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Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising.

ABSTRACT: Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

SUBMITTER: Grussu F

PROVIDER: S-EPMC7378937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAutomated segmentation of spinal magnetic resonance imaging (MRI) plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures is challenging.MethodsThis retrospective study, approved by the ethical committee, involved translating T1-weighted and T2-weighted images into computed tomography (CT) images in a total of 263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared two-dimensional (2D) paired - Pix2Pix, denoising diffusion implicit models (DDIM) image mode, DDIM noise mode - and unpaired (SynDiff, contrastive unpaired translation) image-to-image translation using "peak signal-to-noise ratio" as quality measure. A publicly available segmentation network segmented the synthesized CT datasets, and Dice similarity coefficients (DSC) were evaluated on in-house test sets and the "MRSpineSeg Challenge" volumes. The 2D findings were extended to three-dimensional (3D) Pix2Pix and DDIM.Results2D paired methods and SynDiff exhibited similar translation performance and DCS on paired data. DDIM image mode achieved the highest image quality. SynDiff, Pix2Pix, and DDIM image mode demonstrated similar DSC (0.77). For craniocaudal axis rotations, at least two landmarks per vertebra were required for registration. The 3D translation outperformed the 2D approach, resulting in improved DSC (0.80) and anatomically accurate segmentations with higher spatial resolution than that of the original MRI series.ConclusionsTwo landmarks per vertebra registration enabled paired image-to-image translation from MRI to CT and outperformed all unpaired approaches. The 3D techniques provided anatomically correct segmentations, avoiding underprediction of small structures like the spinous process.Relevance statementThis study addresses the unresolved issue of translating spinal MRI to CT, making CT-based tools usable for MRI data. It generates whole spine segmentation, previously unavailable in MRI, a prerequisite for biomechanical modeling and feature extraction for clinical applications.Key points• Unpaired image translation lacks in converting spine MRI to CT effectively. • Paired translation needs registration with two landmarks per vertebra at least. • Paired image-to-image enables segmentation transfer to other domains. • 3D translation enables super resolution from MRI to CT. • 3D translation prevents underprediction of small structures.

Project description:ObjectiveTo assess relationships between spinal cord MRI (SC-MRI) and retinal measures, and to evaluate whether these measures independently relate to clinical disability in multiple sclerosis (MS).MethodsOne hundred two patients with MS and 11 healthy controls underwent 3-tesla brain and cervical SC-MRI, which included standard T1- and T2-based sequences and diffusion-tensor and magnetization-transfer imaging, and optical coherence tomography with automated segmentation. Clinical assessments included visual acuity (VA), Expanded Disability Status Scale, MS functional composite, vibration sensation threshold, and hip-flexion strength. Regions of interest circumscribing SC cross-sections at C3-4 were used to obtain cross-sectional area (CSA), fractional anisotropy (FA), perpendicular diffusivity (λ⊥), and magnetization transfer ratio. Multivariable regression assessed group differences and SC, retinal, and clinical relationships.ResultsIn MS, there were correlations between SC-CSA, SC-FA, SC-λ⊥, and peripapillary retinal nerve fiber layer (pRNFL) (p = 0.01, p = 0.002, p = 0.001, respectively) after adjusting for age, sex, prior optic neuritis, and brain atrophy. In multivariable clinical models, when SC-CSA, pRNFL, and brain atrophy were included simultaneously, SC-CSA and pRNFL retained independent relationships with low-contrast VA (p = 0.04, p = 0.002, respectively), high-contrast VA (p = 0.06, p = 0.008), and vibration sensation threshold (p = 0.01, p = 0.05). SC-CSA alone retained independent relationships with Expanded Disability Status Scale (p = 0.001), hip-flexion strength (p = 0.001), and MS functional composite (p = 0.004).ConclusionsIn this cross-sectional study of patients with MS, correlations exist between SC-MRI and retinal layers, and both exhibit independent relationships with clinical dysfunction. These findings suggest that the SC and optic nerve reflect ongoing global pathologic processes that supplement measures of whole-brain atrophy, highlighting the importance of combining measures from unique compartments to facilitate a thorough examination of regional and global disease processes that contribute to clinical disability in MS.

Project description:INTRODUCTION:Magnetic resonance imaging (MRI) shows slight spatial variations in brain white matter (WM). We used quantitative multi-parametric MRI to evaluate in what respect these inhomogeneities could correspond to WM subtypes with specific characteristics and spatial distribution. MATERIALS AND METHODS:Twenty-six controls (12 women, 38 ±9 Y) took part in a 60-min session on a 3T scanner measuring 7 parameters: R1 and R2, diffusion tensor imaging which allowed to measure Axial and Radial Diffusivity (AD, RD), magnetization transfer imaging which enabled to compute the Macromolecular Proton Fraction (MPF), and a susceptibility-weighted sequence which permitted to quantify R2* and magnetic susceptibility (?m). Spatial independent component analysis was used to identify WM subtypes with specific combination of quantitative parameters values. RESULTS:Three subtypes could be identified. t-WM (track) mostly mapped on well-formed projection and commissural tracts and came with high AD values (all p < 10(-18)). The two other subtypes were located in subcortical WM and overlapped with association fibers: f-WM (frontal) was mostly anterior in the frontal lobe whereas c-WM (central) was underneath the central cortex. f-WM and c-WM had higher MPF values, indicating a higher myelin content (all p < 1.7 10(-6)). This was compatible with their larger ?m and R2, as iron is essentially stored in oligodendrocytes (all p < 0.01). Although R1 essentially showed the same, its higher value in t-WM relative to c-WM might be related to its higher cholesterol concentration. CONCLUSIONS:Thus, f- and c-WMs were less structured, but more myelinated and probably more metabolically active regarding to their iron content than WM related to fasciculi (t-WM). As known WM bundles passed though different WM subtypes, myelination might not be uniform along the axons but rather follow a spatially consistent regional variability. Future studies might examine the reproducibility of this decomposition and how development and pathology differently affect each subtype.

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Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising.

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