Human Brain Imaging Research Articles

3D CEST MRI with an unevenly segmented RF irradiation scheme: A feasibility study in brain tumor imaging.

To integrate 3D CEST EPI with an unevenly segmented RF irradiation module and preliminarily demonstrate it in the clinical setting. A CEST MRI with unevenly segmented RF saturation was implemented, including a long primary RF saturation to induce the steady-state CEST effect, maintained with repetitive short secondary RF irradiation between readouts. This configuration reduces relaxation-induced blur artifacts during acquisition, allowing fast 3D spatial coverage. Numerical simulations were performed to select parameters such as flip angle (FA), short RF saturation duration (Ts2), and the number of readout segments. The sequence was validated experimentally with data from a phantom, healthy volunteers, and a brain tumor patient. Based on the numerical simulation and l-carnosine gel phantom experiment, FA, Ts2, and the number of segments were set to 20°, 0.3 s, and the range from 4 to 8, respectively. The proposed method minimized signal modulation in the human brain images in the kz direction during the acquisition and provided the blur artifacts-free CEST contrast over the whole volume. Additionally, the CEST contrast in the tumor tissue region is higher than in the contralateral normal tissue region. It is feasible to implement a highly accelerated 3D EPI CEST imaging with unevenly segmented RF irradiation.

CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses

Coordinate-based meta-analysis (CBMA) is a powerful technique in the field of human brain imaging research. Due to its intense usage, several procedures for data preparation and post hoc analyses have been proposed so far. However, these steps are often performed manually by the researcher, and are therefore potentially prone to error and time-consuming. We hence developed the Coordinate-Based Meta-Analyses Toolbox (CBMAT) to provide a suite of user-friendly and automated MATLAB® functions allowing one to perform all these procedures in a fast, reproducible and reliable way. Besides the description of the code, in the present paper we also provide an annotated example of using CBMAT on a dataset including 34 experiments. CBMAT can therefore substantially improve the way data are handled when performing CBMAs. The code can be downloaded from https://github.com/Jordi-Manuello/CBMAT.git.

Messing up to clean up: Semi-randomized frequency selective space-filling curves to suppress physiological signal fluctuations in MRI.

Rapid 3D steady-state sequences are widely used but are also known to be sensitive to semi-periodic physiological signal fluctuations due to, for example, cardiac pulsation, breathing, and eye/eyelids movement. This semi-periodicity results in repeating artifacts in the image whose intensity depends on the scan parameters. The purpose of this study is to design a reordering of the 2D phase encodes (within the 3D acquisition) that reduces these artifacts. A randomized order of the phase encodes can suppress repeating artifact but may also introduce its own apparent noise, for example, in cases of slow subject movement or gradual changes in eddy currents. In a new design a semi-randomized space-filling curve is generated by scrambling the local order of the phase encodes to achieve a controlled frequency selective effect, that is, eliminating artifacts above a chosen (fluctuation) frequency threshold while leaving lower frequencies untouched, thus overcoming the limitations of a randomized order. The method was characterized in simulations and substantiated by human brain imaging at 7 T using two steady-state gradient echo variants that suffer from pulsation, either near blood vessels or near the ventricles. The simulations with a point source show that the maximum artifact intensity can be reduced by factors of 10-50 depending on the scan parameters. In human scanning, the new approach drastically reduced physiologically induced artifacts and was superior in this regard to both full randomization and a generalized Hilbert curve, another semi-randomized approach. The phase-encodes reordering presented here effectively removes artifacts arising from local fluctuations.

Reproducibility of 3D MRSI for imaging human brain glucose metabolism using direct (2H) and indirect (1H) detection of deuterium labeled compounds at 7T and clinical 3T

IntroductionDeuterium metabolic imaging (DMI) and quantitative exchange label turnover (QELT) are novel MR spectroscopy techniques for non-invasive imaging of human brain glucose and neurotransmitter metabolism with high clinical potential. Following oral or intravenous administration of non-ionizing [6,6′-2H2]-glucose, its uptake and synthesis of downstream metabolites can be mapped via direct or indirect detection of deuterium resonances using 2H MRSI (DMI) and 1H MRSI (QELT), respectively. The purpose of this study was to compare the dynamics of spatially resolved brain glucose metabolism, i.e., estimated concentration enrichment of deuterium labeled Glx (glutamate+glutamine) and Glc (glucose) acquired repeatedly in the same cohort of subjects using DMI at 7T and QELT at clinical 3T. MethodsFive volunteers (4 m/1f) were scanned in repeated sessions for 60 min after overnight fasting and 0.8 g/kg oral [6,6′-2H2]-glucose administration using time-resolved 3D 2H FID-MRSI with elliptical phase encoding at 7T and 3D 1H FID-MRSI with a non-Cartesian concentric ring trajectory readout at clinical 3T. ResultsOne hour after oral tracer administration regionally averaged deuterium labeled Glx4 concentrations and the dynamics were not significantly different over all participants between 7T 2H DMI and 3T 1H QELT data for GM (1.29±0.15 vs. 1.38±0.26 mM, p=0.65 & 21±3 vs. 26±3 µM/min, p=0.22) and WM (1.10±0.13 vs. 0.91±0.24 mM, p=0.34 & 19±2 vs. 17±3 µM/min, p=0.48). Also, the observed time constants of dynamic Glc6 data in GM (24±14 vs. 19±7 min, p=0.65) and WM (28±19 vs. 18±9 min, p=0.43) dominated regions showed no significant differences. Between individual 2H and 1H data points a weak to moderate negative correlation was observed for Glx4 concentrations in GM (r=-0.52, p<0.001), and WM (r=-0.3, p<0.001) dominated regions, while a strong negative correlation was observed for Glc6 data GM (r=-0.61, p<0.001) and WM (r=-0.70, p<0.001). ConclusionThis study demonstrates that indirect detection of deuterium labeled compounds using 1H QELT MRSI at widely available clinical 3T without additional hardware is able to reproduce absolute concentration estimates of downstream glucose metabolites and the dynamics of glucose uptake compared to 2H DMI data acquired at 7T. This suggests significant potential for widespread application in clinical settings especially in environments with limited access to ultra-high field scanners and dedicated RF hardware.

Herpes Simplex Virus‐1 Proteins as Drivers for Alzheimer’s Disease Pathologies

AbstractBackgroundDNA and RNA of Herpes Simplex Virus 1 (HSV‐1) were found in the brains and serological samples of Alzheimer’s disease (AD) patients. Such molecular presence of HSV‐1 in AD patients is especially intriguing as HSV‐1 virions are rarely detected in AD brains.MethodTo follow the molecular footsteps detected, we imaged viral proteins in postmortem human AD brains at superior resolution using expansion microscopy, a tissue manipulation method that physically expands the samples by a factor of 4.5x, allowing a 40 nm imaging resolution, and immunolabeled herpetic proteins, AD pathologies and cell markers.ResultWe found an abundance of herpetic proteins, previously undetectable with standard methods, across large brain areas. Importantly, we found that HSV‐1 proteins strongly co‐localized with AD pathologies. Consequently, we hypothesized that expression of HSV‐1 proteins during latency may be linked to AD pathology. We are now in the process of characterizing the HSV‐1 proteome in AD brains by imaging key proteins in expanded AD brain slices and examining their colocalization with AD pathologies across brain areas and disease stages. As a complementary system to the fixed human brain slices, we are exposing live human brain organoids, to HSV‐1, and imaging the relationships between viral proteins and the formation of AD pathologies via expansion microscopy.ConclusionPathogens may be triggers of immune responses driving AD; this study would shed light on one common pathogen, HSV‐1, while serving as a framework to unveiling molecular causation between infectious agents and AD hallmarks.

Photoacoustic digital brain and deep-learning-assisted image reconstruction

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals’ distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull’s acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

Correction of errors in estimates of T1ρ at low spin-lock amplitudes in the presence of B0 and B1 inhomogeneities.

Relaxation rates R1ρ in the rotating frame measured by spin-lock methods at very low locking amplitudes (≤ 100 Hz) are sensitive to the effects of water diffusion in intrinsic gradients and may provide information on tissue microvasculature, but accurate estimates are challenging in the presence of B0 and B1 inhomogeneities. Although composite pulse preparations have been developed to compensate for nonuniform fields, the transverse magnetization comprises different components and the spin-lock signals measured do not decay exponentially as a function of locking interval at low locking amplitudes. For example, during a typical preparation sequence, some of the magnetization in the transverse plane is nutated to the Z-axis and later tipped back, and so does not experience R1ρ relaxation. As a result, if the spin-lock signals are fit to a monoexponential decay with locking interval, there are residual errors in quantitative estimates of relaxation rates R1ρ and their dispersion with weak locking fields. We developed an approximate theoretical analysis to model the behaviors of the different components of the magnetization, which provides a means to correct these errors. The performance of this correction approach was evaluated both through numerical simulations and on human brain images at 3 T, and compared with a previous correction method using matrix multiplication. Our correction approach has better performance than the previous method at low locking amplitudes. Through careful shimming, the correction approach can be applied in studies using low spin-lock amplitudes to assess the contribution of diffusion to R1ρ dispersion and to derive estimates of microvascular sizes and spacings. The results of imaging eight healthy subjects suggest that R1ρ dispersion in human brain at low locking fields arises from diffusion among inhomogeneities that generate intrinsic gradients on a scale of capillaries (~7.4 ± 0.5 μm).

5T magnetic resonance imaging: radio frequency hardware and initial brain imaging.

We aimed to demonstrate the feasibility of generating high-resolution human brain magnetic resonance imaging (MRI) at 5 Tesla (T) using a quadrature birdcage transmit/48-channel receiver coil assembly. A quadrature birdcage transmit/48-channel receiver coil assembly was designed for human brain imaging at 5T. The radio frequency (RF) coil assembly was validated by electromagnetic (EM) simulations and phantom imaging experimental studies. The simulated B1+ field inside a human head phantom and inside a human head model generated by the birdcage coils driven in circularly polarized (CP) mode at 3T, 5T and 7T was compared. Signal-to-noise ratio (SNR) maps, the inverse g-factor maps for evaluation of parallel imaging performance, anatomic images, angiography images, vessel wall images and susceptibility weighted images (SWI) were acquired using the RF coil assembly at 5T and compared to those acquired using a 32-channel head coil on a 3T MRI scanner. For the EM simulations, 5T MRI provided less RF inhomogeneity compared to that of 7T. In the phantom imaging study, the distributions of the measured B1+ field were consistent with the distributions of the simulated B1+ field. In the human brain imaging study, the average SNR value of the brain in the transversal plane at 5T was 1.6 times of that at 3T. The 48-channel head coil at 5T had higher parallel acceleration capability than the 32-channel head coil at 3T. The anatomic images at 5T also showed higher SNR than those at 3T. Improved delineation of the hippocampus, lenticulostriate arteries, and basilar arteries was observed at 5T compared to 3T. SWI with a higher resolution of 0.3 mm ×0.3 mm ×1.2 mm could be acquired at 5T, which enabled better visualization of small blood vessels compared to that at 3T. 5T MRI can provide significant SNR improvement compared to that of 3T with less RF inhomogeneity than that of 7T. The ability to obtain high quality in vivo human brain images at 5T using the quadrature birdcage transmit/48-channel receiver coil assembly has significant in clinical and scientific research applications.

APART-QSM: An improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method

The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2′ in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2′ and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2′ relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.

Benefits of high-dielectric pad for neuroimaging study in 7-Tesla MRI

This study aimed to evaluate whether the use of a high-dielectric pad is effective in increasing transmit and receive sensitivity in areas of low signal intensity in the human brain at high magnetic fields and assess its usefulness in neuroimaging studies. The novelty of this study lies in the first reported use of diffusion tensor imaging (DTI) results to evaluate the effect of the pad on neuroimaging. Six volunteers underwent MR scanning using a 7 T MR system. T1-weighted images (T1w) and diffusion-weighted images (DWI) were acquired to demonstrate the benefits of a high-dielectric pad made of barium titanate (BaTiO3). For all imaging experiments, two datasets were acquired per person, one with and one without a high-dielectric pad. Enhancement of signal sensitivity in neuroimaging has been analyzed by DTI study. Higher signal intensities and spatial contrast were demonstrated in the in T1w images acquired using high-dielectric pad than in those acquired without high-dielectric pad. Especially in DTI studies, increased quantitative anisotropy (QA) signals were observed in the corticospinal tract (CST), frontopontine tract (FPT), splenium of corpus callosum (SCC), fornix (FX), inferior fronto-occipital fasciculus (IFOF), cerebellum (CB), middle cerebellar peduncle (MCP), and body of corpus callosum (BCC) (FDR < 0.05). The signal differences accounted for an overall 20% increase. A high-dielectric pad is effective in enhancing signal intensity in human brain images acquired using 7 T MRI. Our results show that the use of such pad can increase the spatial resolution, tissue contrast, and signal intensity in neuroimaging studies. These findings suggest that high-dielectric pads may provide a relatively simple and low-cost method for spatiotemporal brain imaging studies.

Improving the study of brain-behavior relationships by revisiting basic assumptions.

Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.

Methodological Research on Image Registration Based on Human Brain Tissue In Vivo

As one of the critical steps in brain imaging analysis and processing, brain image registration plays a significant role. In this paper, we proposed a technique of human brain image registration based on tissue morphology in vivo to address the problems of previous image registration. First, different feature points were extracted and combined, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image. Second, feature points were screened through eliminating their wrong matching pairs between moving image and reference image. Finally, the remaining matching pairs of feature points were used to generate the model parameters of spatial transformation, with which the brain image registration can be finished by combining interpolation techniques. Results showed that compared with the Surf, Demons, and Sift algorithms, the proposed method can perform better not only for four quantitative indicators (mean square differences, normalized cross correlation, normalized mutual information and mutual information) but also in spatial location, size, appearance contour, and registration details. The findings may suggest that the proposed method will be of great value for brain image reconstruction, fusion, and statistical comparison analysis.

Oxidative stress biomarkers and longitudinal changes in human brain imaging across the Alzheimer’s disease continuum

AbstractBackgroundOxidative stress is critical for Alzheimer’s disease (AD) pathophysiology. Brain mitochondrial function, proteins, and lipids are susceptible to oxidative damage caused by reactive oxygen species. In turn, this damage leads to key AD‐like changes in the brain, such as impaired glucose metabolism (i.e., lower fluorodeoxyglucose, or FDG‐PET) and gray matter volume atrophy in AD‐sensitive regions. It is unknown if oxidative stress biomarkers like superoxide dismutase (SOD) proteins track these changes using longitudinal brain imaging data, and what mechanisms may be at work (Figure 1).MethodUsing Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, 192 aged adults (75 unimpaired, 48 MCI, 69 AD) had annual CSF and FDG data available for over 6 years. A two‐phase MRM targeted proteomics panel was used to gauge CSF SOD1 levels from serum. Standard assays in CSF determined Aβ, p‐tau‐181, and total tau. Brain glucose uptake was determined using FDG SUVR calibrated to the pons. Brain volume was examined longitudinally using FreeSurfer.ResultLongitudinally, among SOD1 quartiles (Figure 2), the lowest SOD1 group showed marked hypometabolism among older adults at baseline (R2=0.528, p&lt;.001, “blue line”), but 6 years later instead predicted hypermetabolism (R2=0.389, p&lt;.001). The highest SOD1 group showed no association at baseline but at 6 years later predicted hypometabolism among older adults (R2=0.151, p&lt;.05, “dotted red line”). CSF Aβ and p‐tau‐181 fully mediated these effects (ps&lt;.01), such that lower Aβ and higher p‐tau‐181 over time resulted in higher SOD1 predicting less glucose uptake. Similarly, higher SOD1 was not related to atrophy in AD‐sensitive regions like parahippocampus at baseline, but 5 years later was strongly related to more brain atrophy (R2=0.207,p&lt;.001) (Figure 3), which was mediated by higher CSF total tau (p&lt;.001).ConclusionLike previous reports, we found that SOD1 initially showed neutral or ameliorative patterns, reflecting its classical role in reducing oxidative stress. With increased Aβ or p‐tau‐181 deposition over time, however, SOD1 became a marker of the degree of oxidative damage that may have occurred in neural tissue due to Aβ or tau.

Machine-Learning-Based Inversion Scheme for Super-Resolution Three-Dimensional Microwave Human Brain Imaging

To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost.

A 3-D Full Convolution Electromagnetic Reconstruction Neural Network (3-D FCERNN) for Fast Super-Resolution Electromagnetic Inversion of Human Brain.

Three-dimensional (3-D) super-resolution microwave imaging of human brain is a typical electromagnetic (EM) inverse scattering problem with high contrast. It is a challenge for the traditional schemes based on deterministic or stochastic inversion methods to obtain high contrast and high resolution, and they require huge computational time. In this work, a dual-module 3-D EM inversion scheme based on deep neural network is proposed. The proposed scheme can solve the inverse scattering problems with high contrast and super-resolution in real time and reduce a huge computational cost. In the EM inversion module, a 3-D full convolution EM reconstruction neural network (3-D FCERNN) is proposed to nonlinearly map the measured scattered field to a preliminary image of 3-D electrical parameter distribution of the human brain. The proposed 3-D FCERNN is completely composed of convolution layers, which can greatly save training cost and improve model generalization compared with fully connected networks. Then, the image enhancement module employs a U-Net to further improve the imaging quality from the results of 3-D FCERNN. In addition, a dataset generation strategy based on the human brain features is proposed, which can solve the difficulty of human brain dataset collection and high training cost. The proposed scheme has been confirmed to be effective and accurate in reconstructing the distribution of 3-D super-resolution electrical parameters distribution of human brain through noise-free and noisy examples, while the traditional EM inversion method is difficult to converge in the case of high contrast and strong scatterers. Compared with our previous work, the training of FCERNN is faster and can significantly decrease computational resources.

Breaking the binary: Gender versus sex analysis in human brain imaging

Despite decades of pursuit, human brain imaging has yet to uncover clear neural correlates of male-female behavioral differences. Given that such behavior does not always align with sex categories, we argue that neuroimaging research may find more success by partitioning subjects along nonbinary gender attributes in addition to sex. We review the handful of studies that have done this, several of which find as good or better association between brain measures and “gender” as they do with “sex.” Recent advances in operationalizing “gender” as a multidimensional variable should facilitate such studies, along with discovery-based approaches that mine brain imaging data for gender-associated attributes, independent of sex.

Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI.

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Spatiotemporal encoding MRI using subspace-constrained sampling and locally-low-rank regularization: Applications to diffusion weighted and diffusion kurtosis imaging of human brain and prostate

The benefits of performing locally low-rank (LLR) reconstructions on subsampled diffusion weighted and diffusion kurtosis imaging data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved diffusion-oriented acquisitions, and allows one to overcome distortions otherwise observed along EPI's phase-encoded dimension. In combination with LLR-based reconstructions of the pooled imaging data and with a joint subsampling of b-weighted and interleaved images, additional improvements in terms of sensitivity as well as shortened acquisition times are demonstrated, without noticeable penalties. Details on how the LLR-regularized, subspace-constrained image reconstructions were adapted to SPEN are given; the improvements introduced by adopting these reconstruction frameworks for the accelerated acquisition of diffusivity and of kurtosis imaging data in both relatively homogeneous regions like the human brain and in more challenging regions like the human prostate, are presented and discussed within the context of similar efforts in the field.

Design of an adaptive winner takes all circuit explaining features of binocular rivalry in visual brain

The paper presents an adaptive WTA circuit where the firing of a neuron depends on input stimuli and the circuit self-adapts which in turn allow the loosing neuron to win. The CMOS based design have been simulated at 45 nm CMOS process which occupies less chip area (of about 12 × 25 μm2) and consume low power (about 124 nW). The design is stable as temperature coefficient is about 11.5 μV/°C and noise figure is about 11.6 nV/Hz. The self-adaption feature depends on the value of capacitor ratio which is equal to 50 fF. A visual brain feature called Binocular Rivalry (BR) has been demonstrated and explained using simulated results of proposed circuit. Three features of visual brain; superimposition of images obtained from two eyes; binocular rivalry when different images are presented to eyes: suppression and dominance of images, and neural adaption, have been explained using circuit simulation. The real time images processed using image processing illustrating BR features has also been implemented to circuit for real time operations. The time taken for neural adaption which causes switching of images in human brain when distinguish images are provided to eyes is about 0.2s, as demonstrated in circuit simulations using BR images. The proposed design can be implanted in machines (humanly robots) with which it can see, learn and self-adapt like a human visual brain. The circuit and the concept can also be implemented in devices used in assistive technology for helping visually disabled or less able persons. The Papers proposes a novel idea of CMOS based hardware for implementing BR features in machines.

A Monopole and Dipole Hybrid Antenna Array for Human Brain Imaging at 10.5 Tesla.

In this letter, we evaluate antenna designs for ultra-high frequency and field (UHF) human brain magnetic resonance imaging (MRI) at 10.5 tesla (T). Although MRI at such UHF is expected to provide major signal-to-noise gains, the frequency of interest, 447 MHz, presents us with challenges regarding improved B1 + efficiency, image homogeneity, specific absorption rate (SAR), and antenna element decoupling for array configurations. To address these challenges, we propose the use of both monopole and dipole antennas in a novel hybrid configuration, which we refer to as a mono-dipole hybrid antenna (MDH) array. Compared to an 8-channel dipole antenna array of the same dimensions, the 8-channel MDH array showed an improvement in decoupling between adjacent array channels, as well as ~18% higher B1 + and SAR efficiency near the central region of the phantom based on simulation and experiment. However, the performances of the MDH and dipole antenna arrays were overall similar when evaluating a human model in terms of peak B1 + efficiency, 10 g SAR, and SAR efficiency. Finally, the concept of an MDH array showed an advantage in improved decoupling, SAR, and B1 + near the superior region of the brain for human brain imaging.