Pasquale Borrelli

In vivo dentate nucleus MRI relaxometry correlates with previous administration of Gadolinium-based contrast agents

AbstractObjectivesTo evaluate changes in T1 and T2* relaxometry of dentate nuclei (DN) with respect to the number of previous administrations of Gadolinium-based contrast agents (GBCA).MethodsIn 74 relapsing-remitting multiple sclerosis (RR-MS) patients with variable disease duration (9.8±6.8 years) and severity (Expanded Disability Status Scale scores:3.1±0.9), the DN R1 (1/T1) and R2* (1/T2*) relaxation rates were measured using two unenhanced 3D Dual-Echo spoiled Gradient-Echo sequences with different flip angles. Correlations of the number of previous GBCA administrations with DN R1 and R2* relaxation rates were tested, including gender and age effect, in a multivariate regression analysis.ResultsThe DN R1 (normalized by brainstem) significantly correlated with the number of GBCA administrations (p<0.001), maintaining the same significance even when including MS-related factors. Instead, the DN R2* values correlated only with age (p=0.003), and not with GBCA administrations (p=0.67). In a subgroup of 35 patients for whom the administered GBCA subtype was known, the effect of GBCA on DN R1 appeared mainly related to linear GBCA.ConclusionsIn RR-MS patients, the number of previous GBCA administrations correlates with R1 relaxation rates of DN, while R2* values remain unaffected, suggesting that T1-shortening in these patients is related to the amount of Gadolinium given.Key Points• In multiple sclerosis, previous Gadolinium administrations correlate with dentate nuclei T1 relaxometry. • Such correlation is linked to linear Gadolinium chelates and unrelated to disease duration or severity. • Dentate nuclei T2* relaxometry is age-related and independent of previous Gadolinium administrations. • Changes in dentate nuclei T1 relaxometry are not determined by iron accumulation. • MR relaxometry can quantitatively assess Gadolinium accumulation in dentate nuclei.

Improving signal-to-noise ratio in susceptibility weighted imaging: A novel multicomponent non-local approach

In susceptibility-weighted imaging (SWI), the high resolution required to obtain a proper contrast generation leads to a reduced signal-to-noise ratio (SNR). The application of a denoising filter to produce images with higher SNR and still preserve small structures from excessive blurring is therefore extremely desirable. However, as the distributions of magnitude and phase noise may introduce biases during image restoration, the application of a denoising filter is non-trivial. Taking advantage of the potential multispectral nature of MR images, a multicomponent approach using a Non-Local Means (MNLM) denoising filter may perform better than a component-by-component image restoration method. Here we present a new MNLM-based method (Multicomponent-Imaginary-Real-SWI, hereafter MIR-SWI) to produce SWI images with high SNR and improved conspicuity. Both qualitative and quantitative comparisons of MIR-SWI with the original SWI scheme and previously proposed SWI restoring pipelines showed that MIR-SWI fared consistently better than the other approaches. Noise removal with MIR-SWI also provided improvement in contrast-to-noise ratio (CNR) and vessel conspicuity at higher factors of phase mask multiplications than the one suggested in the literature for SWI vessel imaging. We conclude that a proper handling of noise in the complex MR dataset may lead to improved image quality for SWI data.

Unbiased noise estimation and denoising in parallel magnetic resonance imaging

In magnetic resonance (MR) clinical practice, noise estimation is usually performed on Rayleigh-distributed background (no signal area) of magnitude images. Although noise variance in quadrature MR images is considered spatially independent, parallel MRI (pMRI) techniques as SENSE or GRAPPA generate spatially varying noise (SVN) distribution. In this scenario noise estimation from background may produce biased results. To address these limitations we introduce a novel noise estimation scheme based on local statistics. Our method turns out to be more accurate than the other pMRI noise estimation schemes previously described in the literature. Denoising performances, measured by visual inspection and peak signal-to-noise ratio (PSNR), of Non-Local Means denoising filters (NLM) are considerably improved using SVN-NLM in case of inhomogeneous noise. Furthermore, SVN-NLM behaves as well as standard NLM when homogeneous noise was added, thus proving to be a robust and powerful denoising algorithm for arbitrary MRI datasets.

A Novel Multiparametric Approach to 3D Quantitative MRI of the Brain.

Magnetic Resonance properties of tissues can be quantified in several respects: relaxation processes, density of imaged nuclei, magnetism of environmental molecules, etc. In this paper, we propose a new comprehensive approach to obtain 3D high resolution quantitative maps of arbitrary body districts, mainly focusing on the brain. The theory presented makes it possible to map longitudinal (R 1), pure transverse (R 2) and free induction decay (R2*) rates, along with proton density (PD) and magnetic susceptibility (χ), from a set of fast acquisition sequences in steady-state that are highly insensitive to flow phenomena. A novel denoising scheme is described and applied to the acquired datasets to enhance the signal to noise ratio of the derived maps and an information theory approach compensates for biases from radio frequency (RF) inhomogeneities, if no direct measure of the RF field is available. Finally, the results obtained on sample brain scans of healthy controls and multiple sclerosis patients are presented and discussed.

A multiparametric and multiscale approach to automated segmentation of brain veins

Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2*- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.

Longitudinal assessment of dentate nuclei relaxometry during massive gadobutrol exposure

We report the assessment of Dentate Nuclei (DN) R1 (1/T1) and R2* (1/T2*) values in a patient with relapsing-remitting Multiple Sclerosis, exposed to 22 standard (0.1 mmol/kg) doses of gadobutrol, who underwent eight relaxometric MR measurements within 2 years. DN R1 did not significantly increase nor correlated with cumulative gadobutrol administration, even after a total dose of 130 ml. Likewise, DN R2* relaxometry remained unchanged. In conclusion, massive gadobutrol exposure did not induce significant DN relaxometry changes.

RESUME: Turning an SWI acquisition into a fast qMRI protocol

Susceptibility Weighted Imaging (SWI) is a common MRI technique that exploits the magnetic susceptibility differences between the tissues to provide valuable image contrasts, both in research and clinical contexts. However, despite its increased clinical use, SWI is not intrinsically suitable for quantitation purposes. Conversely, quantitative Magnetic Resonance Imaging (qMRI) provides a way to disentangle the sources of common MR image contrasts (e.g. proton density, T1, etc.) and to measure physical parameters intrinsically related to tissue microstructure. Unfortunately, the poor signal-to-noise ratio and resolution, coupled with the long imaging time of most qMRI strategies, have hindered the integration of quantitative imaging into clinical protocols. Here we present the RElaxometry and SUsceptibility Mapping Expedient (RESUME) to show that the standard acquisition leading to a clinical SWI dataset can be easily turned into a thorough qMRI protocol at the cost of a further 50% of the SWI scan time. The R1, R2*, proton density and magnetic susceptibility maps provided by the RESUME scheme alongside the SWI reconstruction exhibit high reproducibility and accuracy, and a submillimeter resolution is proven to be compatible with a total scan time of 7 minutes.

MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions

Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.

Redefining the Pulvinar Sign in Fabry Disease

BACKGROUND AND PURPOSE: The pulvinar sign refers to exclusive T1WI hyperintensity of the lateral pulvinar. Long considered a common sign of Fabry disease, the pulvinar sign has been reported in many pathologic conditions. The exact incidence of the pulvinar sign has never been tested in representative cohorts of patients with Fabry disease. The aim of this study was to assess the prevalence of the pulvinar sign in Fabry disease by analyzing T1WI in a large Fabry disease cohort, determining whether relaxometry changes could be detected in this region independent of the pulvinar sign positivity. MATERIALS AND METHODS: We retrospectively analyzed brain MR imaging of 133 patients with Fabry disease recruited through specialized care clinics. A subgroup of 26 patients underwent a scan including 2 FLASH sequences for relaxometry that were compared with MRI scans of 34 healthy controls. RESULTS: The pulvinar sign was detected in 4 of 133 patients with Fabry disease (3.0%). These 4 subjects were all adult men (4 of 53, 7.5% of the entire male population) with renal failure and under enzyme replacement therapy. When we tested for discrepancies between Fabry disease and healthy controls in quantitative susceptibility mapping and relaxometry maps, no significant difference emerged for any of the tested variables. CONCLUSIONS: The pulvinar sign has a significantly lower incidence in Fabry disease than previously described. This finding, coupled with a lack of significant differences in quantitative MR imaging, allows hypothesizing that selective involvement of the pulvinar is a rare neuroradiologic sign of Fabry disease.

Improving SNR in Susceptibility Weighted Imaging by a NLM-based denoising scheme

The combination of magnitude and phase information inherent in Susceptibility-Weighted Imaging (SWI) greatly benefits from high-resolution MRI acquisitions. The application of a denoising filter to produce SWI images with higher signal-to-noise ratio (SNR) while preserving small structures from excessive blurring is therefore extremely desirable, but non-trivial, as the distribution of magnitude and phase noise may introduce biases during image restoration. Here we present a new dedicated noise removal algorithm based on the Non-Local Means (NLM) filter and compare its results with the original SWI and “standard” NLM-denoised human brain images. Both the visual assessment by two expert readers and the quantitative evaluation of the contrast changes of the voxel intensities demonstrated that the images restored with the proposed algorithm fared consistently better than the other two schemes, showing that a proper handling of noise in the complex MRI dataset may lead to visible improvements of the overall SWI quality.