Theoretical model for water diffusion in tissues

Time-dependent diffusion magnetic resonance imaging for the analysis of parotid gland tumors

BackgroundDifferent parotid tumors differ in terms of treatment strategies due to their distinct biological behaviors. Time-dependent diffusion magnetic resonance imaging (td-dMRI) can characterize and quantify the cytological indexes, and then aid the differential diagnosis of various tumors. However, the value of td-dMRI in the analysis of parotid gland tumors remains unclear.PurposeTo investigate the value of quantitative parameters derived from td-dMRI in the analysis of parotid gland tumors.Material and MethodsIn total, 39 patients with parotid gland tumors were prospectively enrolled, including 24 patients with polymorphic adenomas (PAs), eight with Warthin's tumors (WTs), and seven with malignant tumors (MTs). Td-dMRI was performed for preoperative evaluation. Intracellular volume fraction (Vin), mean cell size (d), extracellular diffusion coefficient (Dex), and cellularity were obtained based on the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion model, and compared among the three groups. One-way ANOVA, Kruskal-Wallis test, and receiver operating characteristic (ROC) curve analysis were performed for further statistical analysis as appropriate.ResultsSignificant differences were found in all td-dMRI-derived indexes among PAs, WTs, and MTs (all P < 0.05). Vin was the sole parameter with significant differences for all sub-group comparisons (PAs vs. WTs, P < 0.001; PAs vs. MTs, P = 0.031; WTs vs. MTs, P = 0.047). With Vin values of 0.267, 0.231, and 0.260 as threshold, respectively, optimal performance levels were obtained for differentiating PAs from WTs (area under the ROC curve [AUC]=0.932, sensitivity=0.917, and specificity=0.875), PAs from MTs (AUC=0.744, sensitivity=0.833, and specificity=0.714), and WTs from MTs (AUC=0.750, sensitivity=0.875, and specificity=0.714).ConclusionMicrostructural parameters derived from td-dMRI, especially Vin, might be promising imaging biomarkers for characterizing parotid gland tumors.

When CEST meets diffusion: Multi-echo diffusion-encoded CEST (dCEST) MRI to measure intracellular and extracellular CEST signal distributions

PURPOSE

To develop a multi-echo, diffusion-encoded chemical exchange saturation transfer (dCEST) imaging technique for estimating the intracellular and extracellular/intravascular contributions to the conventional CEST signal.

METHODS

A dCEST pulse sequence was developed to quantify the signal fractions, transverse relaxation times (T2), and apparent diffusion coefficient (ADC) of the intracellular and extracellular/intravascular water compartments. dCEST images were acquired across a wide range of TE, b-values, RF saturation strengths, and frequency offsets. The data were analyzed using a two-compartment model with distinct diffusivities and T2 values. Intracellular and extracellular fractions of conventional water-saturation spectra (Z-spectra) and corresponding amide proton transfer (APT) signals were estimated from human brain scans of healthy volunteers at 3 T.

RESULTS

The multi-echo diffusion results showed that the intracellular water fractions were significantly higher than the extracellular water fractions, whereas the intracellular T2 values were shorter than those of the extracellular/intravascular compartments. The ADC for the intracellular compartment was significantly lower than that of the extracellular compartment. The dCEST analysis showed that the average intracellular and extracellular fractions of the Z-spectra were 85 ± 7% and 15 ± 4%, respectively. The overall intracellular APT-weighted values were higher than the total (i.e., intracellular + extracellular) APT-weighted values.

CONCLUSIONS

The dCEST imaging technique provides valuable insight into the source of signals in conventional CEST MRI, offering potential utility for clinical applications.

Imaging Microstructural Parameters of Breast Tumor in Patient Using Time-Dependent Diffusion: A Feasibility Study

Objectives: To explore the feasibility of time-dependent diffusion in clinical applications of breast MRI, as well as the capacity of quantitative microstructural mapping for characterizing the cellular properties in malignant and benign breast tumors. Methods: 38 patients with 45 lesions were enrolled. Diffusion MRI acquisition was conducted with a combination of pulsed gradient spin-echo sequences (PGSE) and oscillating gradient spin-echo (OGSE) on a 3T MRI scanner. The microstructural parameters including cellularity extracellular diffusivity (Dex), mean cell size, intracellular volume fraction (νin), and the apparent diffusion coefficient (ADC) values were calculated. Each parameter was compared using the unpaired t-test between malignant and benign tumors. The area under the receiver operating characteristic curve (AUC) values was used to evaluate the diagnostic performance of different indices. Results: The mean diameter, Dex, ADC0Hz, ADC25Hz, and ADC50Hz were significantly lower in the malignant group than in the benign group (p < 0.001), while νin and cellularity were significantly higher in the malignant group (p < 0.001). All the microstructural parameters and time-dependent ADC values achieved high accuracy in differentiating between malignant and benign tumors of the breast. For microstructural parameters, the AUC of the cellularity was greater than others (AUC = 0.936). In an immunohistochemical subgroup comparison, the PR-positive group had significantly lower νin and cellularity, and significantly elevated Dex and ADC0Hz compared to the negative groups (p < 0.05). When combining diffusion parameters (cellularity, diameter, and ADC25Hz), the highest diagnostic performance was obtained with an AUC of 0.969. Conclusions: DWI with a short diffusion time is capable of providing additional microstructural parameters in differentiating between benign and malignant breast tumors. The time-dependent diffusion MRI parameters have the potential to serve as a non-invasive tool to probe the differences in the internal structures of breast lesions.

Microstructural brain changes following prospective memory rehabilitation in traumatic brain injury: An observational study

Prospective memory (PM) impairment is a common consequence of moderate-severe traumatic brain injury (TBI). Compensatory strategy training and rehabilitation (COMP) is the usual treatment of PM deficits through environmental modification and the use of assistive methods such as diaries and routines. The study intends to examine the changes in white matter integrity, as measured by advanced diffusion magnetic resonance imaging (dMRI) following COMP intervention in moderate-severe TBI patients. Nine COMP intervention and twelve routine care comparison cohort moderate-severe TBI patients were recruited from level 1 trauma centres in the Brisbane metropolitan area. Both groups were imaged at least one-month post-TBI for a baseline scan. COMP group was imaged again after a 6-week COMP intervention program and the comparison group was imaged again at least 6 weeks after the baseline scan. MRI scan included structural imaging and dMRI, which the latter fitted for the Neurite Orientation Dispersion and Density Imaging (NODDI) model. Only the comparison group had decreased Neurite Density Index in the major white matter tracts and increased isotropic diffusion in the fluid space between the cortical folds. Our results indicated that COMP intervention slowed down the neural degeneration in moderate-severe TBI patients as compared to routine medical care/rehabilitation.

Tuned exchange imaging: Can the filter exchange imaging pulse sequence be adapted for applications with thin slices and restricted diffusion?

Filter exchange imaging (FEXI) is a double diffusion-encoding (DDE) sequence that is specifically sensitive to exchange between sites with different apparent diffusivities. FEXI uses a diffusion-encoding filtering block followed by a detection block at varying mixing times to map the exchange rate. Long mixing times enhance the sensitivity to exchange, but they pose challenges for imaging applications that require a stimulated echo sequence with crusher gradients. Thin imaging slices require strong crushers, which can introduce significant diffusion weighting and bias exchange rate estimates. Here, we treat the crushers as an additional encoding block and consider FEXI as a triple diffusion-encoding sequence. This allows the bias to be corrected in the case of multi-Gaussian diffusion, but not easily in the presence of restricted diffusion. Our approach addresses challenges in the presence of restricted diffusion and relies on the ability to independently gauge sensitivities to exchange and restricted diffusion for arbitrary gradient waveforms. It follows two principles: (i) the effects of crushers are included in the forward model using signal cumulant expansion; and (ii) timing parameters of diffusion gradients in filter and detection blocks are adjusted to maintain the same level of restriction encoding regardless of the mixing time. This results in the tuned exchange imaging (TEXI) protocol. The accuracy of exchange mapping with TEXI was assessed through Monte Carlo simulations in spheres of identical sizes and gamma-distributed sizes, and in parallel hexagonally packed cylinders. The simulations demonstrate that TEXI provides consistent exchange rates regardless of slice thickness and restriction size, even with strong crushers. However, the accuracy depends on b-values, mixing times, and restriction geometry. The constraints and limitations of TEXI are discussed, including suggestions for protocol adaptations. Further studies are needed to optimize the precision of TEXI and assess the approach experimentally in realistic, heterogeneous substrates.

Apparent diffusion coefficient in intrahepatic cholangiocarcinoma diffusion-weighted magnetic resonance imaging noninvasively predicts Ki-67 expression

AIM

Tumor Ki-67 expression reflects prognosis and cancer grade, and biopsy-based preoperative assessment of Ki-67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki-67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki-67 expression in intrahepatic cholangiocarcinoma (ICC).

METHOD

We retrospectively analyzed 39 cases of ICC confirmed by surgical pathology. All patients had undergone magnetic resonance imaging, and ADC values (mean, minimum, and maximum) were calculated. Ki-67 expression was assessed by immunohistochemistry, and patients were divided into groups of high (n = 18) and low (n = 21) Ki-67 expression. To assess the diagnostic performance of the ADC values for Ki-67 expression, we used the receiver operating characteristic curve and compared the areas under the curve (AUC).

RESULTS

The mean and minimum ADC values were significantly lower in the group with high Ki-67 expression. For predicting high Ki-67 expression, the AUC values were 0.701 for mean ADC, 0.818 for minimum ADC, and 0.571 for maximum ADC. The diagnostic sensitivity and specificity of the minimum ADC values were 88.9% and 76.2%, respectively. In addition, with ADC values combined, the AUC increased to 0.831. Apparent diffusion coefficient is a useful predictor of Ki-67 expression level in ICC.

CONCLUSION

Apparent diffusion coefficient values, especially minimum ADC values, can noninvasively predict ICC associated with high Ki-67 expression.

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

BACKGROUND

Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second-line diagnostic method after ultrasound for the assessment of uterine masses.

OBJECTIVES

To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.

SEARCH STRATEGY

A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023.

SELECTION CRITERIA

All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.

DATA COLLECTION AND ANALYSIS

Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).

RESULTS

Eight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84-0.94), specificity of 0.96 (95% CI 0.96-0.97), positive likelihood ratio of 13.55 (95% CI 6.20-29.61), negative likelihood ratio of 0.08 (95% CI 0.02-0.32), diagnostic odds ratio of 175.13 (95% CI 46.53-659.09), and area under the curve of 0.9759.

CONCLUSIONS

MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

A cross-comparative analysis of in vivo versus ex vivo MRI indices in a mouse model of concussion

BACKGROUND

We present a cross-sectional, case-matched, and pair-wise comparison of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) measures in vivo and ex vivo in a mouse model of concussion, thus aiming to establish the concordance of structural and diffusion imaging findings in living brain and after fixation.

METHODS

We allocated 28 male mice aged 3-4 months to sham injury and concussion (CON) groups. CON mice had received a single concussive impact on day 0 and underwent MRI at day 2 (n = 9) or 7 (n = 10) post-impact, and sham control mice likewise underwent imaging at day 2 (n = 5) or 7 (n = 4). Immediately after the final scanning, we collected the perfusion-fixed brains, which were stored for imaging ex vivo 6-12 months later. We then compared the structural imaging, DTI, and NODDI results between different methods.

RESULTS

In vivo to ex vivo structural and DTI/NODDI findings were in notably poor agreement regarding the effects of concussion on structural integrity of the brain.

COMPARISON WITH EXISTING METHODS

ex vivo imaging was frequently done to study the effects of diseases and treatments, but our results showed that ex vivo and in vivo imaging can detect completely opposite and contradictory results. This is also the first study that compares in vivo and ex vivo NODDI.

CONCLUSION

Our findings call for caution in extrapolating translational capabilities obtained ex vivo to physiological measurements in vivo. The divergent findings may reflect fixation artefacts and the contribution of the glymphatic system changes.

Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations

Aquaporins provide a unique approach for imaging genetic activity in deep tissues by increasing the rate of cellular water diffusion, which generates a magnetic resonance contrast. However, distinguishing aquaporin signals from the tissue background is challenging because water diffusion is influenced by structural factors, such as cell size and packing density. Here, we developed a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on subtracting signals at two diffusion times can improve specificity by unambiguously isolating aquaporin signals from the tissue background. We further used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin and established a mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. The quantitative framework developed in this study will enable a broad range of applications in biomedical synthetic biology, requiring the use of aquaporins to noninvasively monitor the location and function of genetically engineered devices in live animals.

Realistic numerical simulations of diffusion tensor cardiovascular magnetic resonance: The effects of perfusion and membrane permeability

PURPOSE

To study the sensitivity of diffusion tensor cardiovascular magnetic resonance (DT-CMR) to microvascular perfusion and changes in cell permeability.

METHODS

Monte Carlo (MC) random walk simulations in the myocardium have been performed to simulate self-diffusion of water molecules in histology-based media with varying extracellular volume fraction (ECV) and permeable membranes. The effect of microvascular perfusion on simulations of the DT-CMR signal has been incorporated by adding the contribution of particles traveling through an anisotropic capillary network to the diffusion signal. The simulations have been performed considering three pulse sequences with clinical gradient strengths: monopolar stimulated echo acquisition mode (STEAM), monopolar pulsed-gradient spin echo (PGSE), and second-order motion-compensated spin echo (MCSE).

RESULTS

Reducing ECV intensifies the diffusion restriction and incorporating membrane permeability reduces the anisotropy of the diffusion tensor. Widening the intercapillary velocity distribution results in increased measured diffusion along the cardiomyocytes long axis when the capillary networks are anisotropic. Perfusion amplifies the mean diffusivity for STEAM while the opposite is observed for short diffusion encoding time sequences (PGSE and MCSE).

CONCLUSION

The effect of perfusion on the measured diffusion tensor is reduced using an increased reference b-value. Our results pave the way for characterization of the response of DT-CMR to microstructural changes underlying cardiac pathology and highlight the higher sensitivity of STEAM to permeability and microvascular circulation due to its longer diffusion encoding time.

Monte Carlo simulation of water diffusion through cardiac tissue models

Monte Carlo diffusion simulations are commonly used to establish a reliable ground truth of tissue microstructure, including for the validation of diffusion-weighted MRI. However, selecting simulation parameters is challenging and affects validity and reproducibility. We conducted experiments to investigate critical conditions in Monte Carlo simulations, such as tissue representation complexity, simulated molecules, update duration, and compartment size. Results show significant changes in microstructure characteristics when parameters are altered, emphasizing the importance of careful control for a reliable ground truth.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

Hippocampal demyelination is associated with increased magnetic susceptibility in a mouse model of concussion

Structural and functional deficits in the hippocampus are a prominent feature of moderate-severe traumatic brain injury (TBI). In this work, we investigated the potential of Quantitative Susceptibility Imaging (QSM) to reveal the temporal changes in myelin integrity in a mouse model of concussion (mild TBI). We employed a cross-sectional design wherein we assigned 43 mice to cohorts undergoing either a concussive impact or a sham procedure, with QSM imaging at day 2, 7, or 14 post-injury, followed by Luxol Fast Blue (LFB) myelin staining to assess the structural integrity of hippocampal white matter (WM). We assessed spatial learning in the mice using the Active Place Avoidance Test (APA), recording their ability to use visual cues to locate and avoid zone-dependent mild electrical shocks. QSM and LFB staining indicated changes in the stratum lacunosum-molecular layer of the hippocampus in the concussion groups, suggesting impairment of this key relay between the entorhinal cortex and the CA1 regions. These imaging and histology findings were consistent with demyelination, namely increased magnetic susceptibility to MR imaging and decreased LFB staining. In the APA test, sham animals showed fewer entries into the shock zone compared to the concussed cohort. Thus, we present radiological, histological, and behavioral findings that concussion can induce significant and alterations in hippocampal integrity and function that evolve over time after the injury.

Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations

Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.

Coregistered histology sections with diffusion tensor imaging data at 200 µm resolution in meningioma tumors

A significant problem in diffusion MRI (dMRI) is the lack of understanding regarding which microstructural features account for the variability in the diffusion tensor imaging (DTI) parameters observed in meningioma tumors. A common assumption is that mean diffusivity (MD) and fractional anisotropy (FA) from DTI are inversely proportional to cell density and proportional to tissue anisotropy, respectively. Although these associations have been established across a wide range of tumors, they have been challenged for interpreting within-tumor variations where several additional microstructural features have been suggested as contributing to MD and FA. To facilitate the investigation of the biological underpinnings of DTI parameters, we performed ex-vivo DTI at 200 µm isotropic resolution on sixteen excised meningioma tumor samples. The samples exhibit a variety of microstructural features because the dataset includes meningiomas of six different meningioma types and two different grades. Diffusion-weighted signal (DWI) maps, DWI maps averaged over all directions for given b-value, signal intensities without diffusion encoding (S₀) as well as DTI parameters: MD, FA, in-plane FA (FA_IP), axial diffusivity (AD) and radial diffusivity (RD), were coregistered to Hematoxylin & Eosin- (H&E) and Elastica van Gieson-stained (EVG) histological sections by a non-linear landmark-based approach. Here, we provide DWI signal and DTI maps coregistered to histology sections and describe the pipeline for processing the raw DTI data and the coregistration. The raw, processed, and coregistered data are hosted by Analytic Imaging Diagnostics Arena (AIDA) data hub registry, and software tools for processing are provided via GitHub. We hope that data can be used in research and education concerning the link between the meningioma microstructure and parameters obtained by DTI.

Diffusion time dependency of extracellular diffusion

PURPOSE

To quantify the variations of the power-law dependences on diffusion time t or gradient frequencyf $$ f $$ of extracellular water diffusion measured by diffusion MRI (dMRI).

METHODS

Model cellular systems containing only extracellular water were used to investigate thet / f $$ t/f $$ dependence ofD ex $$ {D}_{ex} $$ , the extracellular diffusion coefficient. Computer simulations used a randomly packed tissue model with realistic intracellular volume fractions and cell sizes. DMRI measurements were performed on samples consisting of liposomes containing heavy water(D2 O, deuterium oxide) dispersed in regular water (H2 O).D ex $$ {D}_{ex} $$ was obtained over a broadt $$ t $$ range (∼1-1000 ms) and then fit power-law equationsD ex ( t ) = D const + const · t - ϑ t $$ {D}_{ex}(t)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {t}^{-{\vartheta}_t} $$ andD ex ( f ) = D const + const · f ϑ f $$ {D}_{ex}(f)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {f}^{\vartheta_f} $$ .

RESULTS

Both simulated and experimental results suggest that no single power-law adequately describes the behavior ofD ex $$ {D}_{ex} $$ over the range of diffusion times of most interest in practical dMRI. Previous theoretical predictions are accurate over only limitedt $$ t $$ ranges; for example,θ t = θ f = - 1 2 $$ {\theta}_t={\theta}_f=-\frac{1}{2} $$ is valid only for short times, whereasθ t = 1 $$ {\theta}_t=1 $$ orθ f = 3 2 $$ {\theta}_f=\frac{3}{2} $$ is valid only for long times but cannot describe other ranges simultaneously. For the specifict $$ t $$ range of 5-70 ms used in typical human dMRI measurements,θ t = θ f = 1 $$ {\theta}_t={\theta}_f=1 $$ matches the data well empirically.

CONCLUSION

The optimal power-law fit of extracellular diffusion varies with diffusion time. The dependency obtained at short or longt $$ t $$ limits cannot be applied to typical dMRI measurements in human cancer or liver. It is essential to determine the appropriate diffusion time range when modeling extracellular diffusion in dMRI-based quantitative microstructural imaging.

CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI)

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

Association of sub-acute changes in plasma amino acid levels with long-term brain pathologies in a rat model of moderate-severe traumatic brain injury

Introduction

Traumatic brain injury (TBI) induces a cascade of cellular alterations that are responsible for evolving secondary brain injuries. Changes in brain structure and function after TBI may occur in concert with dysbiosis and altered amino acid fermentation in the gut. Therefore, we hypothesized that subacute plasma amino acid levels could predict long-term microstructural outcomes as quantified using neurite orientation dispersion and density imaging (NODDI).

Methods

Fourteen 8-10-week-old male rats were randomly assigned either to sham (n = 6) or a single moderate-severe TBI (n = 8) procedure targeting the primary somatosensory cortex. Venous blood samples were collected at days one, three, seven, and 60 post-procedure and NODDI imaging were carried out at day 60. Principal Component Regression analysis was used to identify time dependent plasma amino acid concentrations after in the subacute phase post-injury that predicted NODDI metric outcomes at day 60.

Results

The TBI group had significantly increased plasma levels of glutamine, arginine, alanine, proline, tyrosine, valine, isoleucine, leucine, and phenylalanine at days three-seven post-injury. Higher levels of several neuroprotective amino acids, especially the branched-chain amino acids (valine, isoleucine, leucine) and phenylalanine, as well as serine, arginine, and asparagine at days three-seven post-injury were also associated with lower isotropic diffusion volume fraction measures in the ventricles and thus lesser ventricular dilation at day 60.

Discussion

In the first such study, we examined the relationship between the long-term post-TBI microstructural outcomes across whole brain and the subacute changes in plasma amino acid concentrations. At days three to seven post-injury, we observed that increased plasma levels of several amino acids, particularly the branched-chain amino acids and phenylalanine, were associated with lesser degrees of ventriculomegaly and hydrocephalus TBI neuropathology at day 60 post-injury. The results imply that altered amino acid fermentation in the gut may mediate neuroprotection in the aftermath of TBI.

Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

BACKGROUND

Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level.

PURPOSE

To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters.

MATERIALS AND METHODS

We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FA_IP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FA_IP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R²_OS) on the intra-tumor level and within-sample R² across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FA_IP, respectively.

RESULTS

Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R²_OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FA_IP (median R²_OS = 0.31, 0.20-0.42). Samples with low R²_OS for FA_IP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R² = 0.60) and FA_IP (R² = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FA_IP is high in the presence of elongated and aligned cell structures, but low otherwise.

CONCLUSION

Cell density and structure anisotropy account for variability in MD and FA_IP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

Breast Magnetic Resonance Imaging Can Predict Ki67 Discordance Between Core Needle Biopsy and Surgical Samples

BACKGROUND

Disagreement in assessments of Ki67 expression based on core-needle biopsy and matched surgical samples complicates decisions in the treatment of breast cancer.

PURPOSE

To examine whether preoperative breast MRI could be useful in predicting Ki67 discordance between core-needle biopsy and surgical samples.

STUDY TYPE

Retrospective.

POPULATION

Three hundred and sixty-five breast cancer patients with MRI scans and having both core-needle biopsy and surgical samples from 2017 to 2019.

FIELD STRENGTH/SEQUENCE

3.0 T, T2-weighted iterative decomposition of water and fat with echo asymmetry and least squares estimation sequence, diffusion-weighted sequence using b-values 0/1000, dynamic contrast enhanced image by volume imaged breast assessme NT.

ASSESSMENT

We collected clinicopathologic variables and preoperative MRI features (tumor size, lesion type, shape of mass, spiculated margin, internal enhancement, peri-tumoral edema, intra-tumoral necrosis, multifocal/multicentric, apparent diffusion coefficient [ADC] minimum, ADC mean, ADC maximum, ADC difference).

STATISTICAL TESTS

K-means clustering, multivariable logistic regression, receiver operating characteristic curve.

RESULTS

Sixty-one patients showed Ki67 discordance and 304 patients show Ki67 concordance according to our definition using K-means clustering. Multivariable regression analysis showed that the following parameters were independently associated with Ki67 discordance: peri-tumoral edema, odds ratio (OR) 2.662, 95% confidence interval (CI) 1.432-4.948; ADCmin ≤ 0.829 × 10^-3  mm² /sec, OR 2.180, 95% CI 1.075-4.418; and ADCdiff > 0.317 × 10^-3  mm² /sec, OR 3.365, 95% CI 1.698-6.669. This multivariable model resulted in an AUC of 0.758 (95% CI 0.711-0.802) with sensitivity and specificity being 0.803 and 0.621, respectively.

CONCLUSION

Presence of peri-tumoral edema, smaller ADCmin and greater ADCdiff in preoperative breast MRI may indicate high risk of Ki67 discordance between core-needle biopsy and surgical samples. For patients with these MRI-based risk factors, clinicians should not rely on Ki67 assessment only from core-needle biopsy.

Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange

Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.

Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy

Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.

A model to visualize the fate of iron after intracranial hemorrhage using isotopic tracers and elemental bioimaging

Hemoglobin-iron is a red-blood-cell toxin contributing to secondary brain injury after intracranial bleeding. We present a model to visualize an intracerebral hematoma and secondary hemoglobin-iron distribution by detecting 58Fe-labeled hemoglobin (Hb) with laser ablation-inductively coupled plasma-mass spectrometry on mouse brain cryosections after stereotactic whole blood injection for different time periods. The generation of 58Fe-enriched blood and decisive steps in the acute hemorrhage formation and evolution was evaluated. The model allows to visualize and quantify 58Fe with high spatial resolution and striking signal-to-noise ratio. Script-based evaluation of the delocalization-depth revealed ongoing 58Fe delocalization in the brain even six days after hematoma induction. Collectively, the model can quantify the distribution of Hb-derived iron post-bleeding, providing a methodological framework to study the pathophysiological basis of cell-free Hb toxicity in hemorrhagic stroke.

Random walk diffusion simulations in semi-permeable layered media with varying diffusivity

In this paper we present random walk based solutions to diffusion in semi-permeable layered media with varying diffusivity. We propose a novel transit model for solving the interaction of random walkers with a membrane. This hybrid model is based on treating the membrane permeability and the step change in diffusion coefficient as two interactions separated by an infinitesimally small layer. By conducting an extensive analytical flux analysis, the performance of our hybrid model is compared with a commonly used membrane transit model (reference model). Numerical simulations demonstrate the limitations of the reference model in dealing with step changes in diffusivity and show the capability of the hybrid model to overcome this limitation and to offer substantial gains in computational efficiency. The suitability of both random walk transit models for the application to simulations of diffusion tensor cardiovascular magnetic resonance (DT-CMR) imaging is assessed in a histology-based domain relevant to DT-CMR. In order to demonstrate the usefulness of the new hybrid model for other possible applications, we also consider a larger range of permeabilities beyond those commonly found in biological tissues.

Subacute cytokine changes after a traumatic brain injury predict chronic brain microstructural alterations on advanced diffusion imaging in the male rat

INTRODUCTION

The process of neuroinflammation occurring after traumatic brain injury (TBI) has received significant attention as a potential prognostic indicator and interventional target to improve patients' outcomes. Indeed, many of the secondary consequences of TBI have been attributed to neuroinflammation and peripheral inflammatory changes. However, inflammatory biomarkers in blood have not yet emerged as a clinical tool for diagnosis of TBI and predicting outcome. The controlled cortical impact model of TBI in the rodent gives reliable readouts of the dynamics of post-TBI neuroinflammation. We now extend this model to include a panel of plasma cytokine biomarkers measured at different time points post-injury, to test the hypothesis that these markers can predict brain microstructural outcome as quantified by advanced diffusion-weighted magnetic resonance imaging (MRI).

METHODS

Fourteen 8-10-week-old male rats were randomly assigned to sham surgery (n = 6) and TBI (n = 8) treatment with a single moderate-severe controlled cortical impact. We collected blood samples for cytokine analysis at days 1, 3, 7, and 60 post-surgery, and carried out standard structural and advanced diffusion-weighted MRI at day 60. We then utilized principal component regression to build an equation predicting different aspects of microstructural changes from the plasma inflammatory marker concentrations measured at different time points.

RESULTS

The TBI group had elevated plasma levels of IL-1β and several neuroprotective cytokines and chemokines (IL-7, CCL3, and GM-CSF) compared to the sham group from days 3 to 60 post-injury. The plasma marker panels obtained at day 7 were significantly associated with the outcome at day 60 of the trans-hemispheric cortical map transfer process that is a frequent finding in unilateral TBI models.

DISCUSSION

These results confirm and extend prior studies showing that day 7 post-injury is a critical temporal window for the reorganisation process following TBI. High plasma level of IL-1β and low plasma levels of the neuroprotective IL-7, CCL3, and GM-CSF of TBI animals at day 60 were associated with greater TBI pathology.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum

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

D* from diffusion MRI reveals a correspondence between ventricular cerebrospinal fluid volume and flow in the ischemic rodent model

Quantitative measurement of cerebrospinal fluid (CSF) flow and volume and longitudinal monitoring of CSF dynamics provide insights into the compensatory characteristics of post-stroke CSF. In this study, we compared the MRI pseudo-diffusion index (D*) of live and sacrificed rat brains to confirm the effect of ventricular CSF flow on diffusion signals. We observed the relationship between the CSF peak velocities and D* through Monte Carlo (MC) simulations to further understand the source of D* contrast. We also determined the dominant CSF flow using D* in three directions. Finally, we investigated the dynamic evolutions of ventricular CSF flow and volume in a stroke rat model (n = 8) from preoperative to up to 45 days after surgery and determined the correlation between ventricular CSF volume and flow. MC simulations showed a strong positive correlation between the CSF peak velocity and D* (r = 0.99). The dominant CSF flow variations in the 3D ventricle could be measured using the maximum D* map. A longitudinal positive correlation between ventricular CSF volume and D* was observed in the lateral (r = 0.74) and ventral-third (r = 0.81) ventricles, respectively. The directional D* measurements provide quantitative CSF volume and flow information, which would provide useful insights into ischemic stroke with diffusion MRI.

DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnet_DTI) and for neurite orientation dispersion and density imaging (DIFFnet_NODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.

Objectives

This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients.

Method

The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.

Results

The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07).

Conclusions

ADC values cannot be used to discriminate between molecular subtypes of BC.

Quantitative magnetic resonance imaging of chronic kidney disease: an experimental in vivo study using rat chronic kidney disease models

BACKGROUND

Recent advances in magnetic resonance imaging (MRI) may allow it to be an alternative emerging tool for the non-invasive evaluation of renal parenchymal disease.

PURPOSE

To validate the usefulness of quantitative multiparametric MRI protocols and suggest the suitable quantitative MR sequence protocol to evaluate parenchymal fibrosis using an animal model of chronic kidney disease (CKD) by long-term adenine intake.

MATERIAL AND METHODS

In this prospective animal study, 16 male Wistar rats were analyzed and categorized into three groups. Rats in the CKD groups underwent 0.25% adenine administration for three or six weeks. Quantitative MRI protocols, including diffusion-weighted imaging (DWI), T1ρ (T1 rho), and T2* mapping were performed using a 9.4-T animal MR scanner. A semi-quantitative histopathologic analysis for renal fibrosis was conducted. Quantitative MR values measured from anatomic regions of kidneys underwent intergroup comparative analyses.

RESULTS

The apparent diffusion coefficient (ADC) and T1 (T1 rho) values were significantly increased in all CKD groups. Values measured from the cortex and outer medulla showed significant intergroup differences. Total ADC values tended to increase according to periods, and T1ρ values increased in three weeks and decreased in six weeks.

CONCLUSION

Quantitative MRI protocols could be a non-invasive assessment modality in the diagnosis and evaluation of CKD. Particularly, T1ρ may be a suitable MR sequence to quantitatively assess renal parenchymal fibrosis.

The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations

PURPOSE

Diffusion-weighted imaging (DWI) may aid accurate tumor grading. Decreased diffusivity and increased diffusion heterogeneity measures have been observed in high-grade gliomas using the non-monoexponential models for DWI. However, DWI measures concerning tissue characteristics in terms of pathophysiological and structural changes are yet to be established. Thus, this study aims to investigate the relationship between the diffusion measurements and microstructural changes in the presence of high-grade gliomas using a three-dimensional Monte Carlo simulation with systematic changes of microstructural parameters.

METHODS

Water diffusion was simulated in a microenvironment along with changes associated with the presence of high-grade gliomas, including increases in cell density, nuclear volume, extracellular volume (VF_ex), and extracellular tortuosity (λ_ex), and changes in membrane permeability (P_mem). DWI signals were simulated using a pulsed gradient spin-echo sequence. The sequence parameters, including the maximum gradient strength and diffusion time, were set to be comparable to those of clinical scanners and advanced human MRI systems. The DWI signals were fitted using the gamma distribution and diffusional kurtosis models with b-values up to 6000 and 2500 s/mm², respectively.

RESULTS

The diffusivity measures (apparent diffusion coefficients (ADC), D_gamma of the gamma distribution model and D_app of the diffusional kurtosis model) decreased with increases in cell density and λ_ex, and a decrease in P_mem. These diffusivity measures increased with increases in nuclear volume and VF_ex. The diffusion heterogeneity measures (σ_gamma of the gamma distribution model and K_app of the diffusional kurtosis model) increased with increases in cell density or nuclear volume at the low P_mem, and a decrease in P_mem. Increased σ_gamma was also associated with an increase in VF_ex.

CONCLUSION

Among simulated microstructural changes, only increases in cell density at low P_mem or decreases in P_mem corresponded to both the decreased diffusivity and increased diffusion heterogeneity measures. The results suggest that increases in cell density at low P_mem or decreases in P_mem may be associated with the diffusion changes observed in high-grade gliomas.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT

Simple Summary

Although diffusion-weighted imaging (DWI) can be valuable for differential diagnosis of lung cancer from benign pulmonary nodules and masses (PNMs), the diagnostic capability may not be perfect. This study’s purpose was to compare the diagnostic efficacy of 18-fluoro-2-deoxy-glucose positron emission tomography–computed tomography (FDG-PET/CT) and magnetic resonance imaging (MRI) of DWI and T2-weighted imaging (T2WI) in PNMs. There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The sensitivity of the maximum standardized uptake value (SUVmax) was significantly lower than that of the apparent diffusion coefficient (ADC) and the T2 contrast ratio (T2 CR). The accuracy of SUVmax was significantly lower than that of ADC and that of T2 CR. The sensitivity and accuracy of MRI were significantly higher than those of FDG-PET/CT. MRI can replace FDG-PET/CT for differential diagnosis of PNMs.

Abstract

The purpose of this retrospective study was to compare the diagnostic efficacy of FDG-PET/CT and MRI in discriminating malignant from benign pulmonary nodules and masses (PNMs). There were 278 lung cancers and 50 benign PNMs that were examined by FDG-PET/CT and MRI. The T2 contrast ratio (T2 CR) was designated as the ratio of T2 signal intensity of PNM divided by T2 signal intensity of the rhomboid muscle. The optimal cut-off values (OCVs) for differential diagnosis were 3.605 for maximum standardized uptake value (SUVmax), 1.459 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and 2.46 for T2 CR. Areas under the receiver operating characteristics curves were 67.5% for SUVmax, 74.3% for ADC, and 72.4% for T2 CR, respectively. The sensitivity (0.658) of SUVmax was significantly lower than that (0.838) of ADC (p < 0.001) and that (0.871) of T2 CR (p < 0.001). The specificity (0.620) of SUVmax was that the same as (0.640) ADC and (0.640) of T2 CR. The accuracy (0.652) of SUVmax was significantly lower than that (0.808) of ADC (p < 0.001) and that (0.835) of T2 CR (p < 0.001). The sensitivity and accuracy of DWI and T2WI in MRI were significantly higher than those of FDG-PET/CT. Ultimately, MRI can replace FDG PET/CT for differential diagnosis of PNMs saving healthcare systems money while not sacrificing the quality of care.

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

PURPOSE

Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting.

METHODS

We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data.

RESULTS

When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations.

CONCLUSION

This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.

Assessment of the effects of mimicking tissue microstructural properties on apparent diffusion coefficient and apparent exchange rate in diffusion MRI via a series of specially designed phantoms

PURPOSE

Diffusion MRI provides a valuable tool for imaging tissue microstructure. However, due to the lack of related experimental methods and specially designed phantoms, no experimental study has been conducted yet to quantitatively assess the effects of membrane permeability, intracellular volume fraction (IVF), and intracellular diffusivity on the apparent diffusion coefficient (ADC) obtained from diffusion weighted imaging (DWI), and the effects of membrane permeability on the apparent exchange rate (AXR) obtained from filter exchange imaging (FEXI).

METHODS

A series of phantoms with three adjustable parameters was designed to mimic tissue microstructural properties including membrane permeability, IVF, and intracellular diffusivity. Quantitative experiments were conducted to assess the effects of these properties on ADC and AXR. DWI scans were performed to obtain axial and radial ADC values. FEXI scans were performed to obtain AXR values.

RESULTS

Axial ADC values range from 1.148 μm² /ms to 2.157 μm² /ms, and radial ADC values range from 0.904 μm² /ms to 2.067 μm² /ms. Radial ADC decreased with a decrease in fiber permeability. Decreased axial and radial ADC values with increased intra-fiber volume fraction, and increased polyvinylpyrrolidone (PVP) concentration of the intra-fiber space were observed. AXR values range from 2.1 s^-1 to 4.9 s^-1 . AXR increases with fiber permeability.

CONCLUSION

The proposed phantoms can quantitatively evaluate the effects of mimicking tissue microstructural properties on ADC and AXR. This new phantom design provides a potential method for further understanding the biophysical mechanisms underlying the change in ADC and diffusion exchange.

Ultrahigh-b diffusion-weighted imaging for quantitative evaluation of myelination in shiverer mouse spinal cord

PURPOSE

To perform a quantitative evaluation of myelination on WT and myelin-deficient (shiverer) mouse spinal cords using ultrahigh-b diffusion-weighted imaging (UHb-DWI).

METHODS

UHb-DWI of ex vivo on spinal cord specimens of two shiverer (C3HeB/FeJ-shiverer, homozygous genotype for MbP_shi ) and six WT (Black Six, C3HeB/FeJ) mice were acquired using 3D multishot diffusion-weighted stimulated-echo EPI, a homemade RF coil, and a small-bore 7T MRI system. Imaging was performed in transaxial plane with 75 × 75 μm² in-plane resolution, 1-mm-slice thickness, and radial DWI using b_max = 42,890 s/mm² . Histological evaluation was performed on upper thoracic sections using optical and transmission electron microscopy. Numerical Monte Carlo simulations (MCSs) of water diffusion were performed to facilitate interpretation of UHb-DWI signal-b curves.

RESULTS

The white matter ultrahigh-b radial DWI (UHb-rDWI) signal-b curves of WT mouse cords behaved biexponentially with high-b diffusion coefficient D_H < 0.020 × 10^-3 mm² /s. However, as expected with less myelination, the signal-b of shiverer mouse cords behaved monoexponentially with significantly greater D_H = 0.162 × 10^-3 , 0.142 × 10^-3 , and 0.164 × 10^-3 mm² /s at anterodorsal, posterodorsal, and lateral columns, respectively. The axial DWI signals of all mouse cords behaved monoexponentially with D = (0.718-1.124) × 10^-3 mm² /s. MCS suggests that these elevated D_H are mainly induced by increased water exchange at the myelin sheath. Microscopic results were consistent with the UHb-rDWI findings.

CONCLUSION

UHb-DWI provides quantitative differences in myelination of spinal cords from myelin-deficit shiverer and WT mice. UHb-DWI may become a powerful tool to evaluate myelination in demyelinating disease models that may translate to human diseases, including multiple sclerosis.

SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI

The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called 'b-tensor' encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.

Multi-modal magnetic resonance imaging in a mouse model of concussion

This data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3-4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal's backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.

Novel Insights of T2-Weighted Imaging: Significance for Discriminating Lung Cancer from Benign Pulmonary Nodules and Masses

Diffusion-weighted imaging is useful for discriminating lung cancer from benign pulmonary nodules and masses (BPNMs), however the diagnostic capability is not perfect. The aim of this research was to clarify whether T2-weighted imaging (T2WI) is efficient in discriminating lung cancer from BPNMs, especially from pulmonary abscesses. A T2 contrast ratio (T2 CR) for a pulmonary nodule is defined as the ratio of T2 signal intensity of a pulmonary nodule divided by the T2 signal intensity of the rhomboid muscle. There were 52 lung cancers and 40 inflammatory BPNMs (mycobacteria disease 12, pneumonia 13, pulmonary abscess 9, other 6) and seven non-inflammatory BPNMs. The T2 CR (2.14 ± 0.63) of lung cancers was significantly lower than that (2.68 ± 1.04) of BPNMs (p = 0.0021). The T2 CR of lung cancers was significantly lower than that (2.93 ± 0.26) of pulmonary abscesses (p = 0.011). When the optical cutoff value of T2 CR was set as 2.44, the sensitivity was 0.827 (43/52), the specificity 0.596 (28/47), the accuracy 0.717 (71/99), the positive predictive value 0.694 (43/62), and the negative predictive value 0.757 (28/37). T2 CR of T2WI is useful in discriminating lung cancer from BPNMs. Pulmonary abscesses, which show strong restricted diffusion in DWI, can be differentiated from lung cancers using T2WI.

How to Discriminate Lung Cancer From Benign Pulmonary Nodules and Masses? Usefulness of Diffusion-Weighted Magnetic Resonance Imaging With Apparent Diffusion Coefficient and Inside/Wall Apparent Diffusion Coefficient Ratio

Background:

Although diffusion-weighted imaging (DWI) is useful for differential diagnosis between lung cancers and benign pulmonary nodules and masses (BPNMs), it is difficult to differentiate pulmonary abscesses from lung cancers because pulmonary abscesses show restricted diffusion. With this research we will present how to assess the total apparent diffusion coefficient (ADC) and inside/wall ADC ratio for these pulmonary nodules and masses (PNMs).

Methods:

The pulmonary lesions were divided into next 3 groups. There were 40 lung cancers, 41 inflammatory benign PNMs (mycobacteria disease 13, pneumonia 12, pulmonary abscess 10, other 6) and 7 noninflammatory benign PNMs. Definitions were as follows: wall ADC = ADC value in outer one-third of the lesion; inside ADC = ADC value in central two-thirds of the lesion: inside/wall ADC ratio = ratio of inside ADC/wall ADC.

Results:

Mean total ADC (1.26 ± 0.32 × 10⁻³ mm²/s) of the lung cancers was remarkably lower than that (1.53 ± 0.53) of the BPNMs. The mean total ADC values were 1.26 ± 0.32 in lung cancer, 1.45 ± 0.47 in inflammatory BPNM and 2.04 ± 0.63 in noninflammatory BPNM, and there were significant differences among them. The mean inside ADC value (1.33 ± 0.32) of the lung cancers was remarkably higher than that (0.94 ± 0.42) of the pulmonary abscesses. The mean inside/wall ADC ratio (1.20 ± 0.28) of the lung cancers was remarkably higher than that (0.74 ± 0.14) of the pulmonary abscesses.

Conclusions:

Although ADC of DWI could differentiate lung cancer from BPNM, the inside/wall ADC ratio of DWI is efficient for differentiation between lung cancer and lung abscess. The inside/wall ADC ratio of DWI strengthens a weak point of DWI.

Cell-permeable lanthanide-platinum(IV) anti-cancer prodrugs

Platinum compounds are a vital part of our anti-cancer arsenal, and determining the location and speciation of platinum compounds is crucial. We have synthesised a lanthanide complex bearing a salicylic group (Ln = Gd, Eu) which demonstrates excellent cellular accumulation and minimal cytotoxicity. Derivatisation enabled access to bimetallic lanthanide-platinum(ii) and lanthanide-platinum(iv) complexes. Luminescence from the europium-platinum(iv) system was quenched, and reduction to platinum(ii) with ascorbic acid resulted in a "switch-on" luminescence enhancement. We used diffusion-based 1H NMR spectroscopic methods to quantify cellular accumulation. The gadolinium-platinum(ii) and gadolinium-platinum(iv) complexes demonstrated appreciable cytotoxicity. A longer delay following incubation before cytotoxicity was observed for the gadolinium-platinum(iv) compared to the gadolinium-platinum(ii) complex. Functionalisation with octanoate ligands resulted in enhanced cellular accumulation and an even greater latency in cytotoxicity.

Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts

In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.

A computational method for the covariance matrix associated with extracellular diffusivity on disordered models of cylindrical brain axons

This study developed a method to approximate the covariance matrix associated with the simulation of water molecular diffusion inside the brain tissue. The computation implements the Discontinuous Galerkin method of the diffusion equation. A physically consistent numerical flux is applied to model the interaction between the axon walls and extracellular regions. This numerical flux yields an efficient GPU-CUDA implementation. We consider the two-dimensional case of high axon pack density, valid, for instance, in the brain's corpus callosum region.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Simple Summary

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10⁻³ mm²/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Combination Assessment of Diffusion-Weighted Imaging and T2-Weighted Imaging Is Acceptable for the Differential Diagnosis of Lung Cancer from Benign Pulmonary Nodules and Masses

Simple Summary

The purpose of this study is to determine whether the combination assessment of DWI and T2WI improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). As using the OCV (1.470 × 10⁻³ mm²/s) for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV (2.45) for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Abstract

The purpose of this study is to determine whether the combination assessment of DWI and T2-weighted imaging (T2WI) improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). The optimal cut-off value (OCV) for differential diagnosis was set at 1.470 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and at 2.45 for T2 contrast ratio (T2 CR). The ADC (1.24 ± 0.29 × 10⁻³ mm²/s) of lung cancer was significantly lower than that (1.69 ± 0.58 × 10⁻³ mm²/s) of BPNM. The T2 CR (2.01 ± 0.52) of lung cancer was significantly lower than that (2.74 ± 1.02) of BPNM. As using the OCV for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Improved gradient waveforms for oscillating gradient spin-echo (OGSE) diffusion tensor imaging

The dependence of the diffusion tensor on frequency is of great interest in studies of tissue microstructure because it reveals restrictions to the Brownian motion of water molecules caused by cell membranes. Oscillating gradient spin-echo (OGSE) sequences can sample this dependence with gradient shapes for which the power spectrum of the zeroth moment is focused at a target frequency. In order to maintain the total spectral power (ie the b-value), oscillating gradient amplitudes must grow with the frequency squared. For this reason, OGSE applications on clinical MRI scanners are limited to low frequencies, for which it is difficult to obtain a narrow frequency bandwidth of the gradient moment in a useful echo time. In particular, the commonly used pair of single-period trapezoidal-cosine pulses separated by a half-period produces significant side lobes away from the target frequency. To mitigate this effect, improved OGSE waveforms are proposed, which reduce the gap between the two gradient pulses to the minimum duration required for the refocusing RF pulse. Additionally, a slight deviation from the periodicity of the waveforms is proposed in order to permit using the maximum slew rate of the gradient system for all lobes of trapezoidal waveforms while maintaining advantageous spectral properties, which is not the case for the currently used OGSE sequences. Numerical calculations validate these changes, showing that both modifications significantly narrow the gradient moment power spectrum and increase the contribution of its main lobe to the b-value, thus improving the specificity of the measurement. The utility of the new shapes is demonstrated by diffusion tensor measurements of human white matter in vivo over the range of 30-75 Hz with a b-value of nearly 1000 s/mm² , using a high-performance gradient insert. However, the improvement should increase the sampling precision of OGSE experiments for all gradient systems.

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

BACKGROUND

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease.

NEW METHOD

This is the second review on the topic of g-ratio mapping using MRI.

RESULTS

This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches.

COMPARISON WITH EXISTING METHODS

Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology.

CONCLUSIONS

We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.

A roadmap of brain recovery in a mouse model of concussion: insights from neuroimaging

Concussion or mild traumatic brain injury is the most common form of traumatic brain injury with potentially long-term consequences. Current objective diagnosis and treatment options are limited to clinical assessment, cognitive rest, and symptom management, which raises the real danger of concussed patients being released back into activities where subsequent and cumulative injuries may cause disproportionate damages. This study conducted a cross-sectional multi-modal examination investigation of the temporal changes in behavioural and brain changes in a mouse model of concussion using magnetic resonance imaging. Sham and concussed mice were assessed at day 2, day 7, and day 14 post-sham or injury procedures following a single concussion event for motor deficits, psychological symptoms with open field assessment, T2-weighted structural imaging, diffusion tensor imaging (DTI), neurite orientation density dispersion imaging (NODDI), stimulus-evoked and resting-state functional magnetic resonance imaging (fMRI). Overall, a mismatch in the temporal onsets and durations of the behavioural symptoms and structural/functional changes in the brain was seen. Deficits in behaviour persisted until day 7 post-concussion but recovered at day 14 post-concussion. DTI and NODDI changes were most extensive at day 7 and persisted in some regions at day 14 post-concussion. A persistent increase in connectivity was seen at day 2 and day 14 on rsfMRI. Stimulus-invoked fMRI detected increased cortical activation at day 7 and 14 post-concussion. Our results demonstrate the capabilities of advanced MRI in detecting the effects of a single concussive impact in the brain, and highlight a mismatch in the onset and temporal evolution of behaviour, structure, and function after a concussion. These results have significant translational impact in developing methods for the detection of human concussion and the time course of brain recovery.

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.