Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Consistency of muscle fibers directionality in human thigh derived from diffusion-weighted MRI

Objective.Diffusion-weighted MR imaging (DW-MRI) is known to quantify muscle fiber directionality and thus may be useful for radiotherapy target definition in sarcomas. Here, we investigate the variability of tissue anisotropy derived from diffusion tensor (DT) in the human thigh to establish the baseline parameters and protocols for DW-MRI acquisition for future studies in sarcoma patients.Approach.We recruited ten healthy volunteers to acquire diffusion-weighted MR images of the left and right thigh. DW-MRI data were used to reconstruct DT eigenvectors within each individual thigh muscle. Deviations of the principal eigenvector from its mean were calculated for different experimental conditions.Main results.Within the majority of muscles in most subjects, the mode of the histogram of the angular deviation of the principal eigenvector of the water DT from its muscle-averaged value did not exceed 20°. On average for all subjects, the mode ranged from 15° to 24°. Deviations much larger than 20° were observed in muscles far from the RF coil, including cases with significant amounts of subcutaneous fat and muscle deformation under its own weight.Significance.Our study is a robust characterization of angular deviations of muscle fiber directionality in the thigh as determined by DW-MRI. We show that an appropriate choice of experimental conditions reduces the variability of the observed directionality. Precise determination of tissue directionality will enable reproducible models of microscopic tumor spread, with future application in defining the clinical target volume for soft tissue sarcoma.

Correlations between DKI and DWI with Ki-67 in gastric adenocarcinoma

BACKGROUND

Diffusion kurtosis imaging (DKI) has been applied for gastric adenocarcinoma. Correlations between its parameters and Ki-67 are unclear.

PURPOSE

To investigate the correlation between DKI and diffusion-weighted imaging (DWI) parameters with the Ki-67 index in gastric adenocarcinoma.

MATERIAL AND METHODS

A total of 54 patients with gastric adenocarcinoma were enrolled in the study and underwent DWI and DKI at 3.0-T MRI before surgery. Based on the settings of the regions of interest, the DWI and DKI parameters (including apparent diffusion coefficient [ADC], diffusion kurtosis [K], and diffusion coefficient [DK]) of each patient's gastric adenocarcinoma were measured and calculated. The participants were divided into two groups (low Ki-67 group and high Ki-67 groups). The intraclass correlation coefficient (ICC) and independent-sample t-test were used to compare differences in each parameter between two groups. Spearman's correlation coefficient was calculated to determine the correlation between Ki-67 and the parameters. Each parameter was compared using the area under the receiver operating characteristic curve. All parameters were included in the multivariate logistic regression analysis to explore the relationship between each parameter and high Ki-67 index.

RESULTS

ADC and DK were negatively relevant with Ki-67 and K was positively relevant with Ki-67 in gastric adenocarcinoma. ADC, DK, and K had diagnostic efficiency in differentiating the low Ki-67 group from the high Ki-67 group. A higher K value independently predicted a high Ki-67 status.

CONCLUSION

DWI and DKI reflected the proliferative characteristics of gastric adenocarcinoma. K was the strongest independent factor for predicting high Ki-67 status.

MP-PCA denoising for diffusion MRS data: promises and pitfalls

Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.

THE ROLE OF THE APPARENT DIFFUSION COEFFICIENT OF THE BIPARAMETRIC MRI AS AN IMAGING MARKER OF PROSTATE CANCER

Prostate cancer (PCa) is the most common malignancy in men. The role of the apparent diffusion coefficient (ADC)of biparametric MRI (biMRI) which is a study without the use of dynamic contrast enhancement (DCE), in detectionof PCa is still not comprehensively investigated.

OBJECTIVE

The goal of the study was to assess the role of ADC of biMRI as an imaging marker of clinically significant PCaMaterials and methods. The study involved 78 men suspected of having PCa. All patients underwent a comprehensive clinical examination, which included multiparametric MRI of the prostate, a component of which was biMRI. TheMRI data was evaluated according to the PIRADS system version 2.1.

RESULTS

The distribution of patients according to the PIRADS system was as follows: 1 point - 9 (11.54 %)patients, 2 points - 12 (15.38 %) patients, 3 points - 25 (32.05 %) patients, 4 points - 19 (24.36 %) patients and5 points - 13 (16.67 %) patients. In a subgroup of patients with 5 points, clinically significant PCa was detected in 100 % of cases. In the subgroup of patients with tumors of 4 points clinically significant PCa was diagnosed in 16of 19 (84.21 %) cases, and in 3 (15.79 %) patients - clinically insignificant tumor. In the subgroup of patients with3 points, clinically significant PCa was diagnosed in 11 of 25 (44.0 %) cases, in 8 (32.0 %) patients - clinicallyinsignificant tumor and in 6 (24.0 %) patients - benign prostatic hyperplasia. PCa with a score of 7 on the Gleasonscale showed significantly lower mean values of ADC of the diffusion weighted MRI images compared to tumors witha score of < 7 on the Gleason scale: (0.86 ± 0.07) x 103 mm2/s vs (1.08 ± 0.04) x 103 mm2/s (р < 0.05).

CONCLUSIONS

The obtained results testify to the high informativeness of biMRI in the diagnosis of prostate cancer.The use of ADC allowed to differentiate clinically significant and insignificant variants of the tumor, as well asbenign changes in prostate tissues and can be considered as a potential imaging marker of PCa.

The variability of MR axon radii estimates in the human white matter

The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.

Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile

Purpose

Glioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.

Methods

Pre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.

Results

Grade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.

Conclusions

ADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data

Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson-Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b-matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b-values in contrast to the perhaps common assumption that only high b-value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable.

Harmonization of diffusion MRI data sets with adaptive dictionary learning

Diffusion magnetic resonance imaging can indirectly infer the microstructure of tissues and provide metrics subject to normal variability in a population. Potentially abnormal values may yield essential information to support analysis of controls and patients cohorts, but subtle confounds could be mistaken for purely biologically driven variations amongst subjects. In this work, we propose a new harmonization algorithm based on adaptive dictionary learning to mitigate the unwanted variability caused by different scanner hardware while preserving the natural biological variability of the data. Our harmonization algorithm does not require paired training data sets, nor spatial registration or matching spatial resolution. Overcomplete dictionaries are learned iteratively from all data sets at the same time with an adaptive regularization criterion, removing variability attributable to the scanners in the process. The obtained mapping is applied directly in the native space of each subject toward a scanner-space. The method is evaluated with a public database which consists of two different protocols acquired on three different scanners. Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner. Experiments with alterations using a free water compartment, which is not simulated in the training data, shows that the modifications applied to the diffusion weighted images are preserved in the diffusion metrics after harmonization, while still reducing global variability at the same time. The algorithm could help multicenter studies pooling their data by removing scanner specific confounds, and increase statistical power in the process.

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

Automated classification of depression from structural brain measures across two independent community-based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.

Diagnostic accuracy of contrast-enhanced diffusion-weighted MRI for liver metastases of pancreatic cancer: towards adequate staging and follow-up of pancreatic cancer - DIA-PANC study: study protocol for an international, multicenter, diagnostic trial

BACKGROUND

At the time of surgery, approximately 10-20% of the patients with pancreatic cancer are considered unresectable because of unexpected liver metastasis, peritoneal carcinomatosis or locally advanced disease. This leads to futile surgical treatment with all the associated morbidity, mortality and costs. More than 50% of all liver metastases develop in the first six months postoperatively. These (subcentimeter) liver metastases are most likely already present at the time of diagnosis and have not been identified pre-operatively, due to the poor sensitivity of routine preoperative contrast-enhanced CT (CECT).

METHODS

The DIA-PANC study is a prospective, international, multicenter, diagnostic cohort study investigating diffusion-weighted, contrast-enhanced MRI for the detection of liver metastases in patients with all stages of pancreatic cancer. Indeterminate or malignant liver lesions on MRI will be further investigated histopathologically. For patients with suspected liver lesions without histopathological proof, follow up imaging with paired CT and MRI at 3-, 6- and 12-months will serve as an alternative reference standard.

DISCUSSION

The DIA-PANC trial is expected to report high-level evidence of the diagnostic accuracy of MRI for the detection of liver metastases, resulting in significant value for clinical decision making, guideline development and improved stratification for treatment strategies and future trials. Furthermore, DIA-PANC will contribute to our knowledge of liver metastases regarding incidence, imaging characteristics, their number and extent, and their change in time with or without treatment. It will enhance the worldwide implementation of MRI and consequently improve personalized treatment of patients with suspected pancreatic ductal adenocarcinoma.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03469726 . Registered on March 19th 2018 - Retrospectively registered.

Evaluation of diffusion-weighted magnetic resonance imaging at 3.0 Tesla for differentiation between intracranial neoplastic and noninfectious inflammatory lesions in dogs

OBJECTIVE

To evaluate the utility of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values obtained by diffusion-weighted MRI (DWI) at 3.0 T for differentiating intracranial neoplastic lesions from noninfectious inflammatory lesions (NIILs) in dogs.

ANIMALS

54 dogs that met inclusion criteria (ie, had a histologically confirmed intracranial lesion and DWI of the brain performed) with 5 lesion types: meningioma [n = 18], glioma [14], metastatic hemangiosarcoma [3], other metastatic neoplasms [5], and NIIL [14]).

PROCEDURES

Two observers, who were blinded to the histologic diagnoses, independently determined the mean ADC and FA values for each evaluated intracranial lesion on the basis of 3 circular regions of interest on DWI images. Findings were compared among the 5 lesion types, between all neoplasms combined and NIILs, and between the 5 legion types and previously determined values for corresponding locations for neurologically normal dogs.

RESULTS

The mean ADC and FA values did not differ significantly among the 5 lesion types or between all neoplasms combined and NIILs. However, 35% (14/40) of the neoplastic lesions had an ADC value ≥ 1.443 × 10^-3 mm²/s, whereas all NIILs had ADC values < 1.443 × 10^-3 mm²/s. Meningiomas and NIILs had FA values that were significantly lower than those for neurologically normal dogs.

CONCLUSIONS AND CLINICAL RELEVANCE

In this population of dogs, the FA values for meningiomas and NIILs differed significantly from those previously reported for neurologically normal dogs. In addition, an ADC cutoff value of 1.443 × 10^-3 mm²/s appeared to be highly specific for diagnosing neoplastic lesions (vs NIILs), although the sensitivity and accuracy were low.

Diffusion-Weighted Magnetic Resonance Imaging as a Noninvasive Parameter for Differentiating Benign and Malignant Intraperitoneal Collections

Background and Objective: The imaging differentiation of benign from malignant intraperitoneal collections (IPCs) relies on the tumoral morphological modifications of the peritoneum, which are not always advocating for malignancy. We aimed to assess ascitic fluid with the apparent diffusion coefficient (ADC) to determine non-invasive, stand-alone, differentiation criteria for benign and malignant intraperitoneal effusions. Materials and Methods: Sixty-one patients with known IPCs who underwent magnetic resonance examinations for reasons such as tumor staging, undetermined abdominal mass and disease follow up were retrospectively included in this study. All subjects had a final diagnosis of the fluid based on pathological examinations, which were divided into benign (n = 37) and malignant (n = 24) IPCs groups. ADC values were measured separately by two radiologists, and the average values were used for comparing the two groups by consuming the independent samples t-test. The receiver operating characteristic analysis was performed to test the ADC values' diagnostic ability to distinguish malignant from benign collections. Results: The differentiation between benign and malignant IPCs based on ADC values was statistically significant (p = 0.0034). The mean ADC values were higher for the benign (3.543 × 10^-3 mm²/s) than for the malignant group (3.057 × 10^-3 mm²/s). The optimum ADC cutoff point for the diagnosis of malignant ascites was <3.241 × 10^-3 mm²/s, with a sensitivity of 77.78% and a specificity of 80%. Conclusions: ADC represents a noninvasive and reproducible imaging parameter that may help to assess intraperitoneal collections. Although successful in distinguishing malignant from benign IPCs, further research must be conducted in order to certify if the difference in ADC values is a consequence of the physical characteristics of the ascitic fluids or their appurtenance to a certain histopathological group.

A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study

Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB ​= ​2) dMRI protocol. The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 ¹⁹F NMR probes. The time variation of 0^th, 1^st and 2^nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps. ¹⁹F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps. In conclusion, we demonstrate that up-to-2^nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes

BACKGROUND

Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients.

METHODS

Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists.

RESULTS

Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model.

CONCLUSION

IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

Adaptive phase correction of diffusion-weighted images

Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.

Complex diffusion-weighted image estimation via matrix recovery under general noise models

We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.

Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.

Retrieving neuronal orientations using 3D scanning SAXS and comparison with diffusion MRI

While diffusion MRI (dMRI) is currently the method of choice to non-invasively probe tissue microstructure and study structural connectivity in the brain, its spatial resolution is limited and its results need structural validation. Current ex vivo methods employed to provide 3D fiber orientations have limitations, including tissue-distorting sample preparation, small field of view or inability to quantify 3D fiber orientation distributions. 3D fiber orientation in tissue sections can be obtained from 3D scanning small-angle X-ray scattering (3D sSAXS) by analyzing the anisotropy of scattering signals. Here we adapt the 3D sSAXS method for use in brain tissue, exploiting the high sensitivity of the SAXS signal to the ordered molecular structure of myelin. We extend the characterization of anisotropy from vectors to tensors, employ the Funk-Radon-Transform for converting scattering information to real space fiber orientations, and demonstrate the feasibility of the method in thin sections of mouse brain with minimal sample preparation. We obtain a second rank tensor representing the fiber orientation distribution function (fODF) for every voxel, thereby generating fODF maps. Finally, we illustrate the potential of 3D sSAXS by comparing the result with diffusion MRI fiber orientations in the same mouse brain. We show a remarkably good correspondence, considering the orthogonality of the two methods, i.e. the different physical processes underlying the two signals. 3D sSAXS can serve as validation method for microstructural MRI, and can provide novel microstructural insights for the nervous system, given the method's orthogonality to dMRI, high sensitivity to myelin sheath's orientation and abundance, and the possibility to extract myelin-specific signal and to perform micrometer-resolution scanning.

Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.

Systematic review with network meta-analysis: Diagnostic values of ultrasonography, computed tomography, and magnetic resonance imaging in patients with ischemic stroke

BACKGROUND AND OBJECTIVE

Ischemic stroke is a foremost cause for disability and death worldwide. This study is conducted in order to compare the diagnostic values between transcranial Doppler ultrasound (ultrasonography), computed tomography (CT), and magnetic resonance imaging (MRI) in patients suffering from ischemic stroke by performing a network meta-analysis.

METHODS

We made use of Cochrane Library, PubMed, and Embase in order to obtain literature and papers. The combination analysis of both direct and indirect evidence in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy was conducted so as to assess the odds ratios (ORs) and surface under the cumulative ranking curve (SUCRA) values of the seven different imaging methods. These imaging techniques include ultrasonography, computed tomography (traditional CT, computed tomography angiography [CTA], computed tomography perfusion [CTP]), and MRI (traditional MRI, diffusion-weighted imaging [DWI], magnetic resonance angiography), in order to properly diagnose ischemic stroke patients.

RESULTS

Thirteen eligible diagnostic trials were enrolled into this network meta-analysis. The results of the traditional meta-analysis showed that among CT methods, CTP showed higher sensitivity, NPV, and accuracy; among MRI methods, DWI had relatively higher sensitivity, NPV, and accuracy. The results of network meta-analysis showed that DWI had relatively higher sensitivity, NPV, and accuracy when compared with traditional CT, CTA, magnetic resonance angiography and traditional MRI. CTP showed higher SUCRA among CT methods while DWI showed higher SUCRA among MRI methods. A cluster analysis revealed that DWI had the highest diagnostic value in terms of sensitivity, PPV, NPV, and accuracy amongst the aforementioned seven imaging techniques.

CONCLUSION

This network meta-analysis provides supporting evidence to the idea that DWI has a higher diagnostic value regarding ischemic stroke among MRI methods, and CTP has a poor diagnostic value among CT methods, which provide therapeutic considerations for Ischemic stroke intervention.

Mono, bi- and tri-exponential diffusion MRI modelling for renal solid masses and comparison with histopathological findings

PURPOSE

To compare diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and tri-exponential models of the diffusion magnetic resonance imaging (MRI) signal for the characterization of renal lesions in relationship to histopathological findings.

METHODS

Sixteen patients planned to undergo nephrectomy for kidney tumour were scanned before surgery at 3 T magnetic resonance imaging (MRI), with T2-weighted imaging, DTI and diffusion weighted imaging (DWI) using ten b-values. DTI parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squared fitting of the DTI data and bi-, and tri-exponential fit parameters (Dbi, fstar,and Dtri, ffast,finterm) using a nonlinear fit of the multiple b-value DWI data. Average parameters were calculated for regions of interest, selecting the lesions and healthy kidney tissue. Tumour type and specificities were determined after surgery by histological examination. Mean parameter values of healthy tissue and solid lesions were compared using a Wilcoxon-signed ranked test and MANOVA.

RESULTS

Thirteen solid lesions (nine clear cell carcinomas, two papillary renal cell carcinoma, one haemangioma and one oncocytoma) and four cysts were included. The mean MD of solid lesions are significantly (p < 0.05) lower than healthy cortex and medulla, (1.94 ± 0.32*10- 3 mm2/s versus 2.16 ± 0.12*10- 3 mm2/s and 2.21 ± 0.14*10- 3 mm2/s, respectively) whereas ffast is significantly higher (7.30 ± 3.29% versus 4.14 ± 1.92% and 4.57 ± 1.74%) and finterm is significantly lower (18.7 ± 5.02% versus 28.8 ± 5.09% and 26.4 ± 6.65%). Diffusion coefficients were high (≥2.0*10- 3 mm2/s for MD, 1.90*10- 3 mm2/s for Dbi and 1.6*10- 3 mm2/s for Dtri) in cc-RCCs with cystic structures and/or haemorrhaging and low (≤1.80*10- 3 mm2/s for MD, 1.40*10- 3 mm2/s for Dbi and 1.05*10- 3 mm2/s for Dtri) in tumours with necrosis or sarcomatoid differentiation.

CONCLUSION

Parameters derived from a two- or three-component fit of the diffusion signal are sensitive to histopathological features of kidney lesions.

Stability and reproducibility of co-electrospun brain-mimicking phantoms for quality assurance of diffusion MRI sequences

Grey and white matter mimicking phantoms are important for assessing variations in diffusion MR measures at a single time point and over an extended period of time. This work investigates the stability of brain-mimicking microfibre phantoms and reproducibility of their MR derived diffusion parameters. The microfibres were produced by co-electrospinning and characterized by scanning electron microscopy (SEM). Grey matter and white matter phantoms were constructed from random and aligned microfibres, respectively. MR data were acquired from these phantoms over a period of 33 months. SEM images revealed that only small changes in fibre microstructure occurred over 30 months. The coefficient of variation in MR measurements across all time-points was between 1.6% and 3.4% for MD across all phantoms and FA in white matter phantoms. This was within the limits expected for intra-scanner variability, thereby confirming phantom stability over 33 months. These specialised diffusion phantoms may be used in a clinical environment for intra and inter-site quality assurance purposes, and for validation of quantitative diffusion biomarkers.

A supervised learning approach for diffusion MRI quality control with minimal training data

Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. Recently supervised learning approaches based on convolutional neural networks have been shown to be competitive with manual inspection. A drawback of these approaches is they still require a manually labelled dataset for training, which is itself time-consuming to produce and still introduces an element of subjectivity. In this work we demonstrate the need for manual labelling can be greatly reduced by training on simulated data, and using a small amount of labelled data for a final calibration step. We demonstrate its potential for the detection of severe movement artefacts, and compare performance to a classifier trained on manually-labelled real data.

Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System)

CONTEXT

Management of bladder cancer (BC) is primarily driven by stage, grade, and biological potential. Knowledge of each is derived using clinical, histopathological, and radiological investigations. This multimodal approach reduces the risk of error from one particular test, but may present a staging dilemma when results conflict. Multiparametric magnetic resonance imaging (mpMRI) may improve patient care through imaging of the bladder with better resolution of the tissue planes than computed tomography and without radiation exposure.

OBJECTIVE

To define a standardized approach to imaging and reporting mpMRI for BC, by developing a VI-RADS score.

EVIDENCE ACQUISITION

We created VI-RADS (Vesical Imaging-Reporting And Data System) through consensus using existing literature.

EVIDENCE SYNTHESIS

We describe standard imaging protocols and reporting criteria (including size, location, multiplicity, and morphology) for bladder mpMRI. We propose a five-point VI-RADS score, derived using T2-weighted MRI, diffusion-weighted imaging, and dynamic contrast enhancement, which suggests the risks of muscle invasion. We include sample images used to understand VI-RADS.

CONCLUSIONS

We hope that VI-RADS will standardize reporting, facilitate comparisons between patients, and in future years, will be tested and refined if necessary. While we do not advocate mpMRI for all patients with BC, this imaging may compliment pathology or reduce radiation-based imaging. Bladder mpMRI may be most useful in patients with non-muscle-invasive cancers, in expediting radical treatment or for determining response to bladder-sparing approaches.

PATIENT SUMMARY

Magnetic resonance imaging (MRI) scans for bladder cancer are becoming more common and may provide accurate information that helps improve patient care. Here, we describe a standardized reporting criterion for bladder MRI. This should improve communication between doctors and allow better comparisons between patients.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

The utility of measuring the apparent diffusion coefficient for peritumoral zone in assessing infiltration depth of endometrial cancer

BACKGROUND

The invasion depth of endometrial cancer is one of the most important prognosis factors. The aim of the current study was to investigate the diagnostic value of the apparent diffusion coefficient (ADC) of the peritumoral zone for assessing the infiltration depth of endometrial cancer.

METHODS

An institutional review board approved this prospective study, and all study participants provided informed consent. A total of 58 patients (mean age 54 ± 8.3 years, range 34-69 years) with endometrial cancer were prospectively enrolled. Two radiologists assessed all preoperative magnetic resonance images with T1, T2, and diffusion-weighted imaging, and determined the location of the deepest invasion of the tumor. The peritumoral zone was defined as a 5-mm-thick zone surrounding and adjacent to the cancerous endometrium. The mean ADC (ADCm) values of the tumor and the peritumoral zone were measured. Sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve (Az) were calculated for visual inspection, and an ADC cutoff value for the peri-endometrial zone was determined for predicting the myometrial invasion depth.

RESULTS

The ADCm values of tumors and peritumoral zones were 0.83 × 10- 3 mm2/sec and 1.06 × 10- 3 mm2/sec, respectively. There was no significant difference between the ADCm values of the tumors in the superficial and deep myometrial invasion groups (P > 0.05). However, the ADCm value at the peritumoral zone in the deep myometrial invasion group (1.23 × 10- 3 mm2/sec) significantly differed from that in the superficial myometrial invasion group (0.99 × 10- 3 mm2/sec) (p = 0.005). In assessments of deep myometrial invasion, the sensitivity, specificity, negative predictive value, and positive predictive value were 0.58, 0.93, 0.84, and 0.77, respectively, for the ADCm cutoff value of the peritumoral zone, and 0.71, 0.80, 0.87, and 0.60. respectively, for visual inspection. The accuracy of myometrial invasion depth assessment using the ADCm cutoff value and visual inspection were 83 and 78%, respectively. The Az for both was 0.76.

CONCLUSION

ADCm at the peritumoral zone can predict deep myometrial invasion of endometrial cancer. This value can therefore enhance confidence in preoperative endometrial cancer evaluation, and when tailoring surgical approaches.

Validation strategies for the interpretation of microstructure imaging using diffusion MRI

Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.

Role of PROPELLER-DWI of the prostate in reducing distortion and artefact from total hip replacement metalwork

OBJECTIVE

To compare image quality, artefact, and distortion in standard echo-planar imaging (EPI) with periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) for prostate magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with previous total hip replacement (THR).

METHODS

21 male subjects with a clinical suspicion for, or known prostate cancer and previous THR were scanned at 1.5 T using a phased-array body coil. DWI was obtained using single-shot EPI and PROPELLER techniques using fat saturation (PROPELLER-DWI-FS), and without (PROPELLER-DWI-NFS). Image quality (the overall impression of diagnostic quality) was compared to T2-weighted (T2WI) imaging using a 5-point Likert scale, with diffusion sequences additionally scored for artefact and distortion according to a 4-point scale, with artefact defined as the amount of prostate affected and distortion as the degree of warping of the organ. The T2W and DW image volumes were compared to produce quantitative distortion maps. A two-sample Wilcoxon test compared the qualitative scores, with inter-reader variability calculated using Cohen's kappa.

RESULTS

21 patients were included in the study, with an average age of 70.4 years and PSA 9.2 ng/ml. Hip metalwork was present bilaterally in 3 patients, left-sided in 9, and right-sided in 9. PROPELLER-DWI-FS significantly improved image quality (p < 0.01) and reduced distortion (p < 0.01) when compared to standard EP-DWI. Artefact was not shown to be significantly improved. The last 5 patients in the study were additionally imaged with PROPELLER-DWI-NFS, which resulted in a significant reduction in artefact compared to EP-DWI (p < 0.05). Quantitative distortion was significantly lower compared to EP-DWI for both PROPELLER with fat saturation (p < 0.01) and without fat saturation (p < 0.01).

CONCLUSION

PROPELLER-DWI demonstrates better image quality and decreases both artefact and distortion compared to conventional echo planar sequences in patients with hip metalwork.

18F-FDG PET/MR imaging of lymphoma nodal target lesions: Comparison of PET standardized uptake value (SUV) with MR apparent diffusion coefficient (ADC)

To compare positron emission tomography (PET) standardized uptake value (SUV) with magnetic resonance (MR) apparent diffusion coefficient (ADC) of nodal target lesions in patients with F-fluoro-2-deoxyglucose (FDG)-avid lymphomas by simultaneous PET/MR.Patients with histologically proven Hodgkin and non-Hodgkin lymphoma underwent PET/MR limited field of view of FDG-avid target nodal lesions. For PET images, a region of interest (ROI) was drawn around the target nodal lesion and the SUVmax and SUVmean was measured. For MR ADC measurements a ROI was placed over the target nodal lesion on diffusion-weighted imaging (DWI) and ADCmin and ADCmean (mean ADC) values within the ROI were recorded.Thirty-nine patients (19 women, 20 men; 13 patients with Hodgkin lymphoma and 26 with non-Hodgkin lymphoma) were included in the analysis. Sixty-six nodal lesions detected by PET/CT (19 PET-negative and 47 PET-positive) were analyzed by PET/MR. PET/MR quantitative assessments showed that ADCmin and ADCmean were accurate for discriminating positive from negative nodal lymphoma, with an AUC of 0.927 and 0.947, respectively. The ROC curve analysis of ADCmean versus SUVmax and SUVmean was not statistically significant (difference=0.044, P = .08 and difference = 0.045, P = .07; respectively). A substantial inverse association was observed between ADCmean with SUVmean and SUVmax (rho = -0.611; -0.607; P < .0001, respectively). A moderate inverse association was found between ADCmin with SUVmean and SUVmax (rho = -0.529, -0.520; P < .0001, respectively). Interobserver variability of quantitative assessment showed very good agreement for all variables (ICC>0.87).A significant correlation between ADCs and SUVs is found in FDG avid lymphomas. ADCmean is not inferior to PET SUV in discriminating positive and negative nodal lymphomas. Further larger studies are warranted to validate quantitative PET/MR for lymphoma patient management.

Liquid crystal phantom for validation of microscopic diffusion anisotropy measurements on clinical MRI systems

PURPOSE

To develop a phantom for validating MRI pulse sequences and data processing methods to quantify microscopic diffusion anisotropy in the human brain.

METHODS

Using a liquid crystal consisting of water, detergent, and hydrocarbon, we designed a 0.5-L spherical phantom showing the theoretically highest possible degree of microscopic anisotropy. Data were acquired on the Connectome scanner using echo-planar imaging signal readout and diffusion encoding with axisymmetric b-tensors of varying magnitude, anisotropy, and orientation. The mean diffusivity, fractional anisotropy (FA), and microscopic FA (µFA) parameters were estimated.

RESULTS

The phantom was observed to have values of mean diffusivity similar to brain tissue, and relaxation times compatible with echo-planar imaging echo times on the order of 100 ms. The estimated values of µFA were at the theoretical maximum of 1.0, whereas the values of FA spanned the interval from 0.0 to 0.8 as a result of varying orientational order of the anisotropic domains within each voxel.

CONCLUSIONS

The proposed phantom can be manufactured by mixing three widely available chemicals in volumes comparable to a human head. The acquired data are in excellent agreement with theoretical predictions, showing that the phantom is ideal for validating methods for measuring microscopic diffusion anisotropy on clinical MRI systems. Magn Reson Med 79:1817-1828, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives

For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology.

KEY POINTS

• Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.

A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom

PURPOSE

To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics.

METHODS

A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting.

RESULTS

The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available.

CONCLUSIONS

A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.

UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer

OBJECTIVE

Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology.

METHODS

A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control.

RESULTS

The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T.

CONCLUSION

This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs.

Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients

INTRODUCTION

There is a need to develop imaging methods sensitive to axonal injury in multiple sclerosis (MS), given the prominent impact of axonal pathology on disability and outcome. Advanced multi-compartmental diffusion models offer novel indices sensitive to white matter microstructure. One such model, neurite orientation dispersion and density imaging (NODDI), is sensitive to neurite morphology, providing indices of apparent volume fractions of axons (vin), isotropic water (viso) and the dispersion of fibers about a central axis (orientation dispersion index, ODI). NODDI has yet to be studied for its sensitivity to spinal cord pathology. Here, we investigate the feasibility and utility of NODDI in the cervical spinal cord of MS patients.

METHODS

NODDI was applied in the cervical spinal cord in a cohort of 8 controls and 6 MS patients. Statistical analyses were performed to test the sensitivity of NODDI-derived indices to pathology in MS (both lesion and normal appearing white matter NAWM). Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) analysis were also performed to compare with NODDI.

RESULTS

A decrease in NODDI-derived vin was observed at the site of the lesion (p < 0.01), whereas a global increase in ODI was seen throughout white matter (p < 0.001). DKI-derived mean kurtosis (MK) and radial kurtosis (RK) and DTI-derived fractional anisotropy (FA) and radial diffusivity (RD) were all significantly different in MS patients (p < 0.02), however NODDI provided higher contrast between NAWM and lesion in all MS patients.

CONCLUSION

NODDI provides unique contrast that is not available with DKI or DTI, enabling improved characterization of the spinal cord in MS.

Changes in brain microstructure during infancy and childhood using clinical feasible ADC-maps

PURPOSE

The purpose of this study was to examine age-related changes in apparent diffusion coefficient (ADC) during infancy and childhood using routine MRI data.

METHODS

A total of 112 investigations of patients aged 0 to 17.2 years showing a normal degree of myelination and no signal abnormalities on conventional MRI were retrospectively selected from our pool of pediatric MRI examinations at 1.5T. ADC maps based on our routinely included axial diffusion weighted sequence were created from the scanner. ADC values were measured in 35 different brain regions investigating normal age-related changes during the maturation of the human brain in infancy and childhood using clinical feasible sequences at 1.5T.

RESULTS

The relationship between ADC values and age in infancy and childhood can be described as an exponential function. With increasing age, the ADC values decrease significantly in all brain regions, especially during the first 2 years of life. Except in the peritrigonal white matter, no significant differences were found between both hemispheres. Between 0 and 2 years of life, no significant gender differences were detected.

CONCLUSIONS

Using ADC maps based on a routinely performed axial diffusion weighted sequence, it was possible first to describe the relationship between ADC values and age in infancy and childhood as exponential function in the whole brain and second to determine normative age-related ADC values in multiple brain regions.

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion-weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521-540.

Estimating Total Cerebral Microinfarct Burden From Diffusion-Weighted Imaging

BACKGROUND AND PURPOSE

Cerebral microinfarcts (CMI) are important contributors to vascular cognitive impairment. Magnetic resonance imaging diffusion-weighted imaging (DWI) hyperintensities have been suggested to represent acute CMI. We aim to describe a mathematical method for estimating total number of CMI based on the presence of incidental DWI lesions.

METHODS

We reviewed magnetic resonance imaging scans of subjects with cognitive decline, cognitively normal subjects and previously reported subjects with past intracerebral hemorrhage (ICH). Based on temporal and spatial characteristics of DWI lesions, we estimated the annual rate of CMI needed to explain the observed rate of DWI lesion detection in each group. To confirm our estimates, we performed extensive sampling for CMI in the brain of a deceased subject with past lobar ICH who found to have a DWI lesion during life.

RESULTS

Clinically silent DWI lesions were present in 13 of 343 (3.8%) cognitively impaired and 10 of 199 (5%) cognitively intact normal non-ICH patients, both lower than the incidence in the past ICH patients (23 of 178; 12.9%; P<0.0006). The predicted annual incidence of CMI ranges from 16 to 1566 for non-ICH and 50 to 5041 for ICH individuals. Histological sampling revealed a total of 60 lesions in 32 sections. Based on previously reported methods, this density of CMI yields an estimated total brain burden maximum likelihood estimate of 9321 CMIs (95% confidence interval, 7255-11 990).

CONCLUSIONS

Detecting even a single DWI lesion suggests an annual incidence of hundreds of new CMI. The cumulative effects of these lesions may directly contribute to small-vessel-related vascular cognitive impairment.

The effect of Gibbs ringing artifacts on measures derived from diffusion MRI

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI--i.e., Gibbs ringing (GR)--has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially.

Assessing response to treatment of bone metastases from breast cancer: what should be the standard of care?

Bone is the most common site for breast cancer metastases, occurring in up to 70% of those with metastatic disease. In order to effectively manage these patients, it is essential to have consistent, reproducible and validated methods of assessing response to therapy. We present current clinical practice of imaging response assessment of bone metastases. We also review the biology of bone metastases and measures of response assessment including clinical assessment, tumour markers and imaging techniques; bone scans (BSs), computed tomography (CT), positron emission tomography, magnetic resonance imaging (MRI) and whole-body diffusion-weighted MRI (WB DW-MRI). The current standard of care of BSs and CT has significant limitations and are not routinely recommended for the purpose of response assessment in the bones. WB DW-MRI has the potential to address this unmet need and should be evaluated in clinical trials.

An emerging evidence base for PET-CT in the management of childhood rhabdomyosarcoma: systematic review

INTRODUCTION

Rhabdomyosarcoma (RMS) management depends on risk stratification at diagnosis and treatment response. Assessment methods include CT, MRI, bone scintigraphy, histological analysis and bone marrow biopsy. Advanced functional imaging (FI) has potential to improve staging accuracy and management strategies.

METHODS AND ANALYSIS

We conducted a systematic review (PROSPERO 2013:CRD42013006128) of diagnostic accuracy and clinical effectiveness of FI in histologically proven paediatric RMS. PRISMA guidance was followed. We searched 10 databases to November 2013. Studies with ≥10 patients with RMS which compared positron emission tomography (PET), PET-CT or diffusion-weighted imaging (DWI) MRI to conventional imaging at any treatment stage were included. Study quality was assessed. Limited, heterogeneous effectiveness data required narrative synthesis, illustrated by plotting sensitivity and specificity in receiver operating curve (ROC) space.

RESULTS

Eight studies (six PET-CT, two PET) with 272 RMS patients in total were included. No DWI-MRI studies met inclusion criteria. Pooled estimates were not calculated due to sparseness of data. Limited evidence indicated initial PET-CT results were predictive of survival. PET-CT changed management of 7/40 patients. Nodal involvement PET-CT: sensitivity ranged from 80% to 100%; specificity from 89% to 100%. Distant metastatic involvement: PET-CT sensitivity ranged from 95% to 100%; specificity from 80% to100%. Data on metastases in different sites were sparse. Limited data were found on outcome prediction by PET-CT response.

DISSEMINATION AND ETHICS

PET/PET-CT may increase initial staging accuracy in paediatric RMS, specifically in the detection of nodal involvement and distant metastatic spread. There is a need to further assess PET-CT for this population, ideally in a representative, unbiased and transparently selected cohort of patients.

Predictive value of diffusion-weighted MRI for tumor consistency and resection rate of nonfunctional pituitary macroadenomas

BACKGROUNDS

Firm tumor consistency is one of the most important factors that impede sufficient removal of pituitary macroademoas via a transsphenoidal approach. The utility of diffusion-weighted (DW) magnetic resonance imaging (MRI) in predicting the tumor consistency and successfulness of transsphenoidal resection was evaluated in this study.

METHODS

Thirty consecutive primary cases of nonfunctional pituitary macroadenomas were prospectively enrolled. Conventional and DW MRI were done for all the patients and the apparent diffusion coefficient (ADC) values and the signal intensity of the solid tumor were determined. Intraoperative report of tumor consistency, the degree of fibrosis and percentage of collagen content were documented. The 8 weeks postoperative MRI was used for calculation of the tumor resection rate.

RESULTS

The tumor consistency was soft in 10 patients (33.3 %), intermediate in 14 patients (46.7 %) and hard in 6 patients (20 %). The mean collagen content percentage was 10, 23.5 and 66 % (p = 0.009) and the average resection rate was 75, 43 39 % in the three groups respectively (p = 0.001). The mean ADC value was not significantly correlated with the tumor consistency and resection rate. Tumors with isointense to hyperintense signal on DW MRI were more commonly removable by suction and had higher resection rates than those with hypointense signals (p = 0.019). For ADC values within the range of 600-740 × 10(-3) mm(2)/s, a residual volume larger than 20 % of the tumor was more likely.

CONCLUSIONS

DW MRI was useful to predict the tumor consistency, collagen content and the chance of removal of pituitary macroadenomas through endoscopic transsphenoidal surgery, and is recommended in the preoperative patient evaluation.

Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding

Diffusion imaging is a unique noninvasive tool to detect brain white matter trajectory and integrity in vivo. However, this technique suffers from spatial distortion and signal pileup or dropout originating from local susceptibility gradients and eddy currents. Although there are several methods to mitigate these problems, most techniques can be applicable either to susceptibility or eddy-current induced distortion alone with a few exceptions. The present study compared the correction efficiency of FSL tools, "eddy_correct" and the combination of "eddy" and "topup" in terms of diffusion-derived fractional anisotropy (FA). The brain diffusion images were acquired from 10 healthy subjects using 30 and 60 directions encoding schemes based on the electrostatic repulsive forces. For the 30 directions encoding, 2 sets of diffusion images were acquired with the same parameters, except for the phase-encode blips which had opposing polarities along the anteroposterior direction. For the 60 directions encoding, non-diffusion-weighted and diffusion-weighted images were obtained with forward phase-encoding blips and non-diffusion-weighted images with the same parameter, except for the phase-encode blips, which had opposing polarities. FA images without and with distortion correction were compared in a voxel-wise manner with tract-based spatial statistics. We showed that images corrected with eddy and topup possessed higher FA values than images uncorrected and corrected with eddy_correct with trilinear (FSL default setting) or spline interpolation in most white matter skeletons, using both encoding schemes. Furthermore, the 60 directions encoding scheme was superior as measured by increased FA values to the 30 directions encoding scheme, despite comparable acquisition time. This study supports the combination of eddy and topup as a superior correction tool in diffusion imaging rather than the eddy_correct tool, especially with trilinear interpolation, using 60 directions encoding scheme.

Diffusion-weighted MRI, 11C-choline PET and 18F-fluorodeoxyglucose PET for predicting the Gleason score in prostate carcinoma

OBJECTIVES

To evaluate the accuracy of transrectal ultrasound-guided (TRUS) biopsy, diffusion-weighted (DW) magnetic resonance imaging (MRI), (11)C-choline (CHOL) positron emission tomography (PET), and (18)F-fluorodeoxyglucose (FDG) PET in predicting the prostatectomy Gleason risk (GR).

METHODS

The study included 21 patients who underwent TRUS biopsy and multi-technique imaging before radical prostatectomy. Values from five different tests (TRUS biopsy, DW MRI, CHOL PET, FDG PET, and combined DW MRI/CHOL PET) were correlated with the prostatectomy GR using Spearman's ρ. Tests that were found to have significant correlations were used to classify patients into GR groups.

RESULTS

The following tests had significant correlations with prostatectomy GR: TRUS biopsy (ρ = 0.617, P = 0.003), DW MRI (ρ = -0.601, P = 0.004), and combined DW MRI/CHOL PET (ρ = -0.623, P = 0.003). CHOL PET alone and FDG PET only had weak correlations. The correct GR classification rates were 67% with TRUS biopsy, 67% with DW MRI, and 76% with combined DW MRI/CHOL PET.

CONCLUSIONS

DW MRI and combined DW MRI/CHOL PET have significant correlations and high rates of correct classification of the prostatectomy GR, the strength and accuracy of which are comparable with TRUS biopsy.

KEY POINTS

• Accurate determination of the Gleason score is essential for prostate cancer management. • DW MRI ± CHOL PET correlated significantly with prostatectomy Gleason score. • These correlations are similar to that between TRUS biopsy and prostatectomy.

Standardized magnetic resonance imaging acquisition and reporting in pediatric multiple sclerosis

Magnetic resonance (MR) imaging is one of the most important paraclinical tools for the diagnosis of multiple sclerosis (MS), and monitoring of disease progression and treatment response. This article provides clinicians and neuroradiologists caring for children with demyelinating disorders with a suggested standard MR imaging acquisition and reporting protocol, and defines a standard lexicon for lesion features typical of MS in children. As there is considerable overlap between the MR imaging features of pediatric- and adult-onset MS, the recommendations provided herein may be of relevance to radiologists and clinicians caring for adults with multiple sclerosis.

T2-weighted MR imaging early after chemoradiotherapy to evaluate treatment response in head and neck squamous cell carcinoma

BACKGROUND AND PURPOSE

T2-weighted MRI shows potential in early posttreatment assessment of the primary tumor. Residual masses composed entirely of low T2-signal scar tissue suggest local control and those ≥1 cm of similar signal to untreated tumor suggest local failure. The purpose of this study was to investigate the diagnostic accuracy of T2-weighted MR imaging early after chemoradiotherapy for identifying primary tumor treatment failure in squamous cell carcinoma of the head and neck.

MATERIALS AND METHODS

At 6 weeks after treatment, T2-weighted MR images of 37 primary tumors in 37 patients were assessed. Residual masses were divided into 3 patterns: pattern 1 = scar tissue only (flat-edged/retracted mass of low T2 signal intensity); pattern 2 = mass without features described in pattern 1 or 3; and pattern 3 = any pattern that included an expansile mass ≥1 cm of intermediate T2 signal intensity (similar grade of signal intensity to the untreated tumor). T2 patterns were analyzed for local outcome (Fisher exact test) and time to local failure (univariate and multivariate analysis of T2 pattern, age, T stage, and tumor size by use of the Cox regression model).

RESULTS

Residual masses after treatment were present in 34 (92%) of 37 patients. Local failures occurred in residual masses with pattern 1 in 0 (0%) of 14 patients; pattern 2 in 6 (55%) of 11 patients; and pattern 3 in 9 (100%) of 9 patients. Significant associations were found between local control and pattern 1 (P = <.0001; sensitivity, 74%; specificity, 100%; PPV, 100%; NPV, 75%; accuracy, 85%), and between local failure and pattern 3 (P = <.0001; sensitivity, 60%; specificity, 100%; PPV, 100%; NPV, 76%; accuracy, 82%). Pattern 2 showed no significant associations with local outcome. Univariate analysis of time to local failure showed that the T2 pattern was significant (P < .0001) and remained significant on multivariate analysis.

CONCLUSIONS

T2-weighted MR imaging is a potential tool for early posttreatment assessment of primary HNSCC treatment response. Awareness of correlation of the T2 pattern of any residual mass with treatment outcome at the primary site may contribute to patient treatment.

Clinical assessment of standard and generalized autocalibrating partially parallel acquisition diffusion imaging: effects of reduction factor and spatial resolution

BACKGROUND AND PURPOSE

PI improves routine EPI-based DWI by enabling higher spatial resolution and reducing geometric distortion, though it remains unclear which of these is most important. We evaluated the relative contribution of these factors and assessed their ability to increase lesion conspicuity and diagnostic confidence by using a GRAPPA technique.

MATERIALS AND METHODS

Four separate DWI scans were obtained at 1.5T in 48 patients with independent variation of in-plane spatial resolution (1.88 mm(2) versus 1.25 mm(2)) and/or reduction factor (R = 1 versus R = 3). A neuroradiologist with access to clinical history and additional imaging sequences provided a reference standard diagnosis for each case. Three blinded neuroradiologists assessed scans for abnormalities and also evaluated multiple imaging-quality metrics by using a 5-point ordinal scale. Logistic regression was used to determine the impact of each factor on subjective image quality and confidence.

RESULTS

Reference standard diagnoses in the patient cohort were acute ischemic stroke (n = 30), ischemic stroke with hemorrhagic conversion (n = 4), intraparenchymal hemorrhage (n = 9), or no acute lesion (n = 5). While readers preferred both a higher reduction factor and a higher spatial resolution, the largest effect was due to an increased reduction factor (odds ratio, 47 ± 16). Small lesions were more confidently discriminated from artifacts on R = 3 images. The diagnosis changed in 5 of 48 scans, always toward the reference standard reading and exclusively for posterior fossa lesions.

CONCLUSIONS

PI improves DWI primarily by reducing geometric distortion rather than by increasing spatial resolution. This outcome leads to a more accurate and confident diagnosis of small lesions.

Efficient and robust nonlocal means denoising of MR data based on salient features matching

The nonlocal means (NLM) filter has become a popular approach for denoising medical images due to its excellent performance. However, its heavy computational load has been an important shortcoming preventing its use. NLM works by averaging pixels in nonlocal vicinities, weighting them depending on their similarity with the pixel of interest. This similarity is assessed based on the squared difference between corresponding pixels inside local patches centered at the locations compared. Our proposal is to reduce the computational load of this comparison by checking only a subset of salient features associated to the pixels, which suffice to estimate the actual difference as computed in the original NLM approach. The speedup achieved with respect to the original implementation is over one order of magnitude, and, when compared to more recent NLM improvements for MRI denoising, our method is nearly twice as fast. At the same time, we evidence from both synthetic and in vivo experiments that computing of appropriate salient features make the estimation of NLM weights more robust to noise. Consequently, we are able to improve the outcomes achieved with recent state of the art techniques for a wide range of realistic Signal-to-Noise ratio scenarios like diffusion MRI. Finally, the statistical characterization of the features computed allows to get rid of some of the heuristics commonly used for parameter tuning.

MRI is a sensitive marker of subtle white matter pathology in hypoperfused mice

White matter (WM) abnormalities, possibly resulting from hypoperfusion, are key features of the aging human brain. It is unclear, however, whether in vivo magnetic resonance imaging (MRI) approaches, such as diffusion tensor and magnetization transfer MRI are sufficiently sensitive to detect subtle alterations to WM integrity in mouse models developed to study the aging brain. We therefore investigated the use of diffusion tensor and magnetization transfer MRI to measure structural changes in 4 WM tracts following 1 month of moderate hypoperfusion, which results in diffuse WM pathology in C57Bl/6J mice. Following MRI, brains were processed for evaluation of white and gray matter pathology. Significant reductions in fractional anisotropy were observed in the corpus callosum (p = 0.001) and internal capsule (p = 0.016), and significant decreases in magnetization transfer ratio were observed in the corpus callosum (p = 0.023), fimbria (p = 0.032), internal capsule (p = 0.046) and optic tract (p = 0.047) following hypoperfusion. Hypoperfused mice demonstrated diffuse axonal and myelin pathology which was essentially absent in control mice. Both fractional anisotropy and magnetization transfer ratio correlate with markers of myelin integrity/degradation and not axonal pathology. The study demonstrates that in vivo MRI is a sensitive measure of diffuse, subtle WM changes in the murine brain.