CONTEXT: Whether psychoses associated with schizophrenia and affective disorder represent manifestations of different disorders or the same disorder is an important but unresolved question in psychiatry. Results of previous volumetric magnetic resonance imaging investigations indicate that gray matter volume reductions in neocortical regions may be specific to schizophrenia.
OBJECTIVE: To simultaneously evaluate multiple olfactocentric paralimbic regions, which play crucial roles in human emotion and motivation, in first-episode patients with schizophrenia and affective psychosis.
DESIGN: A cross-sectional study using high-spatial resolution magnetic resonance imaging in patients with schizophrenia and affective psychosis at their first hospitalization.
SETTING: Inpatient units at a private psychiatric hospital.
PARTICIPANTS: Fifty-three first-episode patients, 27 with schizophrenia and 26 with affective (mainly manic) psychosis, and 29 control subjects.
MAIN OUTCOME MEASURES: Using high-spatial resolution magnetic resonance imaging, the gray matter volumes of 2 olfactocentric paralimbic regions of interest, the insular cortex and the temporal pole, were evaluated.
RESULTS: A bilateral volume reduction in insular cortex gray matter was specific to first-episode patients with schizophrenia. In contrast, both first-episode psychosis groups showed a volume reduction in left temporal pole gray matter and an absence of normal left-greater-than-right asymmetry. Region of interest correlations showed that only patients with schizophrenia lacked a positive correlation between left temporal pole and left anterior amygdala-hippocampal complex gray matter volumes, whereas both psychosis groups were similar in lacking normal positive correlations between left temporal pole and left anterior superior temporal gyrus gray matter volumes.
CONCLUSIONS: These partially different and partially similar patterns of structural abnormalities in olfactocentric paralimbic regions and their associated abnormalities in other temporolimbic regions may be important factors in the differential and common manifestations of the 2 psychoses.
BACKGROUND: The Heschl gyrus and planum temporale have crucial roles in auditory perception and language processing. Our previous investigation using magnetic resonance imaging (MRI) indicated smaller gray matter volumes bilaterally in the Heschl gyrus and in left planum temporale in patients with first-episode schizophrenia but not in patients with first-episode affective psychosis. We sought to determine whether there are progressive decreases in anatomically defined MRI gray matter volumes of the Heschl gyrus and planum temporale in patients with first-episode schizophrenia and also in patients with first-episode affective psychosis.
METHODS: At a private psychiatric hospital, we conducted a prospective high spatial resolution MRI study that included initial scans of 28 patients at their first hospitalization (13 with schizophrenia and 15 with affective psychosis, 13 of whom had a manic psychosis) and 22 healthy control subjects. Follow-up scans occurred, on average, 1.5 years after the initial scan.
RESULTS: Patients with first-episode schizophrenia showed significant decreases in gray matter volume over time in the left Heschl gyrus (6.9%) and left planum temporale (7.2%) compared with patients with first-episode affective psychosis or control subjects.
CONCLUSIONS: These findings demonstrate a left-biased progressive volume reduction in the Heschl gyrus and planum temporale gray matter in patients with first-episode schizophrenia in contrast to patients with first-episode affective psychosis and control subjects. Schizophrenia but not affective psychosis seems to be characterized by a postonset progression of neocortical gray matter volume loss in the left superior temporal gyrus and thus may not be developmentally fixed.
We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras, we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.
Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.
RATIONALE AND OBJECTIVES: The accuracy of diagnostic test and imaging segmentation is important in clinical practice because it has a direct impact on therapeutic planning. Statistical validations of classification accuracy was conducted based on parametric receiver operating characteristic analysis, illustrated on three radiologic examples, MATERIALS AND METHODS: Two parametric models were developed for diagnostic or imaging data. Example 1: A semi-automated fractional segmentation algorithm was applied to magnetic resonance imaging of nine cases of brain tumors. The tumor and background pixel data were assumed to have bi-beta distributions. Fractional segmentation was validated against an estimated composite pixel-wise gold standard based on multi-reader manual segmentations. Example 2: The predictive value of 100 cases of spiral computed tomography of ureteral stone sizes, distributed as bi-normal after a non-linear transformation, under two treatment options received. Example 3: One hundred eighty cases had prostate-specific antigen levels measured in a prospective clinical trial. Radical prostatectomy was performed in all to provide a binary gold standard of local and advanced cancer stages. Prostate-specific antigen level was transformed and modeled by bi-normal distributions. In all examples, areas under the receiver operating characteristic curves were computed. RESULTS. The areas under the receiver operating characteristic curves were: Example 1: Fractional segmentation of magnetic resonance imaging of brain tumors: meningiomas (0.924-0.984); astrocytomas (0.786-0.986); and other low-grade gliomas (0.896-0.983). Example 3: Ureteral stone size for treatment planning (0.813). Example 2: Prostate-specific antigen for staging prostate cancer (0.768). CONCLUSION: All clinical examples yielded fair to excellent accuracy. The validation metric area under the receiver operating characteristic curves may be generalized to evaluating the performances of several continuous classifiers related to imaging.
PURPOSE: To suggest a quantitative method for assessing the temporal changes in the geometry of individual multiple sclerosis (MS) lesions in follow-up studies of MS patients.
MATERIALS AND METHODS: Computer simulated and in vivo magnetic resonance (MR) imaged MS lesions were studied. Ten in vivo MS lesions were identified from sets of axial MR images acquired from a patient scanned consecutively for 24 times during a one-year period. Each of the lesions was segmented and its three-dimensional surface approximated using spherical harmonics (SH). From the obtained SH polynomial coefficients, indices of shape were defined, and analysis of the temporal changes in each lesion's geometry throughout the year was performed by determining the mean discrete total variation of the shape indices.
RESULTS: The results demonstrate that most of the studied lesions undergo notable geometrical changes with time. These changes are not necessarily associated with similar changes in size/volume. Furthermore, it was found that indices corresponding to changes in lesion shape could be 1.4 to 8.0 times higher than those corresponding to changes in the lesion size/volume.
CONCLUSION: Quantitative three-dimensional shape analysis can serve as a new tool for monitoring MS lesion activity and study patterns of MS lesion evolution over time.
In progressive neurological disorders, such as multiple sclerosis (MS), magnetic resonance imaging (MRI) follow-up is used to monitor disease activity and progression and to understand the underlying pathogenic mechanisms. This article presents image postprocessing methods and validation for integrating multiple serial MRI scans into a spatiotemporal volume for direct quantitative evaluation of the temporal intensity profiles. This temporal intensity signal and its dynamics have thus far not been exploited in the study of MS pathogenesis and the search for MRI surrogates of disease activity and progression. The integration into a four-dimensional data set comprises stages of tissue classification, followed by spatial and intensity normalization and partial volume filtering. Spatial normalization corrects for variations in head positioning and distortion artifacts via fully automated intensity-based registration algorithms, both rigid and nonrigid. Intensity normalization includes separate stages of correcting intra- and interscan variations based on the prior tissue class segmentation. Different approaches to image registration, partial volume correction, and intensity normalization were validated and compared. Validation included a scan-rescan experiment as well as a natural-history study on MS patients, imaged in weekly to monthly intervals over a 1-year follow-up. Significant error reduction was observed by applying tissue-specific intensity normalization and partial volume filtering. Example temporal profiles within evolving multiple sclerosis lesions are presented. An overall residual signal variance of 1.4% +/- 0.5% was observed across multiple subjects and time points, indicating an overall sensitivity of 3% (for axial dual echo images with 3-mm slice thickness) for longitudinal study of signal dynamics from serial brain MRI.
Virtual cystoscopy is a developing technique for bladder cancer screening. In a conventional cystoscopy, an optical probe is inserted into the bladder and an expert reviews the appearance of the bladder wall. Physical limitations of the probe place restrictions on the examination of the bladder wall. In virtual cystoscopy, a computed tomography (CT) scan of the bladder is acquired and an expert reviews the appearance of the bladder wall as shown by the CT. The task of identifying tumors in the bladder wall has often been done without extensive computational aid to the expert. We have developed an image processing algorithm that aids the expert in the detection of bladder tumors. Compared with an expert observer reading the CT, our algorithm achieves 89% sensitivity, 88% specificity, 48% positive predictive value, and 98% negative predictive value.
This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in . In contrast to that work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the multiple shape classes within the image. A parametric model for this new representation is derived by applying principal component analysis to the collection of these multiple signed distance functions. By deriving a parametric model in this manner, we obtain a coupling between the multiple shapes within the image and hence effectively capture the co-variations among the different shapes. The parameters of the multi-shape model are then calculated to minimize a single mutual information-based cost functional for image segmentation. The use of a single cost criterion further enhances the coupling between the multiple shapes as the deformation of any given shape depends, at all times, upon every other shape, regardless of their proximity. We demonstrate the utility of this algorithm to the segmentation of the prostate gland, the rectum, and the internal obturator muscles for MR-guided prostate brachytherapy.
We propose a novel bias correction method for magnetic resonance (MR) imaging that uses complementary body coil and surface coil images. The former are spatially homogeneous but have low signal intensity; the latter provide excellent signal response but have large bias fields. We present a variational framework where we optimize an energy functional to estimate the bias field and the underlying image using both observed images. The energy functional contains smoothness-enforcing regularization for both the image and the bias field. We present extensions of our basic framework to a variety of imaging protocols. We solve the optimization problem using a computationally efficient numerical algorithm based on coordinate descent, preconditioned conjugate gradient, half-quadratic regularization, and multigrid techniques. We show qualitative and quantitative results demonstrating the effectiveness of the proposed method in producing debiased and denoised MR images.
BACKGROUND AND PURPOSE: MR diffusion tensor imaging permits detailed visualization of white matter fiber tracts. This technique, unlike T2-weighted imaging, also provides information about fiber direction. We present findings of normal white matter fiber tract anatomy at high resolution obtained by using line scan diffusion tensor imaging.
METHODS: Diffusion tensor images in axial, coronal, and sagittal sections covering the entire brain volume were obtained with line scan diffusion imaging in six healthy volunteers. Images were acquired for b factors 5 and 1000 s/mm(2) at an imaging resolution of 1.7 x 1.7 x 4 mm. For selected regions, images were obtained at a reduced field of view with a spatial resolution of 0.9 x 0.9 x 3 mm. For each pixel, the direction of maximum diffusivity was computed and used to display the course of white matter fibers.
RESULTS: Fiber directions derived from diffusion tensor imaging were consistent with known white matter fiber anatomy. The principal fiber tracts were well observed in all cases. The tracts that were visualized included the following: the arcuate fasciculus; superior and inferior longitudinal fasciculus; uncinate fasciculus; cingulum; external and extreme capsule; internal capsule; corona radiata; auditory and optic radiation; anterior commissure; corpus callosum; pyramidal tract; gracile and cuneatus fasciculus; medial longitudinal fasciculus; rubrospinal, tectospinal, central tegmental, and dorsal trigeminothalamic tract; superior, inferior, and middle cerebellar peduncle; pallidonigral and strionigral fibers; and root fibers of the oculomotor and trigeminal nerve.
CONCLUSION: We obtained a complete set of detailed white matter fiber anatomy maps of the normal brain by means of line scan diffusion tensor imaging at high resolution. Near large bone structures, line scan produces images with minimal susceptibility artifacts.
New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.
BACKGROUND: Recent investigations using MRI suggest that older persons with mobility impairment have a greater volume of abnormal cerebral white matter compared with persons with normal mobility, thus raising the possibility that those with impairment have lesions in areas critical for the control of mobility. OBJECTIVE: To utilize automated image analysis methods to localize the specific regions of abnormal white matter that distinguish subjects with lower mobility from subjects with higher mobility. METHODS: Tissue classification was performed on subjects' dual-echo long repetition time spin-echo MRI using computer algorithms operating on intensity criteria integrated with anatomic information. Statistical analysis of group differences was obtained after spatially normalizing each brain to a standard reference brain. RESULTS: Four discrete periventricular regions, including bilaterally symmetric frontal and bilateral occipitoparietal regions, were identified as being sensitive (frontal) or specific (occipitoparietal) in discriminating the subjects with lower mobility from subjects with higher mobility. The symmetry of these lesions in individual subjects suggested pathology other than arteriolar infarction. CONCLUSIONS: These results suggest that damage to discrete frontal and occipitoparietal periventricular white matter locations may be associated with a mobility disorder of aging.
This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.
PURPOSE: To assess the reproducibility and accuracy compared to radiologists of three automated segmentation pipelines for quantitative magnetic resonance imaging (MRI) measurement of brain white matter signal abnormalities (WMSA).
MATERIALS AND METHODS: WMSA segmentation was performed on pairs of whole brain scans from 20 patients with multiple sclerosis (MS) and 10 older subjects who were positioned and imaged twice within 30 minutes. Radiologist outlines of WMSA on 20 sections from 16 patients were compared with the corresponding results of each segmentation method.
RESULTS: The segmentation method combining expectation-maximization (EM) tissue segmentation, template-driven segmentation (TDS), and partial volume effect correction (PVEC) demonstrated the highest accuracy (the absolute value of the Z-score was 0.99 for both groups of subjects), as well as high interscan reproducibility (repeatability coefficient was 0.68 mL in MS patients and 1.49 mL in aging subjects).
CONCLUSION: The addition of TDS to the EM segmentation and PVEC algorithms significantly improved the accuracy of WMSA volume measurements, while also improving measurement reproducibility.
OBJECTIVE: The corpus callosum, the largest white matter tract in the brain, is a midline structure associated with the formation of the hippocampus, septum pellucidum, and cingulate cortex, which have been implicated in the pathogenesis of schizophrenia. Corpus callosum shape deformation, therefore, may reflect a midline neurodevelopmental abnormality.
METHOD: Corpus callosum area and shape were analyzed in 14 first-episode psychotic patients with schizophrenia, 19 first-episode psychotic patients with affective disorder, and 18 normal comparison subjects.
RESULTS: No statistically significant corpus callosum area differences between groups were found, but there were differences in the structure's shape between the patients with schizophrenia and the comparison subjects. A correlation between width and angle of the corpus callosum was found in patients with affective disorder.
CONCLUSIONS: Corpus callosum shape abnormalities in first-episode psychotic patients with schizophrenia may reflect a midline neurodevelopmental abnormality.
OBJECTIVE: Disruptions in connectivity between the frontal and temporal lobes may explain some of the symptoms observed in schizophrenia. Conventional magnetic resonance imaging (MRI) studies, however, have not shown compelling evidence for white matter abnormalities, because white matter fiber tracts cannot be visualized by conventional MRI. Diffusion tensor imaging is a relatively new technique that can detect subtle white matter abnormalities in vivo by assessing the degree to which directionally organized fibers have lost their normal integrity. The first three diffusion tensor imaging studies in schizophrenia showed lower anisotropic diffusion, relative to comparison subjects, in whole-brain white matter, prefrontal and temporal white matter, and the corpus callosum, respectively. Here the authors focus on fiber tracts forming temporal-frontal connections.
METHOD: Anisotropic diffusion was assessed in the uncinate fasciculus, the most prominent white matter tract connecting temporal and frontal brain regions, in 15 patients with chronic schizophrenia and 18 normal comparison subjects. A 1.5-T GE Echospeed system was used to acquire 4-mm-thick coronal line-scan diffusion tensor images. Maps of the fractional anisotropy were generated to quantify the water diffusion within the uncinate fasciculus.
RESULTS: Findings revealed a group-by-side interaction for fractional anisotropy and for uncinate fasciculus area, derived from automatic segmentation. The patients with schizophrenia showed a lack of normal left-greater-than-right asymmetry seen in the comparison subjects.
CONCLUSIONS: These findings demonstrate the importance of investigating white matter tracts in vivo in schizophrenia and support the hypothesis of a disruption in the normal pattern of connectivity between temporal and frontal brain regions in schizophrenia.
Evidence suggests that some structural brain abnormalities in schizophrenia are neurodevelopmental in origin. There is also growing evidence to suggest that shape deformations in brain structure may reflect abnormalities in neurodevelopment. While many magnetic resonance (MR) imaging studies have investigated brain area and volume measures in schizophrenia, fewer have focused on shape deformations. In this MR study we used a 3D shape representation technique, based on spherical harmonic functions, to analyze left and right amygdala-hippocampus shapes in each of 15 patients with schizophrenia and 15 healthy controls matched for age, gender, handedness and parental socioeconomic status. Left/right asymmetry was also measured for both shape and volume differences. Additionally, shape and volume measurements were combined in a composite analysis. There were no differences between groups in overall volume or shape. Left/right amygdala-hippocampal asymmetry, however, was significantly larger in patients than controls for both relative volume and shape. The local brain regions responsible for the left/right asymmetry differences in patients with schizophrenia were in the tail of the hippocampus (including both the inferior aspect adjacent to parahippocampal gyrus and the superior aspect adjacent to the lateral geniculate nucleus and more anteriorly to the cerebral peduncles) and in portions of the amygdala body (including the anterior-superior aspect adjacent to the basal nucleus). Also, in patients, increased volumetric asymmetry tended to be correlated with increased left/right shape asymmetry. Furthermore, a combined analysis of volume and shape asymmetry resulted in improved differentiation between groups. Classification function analyses correctly classified 70% of cases using volume, 73.3% using shape, and 87% using combined volume and shape measures. These findings suggest that shape provides important new information toward characterizing the pathophysiology of schizophrenia, and that combining volume and shape measures provides improved group discrimination in studies investigating brain abnormalities in schizophrenia. An evaluation of shape deformations also suggests local abnormalities in the amygdala-hippocampal complex in schizophrenia.
Magnetic resonance diffusion tensor imaging (DTI) is a new technique that can be used to visualize and measure the diffusion of water in brain tissue; it is particularly useful for evaluating white matter abnormalities. In this paper, we review research studies that have applied DTI for the purpose of understanding neuropsychiatric disorders. We begin with a discussion of the principles involved in DTI, followed by a historical overview of magnetic resonance diffusion-weighted imaging and DTI and a brief description of several different methods of image acquisition and quantitative analysis. We then review the application of this technique to clinical populations. We include all studies published in English from January 1996 through March 2002 on this topic, located by searching PubMed and Medline on the key words "diffusion tensor imaging" and "MRI." Finally, we consider potential future uses of DTI, including fiber tracking and surgical planning and follow-up.
BACKGROUND: The fusiform gyrus (occipitotemporal gyrus) is thought to be critical for face recognition and may possibly be associated with impaired facial recognition and interpretation of facial expression in schizophrenia. of postmortem studies have suggested that fusiform gyrus volume is reduced in schizophrenia, but there have been no in vivo structural studies of the fusiform gyrus in schizophrenia using magnetic resonance imaging.
METHODS: High-spatial resolution magnetic resonance images were used to measure the gray matter volume of the fusiform gyrus in 22 patients with first-episode schizophrenia (first hospitalization), 20 with first-episode affective psychosis (mainly manic), and 24 control subjects.
RESULTS: Patients with first-episode schizophrenia had overall smaller relative volumes (absolute volume/intracranial contents) of fusiform gyrus gray matter compared with controls (9%) and patients with affective psychosis (7%). For the left fusiform gyrus, patients with schizophrenia showed an 11% reduction compared with controls and patients with affective psychosis. Right fusiform gyrus volume differed in patients with schizophrenia only compared with controls (8%).
CONCLUSION: Schizophrenia is associated with a bilateral reduction in fusiform gyrus gray matter volume that is evident at the time of first hospitalization and is different from the presentation of affective psychosis.