IPL Publications
Title |
Journal |
Abstract |
Full Article |
Year |
| Voxel processing techniques for the antemortem
study of neuroanatomy and neuropathology using magnetic resonance imaging. |
J Neuropsychiatry Clin Neurosci |
abstract |
pdf (3.5M) |
1993 |
| Automatic atlas-based volume estimation of
human brain regions from MR images. |
J Comput Assist Tomogr |
abstract |
pdf
(2.9M) |
1996 |
| Human frontal cortex: an MRI-based parcellation
method. |
Neuroimage |
abstract |
pdf (6.3M) |
1999 |
| Improving tissue classification in MRI: a
three-dimensional multispectral discriminant analysis method with automated
training class selection. |
J Comput Assist Tomogr |
abstract |
pdf
(3.4M) |
1999 |
| Quantitative in vivo measurement of gyrification
in the human brain: changes associated with aging. |
Cereb Cortex |
abstract |
pdf (3.5M) |
1999 |
| Measurement of brain structures with artificial
neural networks: two- and three-dimensional applications. |
Radiology |
abstract |
pdf (3.3M) |
1999 |
| An MRI-based parcellation method for the temporal lobe. |
Neuroimage |
abstract |
pdf
(6.0M) |
2000 |
| Visualization of subthalamic nuclei with
cortex attenuated inversion recovery MR imaging. |
Neuroimage |
abstract |
pdf
(1.8M) |
2000 |
| A new method for the in vivo volumetric measurement
of the human hippocampus with high neuroanatomical accuracy. |
Hippocampus |
abstract |
pdf
(2.4M) |
2000 |
| Color enhancement of multispectral MR images:
improving the visualization of subcortical structures. |
J Comput Assist Tomogr |
abstract |
pdf (2.5M) |
2001 |
Subcortical, cerebellar, and magnetic resonance based
consistent brain image registration |
Neuroimage |
abstract |
pdf (0.5M) |
2003 |
Andreasen, N. C., T. Cizadlo, et al. (1993). ?Voxel processing
techniques for the antemortem study of neuroanatomy and neuropathology using
magnetic resonance imaging. ? J Neuropsychiatry Clin
Neurosci 5(2): 121-30. BRAINBLAST, a program that uses voxel processing,
was developed in order to produce high-fidelity three-dimensional reconstructions
of the brain. Four steps were used to produce images: washing away cerebrospinal
fluid (via histogramming), dissecting away the blood vessels (via a connectivity
heuristic), highlighting the sulci and gyri (via a lighting model), and resampling
the interior contents of the brain. After reconstruction, the images can be resampled,
rotated, written on, measured, or redissected. The technique has a variety of
applications: study of individual variation in sulcal and gyral patterns, evaluation
of structure/function relationships, measurement of volumes or subregions using
anatomically defined landmarks, and teaching of
neuroanatomy.
pdf (3.5M)
Andreasen, N. C., R. Rajarethinam, et al. (1996). ?Automatic
atlas-based volume estimation of human brain regions from MR images. ? J Comput
Assist Tomogr 20(1): 98-106.OBJECTIVE: MRI offers many opportunities
for noninvasive in vivo measurement of structure-function relationships in the
human brain. Although automated methods are now available for whole-brain measurements,
an efficient and valid automatic method for volume estimation of subregions such
as the frontal or temporal lobes is still needed. MATERIALS AND METHODS: We adapted
the Talairach atlas to the study of brain subregions. We supplemented the atlas
with additional boxes to include the cerebellum. We assigned all the boxes to
1 of 12 regions of interest (ROIs) (frontal, parietal, temporal, and occipital
lobes, cerebellum, and subcortical regions on right and left sides of the brain).
Using T1-weighted MR scans collected with an SPGR sequence (slice thickness =
1.5 mm), we manually traced these ROIs and produced volume estimates. We then
transformed the scans into Talairach space and compared the volumes produced
by the two methods ("traced" versus "automatic"). The traced measurements were
considered to be the "gold
standard" against which the automatic measurements were compared. RESULTS: The
automatic method was found to produce measurements that were nearly identical
to the traced method. We compared absolute measurements of volume produced by
the two methods, as well as the sensitivity and specificity of the automatic
method. We also compared the measurements of cerebral blood flow obtained through
[15O]H2O PET studies in a sample of nine subjects. Absolute measurements of volume
produced by the two methods were very similar, and the sensitivity and specificity
of the automatic method were found to be high for all regions. The flow values
were also found to be very similar by both methods. CONCLUSION: The automatic
atlas-based method for measuring the volume of brain subregions produces results
that are similar to manual techniques. The method is rapid, efficient, unbiased,
and not subject to the problems of rater drift or potentially poor interrater
reliability that plague manual methods. Consequently, this method may be very
useful for the study of structure-function
relationships in the human brain.
pdf (2.9M)
Crespo-Facorro, B., J. J. Kim, et al. (1999). ?Human frontal
cortex: an MRI-based parcellation method. ? Neuroimage 10(5): 500-19.The
frontal lobe is not a single anatomical and functional brain region. Several
lines of research have demonstrated that particular subregions within the frontal
lobe are associated with specific motor and cognitive functions in the human
being. Our main purpose is to develop a magnetic resonance image (MRI)-based
parcellation method of the frontal lobe that permits us to explore plausible
abnormalities in functionally relevant frontal subregions in brain illnesses.
We describe a procedure using MRI for subdividing the entire frontal cortex into
11 subregions: supplementary motor area (SMA), rostral anterior cingulate gyrus
(r-ACiG), caudal anterior cingulate gyrus (c- ACiG), superior cingulate gyrus
(SCiG), medial frontal cortex (MFC), straight gyrus (SG), orbitofrontal cortex
(OFC), precentral gyrus (PCG), superior frontal gyrus (SFG), inferior frontal
gyrus (IFG), and middle frontal gyrus (MFG). Our method posits to conserve the
topographic uniqueness of individual brains and is based on our ability to visualize
both the three-dimensional (3D) rendered brain and the three orthogonal planes
simultaneously. The reliability study for gray matter volume and surface area
of each subregion was performed on a set of 10 MR scans by two raters. The intraclass
R coefficients for gray matter volume of each subregion ranged between 0.86 and
0.99. We describe here a reproducible and reliable topography-based parcellation
method of the frontal lobe that will allow us to use new approaches to understand
the role of particular frontal cortical subregions in schizophrenia and other
brain illnesses.
pdf (6.3M)
Harris, G., N. C. Andreasen, et al. (1999). ?Improving
tissue classification in MRI: a three-dimensional multispectral discriminant
analysis method with automated training class selection. ? J Comput Assist
Tomogr 23(1): 144-54.PURPOSE: To improve the reliability, accuracy,
and computational efficiency of tissue classification with multispectral sequences
[T1, T2, and proton density (PD)], we developed an automated method for identifying
training classes to be used in a discriminant function analysis. We compared
it with a supervised operator-dependent method, evaluating its reliability and
validity. We also developed a fuzzy (continuous) classification to correct for
partial voluming. METHOD: Images were obtained on a 1.5 T GE Signa MR scanner
using three pulse sequences that were co-registered. Training classes for the
discriminant analysis were obtained in two ways. The operator-dependent method
involved defining circular ROIs containing 5-15 voxels
that represented "pure" samples of gray matter (GM), white matter (WM), and cerebrospinal
fluid (CSF), using a total of 150-300 voxels for each tissue type. The automated
method involved selecting a large number of samples of brain tissue with sufficiently
low variance and randomly placed throughout the brain
("plugs"), partitioning these samples into GM, WM, and CSF, and minimizing the
amount of variance within each partition of samples to optimize its "purity." The
purity of the plug was estimated by calculating the variance of 8 voxels in all
modalities (T1, T2, and PD). We also compared "sharp" (discrete) measurements
(which classified tissue only as GM, WM, or CSF) and "fuzzy" (continuous) measurements
(which corrected for partial voluming by weighting the classification based on
the mixture of tissue types in each voxel). RESULTS: Reliability was compared
for the operator- dependent and automated methods as well as for the fuzzy versus
sharp classification. The automated sharp classifications consistently had the
highest interrater and intrarater reliability. Validity was assessed in three
ways: reproducibility of measurements when the same individuals were scanned
on multiple occasions, sensitivity of the method to detecting changes associated
with aging, and agreement between the automated segmentation values and those
produced through expert manual segmentation. The sharp automated classification
emerged as slightly superior to the other three methods according to each of
these validators. Its reproducibility index (intraclass r) was 0.97, 0.98, and
0.98 for total CSF, total GM, and total WM, respectively. Its correlations with
age were 0.54, -0.61, and -0.53, respectively. Its percent agreement with the
expert manually segmented tissue for the three tissue types was 93, 90, and 94%,
respectively. CONCLUSION: Automated identification of training classes for discriminant
analysis was clearly superior to a method that required operator intervention.
A sharp (discrete) classification into three tissue types was also slightly superior
to one that used "fuzzy" classification to produce continuous measurements to
correct for partial voluming. This multispectral automated discriminant analysis
method produces a computationally efficient, reliable, and valid method for classifying
brain tissue into GM, WM, and CSF. It corrects some of the problems with reliability
and computational inefficiency previously observed for operator-dependent approaches
to segmentation.
pdf (3.4M)
Magnotta, V. A., N. C. Andreasen, et al. (1999). ?Quantitative
in vivo measurement of gyrification in the human brain: changes associated with
aging. ? Cereb Cortex 9(2): 151-60.Clinical observation suggests
that the aging process affects gyrification, with the brain appearing more 'atrophic'
with increasing age. Empirical studies of tissue type indicate that gray matter
volume decreases with age while cerebrospinal fluid increases. Quantitative changes
in cortical surface characteristics such as sulcal and gyral shape have not been
measured, however, due to difficulties in developing a method that separates
abutting gyral crowns and opens up the sulci -- the 'problem of buried cortex'.
We describe a quantitative method for measuring brain surface characteristics
that is reliable and valid. This method is used to define the gyral and sulcal
characteristics of atrophic and non-atrophic brains and to examine changes that
occur with aging in a sample of 148 normal individuals from a broad age range.
The shape of gyri and sulci change significantly over time, with the gyri becoming
more sharply and steeply curved, while the sulci become more flattened and less
curved. Cortical thickness also decreases over time. Cortical thinning progresses
more rapidly in males than in females. The progression of these changes appears
to be relatively stable during midlife and to begin to progress some time during
the fourth decade. Measurements of sulcal and gyral shape may be useful in studying
the mechanisms of both neurodevelopmental and neurodegenerative changes that
occur
during brain maturation and aging.
pdf (3.5M)
Magnotta, V. A., D. Heckel, et al. (1999). ?Measurement of brain
structures with artificial neural networks: two- and three-dimensional
applications. ? Radiology 211(3): 781-90.PURPOSE: To evaluate the
ability of an artificial neural network (ANN) to identify brain structures. This
ANN was applied to postprocessed magnetic resonance (MR) images to segment various
brain structures in both two- and three-dimensional applications. MATERIALS AND
METHODS: An ANN was designed that learned from experience to define the corpus
callosum, whole brain, caudate, and putamen. Manual segmentation was used as
a training set for the ANN. The ANN was trained on two-thirds of the manually
segmented images and was tested on the remaining one- third. The reliability
of the ANN was compared against manual segmentations by two technicians. RESULTS:
The ANN was able to identify the brain structures as readily and as well as did
the two technicians. Reliability of the ANN compared with the technicians was
0.96 for the corpus callosum, 0.95 for the whole brain, 0.86 (right) and 0.93
(left) for the caudate, and 0.71 (right) and 0.88 (left) for the putamen. CONCLUSION:
The ANN was able to identify the structures used in this study as well as did
the two technicians. The ANN could do this much more rapidly and without rater
drift. Several other cortical and subcortical structures could also be readily
identified with this method.
pdf (3.3M)
Kim, J. J., B. Crespo-Facorro, et al. (2000). ?An MRI-based
parcellation method for the temporal lobe. ? Neuroimage 11(4):
271-88.The temporal lobe has long been a focus of attention with regard to the
underlying pathology of several major psychiatric illnesses. Previous postmortem
and imaging studies describing regional volume reductions or perfusion defects
in temporal subregions have shown inconsistent findings, which are in part due
to differences in the definition of the subregions and the methodology of measurement.
The development of precise reproducible parcellation systems on magnetic resonance
images may help improve uniformity of results in volumetric MR studies and unravel
the complex activation patterns seen in functional neuroimaging studies. The
present study describes detailed guidelines for the parcellation of the temporal
neocortex. It parcels the entire temporal neocortex into 16 subregions: temporal
pole, heschl's gyrus, planum temporale, planum polare, superior temporal gyrus
(rostral and caudal), middle temporal gyrus (rostral, intermediate, and caudal),
inferior temporal gyrus (rostral, intermediate, and caudal), occipitotemporal
gyrus (rostral and caudal), and parahippocampal gyrus (rostral and caudal). Based
upon topographic landmarks of individual sulci, every subregion was consecutively
traced on a set of serial coronal slices. In spite of the huge variability of
sulcal topography, the sulcal landmarks could be identified reliably due to the
simultaneous display of three orthogonal (transaxial, coronal, and sagittal)
planes, triangulated gray matter isosurface, and a 3-D-rendered image. The reliability
study showed that the temporal neocortex could be parceled successfully and reliably;
intraclass correlation coefficient for each subregion ranged from 0.62 to 0.99.
Ultimately, this method will permit us to detect subtle morphometric impairments
or to find abnormal patterns of functional activation in the temporal subregions
that might reflect underlying neuropathological processes in psychiatric illnesses
such as
schizophrenia.
pdf (6.0M)
Magnotta, V. A., S. Gold, et al. (2000). ?Visualization
of subthalamic nuclei with cortex attenuated inversion recovery
MR imaging. ? Neuroimage 11(4): 341-6.There is a significant amount
of interest in studying the thalamus because of its central location in the brain
and its role as a gatekeeper to higher centers of cognition. Imaging and measuring
of the individual subnuclei of the thalamus has proven extremely difficult in
MR because of the contrast-to-noise (CNR) of the MR sequences used. This report
describes a novel MR pulse sequence known as cortex attenuated inversion recovery
(CAIR), which increases the CNR in images and allows the individual subnuclei
of the thalamus to be visualized by selectively nulling the gray matter in the
brain using an inversion recovery sequence with an inversion time of 700 ms at
1.5 T.
pdf (1.8M)
Pantel, J., D. S. O'Leary, et al. (2000). ?A new method
for the in vivo volumetric measurement of the human hippocampus with high neuroanatomical
accuracy. ? Hippocampus 10(6): 752-8.Accurate and reproducible
in vivo measurement of hippocampal volumes using magnetic resonance (MR) imaging
is complicated by the morphological complexity of the structure. Additionally,
separation of certain parts of the hippocampus from the adjacent brain structures
on MR images is sometimes very difficult. These difficulties have led most investigators
to either use arbitrary landmarks or to exclude certain parts of the structure
from their measurements. Based on three- dimensional MR data, we have developed
a reliable in vivo volumetric measurement of the human hippocampus. In contrast
to most of the previously described volumetric MR-based methods, we aimed to
sample the entire hippocampal formation using its true anatomical definition.
This was accomplished by relying on the capacity of the BRAINS software to simultaneously
visualize in multiple planes,
to "telegraph" tracings or cursor position from one plane to another, and to
simultaneously rely on multispectral data from three different image sets (T1,
T2, and tissue classified). The methods for identifying boundaries and measuring
the hippocampal volume are described. The method has excellent reliability, sensitivity,
and specificity. The method may be of use in studies of structure-function relationships
in neuropsychiatric disorders such as schizophrenia, temporal lobe epilepsy,
and Alzheimer's disease. Future work will use these measurements as training
data for a neural net-based technique to
identify the anatomical boundaries automatically.
pdf (2.4M)
Ward, J., V. Magnotta, et al. (2001). ?Color enhancement of
multispectral MR images: improving the visualization of
subcortical structures. ? J Comput Assist Tomogr 25(6): 942-9.PURPOSE:
The current investigation was undertaken to evaluate a new method for creating
MR multispectral color images, which we call "Superimages." They were developed
to improve the delineation of small brain structures composed of mixed tissue
types, such as the basal ganglia. METHOD: To qualitatively validate the method,
visual comparisons were made of six unimodal and multispectral images, including
the Superimage. Quantitative validation was undertaken by comparing the reliability
values for parcellation of the globus pallidus (GP) using either a gray scale
(T1-weighted) image or the Superimage. RESULTS: Qualitative assessment of the
Superimage revealed enhanced visualization of the GP, caudate, and putamen. Quantitative
assessment resulted in good reliability for Superimage traces. CONCLUSION: The
Superimage significantly improves both the visualization and the parcellation
of structures visualized by MRI.
pdf (2.5M)
Magnotta, V. A., H. J. Bockholt, et al. (2003). ?Subcortical,
cerebellar, and magnetic resonance based
consistent brain image registration.? Neuroimage 19(): 233-45.A
new
landmark-initialized segmentation and intensity-based (LI-SI) inverse-consistent
linear elastic image registration
algorithm is
presented. This method uses manually identified landmarks, segmented
volumetric (anatomical) structures, and normalized image signal
intensity information to co-register datasets. The features used for
image registration and evaluation include: 35 cortical, cerebellar, and
commissure landmarks manually identified by experts, sub-cortical and
cerebellar regions defined semi-automatically by an artificial neural
network and manually trimmed for validity by experts, and tissue
classified images that were generated using a discriminant analysis of
three magnetic resonance image sets representing T1, T2, and PD
modalities. Four groups of results were computed for co-registering 16
datasets with the following registration techniques: rigid registration,
extended Talairach registration, intensity-only inverse-consistent
linear elastic registration, and the new LI-SI registration. Results are
presented showing that relative overlap measurements increased as the
dimensionality of the registration algorithm and amount of anatomical
information increased. The average relative overlap improved from $
0.53$ for the rigid registration to $ 0.55$ for the Talairach
registration to $ 0.74$ for the intensity-only and to $ 0.85$ for the
LI-SI algorithm. We showed a statistically significant improvement for
all but one structure using the intensity-only algorithm as compared to
the Talairach registration. Furthermore, statistically significant
improvements for all structures were achieved using the LI-SI algorithm
compared to the intensity-only algorithm.
pdf (0.5M)
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May 7, 2005
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