Evaluation of 3D gradient filters for estimation of the surface orientation in CTC

The extraction of the gradient information from 3D surfaces plays an important role for many applications including 3D graphics and medical imaging. The extraction of the 3D gradient information is performed by filtering the input data with high pass filters that are typically implemented using 3×3×3 masks. Since these filters extract the gradient information in small neighborhood, the estimated gradient information will be very sensitive to image noise. The development of a 3D gradient operator that is robust to image noise is particularly important since the medical datasets are characterized by a relatively low signal to noise ratio. The aim of this paper is to detail the implementation of an optimized 3D gradient operator that is applied to sample the local curvature of the colon wall in CT data and its influence on the overall performance of our CAD-CTC method. The developed 3D gradient operator has been applied to extract the local curvature of the colon wall in a large number CT datasets captured with different radiation doses and the experimental results are presented and discussed

Modelling and Measurement of Charge Transfer in Multiple GEM Structures

Measurements and numerical simulations on the charge transfer in Gas Electron Multiplier (GEM) foils are presented and their implications for the usage of GEM foils in Time Projection Chambers are discussed. A small test chamber has been constructed and operated with up to three GEM foils. The charge transfer parameters derived from the electrical currents monitored during the irradiation with an Fe-55 source are compared with numerical simulations. The performance in magnetic fields up to 2 T is also investigated.Comment: 21 pages, 16 figures, submitted to NIM-

A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data

Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information has become an increasingly time consuming process whose results can be affected by inter- and intrauser variability. The aim of this paper is to detail the implementation of a fully integrated CAD-CTC system that is able to robustly identify the clinically significant polyps in the CT data. The CAD-CTC system described in this paper is a multistage implementation whose main system components are: 1) automatic colon segmentation; 2) candidate surface extraction; 3) feature extraction; and 4) classification. Our CAD-CTC system performs at 100% sensitivity for polyps larger than 10 mm, 92% sensitivity for polyps in the range 5 to 10 mm, and 57.14% sensitivity for polyps smaller than 5 mm with an average of 3.38 false positives per dataset. The developed system has been evaluated on synthetic and real patient CT data acquired with standard and low-dose radiation levels

Latest developments in 3D analysis of geomaterials by Morpho+

At the Centre for X-ray Tomography of the Ghent University (Belgium) (www.ugct.ugent.be) besides hardware development for high-resolution X-ray CT scanners, a lot of progress is being made in the field of 3D analysis of the scanned samples. Morpho+ is a flexible 3D analysis software which provides the necessary petrophysical parameters of the scanned samples in 3D. Although Morpho+ was originally designed to provide any kind of 3D parameter, it contains some specific features especially designed for the analysis of geomaterial properties like porosity, partial porosity, pore-size distribution, grain size, grain orientation and surface determination. Additionally, the results of the 3D analysis can be visualized which enables to understand and interpret the analysis results in a straightforward way. The complementarities between high-quality X-ray CT images and flexible 3D software are opening up new gateways in the study of geomaterials

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

Advances in Thick GEM-like gaseous electron multipliers. Part I: atmospheric pressure operation

Thick GEM-like (THGEM) gaseous electron multipliers are made of standard printed-circuit board perforated with sub-millimeter diameter holes, etched at their rims. Effective gas multiplication factors of 100000 and 10000000 and fast pulses in the few nanosecond rise-time scale were reached in single- and cascaded double-THGEM elements, in atmospheric-pressure standard gas mixtures with single photoelectrons. High single-electron detection efficiency is obtained in photon detectors combining THGEMs and semitransparent UV-sensitive CsI photocathodes or reflective ones deposited on the top THGEM face; the latter benefits of a reduced sensitivity to ionizing background radiation. Stable operation was recorded with photoelectron fluxes exceeding MHz/mm2. The properties and some potential applications of these simple and robust multipliers are discussed.Comment: 41 pages, 27 figures. Submitted to Nucl. Instr. and Meth. A, Dec 21, 200

Giant Magnetoresistive Biosensors for Time-Domain Magnetorelaxometry: A Theoretical Investigation and Progress Toward an Immunoassay.

Magnetorelaxometry (MRX) is a promising new biosensing technique for point-of-care diagnostics. Historically, magnetic sensors have been primarily used to monitor the stray field of magnetic nanoparticles bound to analytes of interest for immunoassays and flow cytometers. In MRX, the magnetic nanoparticles (MNPs) are first magnetized and then the temporal response is monitored after removing the magnetic field. This new sensing modality is insensitive to the magnetic field homogeneity making it more amenable to low-power portable applications. In this work, we systematically investigated time-domain MRX by measuring the signal dependence on the applied field, magnetization time, and magnetic core size. The extracted characteristic times varied for different magnetic MNPs, exhibiting unique magnetic signatures. We also measured the signal contribution based on the MNP location and correlated the coverage with measured signal amplitude. Lastly, we demonstrated, for the first time, a GMR-based time-domain MRX bioassay. This approach validates the feasibility of immunoassays using GMR-based MRX and provides an alternative platform for point-of-care diagnostics