Michael W. Vannier

Bio: Michael W. Vannier is an academic researcher from University of Chicago. The author has contributed to research in topics: Iterative reconstruction & Tomography. The author has an hindex of 69, co-authored 391 publications receiving 19661 citations. Previous affiliations of Michael W. Vannier include University of Washington & Roy J. and Lucille A. Carver College of Medicine.

Subgenual prefrontal cortex abnormalities in mood disorders

Abstract: Pathological disturbances of mood may follow a 'bipolar' course, in which normal moods alternate with both depression and mania, or a 'unipolar' course, in which only depression occurs. Both bipolar and unipolar disorders can be heritable illnesses associated with neurochemical, neuroendocrine and autonomic abnormalities. The neurobiological basis for these abnormalities has not been established. Using positron emission tomographic (PET) images of cerebral blood flow and rate of glucose metabolism to measure brain activity, we have now localized an area of abnormally decreased activity in the prefrontal cortex ventral to the genu of the corpus callosum in both familial bipolar depressives and familial unipolar depressives. This decrement in activity was at least partly explained by a corresponding reduction in cortical volume, as magnetic resonance imaging (MRI) demonstrated reductions in the mean grey matter volume in the same area of 39 and 48% in the bipolar and unipolar samples, respectively. This region has previously been implicated in the mediation of emotional and autonomic responses to socially significant or provocative stimuli, and in the modulation of the neurotransmitter systems targeted by antidepressant drugs.

Hippocampal atrophy in recurrent major depression

Abstract: Hippocampal volumes of subjects with a history of major depressive episodes but currently in remission and with no known medical comorbidity were compared to matched normal controls by using volumetric magnetic resonance images. Subjects with a history of major depression had significantly smaller left and right hippocampal volumes with no differences in total cerebral volumes. The degree of hippocampal volume reduction correlated with total duration of major depression. In addition, large (diameter > or = 4.5 mm)-hippocampal low signal foci (LSF) were found within the hippocampus, and their number also correlated with the total number of days depressed. These results suggest that depression is associated with hippocampal atrophy, perhaps due to a progressive process mediated by glucocorticoid neurotoxicity.

Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

Abstract: Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.

Three dimensional CT reconstruction images for craniofacial surgical planning and evaluation.

Abstract: Three dimensional reconstruction images of bony and soft tissue surfaces have improved understanding of complex facial deformities. Applied to CT studies of complex craniofacial abnormalities, this method has delineated abnormal facial soft tissue and bony morphology, facilitated surgical planning, and improved quantitative postoperative evaluation. Advanced computer-aided aircraft design techniques were adapted and applied to craniofacial surgical procedure-planning and evaluation using surface contours obtained from CT scans.

Iterative deblurring for CT metal artifact reduction

Abstract: Iterative deblurring methods using the expectation maximization (EM) formulation and the algebraic reconstruction technique (ART), respectively, are adapted for metal artifact reduction in medical computed tomography (CT). In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps. Furthermore, the EM-type algorithm converges faster than the ART-type algorithm in terms of either the I-divergence or Euclidean distance between ideal and reprojected data in the authors' simulation. Also, for a given iteration number, the EM-type deblurring method produces better image clarity but stronger noise than the ART-type reconstruction. The computational complexity of EM- and ART-based iterative deblurring is essentially the same, dominated by reprojection and backprojection. Relevant practical and theoretical issues are discussed.

Marching cubes: A high resolution 3D surface construction algorithm

Abstract: We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

Abstract: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.