Publications

AIM: The sulcogyral pattern of the orbitofrontal cortex (OFC) is characterized by a remarkable inter-individual variability that likely reflects neurobehavioral traits and genetic aspects of neurodevelopment. The aim of the present study was to evaluate the OFC sulcogyral pattern of patients with schizophrenia (SZ) and healthy controls (HC) to determine group differences in OFC sulcogyral pattern as well as gender differences between groups. METHODS: Forty-seven SZ patients (M/F, 23/24) and forty-seven HC (M/F, 17/30), matched on age and gender, were analyzed using magnetic resonance imaging. The sulcogyral pattern was classified into type I, II, or III based on the guidelines set by Chiavaras and Petrides in a previous paper. Chi-squared analysis was used to investigate group and gender differences in the sulcogyral pattern distribution, and categorical regression was used to explore clinical correlations.

The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly.

User interaction is required for reliable segmentation of brain tumors in clinical practice and in clinical research. By incorporating current research tools, 3D Slicer provides a set of interactive, easy to use tools that can be efficiently used for this purpose. One of the modules of 3D Slicer is an interactive editor tool, which contains a variety of interactive segmentation effects. Use of these effects for fast and reproducible segmentation of a single glioblastoma from magnetic resonance imaging data is demonstrated. The innovation in this work lies not in the algorithm, but in the accessibility of the algorithm because of its integration into a software platform that is practical for research in a clinical setting.

BACKGROUND: Patients with schizophrenia have deficits in facial affect expression and detection that hinder social interactions. The goal of this study was to examine whether or not epidemiologically-related antipsychotic-na ıve schizotypal personality disorder (SPD) subjects would have similar deficits as patients with schizophrenia.

One of the most intriguing characteristics of Williams Syndrome individuals is their hypersociability. The amygdala has been consistently implicated in the etiology of this social profile, particularly given its role in emotional and social behavior. This study examined amygdala volume and symmetry in WS individuals and in age and sex matched controls. Magnetic resonance imaging scans were obtained on a GE 1.5-T magnet with 1.5-mm contiguous slices and were used to measure whole gray matter, white matter and cerebrospinal fluid volumes, as well as amygdala volume (right and left). Results revealed significantly reduced intracranial volume in individuals with WS, compared with controls. There were no differences between groups in absolute amygdalae volume, although there was a relative increase in amygdalae volumes, when adjusted for total intracranial content. There were no inter-hemispheric differences in amygdalae volumes in both groups. These results suggest a relative increase in amygdala volume in WS compared with healthy controls that likely reflects abnormal neurodevelopmental processes of midline brain structures.

The current report used structural magnetic resonance imaging (MRI) to objectively measure olfactory bulb volume and olfactory sulcal depth in patients diagnosed with chronic schizophrenia and healthy controls. Additional measures were obtained to assess olfactory function. The olfactory bulb and sulcus were manually traced on structural 3T MRIs for 25 right-handed male patients diagnosed with chronic schizophrenia and 25 matched male healthy controls. A sub-set of subjects received the University of Pennsylvania Smell Identification Test (UPSIT). Olfactory bulb volume was significantly decreased in patients with schizophrenia compared to healthy controls, as was their performance on the UPSIT. Additionally, a positive correlation was seen in patients between right bulb volume and UPSIT scores. Overall, our findings support earlier research studies showing morphometric and functional changes in the olfactory system in patients with schizophrenia.

PURPOSE: To investigates the impact of nonrigid motion correction on pixel-wise pharmacokinetic analysis of free-breathing DCE-MRI in patients with solitary pulmonary nodules (SPNs). Misalignment of focal lesions due to respiratory motion in free-breathing dynamic contrast-enhanced MRI (DCE-MRI) precludes obtaining reliable time-intensity curves, which are crucial for pharmacokinetic analysis for tissue characterization. MATERIALS AND METHODS: Single-slice 2D DCE-MRI was obtained in 15 patients. Misalignments of SPNs were corrected using nonrigid B-spline image registration. Pixel-wise pharmacokinetic parameters K(trans) , v(e) , and k(ep) were estimated from both original and motion-corrected DCE-MRI by fitting the two-compartment pharmacokinetic model to the time-intensity curve obtained in each pixel. The "goodness-of-fit" was tested with χ(2) -test in pixel-by-pixel basis to evaluate the reliability of the parameters. The percentages of reliable pixels within the SPNs were compared between the original and motion-corrected DCE-MRI. In addition, the parameters obtained from benign and malignant SPNs were compared.

BACKGROUND: Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. OBJECTIVE: This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. METHODS: We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts’ findings. We also perform benchmark testing with synthetic data. RESULTS: Our experiments indicated that experts’ visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts’ manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts’ results. However, our approach required far less user input and generated more consistent measurements. CONCLUSION: The sensitivity of experts’ visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts’ segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

MRI can measure several important hemodynamic parameters but might not yet have reached its full potential. The most common MRI method for the assessment of flow is phase-contrast MRI velocity mapping that estimates the mean velocity of a voxel. This estimation is precise only when the intravoxel velocity distribution is symmetric. The mean velocity corresponds to the first raw moment of the intravoxel velocity distribution. Here, a generalized MRI framework for the quantification of any moment of arbitrary velocity distributions is described. This framework is based on the fact that moments in the function domain (velocity space) correspond to differentials in the Fourier transform domain (kv-space). For proof-of-concept, moments of realistic velocity distributions were estimated using finite difference approximations of the derivatives of the MRI signal. In addition, the framework was applied to investigate the symmetry assumption underlying phase-contrast MRI velocity mapping; we found that this assumption can substantially affect phase-contrast MRI velocity estimates and that its significance can be reduced by increasing the velocity encoding range.

Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.