, the algorithms are easily implementable using the closed-form expressions for the derivative of the optimization function with respect to the deformation, and avoid estimation of joint and marginal probability densities governing the image intensities essential to conventional information theoretic image registration methods.

BIOTIC AND ABIOTIC REGULATION OF LIGHTNING FIRE INITIATION IN THE MIXEDWOOD BOREAL FOREST

Lightning fire is the dominant natural disturbance of the western mixedwood boreal forest of North America. We quantified the independent effects of weather and forest composition on lightning fire initiation (a detected and recorded fire start) patterns in Alberta, Canada, to demonstrate how these biotic and abiotic components contribute to ecosystem dynamics in the mixedwood boreal forest. We used logistic regression to describe variation in annual initiation occurrence among 10,000-ha landscape units (voxels) covering a 9 million-ha study region over 11 years. At a voxel scale, forest composition explained more variation in annual initiation than did weather indices. Initiations occurred more frequently in landscapes with more conifer fuels (Picea spp.), and less in aspen-dominated (Populus spp.) ones. Initiations were less frequent in landscapes that had recently burned. Variation in initiation was also influenced by joint weather-lightning indices, but to a lesser degree. For each voxel, these indices quantified the number of days in the fire season when moisture levels were low and lightning was detected. Regional indices of fire weather severity explained substantial interannual variation of initiation, and the effect of forest composition was stronger in years with more severe fire weather. Our study is a conclusive demonstration of biotic and abiotic regulation of lightning fire initiation in the mixedwood boreal forest. The independent effects of forest composition emphasize that vegetation feedbacks strongly regulate disturbance dynamics in the region.

Validation of a personalized dosimetric evaluation tool (Oedipe) for targeted radiotherapy based on the Monte Carlo MCNPX code

Dosimetric studies are necessary for all patients treated with targeted radiotherapy. In order to attain the precision required, we have developed Oedipe, a dosimetric tool based on the MCNPX Monte Carlo code. The anatomy of each patient is considered in the form of a voxel-based geometry created using computed tomography (CT) images or magnetic resonance imaging (MRI). Oedipe enables dosimetry studies to be carried out at the voxel scale. Validation of the results obtained by comparison with existing methods is complex because there are multiple sources of variation: calculation methods (different Monte Carlo codes, point kernel), patient representations (model or specific) and geometry definitions (mathematical or voxel-based). In this paper, we validate Oedipe by taking each of these parameters into account independently. Monte Carlo methodology requires long calculation times, particularly in the case of voxel-based geometries, and this is one of the limits of personalized dosimetric methods. However, our results show that the use of voxel-based geometry as opposed to a mathematically defined geometry decreases the calculation time two-fold, due to an optimization of the MCNPX2.5e code. It is therefore possible to envisage the use of Oedipe for personalized dosimetry in the clinical context of targeted radiotherapy.

Diffusion tractography based group mapping of major white-matter pathways in the human brain

Tractography uses diffusion tensor imaging data to trace white matter pathways in vivo within the brain. We have constructed group maps that represent three major white matter tracts—the anterior callosal fibers, optic radiations, and pyramidal tracts—in a group of 21 volunteers. For each individual tract the fast marching tractography (FMT) algorithm was used to generate a VSC (voxel scale connectivity) map in native space. Using SPM99 these maps were transformed into a standard reference frame and three group mapping techniques were investigated: the first averaged the individual VSC maps, the second produced maps that demonstrate intersubject tract variability and degree of overlap, and the third used an SPM analysis to construct a statistical image that represents the group effect. The group maps reconstructed for each tract under investigation conform well to known anatomy and are consistent with data derived from postmortem human brains. Greater intersubject variability is found around the terminal projections of the tracts adjacent to cerebral cortex, whereas the “core” of each tract is characterized by lower variability. No significant differences were found between the left and right side of the pyramidal tracts and optic radiations. The group mapping techniques utilize the VSC maps in different but complementary ways. In the future, group mapping could investigate in vivo white matter differences between normal subjects and patients affected by neurological and psychiatric diseases.

Measuring myocardial deformations from MR data using information-theoretic nonrigid registration

Magnetic resonance imaging (MRI) is recognized as a relevant modality for dynamically imaging the heart anatomy and function, and achieving satisfying qualitative diagnosis. Reliable methods for quantitatively analyzing cardiac MR images remain, however, to be elaborated. This paper presents a novel approach for measuring myocardial and wall motion from MRI sequences. Based on efficient voxel-based statistical nonrigid registration (NRR), it allows for automatically extracting local/global deformation parameters at the voxel, myocardial segment and whole myocardium scales. Its performances for assessing the myocardial contractile function are illustrated both for the normal heart and for dilated cardiomyopathies (DCM).