Global Tracking of Myocardial Motion in Ultrasound Sequence Images: A Feasibility Study.

The assessment of myocardial motion plays a promising role in the evaluation of cardiac function. This study aims to propose a novel framework of global estimation of the myocardial motion using radio-frequency (RF) data. The framework consists of B-mode image reconstruction, displacement estimation, myocardium extraction, and image fusion.

[Kinetic cluster and α-divergence-based dynamic myocardial factorial analysis of positron-emission computed tomography images].

Objective: We purpose a novel factor analysis method based on kinetic cluster and α-divergence measure for extracting the blood input function and the time-activity curve of the regional tissue from dynamic myocardial positron emission computed tomography(PET) images.

Methods: Dynamic PET images were decomposed into initial factors and factor images by minimizing the α-divergence between the factor model and actual image data. The kinetic clustering as a priori constraint was then incorporated into the model to solve the nonuniqueness problem, and the tissue time-activity curves and the tissue space distributions with physiological significance were generated.

An ultrasound study of altered hydration behaviour of proteoglycan-degraded articular cartilage.

Background: Articular cartilage is a solid-fluid biphasic material covering the bony ends of articulating joints. Hydration of articular cartilage is important to joint lubrication and weight-wearing. The aims of this study are to measure the altered hydration behaviour of the proteoglycan-degraded articular cartilage using high-frequency ultrasound and then to investigate the effect of proteoglycan (PG) degradation on cartilage hydration.

Intensity based image registration by minimizing exponential function weighted residual complexity.

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results.

Ultrasonic reflection coefficient and surface roughness index of OA articular cartilage: relation to pathological assessment.

Background: Early diagnosis of osteoarthritis (OA) is essential for preventing further cartilage destruction and decreasing severe complications. The aims of this study are to explore the relationship between OA pathological grades and quantitative acoustic parameters and to provide more objective criteria for ultrasonic microscopic evaluation of the OA cartilage.

Methods: Articular cartilage samples were prepared from rabbit knees and scanned using ultrasound biomicroscopy (UBM).

[Three-dimensional low-dose CT volume reconstruction based on non-local weights optimization and GPU acceleration].

Concerns have been raised over x-ray radiation dose associated with repeated computed tomography (CT) scans for tumor surveillance and radiotherapy planning. In this paper, we present a low-dose CT image reconstruction method for improving low-dose CT image quality. The method proposed exploited rich redundancy information from previous normal-dose scan image for optimizing the non-local weights construction in the original non-local means (NLM)-based low-dose image reconstruction.

[Adaptive regularized super-resolution reconstruction for magnetic resonance images].

To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.

[A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].

For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed.

[CT image retrieval of the liver with intrahepatic lesions].

This paper presents a method for global feature extraction and the application of the boostmetric distance metric method for medical image retrieval. The global feature extraction method used the low frequency subband coefficient of the wavelet decomposition based on the non-tensor product coefficient for piecewise Gaussian fitting. The local features were extracted after semi-automatic segmentation of the lesion areas in the images in the database.

[Sparse angular CT projection onto convex set reconstruction using nonlocal means iterative modification].

Based on the fact that nonlocal means (NL-means) filtered image can likely produce an acceptable priori solution, we propose a sparse angular CT projection onto convex set (POCS) reconstruction using NL-means iterative modification. The new reconstruction scheme consists of two components, POCS and NL-means filter. In each phase of the sparse angular CT iterative reconstruction, we first used POCS algorithm to meet the identity and non-negativity of projection data, and then performed NL-means filter to the image obtained by POCS method for image quality improvement.

[Research on algorithms based on Markov random models for diffusion tensor-magnetic resonance images].

With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.

[Cerebral blood flow, cerebral blood volume, oxygen utilization and oxygen extraction fraction: the influence of age].

Objective: To analyze the changes of cerebral blood flow (CBF), cerebral blood volume (CBV), oxygen utilization (CMRO2) and oxygen extraction fraction (OEF) with age.

Methods: The PET images of 7 young (21.0-/+1 years old) and 7 aged volunteers (60.

[Cone-beam pseudo lambda tomography under FDK framework].

The medical CT scanner is rapidly evolving from the fan-beam mode to the cone-beam geometry mode. In this paper, a new cone-beam pseudo Lambda tomography was proposed based on the Noo's fan beam super-short scan formula and FDK framework. The proposed pseudo-LT algorithm, which avoids the computation of any PI line and any differential operation, has a significant practical implementation, thus leading to the images with quality improvement and reduced artifacts.

[Adaptive super resolution algorithm for under-sampled images].

A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.

[Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors].

We propose a graph-based three-dimensional (3D) algorithm to automatically segment brain tumors from magnetic resonance images (MRI). The algorithm uses minimum s/t cut criteria to obtain a global optimal result of objective function formed according to Markov Random Field Model and Maximum a posteriori (MAP-MRF) theory, and by combining the expectation-maximization (EM) algorithm to estimate the parameters of mixed Gaussian model for normal brain and tumor tissues. 3D segmentation results of brain tumors are fast achieved by our algorithm.

[Hybrid interpolation for CT metal artifact reducing].

Numerous interpolation-based methods have been described for reducing metal artifacts in CT images, but due to the limit of the interpolation methods, interpolation alone often fails to meet the clinical demands. In this paper, we describe the use of quartic polynomial interpolation in reconstruction of the images of the metal implant followed by linear interpolation to eliminate the streaks. The two interpolation methods are combined according to their given weights to achieve good results.

[Self-adapted segmentation of pulmonary nodule based on region growing].

Objective: To improve the accuracy and efficiency of pulmonary nodule segmentation of thoracic CT image for computer-aided diagnostic (CAD) system, especially for those nodules adhering to the pleural or blood vessels.

Methods: We proposed the automatic process of pulmonary nodule segmentation, and using region growing method based on the contrast and gradient, the pulmonary nodule images were acquired. A self-adapted morphologic segmentation algorithm was presented for the unsuccessful nodule segmentation using region growing.

[A fast 3-D medical image registration algorithm using principal component analysis].

This paper presents a new 3-D image registration method based on the principal component analysis (PCA). Compared with intensity-based registration methods using the whole volume intensity information, our approach utilizes PCA to estimate the centroid and principal axis, and completes the registration by aligning the centroid and principal axis. We evaluated the effectiveness of this approach by applying it to simulated and actual brain image data (MR, CT, PET, and SPECT).

[Elimination of direct derivative of the projection data for image reconstruction based on hybrid filter in helical cone-beam CT].

Objective: We present an alternative approach for precise reconstruction of the images from helical cone-beam projections combining Hilbert filter and Ramp filter.

Methods: Based on the Katsevich algorithm framework, the proposed algorithm combined the FDK-type algorithms and Katsevich algorithm for their respective advantages, to completely avoid the direct derivatives with respect to the coordinates on the detector plane.

Results: The experimental results validated the accuracy of the new algorithm, and this approach significantly improved the resolution of the reconstructed images with much reduced artifacts.

[A new algorithm for magnetic resonance image segmentation based on fuzzy kerne1 clustering].

Fuzzy clustering technique is a popular model widely used in the segmentation of magnetic resonance (MR) images. However, when the conventional fuzzy clustering algorithm is used for image segmentation, the algorithm strictly depending on the current pixels works only on images with less noise. In the paper, we presented a modified fuzzy kernel clustering algorithm for MR image segmentation.

[Medical image segmentation based on Gibbs morphological gradient and distance map Snake model].

Objective: To propose a new algorithm for medical image segmentation based on Gibbs morphological gradient and distance map (DM) Snake model, which allows identification of the correct contours of the objects when processing medical images with noises and pseudo-edges.

Methods: Gibbs morphological gradient was deduced and the method for image segmentation based on Gibbs morphological gradient and distance map Snake model was presented.

Results: This new medical image segmentation algorithm proved to effectively suppress the noises and pseudo-edges when calculating distance map.

[Automatic extraction and segmentation of the intracranial structure from cerebral computed tomography].

This paper describes a new method for extracting and segmenting intracranial structure from serial images of cerebral computerized tomography automatically. A region growing- and morphology-based approach was first developed to extract intracranial structures from the serial images of cerebral computerized tomography, and focusing on the problems of parameter initialization of the expectation maximization (EM) algorithm, an improved EM algorithm based on parameter- limited GMM was presented to segment the intracranial structures successfully. Experimental results of the algorithm showed that this method was effective for all cerebral computerized tomography images from bottom to top of the cerebrum.

[A new unsupervised algorithm for image segmentation based on an inhomogeneous Markov random field model].

A new unsupervised algorithm for image segmentation is proposed using an inhomogeneous Markov random field (MRF) model, in which the parameter is estimated in fuzzy spel affinities. The proposed algorithm improved the accuracy of segmentation. Simulated brain MR image with different noise levels and clinical brain MR image were presented in the experiments.

[Improved reconstruction for PROPELLER data in magnetic resonance imaging based on fuzzy enhancement].

To improve the conventional reconstruction algorithm for PROPELLER MRI data, we propose a new algorithm based on fuzzy enhancement. The motion parameters were extracted from fuzzy enhanced images reconstructed through zero-padding strips. After motion compensation, the image was obtained through gridding reconstruction.

[A new Bayesian reconstruction algorithm for PET images based on correction of the detected sinogram data].

Objective: To improve Bayesian reconstruction of positron-emission tomography (PET) images by devising a novel coupled feedback (CF) iterative model.

Methods: The CF iterative algorithm was applied to update the noisy detected emission sinogram data using the latest reconstructed image in the iterative process of PET reconstruction. The relevant operations included linear filtering, wiener filtering, and projection of the reconstructed images.