Roger P. Woods

Rapid automated algorithm for aligning and reslicing PET images

A computer algorithm for the three-dimensional (3D) alignment of PET images is described. To align two images, the algorithm calculates the ratio of one image to the other on a voxel-by-voxel basis and then iteratively moves the images relative to one another to minimize the variance of this ratio across voxels. Since the method relies on anatomic information in the images rather than on external fiducial markers, it can be applied retrospectively. Validation studies using a 3D brain phantom show that the algorithm aligns images acquired at a wide variety of positions with maximum positional errors that are usually less than the width of a voxel (1.745 mm). Simulated cortical activation sites do not interfere with alignment. Global errors in quantitation from realignment are <2%. Regional errors due to partial volume effects are largest when the gantry is rotated by large angles or when the bed is translated axially by one-half the interplane distance. To minimize such partial volume effects, the algorithm can be used prospectively, during acquisition, to reposition the scanner gantry and bed to match an earlier study. Computation requires 3–6 min on a Sun SPARCstation 2.

Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

PURPOSE We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. METHOD Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. RESULTS All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. CONCLUSION The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.

MRI-PET registration with automated algorithm.

Objective We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject. Methods This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm. Results and Conclusion The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of <3 mm and mean three-dimensional errors of <2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H215O PET images typically requires 20–30 min.

Automated image registration : II. Intersubject validation of Linear and nonlinear models

PURPOSE Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity. METHOD PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks. RESULTS Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration. CONCLUSION Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.

Enhancement of MR images using registration for signal averaging

Purpose: With the advent of noninvasive neuroimaging, a plethora of digital human neuroanatomical atlases has been developed. The accuracy of these atlases is constrained by the resolution and signal-gathering powers of available imaging equipment. In an attempt to circumvent these limitations and to produce a high resolution in vivo human neuroanatomy, we investigated the usefulness of intrasubject registration for post hoc MR signal averaging. Method: Twenty-seven high resolution (7 x 0.78 and 20 x 1.0 mm 3 ) Tl-weighted volumes were acquired from a single subject. along with 12 double echo T2/proton density-weighted volumes. These volumes were automatically registered to a common stereotaxic space in which they were subsampled and intensity averaged. The resulting images were examined for anatomical quality and usefulness for other analytical techniques. Results: The quality of the resulting image from the combination of as few as five Tl volumes was visibly enhanced. The signal-to-noise ratio was expected to increase as the root of the number of contributing scans to 5.2, n = 27. The improvement in the n = 27 average was great enough that fine anatomical details, such as thalamic subnuclei and the gray bridges between the caudate and putamen, became crisply defined. The gray/white matter boundaries were also enhanced. as was the visibility of any finer structure that was surrounded by tissue of varying Tl intensity. The T2 and proton density average images were also of higher quality than single scans, but the improvement was not as dramatic as that of the Tl volumes. Conclusion: Overall, the enhanced signal in the averaged images resulted in higher quality anatomical images, and the data lent themselves to several postprocessing techniques. The high quality of the enhanced images permits novel uses of the data and extends the possibilities for in vivo human neuroanatomy.

Stereotaxic White Matter Atlas Based on Diffusion Tensor Imaging in an ICBM Template

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.

Growth patterns in the developing brain detected by using continuum mechanical tensor maps.

The dynamic nature of growth and degenerative disease processes requires the design of sensitive strategies to detect, track and quantify structural change in the brain in its full spatial and temporal complexity. Although volumes of brain substructures are known to change during development, detailed maps of these dynamic growth processes have been unavailable. Here we report the creation of spatially complex, four-dimensional quantitative maps of growth patterns in the developing human brain, detected using a tensor mapping strategy with greater spatial detail and sensitivity than previously obtainable. By repeatedly scanning children (aged 3–15 years) across time spans of up to four years, a rostro-caudal wave of growth was detected at the corpus callosum, a fibre system that relays information between brain hemispheres. Peak growth rates, in fibres innervating association and language cortices, were attenuated after puberty, and contrasted sharply with a severe, spatially localized loss of subcortical grey matter. Conversely, at ages 3–6 years, the fastest growth rates occurred in frontal networks that regulate the planning of new actions. Local rates, profiles, and principal directions of growth were visualized in each individual child.

Reafferent copies of imitated actions in the right superior temporal cortex

Imitation is a complex phenomenon, the neural mechanisms of which are still largely unknown. When individuals imitate an action that already is present in their motor repertoire, a mechanism matching the observed action onto an internal motor representation of that action should suffice for the purpose. When one has to copy a new action, however, or to adjust an action present in ones motor repertoire to a different observed action, an additional mechanism is needed that allows the observer to compare the action made by another individual with the sensory consequences of the same action made by himself. Previous experiments have shown that a mechanism that directly matches observed actions on their motor counterparts exists in the premotor cortex of monkeys and humans. Here we report the results of functional magnetic resonance experiments, suggesting that in the superior temporal sulcus, a higher order visual region, there is a sector that becomes active both during hand action observation and during imitation even in the absence of direct vision of the imitators hand. The motor-related activity is greater during imitation than during control motor tasks. This newly identified region has all the requisites for being the region at which the observed actions, and the reafferent motor-related copies of actions made by the imitator, interact.

Mathematical/Computational Challenges in Creating Deformable and Probabilistic Atlases of the Human Brain

Striking variations in brain structure, especially in the gyral patterns of the human cortex, present fundamental challenges in human brain mapping. Probabilistic brain atlases, which encode information on structural and functional variability in large human populations, are powerful research tools with broad applications. Knowledge‐based imaging algorithms can also leverage atlased information on anatomic variation. Applications include automated image labeling, pathology detection in individuals or groups, and investigating how regional anatomy is altered in disease, and with age, gender, handedness and other clinical or genetic factors. In this report, we illustrate some of the mathematical challenges involved in constructing population‐based brain atlases. A disease‐specific atlas is constructed to represent the human brain in Alzheimers disease (AD). Specialized strategies are developed for population‐based averaging of anatomy. Sets of high‐dimensional elastic mappings, based on the principles of continuum mechanics, reconfigure the anatomy of a large number of subjects in an anatomic image database. These mappings generate a local encoding of anatomic variability and are used to create a crisp anatomical image template with highly resolved structures in their mean spatial location. Specialized approaches are also developed to average cortical topography. Since cortical patterns are altered in a variety of diseases, gyral pattern matching is used to encode the magnitude and principal directions of local cortical variation. In the resulting cortical templates, subtle features emerge. Regional asymmetries appear that are not apparent in individual anatomies. Population‐based maps of cortical variation reveal a mosaic of variability patterns that segregate sharply according to functional specialization and cytoarchitectonic boundaries. Hum. Brain Mapping 9:81–92, 2000.

Human Brain White Matter Atlas: Identification and Assignment of Common Anatomical Structures in Superficial White Matter

Structural delineation and assignment are the fundamental steps in understanding the anatomy of the human brain. The white matter has been structurally defined in the past only at its core regions (deep white matter). However, the most peripheral white matter areas, which are interleaved between the cortex and the deep white matter, have lacked clear anatomical definitions and parcellations. We used axonal fiber alignment information from diffusion tensor imaging (DTI) to delineate the peripheral white matter, and investigated its relationship with the cortex and the deep white matter. Using DTI data from 81 healthy subjects, we identified nine common, blade-like anatomical regions, which were further parcellated into 21 subregions based on the cortical anatomy. Four short association fiber tracts connecting adjacent gyri (U-fibers) were also identified reproducibly among the healthy population. We anticipate that this atlas will be useful resource for atlas-based white matter anatomical studies.