The generalized Stejskal-Tanner equation for non-uniform magnetic field gradients

The added value of diffusion tensor imaging with systematic bias correction for the assessment of liver morphology and physiology

Diffusion-weighted images of the human liver are prone to artifacts from bulk motions, poor SNR, non-uniformity of magnetic field gradients, and non-optimal choice of diffusion weightings. These factors markedly affect diffusion tensor imaging (DTI) metrics such as mean diffusivity (MD) and fractional anisotropy (FA). This work presents a simple preprocessing pipeline for enhanced magnetic field gradient non-uniformity calibration and analysis of the systematic bias removal attained in each correction step. Liver DTI scans were conducted in two isotropic phantoms and one healthy volunteer. Diffusion tensor was calculated for the original data and after denoising, B1 correction, rigid body registration, and magnetic field gradient non-uniformity correction applying the B-matrix spatial distribution (BSD) method and then, compared with the standard approach (sDTI). MD and FA were determined in three segments of the right lobe from DTI using four different combinations of b-values from the set 0, 400, and 800 s/mm2. Results showed that the proposed preprocessing and BSD methods have a significant impact on MD and FA values in off- and iso-centered isotropic phantoms. The applied corrections applied to the human liver resulted in a 11% change in MD and a - 64% change in FA. By manipulating the b-values used in the diffusion tensor calculation, DTI metrics that reflect only morphology or additional information about liver tissue physiology can be obtained. Accurate quantification of the human liver by diffusion requires appropriate preprocessing and carefully chosen b-value. Noise, B1 inhomogeneity, mis-registration, and non-uniform magnetic field gradients significantly change distributions of DTI metrics in isotropic phantoms and the human liver. Basic preprocessing and the B-matrix spatial distribution (BSD) method perform differently for off-center and isocenter locations. In the human liver, they removed systematic bias of FA and MD by up to -63% and 11%, respectively. Visible variability of FA and MD among b-value sets indicates the possibility of DTI sensitization to different liver compartments.

The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain

Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.

Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score

Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1-3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.

Restricted diffusion characteristics in oscillating gradient spin echo with mesoscopic phantom

In diffusion magnetic resonance imaging, oscillating gradient spin echo (OGSE) has an extremely short diffusion time if motion probing gradient (MPG) is applied to the waveform. Further, it can detect microstructural specificity. OGSE changes sensitivity to spin displacement velocity based on the MPG phase. The current study aimed to investigate the restricted diffusion characteristics of each OGSE waveform using the capillary phantom with various b-values, frequencies, and MPG phases. We performed OGSE (b-value = 300, 500, 800, 1200, 1600, and 2000 s/mm2) for the sine and cosine waveforms using the capillary phantom (6, 12, 25, 50, and 100 μm and free water) with a 9.4-T experimental magnetic resonance imaging system and a solenoid coil. We evaluated the axial and radial diffusivity (AD, RD) of each structure size. The output current of the MPG was assessed with an oscilloscope and analyzed with the gradient modulation power spectra by fast Fourier transform. In sine, the sidelobe spectrum was enhanced with increasing frequency, and the central spectrum slightly increased. The difference in RD was detected at 6 and 12 μm; however, it did not depend on the structure scale at 50 or 100 μm and free water. In cosine, the diffusion spectrum was enhanced, whereas the central spectrum decreased with increasing frequency. Both AD and RD in cosine had a frequency dependence, and AD and RD increased with a higher frequency regardless of structure size. AD and RD in either sine or cosine had no evident b-value dependence. We evaluated the OGSE-restricted diffusion characteristics. The measurements obtained diffusion information similar to the pulsed gradient spin echo. Hence, the cosine measurements indicated that a higher frequency could capture faster diffusion within the diffusion phenomena.

Assessment of Lumbosacral Nerve Roots in Patients with Type 2 Diabetic Peripheral Neuropathy Using Diffusion Tensor Imaging

BACKGROUND

Diffusion tensor imaging (DTI) has found clinical applications in the evaluation of the central nervous system and has been extensively used to image peripheral neuropathy. However, few studies have focused on lumbosacral nerve root fiber damage in diabetic peripheral neuropathy (DPN). The aim of the study was to evaluate whether DTI of the lumbosacral nerve roots can be used to detect DPN.

METHODS

Thirty-two type 2 diabetic patients with DPN and thirty healthy controls (HCs) were investigated with a 3T MRI scanner. DTI with tractography of the L4, L5, and S1 nerve roots was performed. Anatomical fusion with the axial T2 sequences was used to provide correlating anatomical information. Mean fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were measured from tractography images and compared between groups. Diagnostic value was assessed using receiver operating characteristic (ROC) analysis. The Pearson correlation coefficient was used to explore the correlation between DTI parameters and clinical data and the nerve conduction study (NCS) in the DPN group.

RESULTS

In the DPN group, FA was decreased (p < 0.001) and ADC was increased (p < 0.001) compared with the values of the HC group. FA displayed the best diagnostic accuracy, with an area under the ROC curve of 0.716. ADC was positively correlated with HbA1c level (r = 0.379, p = 0.024) in the DPN group.

CONCLUSIONS

DTI of lumbosacral nerve roots demonstrates appreciable diagnostic accuracy in patients with DPN.

The Value of Various Post-Processing Modalities of Diffusion Weighted Imaging in the Detection of Multiple Sclerosis

Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease’s progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.

Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient

BACKGROUND

Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter.

PURPOSE

To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure.

STUDY TYPE

Retrospective.

PHANTOM/POPULATION

Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC).

FIELD STRENGTH/SEQUENCE

A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence.

ASSESSMENT

ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles.

STATISTICAL TEST

Friedman tests and Wilcoxon tests.

RESULTS

The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20 × 10^-3  mm² /sec for both the proposed method and the theoretical GNL method vs. 2.31 × 10^-3  mm² /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P = 0.13).

DATA CONCLUSION

GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 1.

Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer

Background: The aim was to evaluate the feasibility of radiomics features based on diffusion-weighted imaging (DWI) at high b-values for grading bladder cancer and to compare the possible advantages of high-b-value DWI over the standard b-value DWI. Methods: Seventy-four participants with bladder cancer were included in this study. DWI sequences using a 3 T MRI with b-values of 1000, 1700, and 3000 s/mm2 were acquired, and the corresponding ADC maps were generated, followed with feature extraction. Patients were randomly divided into training and testing cohorts with a ratio of 8:2. The radiomics features acquired from the ADC1000, ADC1700, and ADC3000 maps were compared between low- and high-grade bladder cancers by using the Wilcox analysis, and only the radiomics features with significant differences were selected. The least absolute shrinkage and selection operator method and a logistic regression were performed for the feature selection and establishing the radiomics model. A receiver operating characteristic (ROC) analysis was conducted to assess the diagnostic performance of the radiomics models. Results: In the training cohorts, the AUCs of the ADC1000, ADC1700, and ADC3000 model for discriminating between low- from high-grade bladder cancer were 0.901, 0.920, and 0.901, respectively. In the testing cohorts, the AUCs of ADC1000, ADC1700, and ADC3000 were 0.582, 0.745, and 0.745, respectively. Conclusions: The radiomics features extracted from the ADC1700 maps could improve the diagnostic accuracy over those extracted from the conventional ADC1000 maps.

Anatomically Accurate, High-Resolution Modeling of the Human Index Finger Using In Vivo Magnetic Resonance Imaging

Anatomically accurate models of a human finger can be useful in simulating various disorders. In order to have potential clinical value, such models need to include a large number of tissue types, identified by an experienced professional, and should be versatile enough to be readily tailored to specific pathologies. Magnetic resonance images were acquired at ultrahigh magnetic field (7 T) with a radio-frequency coil specially designed for finger imaging. Segmentation was carried out under the supervision of an experienced radiologist to accurately capture various tissue types (TTs). The final segmented model of the human index finger had a spatial resolution of 0.2 mm and included 6,809,600 voxels. In total, 15 TTs were identified: subcutis, Pacinian corpuscle, nerve, vein, artery, tendon, collateral ligament, volar plate, pulley A4, bone, cartilage, synovial cavity, joint capsule, epidermis and dermis. The model was applied to the conditions of arthritic joint, ruptured tendon and variations in the geometry of a finger. High-resolution magnetic resonance images along with careful segmentation proved useful in the construction of an anatomically accurate model of the human index finger. An example illustrating the utility of the model in biomedical applications is shown. As the model includes a number of tissue types, it may present a solid foundation for future simulations of various musculoskeletal disease processes in human joints.

Utility of Diffusion and Magnetization Transfer MRI in Cervical Spondylotic Myelopathy: A Pilot Study

Diffusion tensor imaging (DTI) and magnetization transfer (MT) magnetic resonance imaging (MRI) can help detect spinal cord pathology, and tract-specific analysis of their parameters, such as fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), radial diffusivity (RD) and MT ratio (MTR), can give microstructural information. We performed the tract-based acquisition of MR parameters of three major motor tracts: the lateral corticospinal (CS), rubrospinal (RuS) tract, and lateral reticulospinal (RS) tract as well as two major sensory tracts, i.e., the fasciculus cuneatus (FC) and spinal lemniscus, to detect pathologic change and find correlations with clinical items. MR parameters were extracted for each tract at three levels: the most compressed lesion level and above and below the lesion. We compared the MR parameters of eight cervical spondylotic myelopathy patients and 12 normal controls and analyzed the correlation between clinical evaluation items and MR parameters in patients. RuS and lateral RS showed worse DTI parameters at the lesion level in patients compared to the controls. Worse DTI parameters in those tracts were correlated with weaker power grasp at the lesion level. FC and lateral CS showed a correlation between higher RD and lower FA and MTR with a weaker lateral pinch below the lesion level.

Aberrant Amplitude of Low-Frequency Fluctuation and Degree Centrality within the Default Mode Network in Patients with Vascular Mild Cognitive Impairment

This study aimed to investigate whole-brain spontaneous activities changes in patients with vascular mild cognitive impairment (VaMCI), and to evaluate the relationships between these brain alterations and their neuropsychological assessments. Thirty-one patients with VaMCI and thirty-one healthy controls (HCs) underwent structural MRI and resting-state functional MRI (rs-fMRI) and neuropsychological assessments. The functional alterations were determined by the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC). The gray matter volume (GMV) changes were analyzed using voxel-based morphometry (VBM). Linear regression analysis was used to evaluate the relationships between the structural and functional changes of brain regions and neuropsychological assessments. The VaMCI group had significantly lower scores in the Montreal Cognitive Assessment (MoCA), and higher scores on the Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD). Compared to the HCs, the VaMCI group exhibited GM atrophy in the right precentral gyrus (PreCG) and right inferior temporal gyrus (ITG). VaMCI patients further exhibited significantly decreased brain activity within the default mode network (DMN), including the bilateral precuneus (PCu), angular gyrus (AG), and medial frontal gyrus (medFG). Linear regression analysis revealed that the decreased ALFF was independently associated with lower MoCA scores, and the GM atrophy was independently associated with higher HAMD scores. The current finding suggested that aberrant spontaneous brain activity in the DMN might subserve as a potential biomarker of VaMCI, which may highlight the underlying mechanism of cognitive decline in cerebral small vessel disease.

Effective Connectivity during an Avoidance-Based Pavlovian-to-Instrumental Transfer Task

Pavlovian-to-instrumental transfer (PIT) refers to a phenomenon whereby a classically conditioned stimulus (CS) impacts the motivational salience of instrumental behavior. We examined behavioral response patterns and functional magnetic resonance imaging (fMRI) based effective connectivity during an avoidance-based PIT task. Eleven participants (8 females; Mage = 28.2, SD = 2.8, range = 25-32 years) completed the task. Effective connectivity between a priori brain regions engaged during the task was determined using hemodynamic response function group iterative multiple model estimation (HRF-GIMME). Participants exhibited behavior that was suggestive of specific PIT, a CS previously associated with a reinforcing outcome increased instrumental responding directed at the same outcome. We did not find evidence for general PIT; a CS did not significantly increase instrumental responding towards a different but related outcome. Using HRF-GIMME, we recovered effective connectivity maps among corticostriatal circuits engaged during the task. Group-level paths revealed directional effects from left putamen to right insula and from right putamen to right cingulate. Importantly, a direct effect of specific PIT stimuli on blood-oxygen-level-dependent (BOLD) activity in the left putamen was found. Results provide initial evidence of effective connectivity in key brain regions in an avoidance-based PIT task network. This study adds to the literature studying PIT effects in humans and employing GIMME models to understand how psychological phenomena are supported in the brain.

A 16-Channel Dipole Antenna Array for Human Head Magnetic Resonance Imaging at 10.5 Tesla

For ultra-high field and frequency (UHF) magnetic resonance imaging (MRI), the associated short wavelengths in biological tissues leads to penetration and homogeneity issues at 10.5 tesla (T) and require antenna transmit arrays for efficiently generated 447 MHz B₁⁺ fields (defined as the transmit radiofrequency (RF) magnetic field generated by RF coils). Previously, we evaluated a 16-channel combined loop + dipole antenna (LD) 10.5 T head array. While the LD array configuration did not achieve the desired B₁⁺ efficiency, it showed an improvement of the specific absorption rate (SAR) efficiency compared to the separate 8-channel loop and separate 8-channel dipole antenna arrays at 10.5 T. Here we compare a 16-channel dipole antenna array with a 16-channel LD array of the same dimensions to evaluate B₁⁺ efficiency, 10 g SAR, and SAR efficiency. The 16-channel dipole antenna array achieved a 24% increase in B₁⁺ efficiency in the electromagnetic simulation and MR experiment compared to the LD array, as measured in the central region of a phantom. Based on the simulation results with a human model, we estimate that a 16-channel dipole antenna array for human brain imaging can increase B₁⁺ efficiency by 15% with similar SAR efficiency compared to a 16-channel LD head array.

Formation and Evolution of Nanoscale Calcium Phosphate Precursors under Biomimetic Conditions

Simulated body fluids (SBFs) that mimic human blood plasma are widely used media for in vitro studies in an extensive array of research fields, from biomineralization to surface and corrosion sciences. We show that these solutions undergo dynamic nanoscopic conformational rearrangements on the timescale of minutes to hours, even though they are commonly considered stable or metastable. In particular, we find and characterize nanoscale inhomogeneities made of calcium phosphate (CaP) aggregates that emerge from homogeneous SBFs within a few hours and evolve into prenucleation species (PNS) that act as precursors in CaP crystallization processes. These ionic clusters consist of ∼2 nm large spherical building units that can aggregate into suprastructures with sizes of over 200 nm. We show that the residence times of phosphate ions in the PNS depend critically on the total PNS surface. These findings are particularly relevant for understanding nonclassical crystallization phenomena, in which PNS are assumed to act as building blocks for the final crystal structure.

Study on Hand–Eye Cordination Area with Bare-Hand Click Interaction in Virtual Reality

In virtual reality, users’ input and output interactions are carried out in a three-dimensional space, and bare-hand click interaction is one of the most common interaction methods. Apart from the limitations of the device, the movements of bare-hand click interaction in virtual reality involve head, eye, and hand movements. Consequently, clicking performance varies among locations in the binocular field of view. In this study, we explored the optimal interaction area of hand–eye coordination within the binocular field of view in a 3D virtual environment (VE), and implemented a bare-hand click experiment in a VE combining click performance data, namely, click accuracy and click duration, following a gradient descent method. The experimental results show that click performance is significantly influenced by the area where the target is located. The performance data and subjective preferences for clicks show a high degree of consistency. Combining reaction time and click accuracy, the optimal operating area for bare-hand clicking in virtual reality is from 20° to the left to 30° to the right horizontally and from 15° in the upward direction to 20° in the downward direction vertically. The results of this study have implications for guidelines and applications for bare-hand click interaction interface designs in the proximal space of virtual reality.

Effective Connectivity Analysis of Brain Activated Regions during Distracted Driving

This study aims to use functional magnetic resonance imaging (fMRI) to assess the effective connectivity between the regions of the brain activated when driving and performing a secondary task (addition task). The subjects used an MR-compatible driving simulator ㅊ to manipulate the driving wheel with both hands and control the pedals (accelerator and brake) with their right foot as if they were driving in an actual environment. Effective connectivity analysis was performed for three regions of the right and the left hemispheres with the highest z-scores, and six of the regions of the entire brain (right and left hemisphere) activated during driving by dynamic causal modeling (DCM). In the right hemisphere, a motor control pathway related to movement control for driving performance was discovered; in the left hemisphere, the pathways in the regions related to movement control for driving performance, starting with the region associated with the secondary task, were discovered. In the whole brain, connectivity was discovered in each of the right and left hemispheres. The motor network of declarative memory, which is the connectivity of the right thalamus, left lingual gyrus, and right precentral gyrus, was worth noting. These results seem meaningful, as they demonstrate the connectivity associated with the control of voluntary movement related to memory from human experience, although limited to driving tasks.

Diffusion as a Natural Contrast in MR Imaging of Peripheral Artery Disease (PAD) Tissue Changes. A Case Study of the Clinical Application of DTI for a Patient with Chronic Calf Muscles Ischemia

This paper reports a first application of diffusion tensor imaging with corrections by using the B-matrix spatial distribution method (BSD-DTI) for peripheral artery disease (PAD) detected in the changes of diffusion tensor parameters (DTPs). A 76-year-old male was diagnosed as having PAD, since he demonstrated in angiographic images of lower legs severe arterial stenosis and the presence of lateral and peripheral circulation and assigned to the double-blind RCT using mesenchymal stem cells (MSCs) or placebo for the regenerative treatment of implications of ischemic diseases. In order to indicate changes in diffusivity in calf muscles in comparison to a healthy control, a DTI methodology was developed. The main advantage of the applied protocol was decreased scanning time, which was achieved by reducing b-value and number of scans (to 1), while maintaining minimal number of diffusion gradient directions and high resolution. This was possible due to calibration via the BSD method, which reduced systematic errors and allowed quantitative analysis. In the course of PAD, diffusivities were elevated across the calf muscles in posterior compartment and lost their anisotropy. Different character was noticed for anterior compartment, in which diffusivities along and across muscles were decreased without a significant loss of anisotropy. After the intervention involving a series of injections, the improvement of DTPs and tractography was visible, but can be assigned neither to MSCs nor placebo before unblinding.

BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study

Brain–computer interface (BCI)-guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement. Fourteen chronic stroke subjects received 20 sessions of BCI-guided robot hand training. Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting-state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes, and electrophysical changes derived from the two neuroimaging modalities was also investigated. This work suggested that (a) significant motor function improvement could be obtained after BCI training therapy, (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI, (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change.

Influence of Acquisition Time on MR Image Quality Estimated with Nonparametric Measures Based on Texture Features

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index.

Assessment of the systematic errors caused by diffusion gradient inhomogeneity in DTI-computer simulations

Diffusion tensor imaging (DTI) is a powerful MRI modality that allows the investigation of the microstructure of tissues both in vivo and noninvasively. Its reliability is strictly dependent on the performance of diffusion-sensitizing gradients, of which spatial nonuniformity is a known issue in the case of virtually all clinical MRI scanners. The influence of diffusion gradient inhomogeneity on the accuracy of the diffusion tensor imaging was investigated by means of computer simulations supported by an MRI experiment performed at the isocenter and 15 cm away. The DTI measurements of two diffusion phantoms were simulated assuming a nonuniform diffusion-sensitizing gradient and various levels of noise. Thereafter, the tensors were calculated by two methods: (i) assuming a spatially constant b-matrix (standard DTI) and (ii) applying the b-matrix spatial distribution in the DTI (BSD-DTI) technique, a method of indicating the b-matrix for each voxel separately using an anisotropic phantom as a standard of diffusion. The average eigenvalues and fractional anisotropy across the homogeneous region of interest were calculated and compared with the expected values. Diffusion gradient inhomogeneity leads to overestimation of the largest eigenvalue, underestimation of the smallest one and thus overestimation of fractional anisotropy. The effect is similar to that caused by noise; however, it could not be corrected by increasing SNR. The MRI measurements, performed using a 3 T clinical scanner, revealed that the split of the eigenvalues measured 15 cm away from the isocenter is significant (up to 25%). The BSD-DTI calibration allowed the reduction of the measured fractional anisotropy of the isotropic medium from 0.174 to 0.031, suggesting that gradient inhomogeneity was the main cause of this error. For the phantom measured at the isocenter, however, the split was almost not observed; the average eigenvalues were shifted from the expected value by ~ 5%.