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IntroductionTask-based language fMRI is a non-invasive method of identifying brain regions subserving language that is used to plan neurosurgical resections which potentially encroach on eloquent regions. The use of unstructured fMRI paradigms, such as naturalistic fMRI, to map language is of increasing interest. Their analysis necessitates the use of alternative methods such as machine learning (ML) and deep learning (DL) because task regressors may be difficult to define in these paradigms.MethodsUsing task-based language fMRI as a starting point, this study investigates the use of different categories of ML and DL algorithms to identify brain regions subserving language. Data comprising of seven task-based language fMRI paradigms were collected from 26 individuals, and ML and DL models were trained to classify voxel-wise fMRI time series.ResultsThe general machine learning and the interval-based methods were the most promising in identifying language areas using fMRI time series classification. The geneal machine learning method achieved a mean whole-brain Area Under the Receiver Operating Characteristic Curve (AUC) of 0.97±0.03, mean Dice coefficient of 0.6±0.34 and mean Euclidean distance of 2.7±2.4 mm between activation peaks across the evaluated regions of interest. The interval-based method achieved a mean whole-brain AUC of 0.96±0.03, mean Dice coefficient of 0.61±0.33 and mean Euclidean distance of 3.3±2.7 mm between activation peaks across the evaluated regions of interest.DiscussionThis study demonstrates the utility of different ML and DL methods in classifying task-based language fMRI time series. A potential application of these methods is the identification of language activation from unstructured paradigms.

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

IntroductionThe reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately. Recently, generative adversarial networks (GANs) based on machine learning have shown promise in learning the complex mapping between acquired PET data and reconstructed tomographic images. This study aims to investigate the properties of training images that contribute to GAN performance when non-clinical images are used for training. Additionally, we describe a method to correct common PET imaging artefacts without relying on patient-specific anatomical images.MethodsThe modular GAN framework includes two GANs. Module 1, resembling Pix2pix architecture, is trained on non-clinical sinogram-image pairs. Training data are optimised by considering image properties defined by metrics. The second module utilises adaptive instance normalisation and style embedding to enhance the quality of images from Module 1. Additional perceptual and patch-based loss functions are employed in training both modules. The performance of the new framework was compared with that of existing methods, (filtered backprojection (FBP) and ordered subset expectation maximisation (OSEM) without and with point spread function (OSEM-PSF)) with respect to correction for attenuation, patient motion and noise in simulated, NEMA phantom and human imaging data. Evaluation metrics included structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), relative root mean squared error (rRMSE) for simulated data, and contrast-to-noise ratio (CNR) for NEMA phantom and human data.ResultsFor simulated test data, the performance of the proposed framework was both qualitatively and quantitatively superior to that of FBP and OSEM. In the presence of noise, Module 1 generated images with a SSIM of 0.48 and higher. These images exhibited coarse structures that were subsequently refined by Module 2, yielding images with an SSIM higher than 0.71 (at least 22% higher than OSEM). The proposed method was robust against noise and motion. For NEMA phantoms, it achieved higher CNR values than OSEM. For human images, the CNR in brain regions was significantly higher than that of FBP and OSEM (p

Abstract Background Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. Results Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were − 1.59 ± 2.93% and 0.17 ± 0.07. Conclusions Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

BackgroundCross-modality image estimation can be performed using generative adversarial networks (GANs). To date, SPECT image estimation from another medical imaging modality using this technique has not been considered. We evaluate the estimation of SPECT from MRI and PET, and additionally assess the necessity for cross-modality image registration for GAN training.MethodsWe estimated interictal SPECT from PET and MRI as a single-channel input, and as a multi-channel input to the GAN. We collected data from 48 individuals with epilepsy and converted them to 3D isotropic images for consistence across the modalities. Training and testing data were prepared in native and template spaces. The Pix2pix framework within the GAN network was adopted. We evaluated the addition of the structural similarity index metric to the loss function in the GAN implementation. Root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess how well SPECT images were able to be synthesised.ResultsHigh quality SPECT images could be synthesised in each case. On average, the use of native space images resulted in a 5.4% percentage improvement in SSIM than the use of images registered to template space. The addition of structural similarity index metric to the GAN loss function did not result in improved synthetic SPECT images. Using PET in either the single channel or dual channel implementation led to the best results, however MRI could produce SPECT images close in quality.ConclusionSynthesis of SPECT from MRI or PET can potentially reduce the number of scans needed for epilepsy patient evaluation and reduce patient exposure to radiation.

Abstract Background In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. Methods To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ( $$K_{i}$$ K i ) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. Results Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson’s correlation between $$K_{i}$$ K i estimates from the short imaging window and the validation imaging window exceeded 0.95. Conclusion The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.

Abstract Background The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. Result The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K 1 , k 2 and k 3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p

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.

Abstract Objective Ion channels belonging to subfamily A of voltage‐gated potassium channels (Kv1) are highly expressed on axons, where they play a key role in determining resting membrane potential, in shaping action potentials, and in modulating action potential frequency during repetitive neuronal firing. We aimed to study the genesis of seizures caused by mutations affecting Kv1 channels and searched for potential therapeutic targets. Methods We used a novel in silico model, the laminar cortex model (LCM), to examine changes in neuronal excitability and network dynamics associated with loss‐of‐function mutations in Kv1 channels. The LCM simulates the activities of a network of tens of thousands of interconnected neurons and incorporates the kinetics of 11 types of ion channel and three classes of neurotransmitter receptor. Changes in two types of potassium currents conducted by Kv1 channels were examined: slowly inactivating D‐type currents and rapidly inactivating A‐type currents. Effects on neuronal firing rate, action potential shape, and neuronal oscillation state were evaluated. A systematic parameter scan was performed to identify parameter changes that can reverse the effects of the changes. Results Reduced axonal D‐type currents led to lower firing threshold and widened action potentials, both lowering the seizure threshold. Two potential therapeutic targets for treating seizures caused by loss‐of‐function changes in Kv1 channels were identified: persistent sodium channels and NMDA receptors. Blocking persistent sodium channels restored the firing threshold and reduced action potential width. NMDA receptor antagonists reduced excitatory postsynaptic currents from excessive glutamate release related to widened action potentials. Significance Riluzole reduces persistent sodium currents and excitatory postsynaptic currents from NMDA receptor activation. Our results suggest that this FDA‐approved drug can be repurposed to treat epilepsies caused by mutations affecting axonal Kv1 channels.

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 βSUB and DSUB. 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 α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.

Purpose: The recognition and treatment of high-altitude illness (HAI) is increasingly important in global emergency medicine. High altitude related hypobaric hypoxia can lead to acute mountain sickness (AMS), which may relate to increased expression of vascular endothelial growth factor (VEGF), and subsequent blood-brain barrier (BBB) compromise. This study aimed to establish the relationship between AMS and changes in plasma VEGF levels during a high-altitude ascent. VEGF level changes with dexamethasone, a commonly used AMS medication, may provide additional insight into AMS.Methods: Twelve healthy volunteers ascended Mt Fuji (3,700 m) and blood samples were obtained at distinct altitudes for VEGF analysis. Oxygen saturation (SPO2) measurements were also documented at the same time-point. Six out of the 12 study participants were prescribed dexamethasone for a second ascent performed 48 h later, and blood was again collected to establish VEGF levels.Results: Four key VEGF observations could be made based on the data collected: (i) the baseline VEGF levels between the two ascents trended upwards; (ii) those deemed to have AMS in the first ascent had increased VEGF levels (23.8–30.3 pg/ml), which decreased otherwise (23.8–30.3 pg/ml); (iii) first ascent AMS participants had higher VEGF level variability for the second ascent, and similar to those not treated with dexamethasone; and (iv) for the second ascent dexamethasone participants had similar VEGF levels to non-AMS first ascent participants, and the variability was lower than for first ascent AMS and non-dexamethasone participants. SPO2 changes were unremarkable, other than reducing by around 5% irrespective of whether measurement was taken for the first or second ascent.Conclusion: First ascent findings suggest a hallmark of AMS could be elevated VEGF levels. The lack of an exercise-induced VEGF level change strengthened the notion that elevated plasma VEGF was brain-derived, and related to AMS.

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

Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders. Ultra-high field magnetic resonance imaging (MRI) data obtained using a multi-echo gradient echo sequence have been shown to contain information on myelin, axonal, and extracellular compartments in tissue. Quantitative assessment of water fraction, relaxation time (T2*), and frequency shift using multi-compartment models has been shown to be useful in studying white matter properties via specific tissue parameters. It remains unclear how tissue parameters vary with model selection based on 7T multiple echo time gradient-recalled echo (GRE) MRI data. We applied existing signal compartment models to the corpus callosum and investigated whether a three-compartment model can be reduced to two compartments and still resolve white matter parameters [i.e., myelin water fraction (MWF) and g-ratio]. We show that MWF should be computed using a three-compartment model in the corpus callosum, and the g-ratios obtained using three compartment models are consistent with previous reports. We provide results for other parameters, such as signal compartment frequency shifts.

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.

Abstract Air-core magnetometers are amongst the most commonly used magnetic field detectors in biomedical instruments. They offer excellent sensitivity, low fabrication complexity and a robust, cost-effective solution. However, air-core magnetometers must be tailored to the specific application to achieve high sensitivity, which can be decisive in the accuracy of the diagnoses and the time required for the examination. Existing methods proposed for the design of air-core magnetometers are based on simplified models and simulations using a reduced number of variables, potentially leading to sensitivity that is suboptimal. To circumvent this we chose a method with fewer assumptions and a larger number of decision variables which employed a genetic algorithm, a global optimisation method. Experimental validation shows that the model is appropriate for the design of highly sensitive air-core magnetometers. Moreover, our results support the suitability of a genetic algorithm for optimization in this context. The new method described herein will be made publicly available via our website to facilitate the development of less costly biomedical instruments using air-core magnetometers with unprecedented sensitivity.

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.

PURPOSE:Ultra-high field magnetic resonance imaging poses a number of challenges for robust radio frequency coil designs. A monopole antenna array can potentially overcome key limitations of birdcage coil designs and may provide a useful radio frequency coil for brain imaging. METHODS:Four, 8 and 12 element monopole antenna arrays were simulated using 3 T and 7T magnetic resonance imaging frequencies. For comparison, 4, 8 and 12 element birdcage coils were also simulated. Coil performance was evaluated and compared and the impact of shielding was assessed. A 4 element monopole antenna array was fabricated and bench tested. RESULTS:Comparison of the 4, 8 and 12 element designs suggest that the monopole antenna array leads to better field properties than the birdcage coil in all configurations studied: unloaded, loaded with saline and loaded using a head phantom. Improvements in field properties and homogeneity were evident at both field strengths, implying that the monopole antenna array has potential for head imaging. The monopole antenna array also appears to be more efficient than the comparable birdcage coil design. Additionally, the former is scalable via the addition of more elements whereas our results suggest that this is not the case for the latter. Bench testing results show that the monopole antenna array is well matched with the transmission line, and mutual coupling between elements is sufficiently low. CONCLUSION:We found the monopole antenna array generated a larger field intensity than the birdcage coil design, whilst also producing a more useful magnetic resonance imaging field as measured by radio frequency field homogeneity. Our study suggests that magnetic resonance imaging of the brain can likely benefit from the use of radio frequency monopole antenna arrays.

The aim of the work is to increase the visual information in magnetic resonance imaging based susceptibility weighted images. Our approach is to amplify spatial frequency information of the phase mask used to increase susceptibility weighting using a modified version of the human contrast sensitivity function. Thereby, we propose a modified version of the human contrast sensitivity function for use in phase mask creation. Comparison with conventional susceptibility-weighted imaging was undertaken on a qualitative basis and quantitatively with a number of established image quality metrics on ex vivo mouse brain magnetic resonance images obtained at 16.4 T at various echo times. Four experts also compared the quality of in vivo 1.5 and 3 T human brain magnetic resonance images generated with traditional susceptibility weighted imaging and with the new method. We found that parameters of the modified human contrast sensitivity function can be chosen to improve delineation of structural detail of mouse and human brains. Information contained in susceptibility-weighted images generated using the modified human contrast sensitivity function based phase mask corresponds to that in the conventional method, however the visual range over which it is depicted has improved visual perception. Hence, qualitative evaluation of information contained in susceptibility-weighted images can be improved by amplifying spatial frequencies where human contrast sensitivity is reduced.

OBJECT:We studied the feasibility of generating the variable magnetic fields required for ultra-low field nuclear magnetic resonance relaxometry with dynamically adjustable permanent magnets. Our motivation was to substitute traditional electromagnets by distributed permanent magnets, increasing system portability. MATERIALS AND METHODS:The finite element method (COMSOL®) was employed for the numerical study of a small permanent magnet array to calculate achievable magnetic field strength, homogeneity, switching time and magnetic forces. A manually operated prototype was simulated and constructed to validate the numerical approach and to verify the generated magnetic field. RESULTS:A concentric small permanent magnet array can be used to generate strong sample pre-polarisation and variable measurement fields for ultra-low field relaxometry via simple prescribed magnet rotations. Using the array, it is possible to achieve a pre-polarisation field strength above 100 mT and variable measurement fields ranging from 20-50 μT with 200 ppm absolute field homogeneity within a field-of-view of 5 x 5 x 5 cubic centimetres. CONCLUSIONS:A dynamic small permanent magnet array can generate multiple highly homogeneous magnetic fields required in ultra-low field nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) instruments. This design can significantly reduce the volume and energy requirements of traditional systems based on electromagnets, improving portability considerably.

Texture enhancement is one of the most important techniques in digital image processing and plays an essential role in medical imaging since textures discriminate information. Most image texture enhancement techniques use classical integral order differential mask operators or fractional differential mask operators using fixed fractional order. These masks can produce excessive enhancement of low spatial frequency content, insufficient enhancement of large spatial frequency content, and retention of high spatial frequency noise. To improve upon existing approaches of texture enhancement, we derive an improved Variable Order Fractional Centered Difference (VOFCD) scheme which dynamically adjusts the fractional differential order instead of fixing it. The new VOFCD technique is based on the second order Riesz fractional differential operator using a Lagrange 3-point interpolation formula, for both grey scale and colour image enhancement. We then use this method to enhance photographs and a set of medical images related to patients with stroke and Parkinson's disease. The experiments show that our improved fractional differential mask has a higher signal to noise ratio value than the other fractional differential mask operators. Based on the corresponding quantitative analysis we conclude that the new method offers a superior texture enhancement over existing methods.

Local field potentials (LFPs) are widely used to study the function of local networks in the brain. They are also closely correlated with the blood-oxygen-level-dependent signal, the predominant contrast mechanism in functional magnetic resonance imaging. We developed a new laminar cortex model (LCM) to simulate the amplitude and frequency of LFPs. Our model combines the laminar architecture of the cerebral cortex and multiple continuum models to simulate the collective activity of cortical neurons. The five cortical layers (layer I, II/III, IV, V, and VI) are simulated as separate continuum models between which there are synaptic connections. The LCM was used to simulate the dynamics of the visual cortex under different conditions of visual stimulation. LFPs are reported for two kinds of visual stimulation: general visual stimulation and intermittent light stimulation. The power spectra of LFPs were calculated and compared with existing empirical data. The LCM was able to produce spontaneous LFPs exhibiting frequency-inverse (1/ƒ) power spectrum behaviour. Laminar profiles of current source density showed similarities to experimental data. General stimulation enhanced the oscillation of LFPs corresponding to gamma frequencies. During simulated intermittent light stimulation, the LCM captured the fundamental as well as high order harmonics as previously reported. The power spectrum expected with a reduction in layer IV neurons, often observed with focal cortical dysplasias associated with epilepsy was also simulated.