Electrospun Contrast Agent-Loaded Fibers for Colon-Targeted MRI

Magnetic resonance imaging is a diagnostic tool used for detecting abnormal organs and tissues, often using Gd(III) complexes as contrast-enhancing agents. In this work, core–shell polymer fibers have been prepared using coaxial electrospinning, with the intent of delivering gadolinium (III) diethylenetriaminepentaacetate hydrate (Gd(DTPA)) selectively to the colon. The fibers comprise a poly(ethylene oxide) (PEO) core loaded with Gd(DTPA), and a Eudragit S100 shell. They are homogeneous, with distinct core–shell phases. The components in the fibers are dispersed in an amorphous fashion. The proton relaxivities of Gd(DTPA) are preserved after electrospinning. To permit easy visualization of the release of the active ingredient from the fibers, analogous materials are prepared loaded with the dye rhodamine B. Very little release is seen in a pH 1.0 buffer, while sustained release is seen at pH 7.4. The fibers thus have the potential to selectively deliver Gd(DTPA) to the colon. Mucoadhesion studies reveal there are strong adhesive forces between porcine colon mucosa and PEO from the core, and the dye-loaded fibers can be successfully used to image the porcine colon wall. The electrospun core–shell fibers prepared in this work can thus be developed as advanced functional materials for effective imaging of colonic abnormalities.

Reproducibility of free-breathing cardiovascular magnetic resonance coronary angiography.

OBJECTIVE: Contemporary free-breathing non contrast enhanced cardiovascular magnetic resonance angiography (CMRA) was qualitatively and quantitatively evaluated to ascertain the reproducibility of the method for coronary artery luminal dimension measurements. SUBJECTS AND METHODS: Twenty-two healthy volunteers (mean age 32 +/- 7 years, 12 males) without coronary artery disease were imaged at 2 centers (1 each in Europe and North America) using navigator-gated and corrected SSFP CMRA on a commercial whole body 1.5T System. Repeat images of right (RCA, n = 21), left anterior descending (LAD, n = 14) and left circumflex (LCX, n = 14) coronary arteries were obtained in separate sessions using identical scan protocol and imaging parameters. True visible vessel length, signal-to-noise (SNR), contrast-to-noise ratios (CNR) and the average luminal diameter over the first 4 cm of the vessel were measured. Intra-observer, inter-observer and inter-scan reproducibility of coronary artery luminal diameter were determined using Pearson's correlation, Bland-Altman analysis and intraclass correlation coefficients (ICC). RESULTS: CNR, SNR and the mean length of the RCA, LAD and LCX imaged for original and repeat scans were not significantly different (all p > 0.30). There was a high degree of intra-observer, inter-observer and inter-scan agreements for RCA, LAD and LCX luminal diameter respectively on Bland-Altman and ICC analysis (ICC's for RCA: 0.98. 0.98 and 0.86; LAD: 0.89, 0.89 and 0.63; LCX: 0.95, 0.94 and 0.79). CONCLUSION: In a 2-center study, we demonstrate that free-breathing 3D SSFP CMRA can visualize long continuous segments of coronary vessels with highly reproducible measurements of luminal diameter.

Quantification of aortic flow by phase-contrast magnetic resonance in patients with bicuspid aortic valve.

AIMS: Bicuspid aortic valve (BAV) causes complex flow patterns in the ascending aorta (AAo), which may compromise the accuracy of flow measurement by phase-contrast magnetic resonance (PC-MR). Therefore, we aimed to assess and compare the accuracy of forward flow measurement in the AAo, where complex flow is more dominant in BAV patients, with flow quantification in the left ventricular outflow tract (LVOT) and the aortic valve orifice (AV), where complex flow is less important, in BAV patients and controls. METHODS AND RESULTS: Flow was measured by PC-MR in 22 BAV patients and 20 controls at the following positions: (i) LVOT, (ii) AV, and (iii) AAo, and compared with the left ventricular stroke volume (LVSV). The correlation between the LVSV and the forward flow in the LVOT, the AV, and the AAo was good in BAV patients (r = 0.97/0.96/0.93; P < 0.01) and controls (r = 0.96/0.93/0.93; P < 0.01). However, in relation with the LVSV, the forward flow in the AAo was mildly underestimated in controls and much more in BAV patients [median (inter-quartile range): 9% (4%/15%) vs. 22% (8%/30%); P < 0.01]. This was not the case in the LVOT and the AV. The severity of flow underestimation in the AAo was associated with flow eccentricity. CONCLUSION: Flow measurement in the AAo leads to an underestimation of the forward flow in BAV patients. Measurement in the LVOT or the AV, where complex flow is less prominent, is an alternative means for quantifying the systolic forward flow in BAV patients.

Imaging findings in fetal diaphragmatic abnormalities.

Imaging plays a key role in the detection of a diaphragmatic pathology in utero. US is the screening method, but MRI is increasingly performed. Congenital diaphragmatic hernia is by far the most often diagnosed diaphragmatic pathology, but unilateral or bilateral eventration or paralysis can also be identified. Extralobar pulmonary sequestration can be located in the diaphragm and, exceptionally, diaphragmatic tumors or secondary infiltration of the diaphragm from tumors originating from an adjacent organ have been observed in utero. Congenital abnormalities of the diaphragm impair normal lung development. Prenatal imaging provides a detailed anatomical evaluation of the fetus and allows volumetric lung measurements. The comparison of these data with those from normal fetuses at the same gestational age provides information about the severity of pulmonary hypoplasia and improves predictions about the fetus's outcome. This information can help doctors and families to make decisions about management during pregnancy and after birth. We describe a wide spectrum of congenital pathologies of the diaphragm and analyze their embryological basis. Moreover, we describe their prenatal imaging findings with emphasis on MR studies, discuss their differential diagnosis and evaluate the limits of imaging methods in predicting postnatal outcome.

Novel Magnetic Materials Based on Macrocyclic Ligands: Towards High Relaxivity Contrast Agents and Mononuclear Single-Molecule Magnets

The preparation and characterization of coordination complexes of Schiff-base and crown ether macrocycles is presented, for application as contrast agents for magnetic resonance imaging, Project 1; and single-molecule magnets (SMMs), Projects 2 and 3. In Project 1, a family of eight Mn(II) and Gd(III) complexes of N3X2 (X = NH, O) and N3O3 Schiff-base macrocycles were synthesized, characterized, and evaluated as potential contrast agents for MRI. In vitro and in vivo (rodent) studies indicate that the studied complexes display efficient contrast behaviour, negligible toxicity, and rapid excretion. In Project 2, DyIII complexes of Schiff-base macrocycles were prepared with a view to developing a new family of mononuclear Ln-SMMs with pseudo-D5h geometries. Each complex displayed slow relaxation of magnetization, with magnetically-derived energy barriers in the range Ueff = 4 – 24 K. In Project 3, coordination complexes of selected later lanthanides with various crown ether ligands were synthesized. Two families of complexes were structurally and magnetically analyzed: ‘axial’ or sandwich-type complexes based on 12-crown-4 and 15-crown-5; and ‘equatorial’ complexes based on 18-crown-6. Magnetic data are supported by ab initio calculations and luminescence measurements. Significantly, the first mononuclear Ln-SMM prepared from a crown ether ligand is described.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Segmentació de teixit i lesions en imatges de MRI de malalts d'esclerosi múltiple

Actualment, en l'àmbit mèdic, la ressonància magnètica, MRI Magnetic Resonance Imaging, és un dels sistemes més utilitzats per a la realització de diagnòstics i el seguiment de l'evolució de malalties com l'esclerosi múltiple (EM). No obstant, la gran quantitat d'informació que proporciona aquesta modalitat té com a conseqüència una tasca feixuga d'anàlisi i d'interpretació per part dels radiòlegs i neuròlegs. L'objectiu general d'aquest projecte és desenvolupar un sistema per ajudar als metges a segmentar les imatges de MRI del cervell. S'ha implementat amb MATLAB. Durant tot el procés s'han utilitzat dades sintètiques, de la base de dades simulada BrainWeb, i reals, proporcionades pels grup de metges col•laboradors amb el grup VICOROB. El projecte s'emmarca dins d'un projecte de recerca del grup de Visió per Computador i Robòtica de la Universitat de Girona

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.

Nuclear magnetic resonance characterization of metabolite disorder in orange trees caused by citrus sudden death disease

Citrus sudden death (CSD) is a new disease of sweet orange and mandarin trees grafted on Rangpur lime and Citrus volkameriana rootstocks. It was first seen in Brazil in 1999, and has since been detected in more than four million trees. The CSD causal agent is unknown and the current hypothesis involves a virus similar to Citrus tristeza virus or a new virus named Citrus sudden death-associated virus. CSD symptoms include generalized foliar discoloration, defoliation and root death, and, in most cases, it can cause tree death. One of the unique characteristics of CSD disease is the presence of a yellow stain in the rootstock bark near the bud union. This region also undergoes profound anatomical changes. In this study, we analyse the metabolic disorder caused by CSD in the bark of sweet orange grafted on Rangpur lime by nuclear magnetic resonance (NMR) spectroscopy and imaging. The imaging results show the presence of a large amount of non-functional phloem in the rootstock bark of affected plants. The spectroscopic analysis shows a high content of triacylglyceride and sucrose, which may be related to phloem blockage close to the bud union. We also propose that, without knowing the causal CSD agent, the determination of oil content in rootstock bark by low-resolution NMR can be used as a complementary method for CSD diagnosis, screening about 300 samples per hour.

Anisotropic MRI contrast reveals enhanced ionic transport in plastic crystals

Organic ionic plastic crystals (OIPCs) are attractive as solid-state electrolytes for electrochemical devices such as lithium-ion batteries and solar and fuel cells. OIPCs offer high ionic conductivity, nonflammability, and versatility of molecular design. Nevertheless, intrinsic ion transport behavior of OIPCs is not fully understood, and their measured properties depend heavily on thermal history. Solid-state magnetic resonance imaging experiments reveal a striking image contrast anisotropy sensitive to the orientation of grain boundaries in polycrystalline OIPCs. Probing triethyl(methyl)phosphonium bis(fluorosulfonyl)imide (P1222FSI) samples with different thermal history demonstrates vast variations in microcrystallite alignment. Upon slow cooling from the melt, microcrystallites exhibit a preferred orientation throughout the entire sample, leading to an order of magnitude increase in conductivity as probed using impedance spectroscopy. This investigation describes both a new conceptual window and a new characterization method for understanding polycrystalline domain structure and transport in plastic crystals and other solid-state conductors.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.