Dual‐Modal NIR‐Fluorophore Conjugated Magnetic Nanoparticle for Imaging Amyloid‐β Species In Vivo

Senile plaques, the extracellular deposit of amyloid‐β (Aβ) peptides, are one of the neuropathological hallmarks found in Alzheimer's disease (AD) brain. The current method of brain imaging of amyloid plaques based on positron emission tomography (PET) is expensive and invasive with low spatial resolution. Thus, the development of sensitive and nonradiative amyloid‐β (Aβ)‐specific contrast agents is highly important and beneficial to achieve early AD detection, monitor the disease progression, and evaluate the effectiveness of potential AD drugs. Here a neuroprotective dual‐modal nanoprobe developed by integrating highly Aβ‐specific and turn‐on fluorescence cyanine sensors with superparamagnetic iron oxide nanoparticles as an effective near‐infrared imaging (NIRI)/magnetic resonance imaging (MRI) contrast agent for imaging of Aβ species in vivo is reported. This Aβ‐specific probe is found not only nontoxic and noninvasive, but also highly blood brain barrier permeable. It also shows a potent neuroprotective effect against Aβ‐induced toxicities. This nanoprobe is successfully applied for in vivo fluorescence imaging with high sensitivity and selectivity to Aβ species, and MRI with high spatial resolution in an APP/PS1 transgenic mice model. Its potential as a powerful in vivo dual‐modal imaging tool for early detection and diagnosis of AD in humans is affirmed.     

Quantitative analysis of the SN in Parkinson's disease implementing 3D modeling at 7.0‐T MRI

Parkinson's disease (PD) is a neurodegenerative disorder resulting from the progressive loss of dopaminergic (DA) neurons in the substantia nigra (SN). In our previous study, attempts were made to directly visualize the SN and quantify the differences in shapes and boundaries of the SN between PD subjects and comparison to the normal control subjects using two‐dimensional T2*‐weighted 7.0‐T MRI images (Cho et al., Mov Disord, accepted for publication). However, a two‐dimensional analysis does not represent the entire SN. Therefore, to overcome the limitation of 2D analysis, we acquire 3D image of the SN. For this study, we scanned nine PD patients, along with nine age‐matched control subjects, using a research prototype 7.0‐T MRI scanner in an attempt to visualize the 3D shape of the SN and quantify differences in the volume of the SN between PD subjects and normal control subjects. The shape change of the ventrolateral boundaries of the SN in PD cases was reconfirmed in this 3D study as well as in our previous 2D study (Cho et al., Mov Disord, accepted for publication). Another interesting finding of this study was that 3D MR imaging study demonstrated the potential of the 7.0‐T MRI in the quantification of volume changes in the SN. The measured correlation analyses showed that there is age‐dependent correlation and substantially stronger unified Parkinson's disease rating scale motor score‐dependent correlation in PD patients. These results suggest that 7.0‐T 3D T2*‐weighted MR imaging could provide the quantitative estimation of volume changes in the SN in PD patients in vivo for comparison with normal controls in vivo. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 253–259, 2011;     

Model‐based reconstruction for T1 mapping using single‐shot inversion‐recovery radial FLASH

Quantitative parameter mapping in MRI is typically performed as a two‐step procedure where serial imaging is followed by pixelwise model fitting. In contrast, model‐based reconstructions directly reconstruct parameter maps from raw data without explicit image reconstruction. Here, we propose a method that determines T1 maps directly from multi‐channel raw data as obtained by a single‐shot inversion‐recovery radial FLASH acquisition with a Golden Angle view order. Joint reconstruction of a T1, spin‐density and flip‐angle map is formulated as a nonlinear inverse problem and solved by the iteratively regularized Gauss‐Newton method. Coil sensitivity profiles are determined from the same data in a preparatory step of the reconstruction. Validations included numerical simulations, in vitro MRI studies of an experimental T1 phantom, and in vivo studies of brain and abdomen of healthy subjects at a field strength of 3 T. The results obtained for a numerical and experimental phantom demonstrated excellent accuracy and precision of model‐based T1 mapping. In vivo studies allowed for high‐resolution T1 mapping of human brain (0.5–0.75 mm in‐plane, 4 mm section thickness) and liver (1.0 mm, 5 mm section) within 3.6–5 s. In conclusion, the proposed method for model‐based T1 mapping may become an alternative to two‐step techniques, which rely on model fitting after serial image reconstruction. More extensive clinical trials now require accelerated computation and online implementation of the algorithm. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 254–263, 2016     

Robust brain MRI denoising and segmentation using enhanced non‐local means algorithm

Image denoising is an integral component of many practical medical systems. Non‐local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non‐local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non‐local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c‐means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM‐filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1‐weighted (T1‐w) and T2‐weighted (T2‐w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2‐weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques.     

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     

Magneto‐Plasmonic Au‐Fe Alloy Nanoparticles Designed for Multimodal SERS‐MRI‐CT Imaging

Diagnostic approaches based on multimodal imaging are needed for accurate selection of the therapeutic regimens in several diseases, although the dose of administered contrast drugs must be reduced to minimize side effects. Therefore, large efforts are deployed in the development of multimodal contrast agents (MCAs) that permit the complementary visualization of the same diseased area with different sensitivity and different spatial resolution by applying multiple diagnostic techniques. Ideally, MCAs should also allow imaging of diseased tissues with high spatial resolution during surgical interventions. Here a new system based on multifunctional Au‐Fe alloy nanoparticles designed to satisfy the main requirements of an ideal MCA is reported and their biocompatibility and imaging capability are described. The MCAs show easy and versatile surface conjugation with thiolated molecules, magnetic resonance imaging (MRI) and computed X‐ray tomography (CT) signals for anatomical and physiological information (i.e., diagnostic and prognostic imaging), large Raman signals amplified by surface enhanced Raman scattering (SERS) for high sensitivity and high resolution intrasurgical imaging, biocompatibility, exploitability for in vivo use and capability of selective accumulation in tumors by enhanced permeability and retention effect. Taken together, these results show that Au‐Fe nanoalloys are excellent candidates as multimodal MRI‐CT‐SERS imaging agents.     

NeXt for neuro‐radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

Stereotactic neuro‐radiosurgery is a well‐established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft‐tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C‐Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two‐stage segmentation approach useful to support neuro‐radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast‐enhanced T1‐weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap‐based and distance‐based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).     

Micro‐vascular imaging experiences of time‐of‐flight MRA at 7T for cerebrovascular diseases

Surgical or endovascular approaches have proved effective for large‐vessel diseases over the past decade. However, approaches for small vessel diseases are unlikely to be accomplished by those for large vessels and only few have been applied, because it is hard to access to those small vessels and one could not directly delineate the affected small vessels due to a lack of detection modalities. This study is to examine patients with vascular diseases using ultra‐high field 7T MRI with conventional time‐of‐flight (TOF) sequence, 3D fast low‐angle shot (FLASH) gradient‐echo. We have evaluated several radio‐frequency (RF) coils to find the optimal one for 7T magnetic resonance angiography (MRA), especially for micro‐vascular imaging. We have conducted several comparison studies with vascular disease patients. The results showed that micro‐vessels such as lenticulostriate arteries in the subjects with risk factors like hypertension or stroke patients were significantly less than in the healthy subjects. 7T MRA images in steno‐occlusive patients also showed clearly numerous collateral vessels not visible by 1.5T or 3T MRA. Furthermore, 7T MRA images were comparable to those obtained by digital subtraction angiography (DSA), particularly for micro‐vascular imaging. In this article, we would like to share the clinical experiences on 7T MRA that vascular images of 7T MRA were superior to conventional angiography images including 1.5T and 3T MRA, and even comparable to DSA. We also expect that further technical development and clinical applications of 7T MRA would be a clinically important diagnostic tool, in terms of an early detection of the stroke in a totally non‐invasive manner. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 121–128, 2014     

Transient solutions to nonlinear acousto‐magneto‐mechanical coupling for axisymmetric MRI scanner design

In this work, we simulate the coupled physics describing a magnetic resonance imaging (MRI) scanner by using a higher‐order finite element discretisation and a Newton‐Raphson algorithm. To apply the latter, a linearisation of the nonlinear system of equations is necessary, and we consider two alternative approaches. In the first approach, ie, the nonlinear approach, there is no approximation from a physical standpoint, and the linearisation is performed about the current solution. In the second approach, ie, the linearised approach, we realise that the MRI problem can be described by small dynamic fluctuations about a dominant static solution and linearise about the latter. The linearised approach permits solutions in the frequency domain and provides a computationally efficient way to solve this challenging problem, as it allows the tangent stiffness matrix to be inverted independently of time or frequency. We focus on transient solutions to the coupled system of equations and address the following two important questions: (i) how good is the agreement between the computationally efficient linearised approach compared with the intensive nonlinear approach and (ii) over what range of MRI operating conditions can the linearised approach be expected to provide acceptable results for outputs of interest in an industrial context for MRI scanner design? We include a set of academic and industrially relevant examples to benchmark and illustrate our approach.     

An extended non‐local means algorithm: Application to brain MRI

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient.     

Enhancing Prostate‐Cancer‐Specific MRI by Genetic Amplified Nanoparticle Tumor Homing

Precise localization and visualization of early‐stage prostate cancer (PCa) is critical to improve the success of focal ablation and reduce cancer mortality. However, it remains challenging under the current imaging techniques due to the heterogeneous nature of PCa and the suboptimal sensitivity of the techniques themselves. Herein, a novel genetic amplified nanoparticle tumor‐homing strategy to enhance the MRI accuracy of ultrasmall PCa lesions is reported. This strategy could specifically drive TfR expressions in PCa under PCa‐specific DD3 promoter, and thus remarkably increase Tf‐USPIONs concentrations in a highly accurate manner while minimizing their non‐specific off‐target effects on normal tissues. Consequently, this strategy can pinpoint an ultrasmall PCa lesion, which is otherwise blurred in the current MRI, and thereby addresses the unmet key need in MRI imaging for focal therapy. With this proof‐of‐concept experiment, the synergistic gene–nano strategy holds great promise to boost the MRI effects of a wide variety of commonly used nanoscale and molecular probes that are otherwise limited. In addition, such a strategy may also be translated and applied to MR‐specific imaging of other types of cancers by using their respective tumor‐specific promoters.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

MRI brain segmentation in combination of clustering methods with Markov random field

Medical image segmentation is a preliminary stage of inclusion in identification tools. The correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an accurate detection of the disease diagnosis. Due to in‐homogeneity, low distinction and noise the segmentation of the brain MRI images is treated as the most challenging task. In this article, we proposed hybrid segmentation, by combining the clustering methods with Hidden Markov Random Field (HMRF) technique. This aims to decrease the computational load and improves the runtime of segmentation method, as MRF methodology is used in post‐processing the images. Its evaluation has performed on real imaging data, resulting in the classification of brain tissues with dice similarity metric. These results indicate the improvement in performance of the proposed method with various noise levels, compared with existing algorithms. In implementation, selection of clustering method provides better results in the segmentation of MRI brain images.     

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     

Facile Preparation of Multifunctional WS2/WOx Nanodots for Chelator‐Free 89Zr‐Labeling and In Vivo PET Imaging

While position emission tomography (PET) is an important molecular imaging technique for both preclinical research and clinical disease diagnosis/prognosis, chelator‐free radiolabeling has emerged as a promising alternative approach to label biomolecules or nanoprobes in a facile way. Herein, starting from bottom‐up synthesized WS₂ nanoflakes, this study fabricates a unique type of WS₂/WO_x nanodots, which can function as inherent hard oxygen donor for stable radiolabeling with Zirconium‐89 isotope (⁸⁹Zr). Upon simply mixing, ⁸⁹Zr can be anchored on the surface of polyethylene glycol (PEG) modified WS₂/WO_x (WS₂/WO_x‐PEG) nanodots via a chelator‐free method with surprisingly high labeling yield and great stability. A higher degree of oxidation in the WS₂/WO_x‐PEG sample (WS₂/WO_x (0.4)) produces more electron pairs, which would be beneficial for chelator‐free labeling of ⁸⁹Zr with higher yields, suggesting the importance of surface chemistry and particle composition to the efficiency of chelator‐free radiolabeling. Such ⁸⁹Zr‐WS₂/WO_x (0.4)‐PEG nanodots are found to be an excellent PET contrast agent for in vivo imaging of tumors upon intravenous administration, or mapping of draining lymph nodes after local injection.     

Atomic‐Precision Gold Clusters for NIR‐II Imaging

Near‐infrared II (NIR‐II) imaging at 1100–1700 nm shows great promise for medical diagnosis related to blood vessels because it possesses deep penetration and high resolution in biological tissue. Unfortunately, currently available NIR‐II fluorophores exhibit slow excretion and low brightness, which prevents their potential medical applications. An atomic‐precision gold (Au) cluster with 25 gold atoms and 18 peptide ligands is presented. The Au₂₅ clusters show emission at 1100–1350 nm and the fluorescence quantum yield is significantly increased by metal‐atom doping. Bright gold clusters can penetrate deep tissue and can be applied in in vivo brain vessel imaging and tumor metastasis. Time‐resolved brain blood‐flow imaging shows significant differences between healthy and injured mice with different brain diseases in vivo. High‐resolution imaging of cancer metastasis allows for the identification of the primary tumor, blood vessel, and lymphatic metastasis. In addition, gold clusters with NIR‐II fluorescence are used to monitor high‐resolution imaging of kidney at a depth of 0.61 cm, and the quantitative measurement shows 86% of the gold clusters are cleared from body without any acute or long‐term toxicity at a dose of 100 mg kg^?1.     

An optimal RF shielding method for MR‐PET fusion system with insertable PET

An RF shielding method is proposed to preserve the resonance frequency of an RF coil regardless of the existence of a positron emission tomography (PET) detector module, so that the RF coil can effectively operate for both simultaneous MR‐PET imaging and MR stand‐alone imaging. The RF shield between the RF coil and the PET detector module was manufactured in the form of a hollow acryl cylinder wrapped with gold‐taffeta woven tape. As we adopted a double‐layer RF shield between the RF coil and the PET detector module, it was possible to maintain the resonance frequency of the RF coil and the MR image quality was similar in both cases, with and without the insertable PET detector module. Using the insertable concept of the PET system and the RF coil with an additional double‐layer RF shield, both an MR‐PET fusion system and an MR stand‐alone system were realized. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 263–269, 2014     

Magnetic resonance imaging reconstruction via non‐convex total variation regularization

Magnetic resonance imaging (MRI) reconstruction model based on total variation (TV) regularization can deal with problems such as incomplete reconstruction, blurred boundary, and residual noise. In this article, a non‐convex isotropic TV regularization reconstruction model is proposed to overcome the drawback. Moreau envelope and minmax‐concave penalty are firstly used to construct the non‐convex regularization of L₂ norm, then it is applied into the TV regularization to construct the sparse reconstruction model. The proposed model can extract the edge contour of the target effectively since it can avoid the underestimation of larger nonzero elements in convex regularization. In addition, the global convexity of the cost function can be guaranteed under certain conditions. Then, an efficient algorithm such as alternating direction method of multipliers is proposed to solve the new cost function. Experimental results show that, compared with several typical image reconstruction methods, the proposed model performs better. Both the relative error and the peak signal‐to‐noise ratio are significantly improved, and the reconstructed images also show better visual effects. The competitive experimental results indicate that the proposed approach is not limited to MRI reconstruction, but it is general enough to be used in other fields with natural images.     

Non‐iterative reconstruction with a prior for undersampled radial MRI data

This paper develops an FBP‐MAP (filtered backprojection, maximum a posteriori) algorithm to reconstruct MRI images from undersampled data. An objective function is first set up for the MRI reconstruction problem with a data fidelity term and a Bayesian term. The Bayesian term is a constraint in the temporal dimension. This objective function is minimized using the calculus of variations. The proposed algorithm is non‐iterative. Undersampled dynamic myocardial perfusion MRI data were used to test the feasibility of the proposed technique. It is shown that the non‐iterative Fourier–Bayesian reconstruction method effectively incorporates the temporal constraint and significantly reduces the angular aliasing artifacts caused by undersampling. A significant advantage of the proposed non‐iterative Fourier–Bayesian technique over the iterative techniques is its fast computation time and its ability to reach the optimal solution. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 53–58, 2013.     

Keyhole‐3D phase contrast magnetic resonance angiography: A time‐resolved reconstruction method

In this study, we studied the keyhole imaging technique to 3D Phase‐contrast magnetic resonance angiography (PC MRA) to improve its temporal resolution. Previously, our research group has already studied the 2D PC MRA combined with keyhole technique, and evaluated the applicability. For keyhole‐3D PC MRA, the keyhole factor was used from 12.5% to 50% of the full k‐space. With keyhole factors above 50%, the images were similar to the original image and the vessels in the brain were well observed. We believe the keyhole‐3D PC MRA will give some advantages for improving the temporal resolution of MR systems.