Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging

Menstrual cycle-driven hormone concentrations co-fluctuate with white and gray matter architecture changes across the whole brain

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.

Velocity-compensated intravoxel incoherent motion of the human calf muscle

PURPOSE

To determine whether intravoxel incoherent motion (IVIM) describes the blood perfusion in muscles better, assuming pseudo diffusion (Bihan Model 1) or ballistic motion (Bihan Model 2).

METHODS

IVIM parameters were measured in 18 healthy subjects with three different diffusion gradient time profiles (bipolar with two diffusion times and one with velocity compensation) and 17 b-values (0-600 s/mm2) at rest and after muscle activation. The diffusion coefficient, perfusion fraction, and pseudo-diffusion coefficient were estimated with a segmented fit in the gastrocnemius medialis (GM) and tibialis anterior (TA) muscles.

RESULTS

Velocity-compensated gradients resulted in a decreased perfusion fraction (6.9% ± 1.4% vs. 4.4% ± 1.3% in the GM after activation) and pseudo-diffusion coefficient (0.069 ± 0.046 mm2/s vs. 0.014 ± 0.006 in the GM after activation) compared to the bipolar gradients with the longer diffusion encoding time. Increased diffusion coefficients, perfusion fractions, and pseudo-diffusion coefficients were observed in the GM after activation for all gradient profiles. However, the increase was significantly smaller for the velocity-compensated gradients. A diffusion time dependence was found for the pseudo-diffusion coefficient in the activated muscle.

CONCLUSION

Velocity-compensated diffusion gradients significantly suppress the IVIM effect in the calf muscle, indicating that the ballistic limit is mostly reached, which is supported by the time dependence of the pseudo-diffusion coefficient.

Compensation of concomitant field effects in double diffusion encoding by means of added oscillating gradients

Maxwell or concomitant fields imprint additional phases on the transverse magnetization. This concomitant phase may cause severe image artifacts like signal voids or distort the quantitative parameters due to the induced intravoxel dephasing. In particular, double diffusion encoding (DDE) schemes with two pairs of bipolar diffusion-weighting gradients separated by a refocusing radiofrequency (RF) pulse are prone to concomitant field-induced artifacts. In this work, a method for reducing concomitant field effects in these DDE sequences based on additional oscillating gradients is presented. These oscillating gradient pulses obtained by constrained optimization were added to the original gradient waveforms. The modified sequences reduced the accumulated concomitant phase without significant changes in the original sequence characteristics. The proposed method was applied to a DDE acquisition scheme consisting of 60 pairs of diffusion wave vectors. For phantom as well as for in vivo experiments, a considerable increase in the signal-to-noise ratio (SNR) was obtained. For phantom measurements with a diffusion weighting of b = 2000 s/mm2 for each of the gradient pairs, an SNR increase of up to 40% was observed for a transversal slice that had a distance of 5 cm from the isocenter. For equivalent slice parameters, in vivo measurements in the brain of a healthy volunteer exhibited an increase in SNR of up to 35% for b = 750 s/mm2 for each weighting. These findings are supported by corresponding simulations, which also predict a positive effect on the SNR. In summary, the presented method leads to an SNR gain without additional RF refocusing pulses.

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs

PURPOSE

The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression.

METHODS

Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales.

RESULTS

Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings).

CONCLUSION

All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.

Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND

Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal.

PURPOSE

Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach.

STUDY TYPE

Retrospective.

STUDY POPULATION

A total of 104 patients, aged 24.1-86.4 years.

FIELD STRENGTH/SEQUENCE

A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm² , 750 s/mm² , and 1500 s/mm² . Dynamic contrast enhanced (DCE) sequence.

ASSESSMENT

Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches.

STATISTICAL TESTS

Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient.

RESULTS

DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in μm² /msec), 2.03 vs. 1.26 ( D K in μm² /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ).

DATA CONCLUSION

Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders

INTRODUCTION

Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water.

AIM

To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment.

METHOD

A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons".

RESULTS

Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue.

CONCLUSION

Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.

Nonparametric D-R1-R2 distribution MRI of the living human brain

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R₁ and D-R₂ correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R₂ or R₁ in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R₁-R₂ correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R₁-R₂ correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R₁-R₂ distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R₁-, and R₂-weightings based on the rich information contained in the D-R₁-R₂ distributions.

Magnetic Resonance Imaging Features under Deep Learning Algorithms in Evaluated Cerebral Protection of Craniotomy Evacuation of Hematoma under Propofol Anesthesia

This study aimed to explore the value of magnetic resonance imaging (MRI) features based on deep learning super-resolution algorithms in evaluating the value of propofol anesthesia for brain protection of patients undergoing craniotomy evacuation of the hematoma. An optimized super-resolution algorithm was obtained through the multiscale network reconstruction model based on the traditional algorithm. A total of 100 patients undergoing craniotomy evacuation of hematoma were recruited and rolled into sevoflurane control group and propofol experimental group. Both were evaluated using diffusion tensor imaging (DTI) images based on deep learning super-resolution algorithms. The results showed that the fractional anisotropic image (FA) value of the hind limb corticospinal tract of the affected side of the internal capsule of the experimental group after the operation was 0.67 ± 0.28. The National Institute of Health Stroke Scale (NIHSS) score was 6.14 ± 3.29. The oxygen saturation in jugular venous (SjvO₂) at T4 and T5 was 61.93 ± 6.58% and 59.38 ± 6.2%, respectively, and cerebral oxygen uptake rate (CO₂ER) was 31.12 ± 6.07% and 35.83 ± 7.91%, respectively. The difference in jugular venous oxygen (Da-jvO₂) at T3, T4, and T5 was 63.28 ± 10.15 mL/dL, 64.89 ± 13.11 mL/dL, and 66.03 ± 11.78 mL/dL, respectively. The neuron-specific enolase (NSE) and central-nerve-specific protein (S100β) levels at T5 were 53.85 ± 12.31 ng/mL and 7.49 ± 3.16 ng/mL, respectively. In terms of the number of postoperative complications, the patients in the experimental group were better than the control group under sevoflurane anesthesia, and the differences were substantial (P < 0.05). In conclusion, MRI images based on deep learning super-resolution algorithm have great clinical value in evaluating the degree of brain injury in patients anesthetized with propofol and the protective effect of propofol on brain nerves.

The role of diffusion tensor imaging in idiopathic sensorineural hearing loss: is it significant?

Purpose

To assess the role of diffusion tensor imaging metrics in the evaluation of the microstructural integrity of the central auditory tract in patients with idiopathic sensorineural hearing loss (SNHL), and to compare these patients with healthy controls.

Material and methods

This prospective study, which was conducted upon 30 subjects (21 males, 9 females; age range from 16 to 65 years, mean age 45years) with SNHL proven by audiometric tests. Ten age- and sex-matched healthy volunteers were included as a control group. Patients (n = 30) and volunteers (n = 10) underwent conventional magnetic resonance imaging and diffusion tensor imaging of the brain. Both fractional anisotropy and mean diffusivity (MD) of 3 points along the acoustic tract (inferior colliculus, lateral lemniscus and superior olivary nucleus) were measured bilaterally in all patients and correlated with controls.

Results

Mean fractional anisotropy (FA) values were reduced bilaterally at the superior olivary nucleus and/or lateral lemniscus and more significantly at the inferior colliculus of subjects with SNHL in comparison to the volunteers. In patients of unilateral SNHL, similar results were obtained in the contralateral side when compared to controls with statistically significant difference at the 3 regions (p = 0.001). No significant changes were noticed in the MD parameters either in patient or control groups.

Conclusions

The FA value was a valuable non-invasive biomarker in evaluating the subtle microstructural abnormalities of the central auditory tract in idiopathic SNHL and correlated well with hearing impairment.

Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver

PURPOSE

Intravoxel incoherent motion (IVIM) studies are performed with different acquisition protocols. Comparing them requires knowledge of echo time (TE) dependencies. The TE-dependence of the biexponential perfusion fraction f is well-documented, unlike that of its triexponential counterparts f₁ and f₂ and the biexponential and triexponential pseudodiffusion coefficients D^* , D 1 ∗ , and D 2 ∗ . The purpose was to investigate the TE-dependence of these parameters and to check whether the triexponential pseudodiffusion compartments are associated with arterial and venous blood.

METHODS

Fifteen healthy volunteers (19-58 y; mean: 24.7 y) underwent diffusion-weighted imaging of the abdomen with 24 b-values (0.2-800 s/mm² ) at TEs of 45, 60, 75, and 90 ms. Regions of interest (ROIs) were manually drawn in the liver. One set of bi- and triexponential IVIM parameters per volunteer and TE was determined. The TE-dependence was assessed with the Kruskal-Wallis test.

RESULTS

TE-dependence was observed for f (P < .001), f₁ (P = .001), and f₂ (P < .001). Their median values at the four measured TEs were: f: 0.198/0.240/0.274/0.359, f₁ : 0.113/0.139/0.146/0.205, f₂ : 0.115/0.155/0.182/0.194. D, D^* , D 1 ∗ , and D 2 ∗ showed no significant TE-dependence. Their values were: diffusion coefficient D (10^-4 mm² /s): 9.45/9.63/9.75/9.41, biexponential D^* (10^-2 mm² /s): 5.26/5.52/6.13/5.82, triexponential D 1 ∗ (10^-2 mm² /s): 1.73/2.91/2.25/2.51, triexponential D 2 ∗ (mm² /s): 0.478/1.385/0.616/0.846.

CONCLUSION

f₁ and f₂ show similar TE-dependence as f, ie, increase with rising TE; an effect that must be accounted for when comparing different studies. The diffusion and pseudodiffusion coefficients might be compared without TE correction. Because of the similar TE-dependence of f₁ and f₂ , the triexponential pseudodiffusion compartments are most probably not associated to venous and arterial blood.

Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding

Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.

Brain tissues have single-voxel signatures in multi-spectral MRI

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

Investigating cardiac stimulation limits of MRI gradient coils using electromagnetic and electrophysiological simulations in human and canine body models

Purpose:

Cardiac stimulation (CS) limits to gradient coil switching speed are difficult to measure in humans; instead, current regulatory guidelines (IEC 60601–2-33) are based on animal experiments and electric field–to-dB/dt conversion factors computed for a simple, homogeneous body model. We propose improvement to this methodology by using more detailed CS modeling based on realistic body models and electrophysiological models of excitable cardiac fibers.

Methods:

We compute electric fields induced by a solenoid, coplanar loops, and a commercial gradient coil in two human body models and a canine model. The canine simulations mimic previously published experiments. We generate realistic fiber topologies for the cardiac Purkinje and ventricular muscle fiber networks using rule-based algorithms, and evaluate CS thresholds using validated electrodynamic models of these fibers.

Results:

We were able to reproduce the average measured canine CS thresholds within 5%. In all simulations, the Purkinje fibers were stimulated before the ventricular fibers, and therefore set the effective CS threshold. For the investigated gradient coil, simulated CS thresholds for the x-, y-, and z-axis were at least one order of magnitude greater than the International Electrotechnical Commission limit.

Conclusion:

We demonstrate an approach to simulate gradient-induced CS using a combination of electromagnetic and electrophysiological modeling. Pending additional validation, these simulations could guide the assessment of CS limits to MRI gradient coil switching speed. Such an approach may lead to less conservative, but still safe, operation limits, enabling the use of the maximum gradient amplitude versus slew rate parameter space of recent, powerful gradient systems.