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The shielding of electromagnetic noise is critical in obtaining magnetic resonance imaging measurements in the ultra-low magnetic field regime where the intrinsic signal-to-noise ratio is very small. The traditional approach of using an enclosure for electromagnetic shielding is expensive and hinders system portability. We describe here the use of a CNN-based software gradiometer to suppress the effect of electromagnetic ambient background noise sources that inductively couple into the signal detection coils. The system involves three ambient noise monitoring coils placed at a distance from the magnetic resonance signal detector. The three coils were used to synthesize the ambient noise captured by the signal detector; a convolutional neural network approach was used. Mathematical foundations are provided to justify the noise suppression framework. The results show that as much as 20-fold noise suppression can be achieved using an optimized convolutional neural network and simultaneous ambient noise measurements. The proposed approach has the potential to replace the requirement for magnetically shielded enclosures and make ultra-low field magnetic resonance imaging truly portable.

Objective: Radiomics studies have shown promising results defining image surrogates that predict treatment response and survival in oesophageal squamous cell carcinoma. There are limited studies examining radiogenomics profiles in oesophageal adenocarcinoma (OAC) and this warrants further exploration. Background: Although advances have been made in the treatment of OAC, the five-year overall survival remains poor. Hence, there is a need for biomarkers to improve therapeutic approaches. Methods: Image analysis was performed on pre-treatment PET/CT scans from 80 OAC patients. In the discovery cohort (n=50), CT texture analysis was performed using a filtration-histogram method to evaluate the prognostic value of image features. Whole-genome sequencing, RNA sequencing (RNAseq) and immunohistochemistry characterised the molecular pathways associated with the CT markers. A validation cohort (n=30) was used to confirm radiomics biomarkers. Results: Analysis showed 3 CT markers were significantly associated with both disease-specific and disease-free survival. Combining these markers provided further prognostic significance. Furthermore, we identified a correlation between CT markers and major pathological response rate. Both RNAseq and immunohistochemistry confirmed that lower CD8 T-cell expression in the tumour correlated with the poor survival group predicted using CT markers. RNAseq gene set enrichment analysis identified pathways associated with these radiomics biomarkers. Finally, radiomics analysis in a validation cohort confirmed CT marker (SD, standard deviation) as a biomarker of survival and identified additional image features associated with patient survival. Conclusions: This study demonstrates that radiogenomics has the potential to identify OAC subgroups with prognostic significance and support clinical decision making.

Diffusion kurtosis has become an important magnetic resonance imaging (MRI) modality for non-invasively mapping the microstructural variations in living tissues. Theoretically, the spatial resolution of diffusion kurtosis imaging (DKI) can be significantly improved by acquiring data at ultra-high magnetic fields (UHF, ≤ 7 Tesla) because of the increased signal-to-noise ratios. However, issues such as increased susceptibility artefacts and rapid signal attenuation inherent in UHF-MRI have impeded the adoption of DKI in research and clinics. In this paper, we developed a new image reconstruction algorithm for fast DKI at UHF. By integrating the one-dimensional and two-dimensional principal component analysis and compressed sensing technologies, the new algorithm can reconstruct kurtosis maps from highly undersampled data. The technique was validated using randomly undersampled brain images with a control database of fully sampled DKI acquisitions from healthy human participants. We compared the technique with zero-filling Fourier transform and similar compressed sensing algorithms by evaluating the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between images and assessing the reproducibility of results using the Bland-Altman method. We found that our methods can achieve at least a five-fold reduction in data acquisition time at UHF with high image quality. Moreover, the PSNR and SSIM of five diffusion metrics generated by our methods were superior to the other algorithms when the undersampling rate is high and the echo time is short. The proposed method can be valuable for fast functional and dynamic-contrast imaging techniques at 7 Tesla or higher.

Purpose The purpose of this study is to demonstrate a method for specific absorption rate (SAR) reduction for 2D T2 -FLAIR MRI sequences at 7 T by predicting the required adiabatic radiofrequency (RF) pulse power and scaling the RF amplitude in a slice-wise fashion. Methods We used a time-resampled frequency-offset corrected inversion (TR-FOCI) adiabatic pulse for spin inversion in a T2 -FLAIR sequence to improve B 1 + homogeneity and calculated the pulse power required for adiabaticity slice-by-slice to minimize the SAR. Drawing on the implicit B 1 + inhomogeneity in a standard localizer scan, we acquired 3D AutoAlign localizers and SA2RAGE B 1 + maps in 28 volunteers. Then, we trained a convolutional neural network (CNN) to estimate the B 1 + profile from the localizers and calculated pulse scale factors for each slice. We assessed the predicted B 1 + profiles and the effect of scaled pulse amplitudes on the FLAIR inversion efficiency in oblique transverse, sagittal, and coronal orientations. Results The predicted B 1 + amplitude maps matched the measured ones with a mean difference of 9.5% across all slices and participants. The slice-by-slice scaling of the TR-FOCI inversion pulse was most effective in oblique transverse orientation and resulted in a 1 min and 30 s reduction in SAR induced delay time while delivering identical image quality. Conclusion We propose a SAR reduction technique based on the estimation of B 1 + profiles from standard localizer scans using a CNN and show that scaling the inversion pulse power slice-by-slice for FLAIR sequences at 7T reduces SAR and scan time without compromising image quality.

Background Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. Methods We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. Results The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. Conclusions We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.

Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that produces spatially resolved magnetic susceptibility maps from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but accumulates errors. This study aims to overcome existing limitations by developing a Laplacian-of-Trigonometric-functions (LoT) enhanced deep neural network for near-instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MRI phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the proposed neural networks. The proposed iQFM and iQSM methods in healthy subjects yielded comparable results to those involving the intermediate steps while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. High susceptibility contrast between multiple sclerosis lesions and healthy tissue was also achieved using the proposed methods. Comparative studies indicated that the most significant contributor to iQFM and iQSM over conventional multi-step methods was the elimination of traditional Laplacian unwrapping. The reconstruction time on the order of minutes for traditional approaches was shortened to around 0.1 s using the trained iQFM and iQSM neural networks.

Ultra-high field magnetic resonance imaging data obtained using a multi-echo gradient echo sequence has been shown to contain information on tissue microstructure. Quantitative assessment of water fraction, relaxation time and frequency shift using multi-compartment signal modelling may help improve our understanding of diseases and disorders affecting the human brain. In this study, we explored tissue microstructure information by analysing voxel compartment water fraction and frequency shifts derived from 7 T multi-echo gradient recalled echo MRI data. We aimed to test whether the parameters of a three compartment model could distinguish the normal cortex from the cortex affected by focal cortical dysplasia. We compartmentalised normal and dysplastic cortical regions in patients diagnosed with focal cortical dysplasia. We found the frequency shift parameter of the shortest T2⁎ signal compartment to be sensitive to regions of dysplastic tissue. We conclude that mathematical modelling of echo time dependent gradient recalled echo MRI signals in patients with focal cortical dysplasia can potentially delineate cortical areas that have undergone microstructural changes in comparison to normal tissue.

Highlights • Matrix metalloproteinase (MMP) 7 is elevated in traumatic brain injury. • Blood brain barrier dysfunction as measured by DCE MRI can be expressed as KTrans. • MMP-7 shows a strong correlation with BBB dysfunction shown on MRI. • MMP-7 shows potential to function as a serum biomarker., Objectives To determine if radiological evidence of blood brain barrier (BBB) dysfunction, measured using Dynamic Contrast Enhanced MRI (DCE-MRI), correlates with serum matrix metalloproteinase (MMP) levels in traumatic brain injury (TBI) patients, and thereby, identify a potential biomarker for BBB dysfunction. Patients and Methods 20 patients with a mild, moderate, or severe TBI underwent a DCE-MRI scan and BBB dysfunction was interpreted from KTrans. KTrans is a measure of capillary permeability that reflects the efflux of gadolinium contrast into the extra-cellar space. The serum samples were concurrently collected and later analysed for MMP-1, −2, −7, −9, and −10 levels using an ELISA assay. Statistical correlations between MMP levels and the KTrans value were calculated. Multiple testing was corrected using the Benjamin–Hochberg method to control the false‐discovery rate (FDR). Results Serum MMP-1 values ranged from 1.5 to 49.6 ng/ml (12 ± 12.7), MMP-2 values from 58.3 to 174.1 ng/ml (109.5 ± 26.7), MMP-7 from 1.5 to 31.5 ng/mL (10 ± 7.4), MMP-9 from 128.6 to 1917.5 ng/ml (647.7 ± 749.6) and MMP-10 from 0.1 to 0.6 ng/mL (0.3 ± 0.2). Non-parametric Spearman correlation analysis on the data showed significant positive relationship between KTrans and MMP-7 (r = 0.55, p

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters \b{eta}_SUB and D_SUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters {\alpha} and \b{eta} have shown consistent positive correlation with age in the corpus callosum, indicating {\alpha} and \b{eta} are sensitive to tissue microstructural changes in aging.

Mathematical models are becoming increasingly important in magnetic resonance imaging (MRI), as they provide a mechanistic approach for making a link between tissue microstructure and signals acquired using the medical imaging instrument. The Bloch equations, which describes spin and relaxation in a magnetic field, are a set of integer order differential equations with a solution exhibiting mono-exponential behaviour in time. Parameters of the model may be estimated using a non-linear solver or by creating a dictionary of model parameters from which MRI signals are simulated and then matched with experiment. We have previously shown the potential efficacy of a magnetic resonance fingerprinting (MRF) approach, i.e., dictionary matching based on the classical Bloch equations for parcellating the human cerebral cortex. However, this classical model is unable to describe in full the mm-scale MRI signal generated based on an heterogenous and complex tissue micro-environment. The time-fractional order Bloch equations have been shown to provide, as a function of time, a good fit of brain MRI signals. The time-fractional model has solutions in the form of Mittag–Leffler functions that generalise conventional exponential relaxation. Such functions have been shown by others to be useful for describing dielectric and viscoelastic relaxation in complex heterogeneous materials. Hence, we replaced the integer order Bloch equations with the previously reported time-fractional counterpart within the MRF framework and performed experiments to parcellate human gray matter, which consists of cortical brain tissue with different cyto-architecture at different spatial locations. Our findings suggest that the time-fractional order parameters, α and β, potentially associate with the effect of interareal architectonic variability, which hypothetically results in more accurate cortical parcellation.

Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders in which white matter is implicated, as it has been linked to demyelination, re-myelination, and axonal damage in clinical conditions. Ultra-high field magnetic resonance imaging data obtained using a multi-echo gradient echo sequence has been shown to contain information on myelin, axonal and extracellular compartments in white matter. In this study, we aimed to assess the sensitivity of a three-compartment model to estimate the variation of corresponding compartment parameters (water fraction, relaxation time and frequency shift) of the corpus callosum sub-regions, which are known to have different tissue structure. Additionally, we computed the g-ratio using myelin and axonal water fractions and performed a voxel-by-voxel analysis in the corpus callosum. Based on data acquired for ten participants, we show that the myelin compartment water fraction and T2∗ is consistent across the corpus callosum sub-regions, whilst myelin frequency shift varies. The results show that the variation in water fraction, T2∗ and frequency shift for the myelin signal compartment across the corpus callosum is smaller than for the axonal and extracellular signal compartments. The computed g-ratio was comparable to previously published studies in the corpus callosum. Our study suggests that a multi-echo GRE approach in vivo combined with a complex three-compartment model is sensitive to microstructural parameter variations across the human corpus callosum.

Purpose During the time window of diffusion weighted magnetic resonance imaging experiments (DW-MRI), water diffusion in tissue appears to be anomalous as a transient effect, with a mean squared displacement that is not a linear function of time. A number of statistical models have been proposed to describe water diffusion in tissue, and parameters describing anomalous as well as Gaussian diffusion have previously been related to measures of tissue microstructure such as mean axon radius. We analysed the relationship between white matter tissue characteristics and parameters of existing statistical diffusion models. Methods A white matter tissue model (ActiveAx) was used to generate multiple b-value diffusion-weighted magnetic resonance imaging signals. The following models were evaluated to fit the diffusion signal: 1) Gaussian models - 1a) mono-exponential decay and 1b) bi-exponential decay; 2) Anomalous diffusion models - 2a) stretched exponential, 2b) continuous time random walk and 2c) space fractional Bloch-Torrey equation. We identified the best candidate model based on the relationship between the diffusion-derived parameters and mean axon radius and axial diffusivity, and applied it to the in vivo DW-MRI data acquired at 7.0 T from five healthy participants to estimate the same selected tissue characteristics. Differences between simulation parameters and fitted parameters were used to assess accuracy and in vivo findings were compared to previously reported observations. Results The space fractional Bloch-Torrey model was found to be the best candidate in characterising white matter on the base of the ActiveAx simulated DW-MRI data. Moreover, parameters of the space fractional Bloch-Torrey model were sensitive to mean axon radius and axial diffusivity and exhibited low noise sensitivity based on simulations. We also found spatial variations in the model parameter β to reflect changes in mean axon radius across the mid-sagittal plane of the corpus callosum. Conclusion Simulations have been used to define how the parameters of the most common statistical magnetic resonance imaging diffusion models relate to axon radius and diffusivity. The space fractional Bloch-Torrey equation was identified as the best model for the characterisation of axon radius and diffusivity. This model allows changes in mean axon radius and diffusivity to be inferred from spatially resolved maps of model parameters.

Magnetic resonance imaging can reveal exquisite details about the complex structure and function of human tissue. However, magnetic resonance imaging signal behaviour at high or ultra-high field has shown increased deviation from the classically expected mono-exponential relaxation. The underlying mechanism of anomalous relaxation can contribute to a better understanding of the interaction of spins with their surroundings. The purpose of this work is to explore the utility of the multi-term time-fractional Bloch equations in relation to the anomalous relaxation processes. We proposed an effective predictor–corrector method to solve the multi-term equations. Voxel-level temporal fitting of the magnetic resonance imaging signal was performed based on the model developed from the multi-term time-fractional Bloch equations. A feasible parameter estimation method based on hybrid Nelder–Mead simplex search and particle swarm optimisation was introduced to perform the curve fitting. The extra time-fractional terms provide flexibility in the relaxation process.

Purpose Quantitative susceptibility mapping is a technique to estimate the magnetic property of tissue with particularly high sensitivity at ultra-high field. However, a key challenge at ultra-high field is the combination of phase data acquired using phased array receive coils. Several methods for combining phase data have been proposed, but the influence of coil combination choices on susceptibility quantitation has not been studied systematically. Methods We combined phase data using COMPOSER (COMbining Phase data using a Short Echo-time Reference scan) and a reference-free channel-by-channel method. We investigated the effect of the chosen combination method on susceptibility results in a group of 28 participants at 7 Tesla. Results Our results show that reference scans can bias susceptibility values. Although the proposed reference-free channel-by-channel method cannot remove transmit field phase, it shows comparable results to the COMPOSER method in which a high-resolution ultrashort echo-time reference scan was used. Conclusions We conclude that ultrashort echo-time reference scans reduce quantitation bias and remove the transmit field phase when using COMPOSER to combine phase data, and not combining the phase data before susceptibility processing avoids this bias, resulting in comparable results. Magn Reson Med 79:97–107, 2018. © 2017 InternationalSociety for Magnetic Resonance in Medicine.

In this paper, we investigate anomalous diffusion models in a mouse model of glioblastoma, a grade IV brain tumour, and study how the anomalous diffusion model parameters reflect the change in tumour tissue microstructure. Diffusion-weighted MRI data with multiple b-values at 9.4 T was acquired from mice bearing U87 brain tumour cells at four time points. Voxel-level fitting of the MRI data was performed on the classical mono-exponential model, and four anomalous diffusion models, namely, the stretched exponential model, the sub-diffusion model, the continuous time random walk model and the fractional Bloch-Torrey equation. The performance of the anomalous diffusion parameters for differentiating the three-concentric layers of tumour tissue (i.e., core; intermediate zone; peripheral and hyper-vascularised tumour layer) was evaluated with multinomial logistic regression and multi-class classification analysis. We found that parameter \(\alpha \) from the stretched exponential model, parameter \(\upbeta \) from the sub-diffusion model and parameter \(\upbeta \) from the continuous time random walk model provide a clear delineation of the three layers of tumour tissue. The analysis revealed that the combination of diffusion coefficient D and anomalous diffusion parameter (\(\alpha \) and/or \(\upbeta \)) greatly improved the classification power in terms of F1-scores compared with the current approach in clinics, in which D is used alone. Hence, our mouse brain tumour study demonstrated that anomalous diffusion model parameters are useful for differentiating different tumour layers and normal brain tissue.

Early loss of white matter microstructure integrity is a significant cause of long-term neurological disorders following traumatic brain injury (TBI). White matter abnormalities typically involve axonal loss and demyelination. In-vivo imaging tools to detect and differentiate such microstructural changes are not well-explored. This work utilizes the conjoint potential offered by advanced magnetic resonance imaging techniques, including quantitative susceptibility mapping (QSM) and diffusion tensor imaging (DTI), to discern the underlying white matter pathology at specific time points (5 h, 1, 3, 7, 14, and 30 days) post-injury in the controlled cortical impact mouse model. A total of 42 animals were randomized into six TBI groups (n = 6 per group) and one sham group (n = 6). Histopathology was performed to validate in-vivo findings by performing myelin basic protein (MBP) and glial fibrillary acidic protein (GFAP) immunostaining for the assessment of changes to myelin and astrocytes. After 5 h of injury radial diffusivity (RD) was increased in white matter without a significant change in axial diffusivity (AxD) and susceptibility values. After 1 day post-injury RD was decreased. AxD and susceptibility changes were seen after 3 days post-injury. Susceptibility increases in white matter were observed in both ipsilateral and contralateral regions and persisted for 30 days. In histology, an increase in GFAP immunoreactivity was observed after 3 days post-injury and remained high for 30 days in both ipsilateral and contralateral white matter regions. A loss in MBP signal was noted after 3 days post-injury that continued up to 30 days. In conclusion, these results demonstrate the complementary ability of DTI and QSM in discerning the micro-pathological processes triggered following TBI. While DTI revealed acute and focal white matter changes, QSM mirrored the temporal demyelination in the white matter tracts and diffuse regions at the chronic state.

Dynamically adjustable permanent magnet arrays have been proposed to generate switchable magnetic fields for pre-polarisation in Ultra-Low Field magnetic resonance imaging. However, the optimal switching dynamics of the pre-polarisation magnetic field as well as the energy requirements, mechanical forces and stresses during switching of the pre-polarisation field have not been evaluated. We analysed these requirements numerically and estimated the magnetic resonance signal strength and image quality for two practical switching modes in an instrument suitable for scanning the human head. Von Mises stress analysis showed that although magnetic forces were significantly higher for two specific rungs, the structural integrity of magnet rungs would not be compromised. Our simulations suggest that a significantly higher signal yield is obtained by switching off the pre-polarisation field with the angular velocity in each rung dependent on its location.

Inverse problems are some of the most important mathematical problems in science and mathematics because their solution yields information about parameters that are not directly observable. Artificial neural networks have long been used as a mathematical modelling method and have been used successfully to solve inverse problems for application including denoising and medical image reconstruction. Many inverse problems result from integral processes that can be modelled using a linear formulation. These can be efficiently solved via simple networks which are easily trained with reasonable datasets. An innovative simple neural network architecture, the iterative linear neural network (ILNN), consisting of two non-hidden layer networks, one for the forward model and one for the inverse model, is proposed to solve linear inverse problems. Iteration between the two models refines network outcomes with greater accuracy than a network with only the inverse model. A training procedure accompanying the network is also introduced. The network needs to train only the inverse model with one-hot vectors as targets. The training inputs of the inverse model define the weights of the forward model. The number of targets is finite and equal to the length of the vector. With the defined targets, the training process ensures that the inverse model is at least a left inverse of the forward model. This leads to generalizable networks. The experimental results show that the ILNN produces good results even if its inverse model is not perfectly trained. The proposed network is applied to solve two linear inverse problems, deconvolution and the inverse Radon transform. The network successfully reconstructed original data following blurring and Radon transformation.

Objectives: Tissue microstructure features, namely axon radius and volume fraction, provide important information on the function of white matter pathways. These parameters vary on the scale much smaller than imaging voxels (microscale) yet influence the magnetic resonance imaging diffusion signal at the image voxel scale (macroscale) in an anomalous manner. Researchers have already mapped anomalous diffusion parameters from magnetic resonance imaging data, but macroscopic variations have not been related to microscale influences. With the aid of a tissue model, we aimed to connect anomalous diffusion parameters to axon radius and volume fraction using diffusion-weighted magnetic resonance imaging measurements. Experimental design: An ex vivo human brain experiment was performed to directly validate axon radius and volume fraction measurements in the human brain. These findings were validated using electron microscopy. Additionally, we performed an in vivo study on nine healthy participants to map axon radius and volume fraction along different regions of the corpus callosum projecting into various cortical areas identified using tractography. Principal observations: We found a clear relationship between anomalous diffusion parameters and axon radius and volume fraction. We were also able to map accurately the trend in axon radius along the corpus callosum, and in vivo findings resembled the low-high-low-high behaviour in axon radius demonstrated previously. Conclusions: Axon radius and volume fraction measurements can potentially be used in brain connectivity studies and to understand the implications of white matter structure in brain diseases and disorders. Hum Brain Mapp 38:1068–1081, 2017. © 2016 Wiley Periodicals, Inc.

We describe with a theoretical and numerical analysis the use of small permanent magnets moving along prescribed helical paths for 3D spatial encoding and imaging without sample adjustment in ultra-low field magnetic resonance imaging (ULF-MRI). With our developed method the optimal magnet path and orientation for a given encoding magnet number and instrument architecture can be determined. As a proof-of-concept, we studied simple helical magnet paths and lengths for one and two encoding magnets to evaluate the imaging efficiency for a mechanically operated ULF-MRI instrument with permanent magnets. We demonstrate that a single encoding magnet moving around the sample in a single revolution suffices for the generation of a 3D image by back projection.

Gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms and delineation of putative tissue compartments that contribute to the multi-echo GRE-MRI signal may aid structural characterization. Several studies have adopted the three water-pool compartment model to study white matter brain regions, associating individual compartments with myelin, axonal and extracellular water. However, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We undertook this task for human brain imaging data. Multiple echo time GRE-MRI data were acquired in five healthy participants, specific anatomical structures were segmented in each dataset (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid), and the signal fitted with models comprising one to six signal compartments using a complex-valued plane wave formulation. Information criteria and cluster analysis methods were used to ascertain the number of distinct compartments within the signal from each structure and to determine their respective frequency shifts. We identified five principal signal compartments with different relative contributions to each structure's signal. Voxel-based maps of the volume fraction of each of these compartments were generated and demonstrated spatial correlation with brain anatomy.

Epilepsy is a neurological disorder characterised by spontaneous recurrent seizures. The mechanisms by which multiple molecular and cellular changes lead to seizures is not well understood. Here, we study cortical seizure generation by simulating the activity of neuron groups in a network using the laminar cortex model. We identified a clear boundary between low-amplitude, asynchronous activity and high-amplitude, rhythmic activity, around which small changes in excitatory synaptic gain led to strong oscillatory activity. Neuron groups only responded significantly to stimulation around the boundary. The consequences of biophysical changes induced by epilepsy-related SCN1A mutations were also examined. Marked reduction in neuronal inhibition, as caused by mutations underlying Dravet syndrome, invariably led to strong neuronal firing, whereas small reductions in inhibition could cause significant changes when the network was poised close to the boundary. The study highlights the critical role of network dynamics in seizure genesis.

Intravenous immunoglobulin (IVIg) preparations obtained by fractionating blood plasma, are increasingly being used increasingly as an effective therapeutic agent in treatment of several inflammatory diseases. Its use as a potential therapeutic agent for treatment of stroke and Alzheimer's disease has been proposed, but little is known about the neuroprotective mechanisms of IVIg. In this study, we investigated the effect of IVIg on downstream signaling pathways that are involved in neuronal cell death in experimental models of stroke and Alzheimer's disease. Treatment of cultured neurons with IVIg reduced simulated ischemia- and amyloid βpeptide (Aβ)-induced caspase 3 cleavage, and phosphorylation of the cell death-associated kinases p38MAPK, c-Jun NH2 -terminal kinase and p65, in vitro. Additionally, Aβ-induced accumulation of the lipid peroxidation product 4-hydroxynonenal was attenuated in neurons treated with IVIg. IVIg treatment also up-regulated the anti-apoptotic protein, Bcl2 in cortical neurons under ischemia-like conditions and exposure to Aβ. Treatment of mice with IVIg reduced neuronal cell loss, apoptosis and infarct size, and improved functional outcome in a model of focal ischemic stroke. Together, these results indicate that IVIg acts directly on neurons to protect them against ischemic stroke and Aβ-induced neuronal apoptosis by inhibiting cell death pathways and by elevating levels of the anti-apoptotic protein Bcl2.

Three quadrature transmit/receive radiofrequency magnetic resonance imaging breast coils were designed and built. The coils are geometrically equivalent but were scaled for different breast sizes. The coils were tested using a 4-T Bruker/Siemens whole-body scanner. We used different coil sizes to establish whether it is necessary to match coil size to load size. Moreover, our work investigated how the coil configuration can be changed to improve signal uniformity in the acquired images. We found that improvements can be gained by carefully matching the coil to the sample. In addition, we propose a coil layout that allows images quality to be improved, as measured by the signal-to-noise ratio and field uniformity of the coil.

A method of measuring absolute magnetic susceptibility using T2 and T-weighted magnetic resonance images is described. We used magnitude images derived from spin echo and gradient echo acquisitions, without the use of phase information. The findings are validated analytically and also using experimental phantoms. We show that a combination of magnetic resonance images can provide information needed to deduce the absolute susceptibility of the sample. Careful consideration of sample relaxation times and image acquisition parameters is required to ensure that calculations are accurate. We conducted an investigation of optimal imaging parameters and validated our results. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 41B: 28–36, 2012

Imaging in the presence of nonlinear static and gradient magnetic fields is outlined. Analytic expressions are provided for the signal obtained using specific magnetic resonance imaging (MRI) sequences with the addition of field nonlinearities. We show that using nonlinear Fourier transform image reconstruction, high-quality images can be obtained for the case when the static magnetic field has large peak-to-peak error, and the gradient field exhibits variations beyond currently acceptable levels over the imaging field-of-view. Our results show that MRI can be performed in nonlinear magnetic fields, provided that a versatile technique is used to reconstruct the images.

Visual and haptic organ interactions in real time are essential in virtual reality–based medical simulations to provide training, early diagnosis, and improved treatment planning. This work presents a robust method of computing the deformation of an elastic object when external forces are applied. Point-based haptic interaction between the user of the haptic device and an object is outlined and is formulated as a deformation problem over the surface. The model is expressed in the spherical harmonic basis, from which both haptic device force feedback and object surface updates are calculated. Haptic and visual updates are performed in the same basis, in turn providing significant benefits of computing efficiency and multiresolution modeling. Partially, this is due to the fact that in the model the objects are represented as surfaces. A haptic device is used to probe different prostates that have been segmented and reconstructed from MR images to validate the findings. The calculated deformation and feedback force is further validated against the established finite element method.

A magnetic resonance imaging (MRI) quadrature transmit/receive radio frequency (RF) coil for the purpose of imaging the human wrist in high resolution is outlined. The proposed design is used in a nonconventional manner within the bore of the MRI scanner. Due to restrictions of field linearity, space, and patient comfort, it was deemed necessary to design a coil that can be placed above the head with the arm aligned in the transverse plane of the magnet. This configuration allows the placement of the coil to be near the isocenter of the magnet, away from static field impurities. The RF wrist coil was built and a phantom study was conducted to establish signal-to-noise (SNR) performance and coil uniformity. Our design resulted in a peak SNR of 150 and sufficient uniformity for our application. Subsequent imaging of the human wrist provided excellent detail of the anatomy of interest - i.e., the carpal tunnel. © 2009 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 35B: 191-197, 2009