Lateral tilt during IVC filter placement does not predict the need for advanced filter retrieval techniques

PURPOSEWe aimed to determine if lateral inferior vena cava (IVC) filter tilt at placement predicts the need for subsequent advanced retrieval techniques.METHODSA retrospective chart review was performed of all Gunther Tulip IVC filter placements with subsequent retrievals between February 2015 and October 2017. Chart and imaging review was performed for patient, filter placement, and filter retrieval demographics/characteristics. Degree of agreement between two measurement sets was evaluated with the intraclass correlation (ICC) analysis. Categorical variables were compared with chi-square or Fisher exact test, as appropriate. Kendall rank correlation was used to measure correlation between categorical variables.RESULTSThere was poor agreement between filter tilt angle at the time of placement and retrieval (ICC coefficient, 0.54). Mean difference ± standard deviation between tilt angle at the time of placement and retrieval was 4.6°±4.3° (p = 0.35). Among patient- or procedure-related factors, a common femoral vein access on placement (regression coefficient, -2.90; p = 0.039) was associated with a lower difference between placement and retrieval filter tilt angles compared to internal jugular vein access. Higher filter tilt angle measured at the time of retrieval (OR: 1.19, p = 0.025), hook embedment (OR: 77.3, p < 0.001), and a longer dwell time (OR: 1.25, p = 0.002) were associated with the need for advanced retrieval techniques. However, in univariate and multivariate analysis filter tilt angle at the time of placement was not associated with the subsequent need for advanced retrieval technique (p = 0.16).CONCLUSIONLateral tilt at the time of placement is poorly associated with lateral tilt at the time of retrieval and does not correlate with the need for advanced retrieval technique

Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

Presented at the 2005 MICCAI Workshop on Open-Source Software, October 30th, 2005, Palm Springs, CA, USA. Hosted by The Insight Software Consortium (ISC) and The National Alliance for Medical Image Computing (NA-MIC)An Insight Toolkit (ITK) implementation of our knowledge-based segmentation algorithm applied to brain MRI scans is presented in this paper. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayes’ rule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. We present the results of this code on two different projects. First, we show the effect of applying this code to skull-removed brain MRI scans. Second, we show the effect of applying this code to the extraction of the DLPFC from a user-defined subregion of brain MRI data.We present our results on brain MRI scans, comparing the results of the knowledge-based segmentation to manual segmentations on datasets of schizophrenic patients

A Dorsolateral Prefrontal Cortex Semi-Automatic Segmenter

©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.653643DOI: 10.1117/12.653643Presented at Medical Imaging 2006: Image Processing, 13 February 2006, San Diego, CA, USA.Structural, functional, and clinical studies in schizophrenia have, for several decades, consistently implicated dysfunction of the prefrontal cortex in the etiology of the disease. Functional and structural imaging studies, combined with clinical, psychometric, and genetic analyses in schizophrenia have confirmed the key roles played by the prefrontal cortex and closely linked "prefrontal system" structures such as the striatum, amygdala, mediodorsal thalamus, substantia nigra-ventral tegmental area, and anterior cingulate cortices. The nodal structure of the prefrontal system circuit is the dorsal lateral prefrontal cortex (DLPFC), or Brodmann area 46, which also appears to be the most commonly studied and cited brain area with respect to schizophrenia. In 1986, Weinberger et. al. tied cerebral blood flow in the DLPFC to schizophrenia.1 In 2001, Perlstein et. al. demonstrated that DLPFC activation is essential for working memory tasks commonly deficient in schizophrenia. 2 More recently, groups have linked morphological changes due to gene deletion and increased DLPFC glutamate concentration to schizophrenia.3,4 Despite the experimental and clinical focus on the DLPFC in structural and functional imaging, the variability of the location of this area, differences in opinion on exactly what constitutes DLPFC, and inherent difficulties in segmenting this highly convoluted cortical region have contributed to a lack of widely used standards for manual or semi-automated segmentation programs. Given these implications, we developed a semi-automatic tool to segment the DLPFC from brain MRI scans in a reproducible way to conduct further morphological and statistical studies. The segmenter is based on expert neuroanatomist rules (Fallon-Kindermann rules), inspired by cytoarchitectonic data and reconstructions presented by Rajkowska and Goldman-Rakic.5 It is semi-automated to provide essential user interactivity. We present our results and provide details on our DLPFC open-source tool