Ex vivo assessment of trabecular bone structure from three-dimensional projection reconstruction MR micro-images

Magnetic resonance (MR) imaging has recently been proposed for assessing osteoporosis and predicting fracture risks. However, accurate acquisition techniques and image analysis protocols for the determination of the trabecular bone structure are yet to be defined. The aim of this study was to assess the potential of projection reconstruction (PR) MR microscopy in the analysis of the three-dimensional (3-D) architecture of trabecular bone and in the prediction of its biomechanical properties. High-resolution 3-D PR images (41 x 41 x 82 microm3 voxels) of 15 porcine trabecular bone explants were analyzed to determine the trabecular bone volume fraction (Vv), the mean trabecular thickness (Tb.Th), and the mean trabecular separation (Tb.Sp) using the method of directed secants. These parameters were then compared with those derived from 3-D conventional spin-echo microimages. In both cases, segmentation of the high-resolution images into bone and bone marrow was obtained using a spatial adaptive threshold. The contemporary inclusion of Vv, Tb.Th and 1/Tb.Sp in a multiple regression analysis significantly improved the prediction of Young's modulus (YM). The parameters derived from the PR spin-echo images were found to be stronger predictors of YM (R2 = 0.94, p = 0.004) than those derived from conventional spin-echo images (R2 = 0.79, p = 0.051). Our study indicates that projection reconstruction MR microscopy appears to be more accurate than the conventional Fourier transform method in the quantification of trabecular bone structure and in the prediction of its bioimechanical properties. The proposed PR approach should be readily adaptable to the in vivo MRI studies of osteoporosis.     

Prediction of biomechanical properties of trabecular bone in MR images with geometric features and support vector regression

Whole knee joint MR image datasets were used to compare the performance of geometric trabecular bone features and advanced machine learning techniques in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. Changes of trabecular bone structure throughout the proximal tibia are indicative of several musculoskeletal disorders involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging also allows non-invasive 3-D characterization of bone microstructure. Sophisticated features like the scaling index method (SIM) can estimate local structural and geometric properties of the trabecular bone and may improve the ability of MR imaging to determine local bone quality in vivo. A set of 67 bone cubes was extracted from knee specimens and their biomechanical strength estimated by the yield stress (YS) [in MPa] was determined through mechanical testing. The regional apparent bone volume fraction (BVF) and SIM derived features were calculated for each bone cube. A linear multiregression analysis (MultiReg) and a optimized support vector regression (SVR) algorithm were used to predict the YS from the image features. The prediction accuracy was measured by the root mean square error (RMSE) for each image feature on independent test sets. The best prediction result with the lowest prediction error of RMSE = 1.021 MPa was obtained with a combination of BVF and SIM features and by using SVR. The prediction accuracy with only SIM features and SVR (RMSE = 1.023 MPa) was still significantly better than BVF alone and MultiReg (RMSE = 1.073 MPa). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR-based determination of trabecular bone quality.     

Retrospective correction of MR intensity inhomogeneity byinformation minimization

In this paper, the problem of retrospective correction of intensity inhomogeneity in magnetic resonance (MR) images is addressed. A novel model-based correction method is proposed, based on the assumption that an image corrupted by intensity inhomogeneity contains more information than the corresponding uncorrupted image. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. The degraded image is corrected by the inverse of the image degradation model. The parameters of this model are optimized such that the information of the corrected image is minimized while the global intensity statistic is preserved. The method was quantitatively evaluated and compared to other methods on a number of simulated and real MR images and proved to be effective, reliable, and computationally attractive. The method can be widely applied to different types of MR images because it solely uses the information that is naturally present in an image, without making assumptions on its spatial and intensity distribution. Besides, the method requires no preprocessing, parameter setting, nor user interaction. Consequently, the proposed method may be a valuable tool in MR image analysis.     

Effects of ovariectomy on bone morphology in maxillae of mature rats.

Postmenopausal oestrogen deficiency results in bone loss (osteoporosis) in humans and experimental animals. The loss of trabecular bone in the ovariectomized (OVX) rat provides a useful experimental model of post-menopausal osteoporosis. At 5 months after ovariectomy of 3-month-old female rats, the mid and distal femurs and maxillae were dissected and processed for quantitative backscattered electron microscopic examinations. Histomorphometric analysis of femurs in OVX rats showed significant loss in metaphyseal trabecular bone areas compared with sham-operated controls; no significant bone loss was observed in the cortical bone areas of mid-diaphyses in OVX rats. Net bone areas in the maxillae of OVX rats was similar to that of sham-operated controls. Bone structure of maxillae in OVX rats was also similar to that in controls. Our results suggest that, in this animal model of osteoporosis, prominent bone loss occurs mainly in the bone areas formed by endochondral ossification such as distal femurs, but those areas formed by intramembranous ossification such as mid-femurs and maxillae sustained less effects by OVX.     

Analysis of trabecular bone structure using Fourier transforms andneural networks

Hip fracture due to osteoporosis (OP) and hip osteoarthritis (OA) are both important causes of locomotor morbidity in the elderly population. In osteoporosis, bone mass gradually decreases until the skeleton is too fragile to support the body and a fracture occurs, typically in the femur, wrist or spine. In osteoarthritis, there is a proliferation of bone, leading to a stiffening of the tissue. Current clinical methods for assessment of bone changes in these disorders largely depend on assessing bone mineral density. However, this does not provide any information about bone structure, which is considered to be an equally important factor in assessing bone quality. This paper presents a novel approach for computer analysis of trabecular (or cancellous) bone structure. The technique uses a Fourier transform to generate a “spectral fingerprint” of an image. Principal components analysis is then applied to identify key features from the Fourier transform and this information is passed to a neural network for classification. Testing this on a series of 100 histological sections of trabecular bone from patients with OP and OA and a normal group correctly classified over 90% of the OP group with an overall accuracy of 77%-84%. Such high success rates on a small group suggest that this may provide a simple, but powerful, method for identifying alterations in bone structure     

Automated image analysis for bone density measurements using computed tomography

Quantitative trabecular bone density (TBD) measurements in the appendicular skeleton, using computer tomography, are described. Pixel-value frequency histograms are generated, to determine the spatial coordinates of the centers of mass distribution for both bones in the image field. A line joining these centers is used as an axis for subsequent enhancement of horizontal image features, and for radial fan searches, to separate bone images in the analysis field. One bone image is then deleted and the outer contour of the remaining bone is determined using cross-correlation and mathematical morphology operators. This contour is then used as a template to calculate average linear attenuation coefficients (LACs) over 1-pixel-wide annuli for TBD calculation. Data from 33 individual subjects (1251 images) have been processed using both automatic and manual analysis methods. Both methods give the same numerical values for TBD, but the automatic method has slightly better precision. The analysis method is general and is adaptable to other imaging situations.     

Field distribution in vertebral bodies of the rat during electricalstimulation: a parameter study

The electrical field and current density distributions were found in the various tissues of a mathematical model of the experimental rat used to study systemic osteoporosis. The finite element method was used to solve the boundary value problem derived from Maxwell's equations using a quasistatic approximation for a 60 kHz external output signal applied via skin electrodes. A parametric study was done initially to determine the principle factors which effect the solution of the field in the vertebral bodies. Grid coarseness, model length, and intervertebral space width had little effect on the solution while trabecular bone and abdominal cavity conductivity values had strong effects. The two pair of transversely placed electrodes spaced by at least three vertebral bodies produced the most uniform field distributions and was used in the experimental rat model. The range of current density values in the trabecular bone was determined to be 3.0-5.0 microA/cm2 at the external output signal where evidence of a reversal of bone loss due to castration osteoporosis had been found in the experimental rat.     

3-D active appearance models: segmentation of cardiac MR and ultrasound images

A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R2 = 0.94, 0.97, 0.82, respectively. For echocardiographic analysis, the area correlation was R2 = 0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting.     

Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform

Trabecular or cancellous bone, the type of bone found in the vertebrae and near the joints of long bones, consists of a network of plates and struts. Accurate measurement of trabecular thickness is of significant interest, for example, to assess the effectiveness of anabolic (bone forming) agents of patients with osteoporosis. Here, we introduce a new fuzzy distance transform (FDT)-based thickness computation method that obviates binary segmentation and that can effectively deal with images acquired at a voxel size comparable to the typical trabecular bone thickness. The method's robustness is shown on the basis of micro-CT images of human trabecular bone, resampled at progressively coarser resolution and after application of rotation and addition of noise as a means to simulate the in vivo situation. Reproducibility of the method is demonstrated with micro-CT images by comparing histograms of thickness within and between data sets and with micro-MRI volume data sets of human volunteers imaged repeatedly. Finally, with in vivo micro-MR images from a prior study in rabbits subjected to corticosteroid exposure, it is demonstrated that short-term treatment resulting in trabecular thinning can be quantified with the new method.     

Eye location method based on symmetry analysis and high-order fractal feature

A method for eye location in human facial images based on symmetry analysis and the lacunarity, which is a high-order fractal feature, is proposed. First, the valley field algorithm is applied to the facial image and the eye candidates are identified. Then, principal component analysis is used to detect the symmetry axis of the human face. The eye candidates are grouped to form eye-pair candidates, and the whole image is rotated around the symmetry axis. Finally, a novel approach to estimate lacunarity value is proposed to describe accurately the local structure of eye regions. By comparing the lacunarity values of two eye regions within each eye-pair candidate, the eye-pair candidate with minimum lacunarity value difference is identified as the true one. Numerical experiments demonstrate the effectiveness and reliability of this method.     

MR image denoising method for brain surface 3D modeling

The goal of Optoelectronics Letters is to rapidly report original, new and important results in the fields of photonics and optoelectronics in English, to advance the international academic exchanges. Optoelectronics Letters pays a particularly attention to the cross topics between photonics and electronics.     

MR images reconstruction based on TVWL2–L1 model

Compressive sensing (CS) theory, which has been widely used in magnetic resonance (MR) image processing, indicates that a sparse signal can be reconstructed by the optimization programming process from non-adaptive linear projections. Since MR Images commonly possess a blocky structure and have sparse representations under certain wavelet bases, total variation (TV) and wavelet domain ?₁ norm regularization are enforced together (TV-wavelet L1 method) to improve the recovery accuracy. However, the components of wavelet coefficients are different: low-frequency components of an image, that carry the main energy of the MR image, perform a decisive impact for reconstruction quality. In this paper, we propose a TV and wavelet L2–L1 model (TVWL2–L1) to measure the low frequency wavelet coefficients with ?₂ norm and high frequency wavelet coefficients with ?₁ norm. We present two methods to approach this problem by operator splitting algorithm and proximal gradient algorithm. Experimental results demonstrate that our method can obviously improve the quality of MR image recovery comparing with the original TV-wavelet method.     

Application of autoregressive moving average parametric modeling in magnetic resonance image reconstruction

The modeling of data is an alternative to conventional use of the fast Fourier transform (FFT) algorithm in the reconstruction of magnetic resonance (MR) images. The application of the FFT leads to artifacts and resolution loss in the image associated with the effective window on the experimentally-truncated phase encoded MR data. The transient error modeling method treats the MR data as a subset of the transient response of an infinite impulse filter (H(z) = B(z)IA(z)). Thus, the data are approximated by a deterministic autoregressive moving average (ARMA) model. The algorithm for calculating the filter coefficients is described. It is demonstrated that using the filter coefficients to reconstruct the image removes the truncation artifacts and improves the resolution. However, determining the autoregressive (AR) portion of the ARMA filter by algorithms that minimize the forward and backward prediction errors (e.g., Burg) leads to significant image degradation. The moving average (MA) portion is determined by a computationally efficient method of solving a finite difference equation with initial values. Special features of the MR data are incorporated into the transient error model. The sensitivity to noise and the choice of the best model order are discussed. MR images formed using versions of the transient error reconstruction (TERE) method and the conventional FFT algorithm are compared using data from a phantom and a human subject. Finally, the computational requirements of the algorithm are addressed.     

Automatic quantification of changes in bone in serial MR images of joints

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.     

Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry

The accurate diagnosis of osteoporosis has gained increasing importance due to the aging of our society. Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is an established criterion in the diagnosis of osteoporosis. This measure, however, is limited by its two-dimensionality. This work presents a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image used in clinical routine. A statistical model of the combined shape and BMD distribution is presented, together with a method for its construction from a set of quantitative computed tomography (QCT) scans. A reconstruction is acquired in an intensity based 3D-2D registration process whereby an instance of the model is found that maximizes the similarity between its projection and the DXA image. Reconstruction experiments were performed on the DXA images of 30 subjects, with a model constructed from a database of QCT scans of 85 subjects. The accuracy was evaluated by comparing the reconstructions with the same subject QCT scans. The method presented here can potentially improve the diagnosis of osteoporosis and fracture risk assessment from the low radiation dose and low cost DXA devices currently used in clinical routine.     

基于多模态融合的2D MR脑肿瘤图像分割算法研究

针对不同模态MR脑肿瘤图像呈现的肿瘤状态差异以及卷积神经网络(convolutional neural networks, CNNs)提取特征局限性的问题,提出了一种基于多模态融合的MR脑肿瘤图像分割方法。分割模型以U-net网络为原型,创新一种多模态图像融合方式以加强特征提取能力,同时引入通道交叉注意力机制(channel cross transformer, CCT)代替U-net中的跳跃连接结构,进一步弥补深浅层次的特征差距与空间依赖性,有效融合多尺度特征,加强对肿瘤的分割能力。实验在BraTS数据集上进行了多目标分割结果验证,通过定量分析对比前沿网络分割结果,表明该方法确有良好的分割性能,其分割出三种肿瘤区域的Dice系数分别达到80%、74%、71%。     

A Texture-Based Classification of Crackles and Squawks Using Lacunarity

An automatic classification method to efficiently discriminate the types of discontinuous breath sounds (DBSs), i.e., fine crackles (FCs), coarse crackles (CC), and squawks (SQ), is presented in this paper. Using the lacunarity of the acquired DBS, the proposed classification method, namely LAC, introduces a texture-based approach that captures the differences in the distribution of FC, CC, and SQ across the breathing cycle, which may lead to more accurate characterization of the pulmonary acoustical changes due to the related pathology. Prior to the lacunarity analysis, wavelet-based denoising of DBS is employed to eliminate effects of the vesicular sound (background noise) to DBS oscillatory pattern. LAC analysis builds its classification power both upon the use of lacunarity at an optimum scale and the approximation of its trajectory across an optimum range of scales using a three-parameter hyperbola model. LAC is applied to 363 DBS corresponding to 25 cases included in four lung sound databases. Results show that LAC efficiently classifies the three DBS categories in the comparison groups of FC-CC, FC-SQ (both with mean accuracy of 100%), CC-SQ (mean accuracy of 99.62%-100%), and FC-CC-SQ (mean accuracy of 99.75%-100%). When compared to other classification tools, LAC seems quite attractive, since, without employing high computational complexity, it results in high classification accuracy. Moreover, LAC introduces a ldquotexturerdquo concept in the analysis of breath sounds, something that strongly relates to the perception of the bioacoustic signals by the physician. Due to its simplicity, LAC could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.     

A new adaptive coupled diffusion PDE for MRI Rician noise

Among different methods of image de-noising, partial differential equation (PDE)-based de-noising attracted much attention in the field of medical image processing. The benefit of PDE-based de-noising methods is the ability to smooth image in a nonlinear way, which effectively removes the noise as well as preserving edge through anisotropic diffusion (AD) controlled by the diffusive function. Today, AD filtering such as Perona and Malik (P–M) model is widely used for MR Image enhancement. However, the AD filter is non-optimal for MR images that have Rician noise. Originally, the PDE-based de-noising designed for additive Gaussian distributed noise was signal independent, but the Rician noise was signal dependent. In this paper, we proposed a new adaptive coupled diffusion PDE fitted with MRI Rician noise which not only preserved the edges and fine structures, but also performed efficient de-noising. Our method was an improved version of AADM (automatic parameter selection anisotropic diffusion for MR Images). For this purpose, we have presented a new adaptive method to estimate the standard deviation of noise. As the simulation results showed, our proposed diffusion effectively improved the improved signal-to-noise ratio (ISNR) and preserved edges more than P–M, AADM and unbiased NLM (UNLM—unbiased non-local means) methods to remove Rician noise in MR Images.     

Multispectral code excited linear prediction coding and itsapplication in magnetic resonance images

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.