Multiview cluster ensembles for multimodal MRI segmentation

It has been shown that the combination of multimodal magnetic resonance imaging (MRI) images can improve the discrimination of diseased tissue. The fusion of dissimilar imaging data for classification and segmentation purposes, however, is not a trivial task, as there is an inherent difference in information domains, dimensionality, and scales. This work proposed a multiview consensus clustering methodology for the integration of multimodal MR images into a unified segmentation aiming at heterogeneity assessment in tumoral lesions. Using a variety of metrics and distance functions this multiview imaging approach calculated multiple vectorial dissimilarity‐spaces for each MRI modality and it maked use of cluster ensembles to combine a set of unsupervised base segmentations into an unified partition of the voxel‐based data. The methodology was demonstrated with simulated data in application to dynamic contrast enhanced MRI and diffusion tensor imaging MR, for which a manifold learning step was implemented in order to account for the geometric constrains of the high dimensional diffusion information. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 56–67, 2015     

Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework

The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015     

A modified POCS‐based reconstruction method for compressively sampled MR imaging

One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled k‐space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft‐thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Mary's Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal‐to‐noise ratio, artifact power, and correlation as compared to the reconstruction through low‐resolution and POCS algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 203–207, 2014     

A Nanoparticle Ink Allowing the High Precision Visualization of Tissue Engineered Scaffolds by MRI (Small 30/2023)

Hydrogels are widely used as cell scaffolds in several biomedical applications. Once implanted in vivo, cell scaffolds must often be visualized, and monitored overtime. However, cell scaffolds appear poorly contrasted in most biomedical imaging modalities such as magnetic resonance imaging (MRI). MRI is the imaging technique of choice for high-resolution visualization of low-density, water-rich tissues. Attempts to enhance hydrogel contrast in MRI are performed with “negative” contrast agents that produce several image artifacts impeding the delineation of the implant's contours. In this study, a magnetic ink based on ultra-small iron oxide nanoparticles (USPIONs; <5 nm diameter cores) is developed and integrated into biocompatible alginate hydrogel used in cell scaffolding applications. Relaxometric properties of the magnetic hydrogel are measured, as well as biocompatibility and MR-visibility (T₁-weighted mode; in vitro and in vivo). A 2-week MR follow-up study is performed in the mouse model, demonstrating no image artifacts, and the retention of “positive” contrast overtime, which allows very precise delineation of tissue grafts with MRI. Finally, a 3D-contouring procedure developed to facilitate graft delineation and geometrical conformity assessment is applied on an inverted template alginate pore network. This proof-of-concept establishes the possibility to reveal precisely engineered hydrogel structures using this USPIONs ink high-visibility approach.     

Image‐guided navigation of single‐element focused ultrasound transducer

The spatial specificity and controllability of focused ultrasound (FUS), in addition to its ability to modify the excitability of neural tissue, allows for the selective and reversible neuromodulation of the brain function, with great potential in neurotherapeutics. Intraoperative magnetic resonance imaging (MRI) guidance has limitations due to its complicated examination logistics, such as fixation through skull screws to mount the stereotactic frame, simultaneous sonication in the MRI environment, and restrictions in choosing MR‐compatible materials. To overcome these limitations, an image‐guidance system based on optical tracking and preoperative imaging data is developed, separating the imaging acquisition for guidance and sonication procedure for treatment. Techniques to define the local coordinates of the focal point of sonication are presented. First, mechanical calibration detects the concentric rotational motion of a rigid‐body optical tracker, attached to a straight rod mimicking the sonication path, pivoted at the virtual FUS focus. The spatial error presented in the mechanical calibration was compensated further by MRI‐based calibration, which estimates the spatial offset between the navigated focal point and the ground‐truth location of the sonication focus obtained from a temperature‐sensitive MR sequence. MRI‐based calibration offered a significant decrease in spatial errors (1.9 ± 0.8 mm; 57% reduction) compared to the mechanical calibration method alone (4.4 ± 0.9 mm). Using the presented method, pulse‐mode FUS was applied to the motor area of the rat brain, and successfully stimulated the motor cortex. The presented techniques can be readily adapted for the transcranial application of FUS to intact human brain. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 177–184, 2012     

A novel Markov random field model based on region adjacency graph for T1 magnetic resonance imaging brain segmentation

Tissue segmentation in magnetic resonance brain scans is the most critical task in different aspects of brain analysis. Because manual segmentation of brain magnetic resonance imaging (MRI) images is a time‐consuming and labor‐intensive procedure, automatic image segmentation is widely used for this purpose. As Markov Random Field (MRF) model provides a powerful tool for segmentation of images with a high level of artifacts, it has been considered as a superior method. But because of the high computational cost of MRF, it is not appropriate for online processing. This article has proposed a novel method based on a proper combination of MRF model and watershed algorithm in order to alleviate the MRF's drawbacks. Results illustrate that the proposed method has a good ability in MRI image segmentation, and also decreases the computational time effectively, which is a valuable improvement in the online applications. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 78–88, 2017     

Integrating Fluorinated Polymer and Manganese‐Layered Double Hydroxide Nanoparticles as pH‐activated 19F MRI Agents for Specific and Sensitive Detection of Breast Cancer

¹⁹F magnetic resonance imaging (¹⁹F MRI) agents capable of being activated upon interactions with cancer triggers are attracting increasing attention, although challenges still remain for precise and specific detection of cancer tissues. In this study, a novel hybrid ¹⁹F MRI agent for pH‐sensitive detection of breast cancer tissues is reported, a composite system designed by conjugating a perfluoropolyether onto the surface of manganese‐incorporated layered double hydroxide (Mn‐LDH@PFPE) nanoparticles. The ¹⁹F NMR/MRI signals from aqueous solutions of Mn‐LDH@PFPE nanoparticles are quenched at pH 7.4, but “turned on” following a reduction in pH to below 6.5. This is due to partial dissolution of Mn²⁺ from the Mn‐LDH nanoparticles and subsequent reduction in the effect of paramagnetic relaxation. Significantly, in vivo experiments reveal that an intense ¹⁹F MR signal can be detected only in the breast tumor tissue after intravenous injection of Mn‐LDH@PFPE nanoparticles due to such a specific activation. Thus pH‐activated Mn‐LDH@PFPE nanoparticles are a potential “smart” ¹⁹F MRI agent for precise and specific detection of cancer diseases.     

Manganese‐Based Layered Double Hydroxide Nanoparticles as a T1‐MRI Contrast Agent with Ultrasensitive pH Response and High Relaxivity

Recently, Mn(II)‐containing nanoparticles have been explored widely as an attractive alternative to Gd(III)‐based T₁‐weighted magnetic resonance imaging (MRI) contrast agents (CAs) for cancer diagnosis. However, as far as it is known, no Mn‐based MRI CAs have been reported to sensitively respond to a very weakly acidic environment (pH 6.5–7.0, i.e., the pH range in a tumor microenvironment) with satisfactory imaging performance. Here, recently devised pH‐ultrasensitive Mn‐based layered double hydroxide (Mn‐LDH) nanoparticles with superb longitudinal relaxivity (9.48 mm ^?1 s^?1 at pH 5.0 and 6.82 mm ^?1 s^?1 at pH 7.0 vs 1.16 mm ^?1 s^?1 at pH 7.4) are reported, which may result from the unique microstructure of Mn ions in Mn‐LDH, as demonstrated by extended X‐ray absorption fine structure. Further in vivo imaging reveals that Mn‐LDH nanoparticles show clear MR imaging for tumor tissues in mice for 2 d post intravenous injection. Thus, this novel Mn‐doped LDH nanomaterial, together with already demonstrated capacity for drug and gene delivery, is a very potential theranostic agent for cancer diagnosis and treatment.     

Temperature mapping near the surface of ultrasound transducers using susceptibility- compensated magnetic resonance imaging

Magnetic resonance imaging (MRI)-based temperature mapping very close to the surface of an ultrasound transducer is not possible due to the large magnetic susceptibility-induced image artifacts that arise from the materials used in transducer construction. Here, it is shown in phantoms that "susceptibility-compensated" MRI sequences can be used to measure thermal increases ~1 mm from the surface of a 4-element cymbal array transducer, which has been used widely for noninvasive transdermal drug delivery. The estimated temperatures agree well with those measured using thermocouples.     

Small unilamellar vesicles: a platform technology for molecular imaging of brain tumors

Molecular imaging enables the non-invasive investigation of cellular and molecular processes. Although there are challenges to overcome, the development of targeted contrast agents to increase the sensitivity of molecular imaging techniques is essential for their clinical translation. In this study, spontaneously forming, small unilamellar vesicles (sULVs) (30 nm diameter) were used as a platform to build a bimodal (i.e., optical and magnetic resonance imaging (MRI)) targeted contrast agent for the molecular imaging of brain tumors. sULVs were loaded with a gadolinium (Gd) chelated lipid (Gd-DPTA-BOA), functionalized with targeting antibodies (anti-EGFR monoclonal and anti-IGFBP7 single domain), and incorporated a near infrared dye (Cy5.5). The resultant sULVs were characterized in vitro using small angle neutron scattering (SANS), phantom MRI and dynamic light scattering (DLS). Antibody targeted and nontargeted Gd loaded sULVs labeled with Cy5.5 were assessed in vivo in a brain tumor model in mice using time domain optical imaging and MRI. The results demonstrated that a spontaneously forming, nanosized ULVs loaded with a high payload of Gd can selectively target and image, using MR and optical imaging, brain tumor vessels when functionalized with anti-IGFBP7 single domain antibodies. The unique features of these targeted sULVs make them promising molecular MRI contrast agents.     

In vivo correlation between semi‐quantitative hemodynamic parameters and Ktrans derived from DCE‐MRI of brain tumors

Dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) has become more and more widely applied in cancer diagnosis and treatment follow‐up. Without complicated calculation, a semiquantitative parameter – modified initial area under the curve (mIAUC_c) – was proposed for better correlation with volume transfer constant (K^trans) by computer simulation. In this study, we aim to further investigate the correlation between mIAUC_c and K^trans in clinical. A total of 10 patients with brain tumors participated in this study and images were acquired by using a 3‐Tesla clinical MR scanner. The results showed that mIAUC_c was highly correlated with K^trans with the correlation coefficient of 0.913. Although the ideals of K^trans and mIAUC_c are different, mIAUC_c does the trick for brain tumors evaluations in DCE‐MRI. It reveals that mIAUC_c could be an alternative for physiological condition evaluation in DCE‐MRI. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 132–136, 2012     

Automatic segmentation of cerebral hemispheres in MR human head scans

Automatic segmentation of cerebral hemispheres in magnetic resonance (MR) brain images help to quantify the brain asymmetry and correct several MR brain deformities. The detection of mid‐sagittal plane (MSP) in human brain image is necessary to segment the hemispheres for both operator‐based and automated brain image asymmetric analysis. In this article, a computationally simple and accurate technique to detect MSP in MRI human head scans using curve fitting is developed. The left and right hemispheres are segmented based on the detected MSP. The accuracy of the MSP is evaluated by comparing the segmented left and right hemispheres against the manually segmented ones. Experimental results using 78 volumes of T1, T2 and PD‐weighted MRI brain images show that the proposed method has accurately segmented the cerebral hemispheres based on the detected MSP in axial and coronal orientations of normal and pathological brain images.     

Analysis of denoising filters on MRI brain images

The magnetic resonance imaging (MRI) modality is an effective tool in the diagnosis of the brain. These MR images are introduced with noise during acquisition which reduces the image quality and limits the accuracy in diagnosis. Elimination of noise in medical images is an important task in preprocessing and there exist different methods to eliminate noise in medical images. In this article, different denoising algorithms such as nonlocal means, principal component analysis, bilateral, and spatially adaptive nonlocal means (SANLM) filters are studied to eliminate noise in MR. Comparative analysis of these techniques have been with help of various metrics such as signal‐to‐noise ratio, peak signal‐to‐noise ratio (PSNR), mean squared error, root mean squared error, and structure similarity (SSIM). This comparative study shows that the SANLM denoising filter gives the best performance in terms of better PSNR and SSIM in visual interpretation. It also helps in clinical diagnosis of the brain.     

Hyperbranched Conjugated Polyelectrolyte for Dual‐modality Fluorescence and Magnetic Resonance Cancer Imaging

Herein is reported the synthesis of gadolinium ion (Gd(III))‐chelated hyperbranched conjugated polyelectrolyte (HCPE‐Gd) and its application in fluorescence and magnetic resonance (MR) dual imaging in live animals. The synthesized HCPE‐Gd forms nanospheres with an average diameter of ～42 nm measured by laser light scattering and a quantum yield of 10% in aqueous solution. The absorption spectrum of HCPE‐Gd has two maxima at 318 and 417 nm, and its photoluminescence maximum centers at 591 nm. Confocal laser scanning microscopy studies indicate that the HCPE‐Gd is internalized in MCF‐7 cancer cell cytoplasm with good photostability and low cytotoxicity. Further fluorescence and MR imaging studies on hepatoma H₂₂ tumor‐bearing mouse model reveal that HCPE‐Gd can serve as an efficient optical/MR dual‐modal imaging nanoprobe for in vivo cancer diagnosis.     

Alternatives to the use of the DFT in MRI and spectroscopic reconstructions

Standard and functional magnetic resonance imaging (MRI and f MRI) make of use the two-dimensional (2D) discrete Fourier transform (DFT). Many MR spectroscopic techniques use the 1D DFT. Experimental or time constraints frequently require that the DFT be applied to finite-length (truncated) data sequences. Truncation is essentially a windowing of the data and introduces artifacts and resolution loss in images or spectra. A number of alternative reconstruction algorithms have been proposed to counteract these problems. These algorithms attempt to model the known data and use the modeling information to implicitly or explicitly extrapolate the data to overcome the windowing. One modeling approach, the Transient Error Reconstruction Algorithm (TERA), uses an autoregressive moving average method to recover the missing data. In this article, we briefly discuss variants of the TERA algorithm and development of neural networks to take better account of the differing data properties of MR data sets. Our success with neural networks in fMRI reconstruction has led us to challenge some of the standard approaches to validating MR algorithms and develop our own. These new approaches include k-space phantom generation and automated computer observer (ROC analysis) to evaluate algorithms in terms of their clinical relevance. We have also developed an upgraded image quality measure based on Daly's Visual Differences Predictor. This models the ability of the human visual system to detect significant differences between images produced by different MRI reconstruction algorithms. We also present a protocol for generating Shepp-Logan phantoms which avoids the introduction of the high-frequency k-space data distortion present in the existing approach. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 558–564, 1997     

Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging

We outline a computationally efficient image correction algorithm, which we have applied to diffuse optical tomography (DOT) image time series derived from a magnetic resonance imaging (MRI)-based brain model. Results show that the algorithm increases spatial resolution, decreases spatial bias, and only modestly reduces temporal accuracy for noise levels typically seen in experiment, and produces results comparable to image reconstructions that incorporate information from MRI priors. We demonstrate that this algorithm has robust performance in the presence of noise, background heterogeneity, irregular external and internal boundaries, and error in the initial guess. However, the algorithm introduces artifacts when the absorption and scattering coefficients of the reference medium are overestimated--a situation that is easily avoided in practice. The considered algorithm offers a practical approach to improving the quality of images from time-series DOT, even without the use of MRI priors.     

Zirconium Alloys for Orthopaedic and Dental Applications

Zirconium alloys for biomedical applications are receiving increasing attention due to their two unique properties: 1) the formation of an intrinsic bone‐like apatite layer on their surfaces in body environments, and 2) better compatibility with magnetic resonance imaging (MRI) diagnostics due to their low magnetic susceptibility, as well as their overall excellent biocompatibility, mechanical properties, and bio‐corrosion resistance. In particular, since both of the MRI quality and speed depend on magnetic field strength, there is a compelling drive for use of high magnetic field strength (>3 Tesla) MRI systems. This paper provides a comprehensive review of the characteristics of commercially pure (CP) Zr and Zr‐based alloys as orthopaedic and dental implant materials. These include their 1) phase transformations; 2) unique properties including corrosion resistance, biocompatibility, magnetic susceptibility, shape memory effect, and super‐elasticity; 3) mechanical properties; 4) current orthopaedic and dental applications; and 5) the d‐electron theory for Zr alloy design and novel Zr‐alloys. The mechanical properties of Zr‐based bulk metallic glasses (BMGs) and their application as implant materials are also assessed. Future directions for extending the use of Zr‐alloys as orthopaedic and dental implants are discussed.

     

一种改进的MRI图像刚性平移运动伪影的校正

在磁共振成像过程中由于患者的运动会在图像中造成运动伪影,从而造成图像的退化,严重影响临床诊断．本文对MRI图像刚性平移运动伪影提出了一个改进的后处理方法：首先用谱平移理论消除频率编码方向平移运动;然后建立模糊模型表示图像的背景并对其进行抑制,用数学形态学的方法确定图像的支撑域;最后以能量熵为收敛准则,用相位恢复算法对频率编码方向残余的子像素移动造成的伪影和相位编码方向的伪影进行消除．实验表明,应用本研究提出的方法能够明显地消除图像空间运动造成的伪影．     

A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging

In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm² to 280 × 280 mm². The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.     

医用磁共振成像系统检测方法的研究与评定

根据医用磁共振成像（MRI）系统的技术特性,在实验数据的基础上,分别对主磁场强度、信号噪声比、图像均匀性等MRI系统检测参数和技术指标进行了研究确定,并对主磁场强度的测量结果进行了不确定度评定,评定结果为医用MRI系统主磁场强度测量结果的扩展不确定度U=9.8 mT （k=2）,符合医用MRI系统临床与质量控制的要求。