Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.
Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.
Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.
Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data.
Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.
The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.
The human cerebral cortex is one of the most complicated structures in the body. It has a highly convoluted
structure with much of the cortical sheet buried in sulci. Based on cytoarchitectural and functional imaging
studies, it is possible to segment the cerebral cortex into several subregions. While it is only possible to differentiate
the true anatomical subregions based on cytoarchitecture, the surface morphometry aligns closely with the
underlying cytoarchitecture and provides features that allow the surface of the cortex to be parcellated based on
the sulcal and gyral patterns that are readily visible on the MR images.
We have developed a fully automated pipeline for the generation and registration of cortical surfaces in
the spherical domain. The pipeline initiates with the BRAINS AutoWorkup pipeline. Subsequently, topology
correction and surface generation is performed to generate a genus zero surface and mapped to a sphere. Several
surface features are then calculated to drive the registration between the atlas surface and other datasets. A
spherical diffeomorphic demons algorithm is used to co-register an atlas surface onto a subject surface.
A lobar based atlas of the cerebral cortex was created from a manual parcellation of the cortex. The atlas
surface was then co-registered to five additional subjects using a spherical diffeomorphic demons algorithm. The
labels from the atlas surface were warped on the subject surface and compared to the manual raters. The average
Dice overlap index was 0.89 across all regions.
Schizophrenia is a serious and disabling mental disorder. Diffusion tensor imaging (DTI) studies performed on
schizophrenia have demonstrated white matter degeneration either due to loss of myelination or deterioration of fiber
tracts although the areas where the changes occur are variable across studies. Most of the population based studies
analyze the changes in schizophrenia using scalar indices computed from the diffusion tensor such as fractional
anisotropy (FA) and relative anisotropy (RA). The scalar measures may not capture the complete information from the
diffusion tensor. In this paper we have applied the RADTI method on a group of 9 controls and 9 patients with
schizophrenia. The RADTI method converts the tensors to log-Euclidean space where a linear regression model is
applied and hypothesis testing is performed between the control and patient groups. Results show that there is a
significant difference in the anisotropy between patients and controls especially in the parts of forceps minor, superior
corona radiata, anterior limb of internal capsule and genu of corpus callosum. To check if the tensor analysis gives a
better idea of the changes in anisotropy, we compared the results with voxelwise FA analysis as well as voxelwise
geodesic anisotropy (GA) analysis.
Diffusion imaging provides the ability to study white matter connectivity and integrity noninvasively. Diffusion
weighted imaging contains orientation information that must be appropriately reoriented when applying spatial
transforms to the resulting imaging data. Alexander et al. have introduced two methods to resolve the reorientation
problem. In the first method, the rotation matrix is computed from the transform and the tensors are reoriented. The
second method called as the preservation of principal direction (PPD) method, takes into account the deformation and
rotation components to estimate the rotation matrix. These methods cannot be directly used for higher order diffusion
models (e.g. Q-ball). We have introduced a novel technique called gradient rotation where the rotation is directly applied
to the diffusion sensitizing gradients providing a voxel by voxel estimate of the diffusion gradients instead of a volume
of by volume estimate. A PPD equivalent gradient rotation can be computed using principal component analysis (PCA).
Four subjects were spatially normalized to a template subject using a multistage registration sequence that includes
nonlinear diffeomorphic demons registration. Comparative results of all four methods have been shown. It can be
observed that all the methods work closely to each other, PPD (original and gradient equivalent) being slightly better
than rigid rotation, based on the fact that it includes the shear and scale component. Results also demonstrate that the
multistage registration is a viable method for spatial normalization of diffusion models.
A common procedure performed by many groups in the analysis of neuroimaging data is separating the brain from other
tissues. This procedure is often utilized both by volumetric studies as well as functional imaging studies. Regardless of
the intent, an accurate, robust method of identifying the brain or cranial vault is imperative. While this is a common
requirement, there are relatively few tools to perform this task. Most of these tools require a T1 weighted image and are
therefore not able to accurately define a region that includes surface CSF. In this paper, we have developed a novel brain
extraction technique termed Maximize Uniformity by Summation Heuristic (MUSH) optimization. The algorithm was
designed for extraction of the brain and surface CSF from a multi-modal magnetic resonance (MR) imaging study. The
method forms a linear combination of multi-modal MR imaging data to make the signal intensity within the brain as
uniform as possible. The resulting image is thresholded and simple morphological operators are utilized to generate the
resulting representation of the brain. The resulting method was applied to a sample of 20 MR brain scans and compared
to the results generated by 3dSkullStrip, 3dIntracranial, BET, and BET2. The average Jaccard metrics for the twenty
subjects was 0.66 (BET), 0.61 (BET2), 0.88 (3dIntracranial), 0.91 (3dSkullStrip), and 0.94 (MUSH).
The cerebral cortex is a highly convoluted anatomical structure. The folding pattern defined by sulci and gyri is a
complex pattern that is very heterogeneous across subjects. The heterogeneity across subjects has made the automated
labeling of this structure into its constituent components a challenge to the field of neuroimaging. One way to approach
this problem is to conformally map the surface to another representation such as a plane or sphere. Conformal mapping
of the surface requires that surface to be topologically correct. However, noise and partial volume artifacts in the MR
images frequently cause holes or handles to exist in the surface that must be removed. Topology correction techniques
have been proposed that operate on the cortical surface, the original image data, and hybrid methods have been proposed.
This paper presents an experimental assessment of two different topology correction methods. The first approach is
based on modification of 3D voxel data. The second method is a hybrid approach that determines the location of defects
from the surface representation while repairing the surface by modifying the underlying image data. These methods have
been applied to 10 brains, and a comparison is made among them. In addition, detailed statistics are given based on the
voxel correction method.
Based on these 10 MRI datasets, we have found the hybrid method incapable of correcting the cortical surface
appropriately when a handles and holes exist in close proximity. In several cases, holes in the anatomical surface were
labeled as handles thus resulting in discontinuities in the folding pattern. The image-based approach in this study was
found to correct the topology in all ten cases within a reasonable time. Furthermore, the distance between the original
and corrected surfaces, thickness of brain cortex, curvatures and surface areas are provided as assessments of the
approach based on our datasets.
Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging.
Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like
that being collected in several on going multi-center studies. We have previously reported on using artificial neural
networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen
(0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The
input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We
have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and
cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework.
Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using
15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were
applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was
0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the
cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater
reliability between manual raters and can be achieved without rater intervention.
The ability to study the biochemical composition of the brain is becoming important to better understand
neurodegenerative and neurodevelopmental disorders. Magnetic Resonance Spectroscopy (MRS) can non-invasively
provide quantification of brain metabolites in localized regions. The reliability of MRS is limited in part due to partial
volume artifacts. This results from the relatively large voxels that are required to acquire sufficient signal-to-noise ratios
for the studies. Partial volume artifacts result when a MRS voxel contains a mixture of tissue types. Concentrations of
metabolites vary from tissue to tissue. When a voxel contains a heterogeneous tissue composition, the spectroscopic
signal acquired from this voxel will consist of the signal from different tissues making reliable measurements difficult.
We have developed a novel tool for the estimation of partial volume tissue composition within MRS voxels thus
allowing for the correction of partial volume artifacts. In addition, the tool can localize MR spectra to anatomical regions
of interest. The tool uses tissue classification information acquired as part of a structural MR scan for the same subject.
The tissue classification information is co-registered with the spectroscopic data. The user can quantify the partial
volume composition of each voxel and use this information as covariates for metabolite concentrations.
Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like
that being collected in several on going multi-center studies. We have previously reported on using artificial neural
networks (ANN) to define subcortical brain structures such as the thalamus (0.825), caudate (0.745), and putamen
(0.755). One of the inputs into the ANN is the apriori probability of a structure existing at a given location. In this
previous work, the apriori probability information was generated in Talairach space using a piecewise linear registration.
In this work we have increased the dimensionality of this registration using Thirion's demons registration algorithm. The
input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. The
output of the neural network determined if the voxel was defined as one of the N regions used for training. Training was
performed using a standard back propagation algorithm. The ANN was trained on a set of 15 images for 750,000,000
iterations. The resulting ANN weights were then applied to 6 test images not part of the training set. Relative overlap
calculated for each structure was 0.875 for the thalamus, 0.845 for the caudate, and 0.814 for the putamen. With the
modifications on the neural net algorithm and the use of multi-dimensional registration, we found substantial
improvement in the automated segmentation method. The resulting segmented structures are as reliable as manual raters
and the output of the neural network can be used without additional rater intervention.
KEYWORDS: Functional magnetic resonance imaging, Image registration, Statistical analysis, Image processing, Brain, Computer simulations, Monte Carlo methods, Neuroimaging, Magnetic resonance imaging, Signal attenuation
During functional magnetic resonance imaging (fMRI) brain examinations, the signal extraction from a large number of images is used to evaluate changes in blood oxygenation levels by applying statistical methodology. Image registration is essential as it assists in providing accurate fractional positioning accomplished by using interpolation between sequentially acquired fMRI images. Unfortunately, current subvoxel registration methods found in standard software may produce significant bias in the variance estimator when interpolating with fractional, spatial voxel shifts. It was found that interpolation schemes, as currently applied during the registration of functional brain images, could introduce statistical bias, but there is a possible correction scheme. This bias was shown to result from the "weighted-averaging" process employed by conventional implementation of interpolation schemes. The most severe consequence of inaccurate variance estimators is the undesirable violation of the fundamental 'stationary' assumption required for many statistical methods and Gaussian random field analysis. Thus, this bias violates assumptions of the general linear model (GLM) and/or t-tests commonly used in fMRI studies. Using simulated data as well as actual human data in this, it was demonstrated that this artifact can significantly alter the magnitude and location of the resulting activation patterns/results. Further, the work detailed here introduces a bias correction scheme and evaluates the improved accuracy of its sample variance calculation and influence on fMRI results through comparison with traditional fMRI image registered data.
Clinical signs of radiotherapy failure are often not present until well after treatment has been completed. Methods which could predict the response of tumors either before or early into the radiotherapy schedule would have important implications for patient management. Recent studies performed at our institution suggest that MR perfusion imaging maya be useful in distinguishing between individuals who are likely to benefit from radiation therapy and those who are not. Because MR perfusion imaging reflects tissue vascularity as well as perfusion, quantitative positron emission tomographic (PET) blood flow studies were performed to obtain an independent assessment of tumor perfusion. MR perfusion and PET quantitative blood flow studies were acquired on four women diagnosed with advanced cervical cancer. The MR perfusion studies were acquired on a 1 cm sagittal slice through the epicenter of the tumor mass. Quantitative PET blood flow studies were performed using an autoradiographic technique. The PET and MRI were registered using a manual interactive routine and the mean blood flow in the tumor was compared to the relative signal intensity in a corresponding region on the MR image. The mean blood flow in the cervical tumors ranged form 30-48 ml/min/100 grams. The observed blood flow values are consistent with the assumed relationship between MR contrast enhancement and the distribution of tissue perfusion. The information offered by these studies provides an additional window into the evaluation of the response of cervical tumors to radiation therapy.
Phase contrast magnetic resonance (PC MR) imaging provides an accurate, non- invasive method for blood flow quantification. Unlike conventional MR images that are derived from the amplitude of the proton signal, velocity maps are reconstructed from the phase information of the MR signal. When a magnetic field gradient is applied along the axis of a vessel, intravascular magnetic spins accumulate a phase- shift that is proportional to flow velocity. The phase-shifts are mapped into a 2D array composed of flow velocities. The velocities over an entire cross-sectional area of a blood vessel can then be summed to quantitate actual blood flow. We have used PC MR imaging to quantitate flow in a flow phantom and human subjects. In flow phantom studies, a significant correlation was found between PC MR flow measurements made proximal and distal to a bifurcation (r2 equals 0.999, N equals 5). In 6 human subjects, we found right pulmonary artery (PA) blood flow comprised 53% +/- 1% (mean +/- SEM) of total PA blood flow with the remaining 47% +/- 1% provided by the left PA (difference not statistically significant). Blood flow in the descending aorta, distal to the takeoffs of arteries to the head and upper extremeties, equaled 75% +/- 5% of the blood flow in the ascending aorta. PC MR imaging promises to be a useful tool in the evaluation of blood flow. Advantages of this method include the ability to profile flow velocities over the entire cross-sectional area of the vessel and non-invasive analysis of structures not accessible by other imaging modalities.
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