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. The RF data of murine heart in parasternal long-axis (PLAX) view were collected for B-mode image reconstruction and displacement estimation. The vectorized normalized cross-correlation (VNCC) approach was proposed to globally estimate the displacements of the RF frames, while a sum-table based normalized cross-correlation (STNCC) was performed as reference algorithm. The bimodal fusion images were obtained to visualize the motion and anatomical structure of myocardium by an improved fast mapping algorithm (IFMA). In comparison with STNCC, the computation time of displacement using VNCC reduced by approximate 10s. The myocardial motions of anterior wall and posterior wall during one cardiac cycle were similarly tracked by VNCC as that of STNCC. The averaged absolute error in displacement between the two methods ranges from 1 to 3µm. The obtained myocardial elastographic images using VNCC intuitively present the morphological and mechanical changes during the contraction period of left ventricle. The results demonstrate that the proposed framework is an efficient tool for the estimation of myocardial motion reflecting cardiac systolic function. This approach has potentials to provide visualized information of myocardium for diagnosis and prognosis of cardiovascular diseases (CVDs).

[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. RESULTS: The model was applied to the 82RbPET myocardial perfusion simulation data and compared with the traditional model-based least squares measure and the minimal spatial overlap constraint. The experimental results showed that the proposed model performed better than the traditional model in terms of both accuracy and sensitivity. CONCLUSION: This method can select the optimal measure by α value, and incorporate the prior information of the kinetic clustering of PET image pixels to obtain the accurate time-activity curves of the tissue, which has shown good performance in visual evaluation and quantitative evaluation.

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. METHODS: Twelve porcine patellae with smooth cartilage surface were prepared and evenly divided into two groups: normal group without any enzyme treatment and trypsin group treated with 0.25% trypsin solution for 4 h to digest PG in the tissue. After 40-minute exposure to air at room temperature, the specimens were immerged into the physiological saline solution. The dehydration induced hydration behaviour of the specimen was monitored by the high-frequency (25 MHz) ultrasound pulser/receiver (P/R) system. Dynamic strain and equilibrium strain were extracted to quantitatively evaluate the hydration behaviour of the dehydrated cartilage tissues. RESULTS: The hydration progress of the dehydrated cartilage tissue was observed in M-mode ultrasound image indicating that the hydration behaviour of the PG-degraded specimens decreased. The percentage value of the equilibrium strain (1.84 ± 0.21%) of the PG-degraded cartilage significantly (p < 0.01) decreased in comparison with healthy cartilage (3.46 ± 0.49%). The histological sections demonstrated that almost PG content in the entire cartilage layer was digested by trypsin. CONCLUSION: Using high-frequency ultrasound, this study found a reduction in the hydration behaviour of the PG-degraded cartilage. The results indicated that the degradation of PG decreased the hydration capability of the dehydrated tissue. This study may provide useful information for further study on changes in the biomechanical property of articular cartilage in osteoarthritis.

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. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.

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 quantitative parameters, including the roughness index of the cartilage surface (URI), the reflection coefficients from the cartilage surface (R) and from the cartilage-bone interface (Rbone) were extracted. The osteoarthritis grades of these cartilage samples were qualitatively assessed by histology according to the grading standards of International Osteoarthritis Institute (OARSI). The relationship between these quantitative parameters and the osteoarthritis grades was explored. RESULTS: The results showed that URI increased with the OA grade. URI of the normal cartilage samples was significantly lower than the one of the OA cartilage samples. There was no significant difference in URI between the grade 1 cartilage samples and the grade 2 cartilage samples. The reflection coefficient of the cartilage surface reduced significantly with the development of OA (p < 0.05), while the reflection coefficient of the cartilage-bone interface increased with the increase of grade. CONCLUSION: High frequency ultrasound measurements can reflect the changes in the surface roughness index and the ultrasound reflection coefficients of the cartilage samples with different OA grades. This study may provide useful information for the quantitative ultrasonic diagnosis of early OA.

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

OBJECTIVE: 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. The objective 3D low-dose volume and the previous 3D normal-dose volume were first registered to reduce the anatomic structural dissimilarity between the two datasets, and the optimized non-local weights were constructed based on the registered normal-dose volume. To increase the efficiency of this method, GPU was utilized to accelerate the implementation. The experimental results showed that this method obviously improved the image quality, as compared with the original NLM method, by suppressing the noise-induced artifacts and preserving the edge information.

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

OBJECTIVE: 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. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.

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

OBJECTIVE: 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. The experimental verification of the method using 1688 CT images of the liver containing lesions of liver cancer, liver angioma, and liver cyst confirmed that this feature extraction method improved the detection rate of the lesions with good image retrieval performance.

[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. Simulation experiments showed that the proposed POCS scheme can significantly improve the quality of sparse angular CT image by suppressing the noise and removing the streak-artifacts.

[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 (60.9-/+4.7 years old) were analyzed to identify the areas where CBF, CBV, CMRO2, OEF had significant differences with age. The images were anatomically normalized by statistical parametric mapping (SPM2). A voxel by voxel calculation was performed to obtain the slope with age. Voxels which had statistically significant differences (P<0.05) with age were shown both on global and ROIs brain images. RESULTS: The CBF decreased with age as was consistent with previous reports. The age-related changes in CBV and CMRO2 were similar to CBF, but OEF increased with age. CONCLUSION: CBF, CBV and CMRO2 generally decline with age. The increase in OEF with age suggests a greater reduction in CBF than in CMRO2. The most significant decreases of CBF and CMRO2 occur in the convexity of the frontal cortex and inferior parietal cortex in all the functional images, while in the white matter, the influence of age is minimal.

[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. The results in the simulation studies confirm the observation that the new algorithm can improve the image resolution over the traditional algorithms with noise projection data.

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

OBJECTIVE: 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.

[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.

[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. The validation of the algorithm was tested and showed good accuracy and adaptation under simple interactions with the physicians.

[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. RESULTS AND CONCLUSIONS: Experiments on clinical 2D pulmonary images showed that the solitary pulmonary nodules and those adhering to the pleural or blood vessels could all be segmented. This pulmonary nodule segmentation algorithm is feasible for automated segmentation of the thoracic CT images.

[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). The experimental results indicate that the algorithm is effective, especially for registration of 3-D medical images.

[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. CONCLUSION: The proposed reconstruction formula based on hybrid Hilbert-Ramp filter is an important development of Katsevich reconstruction formula, and the different forms of the Ramp filters can be designed to realize frequency modulation according to the actual clinical application.

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

OBJECTIVE: 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. The new algorithm incorporates a kernel-induced distance mertric and a penalty term that controls the neighborhood effect to the objective function. The results of experiment on both the synthetic images and simulated MR images show that the proposed algorithm is more robust to noise than the standard fuzzy image segmentation algorithms.