Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data

Kumar, Dushyant; Hariharan, Hari; Faizy, Tobias D.; Borchert, Patrick; Siemonsen, Susanne; Fiehler, Jens; Reddy, Ravinder; Sedlacik, Jan

doi:10.1016/j.neuroimage.2018.05.026

Cited by 31 publications

(43 citation statements)

References 38 publications

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“…FA maps are typically acquired at a lower spatial resolution than multi-echo T2 data, as B1 + is assumed to be spatially slowly varying. Using spatial constraints on T2 distributions and FAs across anatomical and pathological structures, as recently described [65], has the potential to improve noise robustness for multi-component T2-mapping.…”

Section: Discussionmentioning

confidence: 99%

“…As the coalesced peak becomes even wider, it can partly extend into the a priori defined short T2 interval, increasing measured MWF. Other regularization approaches have focused on data denoising [66] or used spatial constraints for handling of noisy data [65,[67][68][69]. Although these methodologies yield visually appealing results, the impact of regularization should be carefully evaluated.…”

Section: Discussionmentioning

confidence: 99%

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Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Wiggermann

Vavasour

Kolind

et al. 2020

Preprint

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A revised version of this manuscript was accepted for publication in NMR in Biomedicine.Multi-compartment T 2 -mapping has gained particular relevance for the study of myelin water in brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non-negative least squares fitting of multi-echo T 2 data has been widely employed for the computation of the myelin water fraction (MWF) and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B1 +homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi-compartment T 2 and MWF maps. We generated multi-echo spin-echo T 2 decay curves following the approach of Carr-Purcell-Meiboom-Gill for various myelin concentrations and myelin T 2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra-/extracellular water geometric mean (gm) T 2 . MWF estimates were more stable than myelin water gmT 2 time across different settings of the T 2 analysis. We observed that the lower limit of the T 2 distribution grid should be slightly shorter than TE1. Both TE1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T 2 signal. Among all parameters of interest, the estimated MWF and intra-/extracellular water gmT 2 differed by approximately 0.13-4 percentage points and 3-4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B1 + -inhomogeneities and at lower signal-to-noise ratio. Tailoring acquisition strategies may allow to better characterize the T 2 distribution, including the myelin water, in vivo.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Wiggermann

Vavasour

Kolind

et al. 2020

Preprint

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“…Otherwise, complex TE sampling patterns (such as logarithmic ΔTE) would benefit from separate B 1 mapping, which could be acquired at much lower resolution than the MWI data because of the smooth variation expected of B 1 . 60 The additional acquisition time could be negated by using an auto-calibrating parallel imaging method based on a central calibration region in MWI k-space, rather than coil sensitivity maps acquired separately. 61 In this study, each echo is reconstructed individually from data that is sampled identically, which allowed for reconstruction directly on the scanner.…”

Section: Opportunities For Further Improvementmentioning

confidence: 99%

Multi‐spin echo T₂ relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

Dvorak

Wiggermann

Gilbert

et al. 2020

Magnetic Resonance in Med

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Purpose: Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi-echo T 2 acquisition with gradient echoes (GRASE) or by 2D multislice data collection results in image blurring, contrast changes, and other issues.Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin-echo sequence. Methods: We implemented multi-echo T 2 relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS-PF).Scan-rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS-PF in 7 m:25 s, both with 1.5 × 2 × 3 mm 3 voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm 3 FOV. METRICS was compared with a novel multi-echo spinecho gold-standard (MSE-GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s. Results: METRICS/METRICS-PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra-class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS-PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE-GS. | 1265 DVORAK et Al.

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“…Variations on the CPMG method in recent years have resulted in significantly faster imaging times (118,119,122,123,130). Traditional analysis of the T 2 decay used a non-negative least squares (NNLS) method which makes no a priori assumptions about the number of water environments (129,188), although other approaches also exist (2,52,69,70,134,147,157). Several groups have obtained myelin water images from gradient echo T 2 * decay curve measurement which examines the echo train derived by magnetic field gradient reversals (32, 89,113,142) and measurement of multiple T 1 relaxation components to isolate myelin water has also been used (72,124).…”

Section: Myelin Water Imagingmentioning

confidence: 99%

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Laule

Moore

2018

Brain Pathology

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Damage to myelin is a key feature of multiple sclerosis (MS) pathology. Magnetic resonance imaging (MRI) has revolutionized our ability to detect and monitor MS pathology in vivo. Proton density, T1 and T2 can provide qualitative contrast weightings that yield superb in vivo visualization of central nervous system tissue and have proved invaluable as diagnostic and patient management tools in MS. However, standard clinical MR methods are not specific to the types of tissue damage they visualize, and they cannot detect subtle abnormalities in tissue that appears otherwise normal on conventional MRIs. Myelin water imaging is an MR method that provides in vivo measurement of myelin. Histological validation work in both human brain and spinal cord tissue demonstrates a strong correlation between myelin water and staining for myelin, validating myelin water as a marker for myelin. Myelin water varies throughout the brain and spinal cord in healthy controls, and shows good intra‐ and inter‐site reproducibility. MS plaques show variably decreased myelin water fraction, with older lesions demonstrating the greatest myelin loss. Longitudinal study of myelin water can provide insights into the dynamics of demyelination and remyelination in plaques. Normal appearing brain and spinal cord tissues show reduced myelin water, an abnormality which becomes progressively more evident over a timescale of years. Diffusely abnormal white matter, which is evident in 20%–25% of MS patients, also shows reduced myelin water both in vivo and postmortem, and appears to originate from a primary lipid abnormality with relative preservation of myelin proteins. Active research is ongoing in the quest to refine our ability to image myelin and its perturbations in MS and other disorders of the myelin sheath.

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Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data

Abstract: The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.

Cited by 31 publications

References 38 publications

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Multi‐spin echo T₂ relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

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Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data

Abstract: The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.

Cited by 31 publications

References 38 publications

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2decay data

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2decay data

Multi‐spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

Myelin water imaging to detect demyelination and remyelination and its validation in pathology

Contact Info

Product

Resources

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Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T₂decay data

Multi‐spin echo T₂ relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging