Xiang Li

Cognitive performance and MR markers of cerebral injury in cognitively impaired MS patients

Objective: To relate neuropsychological performance to measures of cerebral injury in persons with MS selected for cognitive impairment. Methods: Participants were 37 individuals with relapsing–remitting (59.5%) and secondary progressive (40.5%) MS. They were tested at baseline as part of a clinical trial to enhance cognition with an acetylcholinesterase inhibitor. Eligibility criteria included at least mild cognitive impairment on a verbal learning and memory task. A modified Brief Repeatable Battery of Neuropsychological Tests formed the core of the behavioral protocol. Neuroimaging measures were central (ventricular) cerebral atrophy, lesion volume, and ratios of N -acetyl aspartate (NAA) to both creatine and choline. Results: A clear, consistent relation was found between cognitive and MR measures. Among neuroimaging measures, central atrophy displayed the highest correlations with cognition, accounting for approximately half the variance in overall cognitive performance. NAA ratios in right hemisphere sites displayed larger correlations than those on the left. Multiple regression models combining the MR measures accounted for well over half the variance in overall cognitive performance. The Symbol Digit Modalities Test was the neuropsychological task most strongly associated with the neuroimaging variables. Conclusions: If a strong and stable association can be firmly established between cognitive and MR variables in appropriate subsets of MS patients, it might aid in the investigation of interventions to enhance cognition and modify the course of the disease.

Reduction of False Positives by Internal Features for Polyp Detection in CT-Based Virtual Colonoscopy

In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological, and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4-30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10-30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4-10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

Noise properties of low-dose CT projections and noise treatment by scale transformations

Projection data acquired for image reconstruction of low-dose computed tomography (CT) are degraded by many factors. These factors complicate noise analysis on the projection data and render a very challenging task for noise reduction. In this study, we first investigate the noise property of the projection data by analyzing a repeatedly acquired experimental phantom data set, in which the phantom was scanned 900 times at a fixed projection angle. The statistical analysis shows that the noise can be regarded as normally distributed with a nonlinear signal-dependent variance. Based on this observation, we then utilize scale transformations to modulate the projection data so that the data variance can be stabilized to be signal independent. By analyzing the relationship between the data standard deviation and the data mean level, we propose a segmented logarithmic transform for the stabilization of the non-stationary noise. After the scale transformations, the noise variance becomes approximately a constant. A two-dimensional Wiener filter is then designed for an analytical treatment of the noise. Experimental results show that the proposed method has a better noise reduction performance without circular artifacts, by visual judgment, as compared to conventional filters, such as the Harming filter.

An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy

Electronic colon cleansing (ECC) aims to segment the colon lumen from a patient abdominal image acquired using an oral contrast agent for colonic material tagging, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) provides fly-through navigation within the colon model, looking for polyps on the inner surface in a manner analogous to that of fiber optic colonoscopy. We have built an ECC pipeline for a commercial VC navigation system. In this paper, we present an improved ECC method. It is based on a partial-volume (PV) image-segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection and computer-aided detection of polyps (CADpolyp) within the cleansed colon lumens obtained using 20 patient datasets. Compared to our previous ECC pipeline, which does not sufficiently consider the PV effect, the method presented in this paper demonstrates improved polyp detection by both visual judgment and CADpolyp measure

Analytical noise treatment for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain

By analyzing the noise properties of calibrated low-dose Computed Tomography (CT) projection data, it is clearly seen that the data can be regarded as approximately Gaussian distributed with a nonlinear signal-dependent variance. Based on this observation, the penalized weighted least-square (PWLS) smoothing framework is a choice for an optimal solution. It utilizes the prior variance-mean relationship to construct the weight matrix and the two-dimensional (2D) spatial information as the penalty or regularization operator. Furthermore, a K-L transform is applied along the z (slice) axis to further consider the correlation among different sinograms, resulting in a PWLS smoothing in the K-L domain. As a tool for feature extraction and de-correlation, the K-L transform maximizes the data variance represented by each component and simplifies the task of 3D filtering into 2D spatial process slice by slice. Therefore, by selecting an appropriate number of neighboring slices, the K-L domain PWLS smoothing fully utilizes the prior statistical knowledge and 3D spatial information for an accurate restoration of the noisy low-dose CT projections in an analytical manner. Experimental results demonstrate that the proposed method with appropriate control parameters improves the noise reduction without the loss of resolution.

Partial Volume Segmentation of Brain Magnetic Resonance Images Based on Maximum a Posteriori Probability

Noise, partial volume (PV) effect, and image-intensity inhomogeneity render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the above-mentioned effects. The objective of this paper is to propose a unified framework, based on the maximum a posteriori probability principle, by taking all these effects into account simultaneously in order to improve image segmentation performance. Instead of labeling each image voxel with a unique tissue type, the percentage of each voxel belonging to different tissues, which we call a mixture, is considered to address the PV effect. A Markov random field model is used to describe the noise effect by considering the nearby spatial information of the tissue mixture. The inhomogeneity effect is modeled as a bias field characterized by a zero mean Gaussian prior probability. The well-known fuzzy C-mean model is extended to define the likelihood function of the observed image. This framework reduces theoretically, under some assumptions, to the adaptive fuzzy C-mean (AFCM) algorithm proposed by Pham and Prince. Digital phantom and real clinical MR images were used to test the proposed framework. Improved performance over the AFCM algorithm was observed in a clinical environment where the inhomogeneity, noise level, and PV effect are commonly encountered.

Adaptive noise reduction toward low-dose computed tomography

An efficient noise treatment scheme has been developed to achieve low-dose CT diagnosis based on currently available CT hardware and image reconstruction technologies. The scheme proposed includes two main parts: filtering in sinogram domain and smoothing in image domain. The acquired projection sinograms were first treated by our previously proposed Karhunen-Loeve (K-L) domain penalized weighted least-square (PWLS) filtering, which fully utilizes the prior statistical noise property and three-dimensional (3D) spatial information for an accurate restoration of the low-dose projections. To treat the streak artifacts due to photon starvation, we also incorporated an adaptive filtering into our PWLS framework, which selectively smoothes those channels contributing most to the streak artifacts. After the sinogram filtering, the image was reconstructed by the conventional filtered backprojection (FBP) method. The image is assumed as piecewise regions each has a unique texture. Therefore, an edge-preserving smoothing (EPS) with locally-adaptive parameters to the noise variation was applied for further noise reduction in image domain. Experimental phantom projections acquired by a GE spiral computed tomography (CT) scanner under 10 mAs tube current were used to evaluate the proposed smoothing scheme. The reconstructed imaged demonstrated that the smoothing scheme with appropriate control parameters provides a significant improvement on noise suppression without sacrificing the spatial resolution.

MRI volumetric analysis of multiple sclerosis: methodology and validation

We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.

Partial volume segmentation of medical images

Image segmentation plays an important role in medical image processing. The aim of conventional hard segmentation methods is to assign a unique label to each voxel. However, due to the limited spatial resolution of medical imaging equipment and the complex anatomic structure of soft tissues, a single voxel in a medical image may be composed of several tissue types, which is called partial volume (PV) effect. Using the hard segmentation methods, the PV effect can substantially decrease the accuracy of quantitative measurements and the quality of visualizing different tissues. In this paper, instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing an image segmentation framework of maximum a posterior (MAP) probability. A new Markov random field (MRF) model was used to reflect the spatial information for the tissue mixture. Parameters of each tissue class were estimated through the expectation-maximization (EM) algorithm during the MAP tissue mixture segmentation. The MAP-EM mixture segmentation methodology was tested by digital phantom MR and patient CT images with PV effect evaluation. Results demonstrated that a hard segmentation method would lose a significant amount of details along the tissue boundaries, while the presented new PV segmentation method can dramatically improve the performance of preserving the details.

An Improved Electronic Colon Cleansing Method for Detection of Polyps by Virtual Colonoscopy

Electronic colon cleansing (ECC) aims to segment the colon lumen from the patient abdominal image acquired with colonic material tagging by oral contrast and other means, so that a virtual colon model can be constructed. Virtual colonoscopy (VC) navigates through the colon model looking for polyps in a similar manner as the fiber optic colonoscopy does. We had built an ECC pipeline for the commercial VC system of Viatronix Inc. In this paper, we present an improved ECC method. It is based on a partial-volume image-segmentation framework, which is derived using the well-established statistical expectation-maximization algorithm. The presented ECC method was evaluated by both visual inspection on the cleansed colon lumens and computer-aided detection of polyps (CADpolyp) using 20 patient datasets. Compared to our previous ECC pipeline, this presented new method demonstrates improvement in both visual judgment and CADpolyp