We present the results of dose and image quality performance evaluation of a novel, prospective ECG-gated Coronary
CT Angiography acquisition mode (SnapShot Pulse, LightSpeed VCT-XT scanner, GE Healthcare, Waukesha, WI), and
compare it to conventional retrospective ECG gated helical acquisition in clinical and phantom studies. Image quality
phantoms were used to measure noise, slice sensitivity profile, in-plane resolution, low contrast detectability and dose,
using the two acquisition modes. Clinical image quality and diagnostic confidence were evaluated in a study of 31
patients scanned with the two acquisition modes. Radiation dose reduction in clinical practice was evaluated by tracking
120 consecutive patients scanned with the prospectively gated scan mode. In the phantom measurements, the
prospectively gated mode resulted in equivalent or better image quality measures at dose reductions of up to 89%
compared to non-ECG modulated conventional helical scans. In the clinical study, image quality was rated excellent by
expert radiologist reviewing the cases, with pathology being identical using the two acquisition modes. The average
dose to patients in the clinical practice study was 5.6 mSv, representing 50% reduction compared to a similar patient
population scanned with the conventional helical mode.
We are developing a computer-aided detection (CAD) system for lung nodules in thoracic CT volumes. During false positive (FP) reduction, the image structures around the identified nodule candidates play an important role in differentiating nodules from vessels. In our previous work, we exploited shape and first-order derivative information of the images by extracting ellipsoid and gradient field features. The purpose of this study was to explore the object shape information using second-order derivatives and the Hessian matrix to further improve the performance of our detection system. Eight features related to the eigenvalues of the Hessian matrix were extracted from a volume of interest containing the object, and were combined with ellipsoid and gradient field features to discriminate nodules from FPs. A data set of 82 CT scans from 56 patients was used to evaluate the usefulness of the FP reduction technique. The classification accuracy was assessed using the area Az under the receiving operating characteristic curve and the number of FPs per section at 80% sensitivity. In the combined feature space, we obtained a test Az of 0.97 ± 0.01, and 0.27 FPs/section at 80% sensitivity. Our results indicate that combining the Hessian, ellipsoid and gradient field features can significantly improve the performance of our FP reduction stage.
Several full-field digital mammography (FFDM) systems have been approved for clinical applications. It is important to develop a CAD system that can easily be adapted to images acquired by FFDM systems from different manufacturers. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multi-resolution preprocessing scheme for image enhancement. Our CAD system performed prescreening to identify mass candidates, segmented the suspicious structures, extracted morphological and texture features, and then classified masses and normal tissue. In this study, we investigated the use of a two-stage gradient field analysis to identify suspicious masses, and the effectiveness of a new gradient field feature extracted from each suspicious object for false positive (FP) reduction. A data set of 104 cases with 243 images acquired with a GE FFDM system was collected. Most cases had two mammographic views, except for 12 cases that had three views and 1 case with only one view. The data set contained 106 masses. The true locations of the masses were identified by an experienced radiologist. Using free-response receiver operating characteristic (FROC) analysis, it was found that our CAD system achieved a cased-based sensitivity of 70%, 80%, and 88% at 0.8, 1.3, and 1.7 FP marks/image, respectively. The high performance indicated the usefulness of the new gradient field analysis method.
We are developing a computer-aided detection system to aid radiologists in diagnosing lung cancer in thoracic
computed tomographic (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of
our algorithm by developing and incorporating a gradient field technique. This technique extracts 3D shape information
from the gray-scale values within a volume of interest. The gradient field feature values are higher for spherical objects,
and lower for elongated and irregularly-shaped objects. A data set of 55 thin CT scans from 40 patients was used to
evaluate the usefulness of the gradient field technique. After initial nodule candidate detection and rule-based first stage
FP reduction, there were 3487 FP and 65 true positive (TP) objects in our data set. Linear discriminant classifiers with
and without the gradient field feature were designed for the second stage FP reduction. The accuracy of these classifiers
was evaluated using the area Az under the receiver operating characteristic (ROC) curve. The Az values were 0.93 and
0.91 with and without the gradient field feature, respectively. The improvement with the gradient field feature was
statistically significant (p=0.01).
This paper presents the results of applying the modified deterministic annealing (DA) algorithm to simulated and clinical magnetic resonance (MR) brain data with multiple sclerosis (MS) lesions. Modified deterministic annealing algorithm is a very efficient segmentation algorithm for isolating MS lesions in the MR images when utilizing all the information contained in all modalities. To fully utilize the information contained in all the modalities, vector segmentation is carried out instead of unimodal segmentation. The vectors to be clustered are formed by multi-modal MR brain data. Through some arithmetic manipulations synthesized image data can be obtained which greatly alleviate the effect of noise and intensity inhomogeneity. Isolated multiple sclerosis lesions are outliers to the brain tissues. Even with noise level up to 7% the MS MR brain data can still be satisfactorily segmented. This method does not need a prior model, and is conceptually very simple. It delineates not only large lesions but small ones as well. The whole process is completely automated without any intervention by an operator, which can be a very promising tool for MS follow-up studies. Comparison between the segmentation results from the simulated MS brain data and from the clinical MS brain data shows that with the current high quality MRI facilities, images with noise above 3% and intensity inhomogeneity above 20% will usually not be produced. Segmentation results for the clinical data are much better and easier to obtain than the simulated noisy data. To get even better results for the MS lesions, inverse problem techniques have to be applied. Noise model and intensity inhomogeneity model have to be established and improved using the given MRI data during iteration.
KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Annealing, 3D image processing, Magnetism, Tissues, Digital filtering, Neuroimaging, Image processing algorithms and systems
This paper presents the results of applying the deterministic annealing (DA) algorithm to simulated magnetic resonance image segmentation. The applicability of this methodology for 3-D segmentation has been rigorously tested by using the simulated MRI volumes of normal brain at the Brain Web [8] for the 181 slices and whole volume of different modalities (T1, T2, and PD) without and with various levels of noise and intensity inhomogeneities. With proper thresholding of the clusters formed by the modified DA almost zero misclassification was achieved without the presence of noise. Even up to 7% addition of noise and 40% inhomogeneity, the average misclassification rates of the voxels belonging to white matter, gray matter, and cerebrospinal fluid were found to be less than 5% after median filtering. The accuracy, stability, global optimization and speed of the DA algorithm for 3-D MR image segmentation could provide a more rigorous tool for identification of diseased brain tissues from 3-D MR images than other existing 3-D segmentation techniques. Further inquiry into the DA algorithm shows that it is a Bayesian classifier with the assumption that the data to be classified follow a multivariate normal distribution. The characteristic of being a Bayesian classifier guarantees its achievement of global optimization.
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