Dynamics of progressive degeneration of major spinal pathways following spinal cord injury: A longitudinal study

Schading, Simon; Dávid, Gergely; Emmenegger, Tim; Achim, Cristian L.; Thompson, Alan; Weiskopf, Nikolaus; Curt, Armin; Freund, Patrick

doi:10.1016/j.nicl.2023.103339

Cited by 2 publications

(2 citation statements)

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“…[36][37][38] One substantial benefit of the tMPPCA denoising technique is that it provides its advantages not only to future studies but also retrospectively increases spatial and temporal SNR of already acquired and published data. This might allow to better delineate small anatomical brain structures 50,51 or detect the dynamics of microstructural changes 52,53 with increased sensitivity. In future MPM studies, tMPPCA denoising can be utilized to considerably reduce scan time for clinical applications or to push the resolution of cutting-edge MPM protocols from the upper to the lower limit of the mesoscopic scale.…”

Section: Discussionmentioning

confidence: 99%

Tensor denoising of quantitative multi‐parameter mapping

Herthum,

Hetzer

2024

Magnetic Resonance in Med

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PurposeQuantitative multi‐parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure‐function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko‐Pastur law (tMPPCA).MethodstMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high‐resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions.ResultsSubstantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan–rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity.ConclusionstMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting‐edge MPM protocols from the upper to the lower limit of the mesoscopic scale.

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Section: Discussionmentioning

confidence: 99%

Tensor denoising of quantitative multi‐parameter mapping

Herthum,

Hetzer

2024

Magnetic Resonance in Med

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“…The validity and applicability of synT 1 -w images in clinical studies was assessed by directly comparing CSA measurements derived from MPRAGE with those derived from synT 1 -w in a longitudinal study that involved healthy participants and SCI patients ( Azzarito et al, 2020 ;Freund et al, 2013b ;Grabher et al, 2015 ;Schading et al, 2023 ;Seif et al, 2018aSeif et al, , 2018bZiegler et al, 2018 ). As the CSA has been proposed as a marker for neurodegeneration in clinical trials ( Casserly et al, 2018 ;Freund et al, 2013a ;Kearney et al, 2015 ;Schmierer and Miquel, 2018 ;Seif et al, 2019 ), optimizing acquisition time of study protocols can help to improve patient comfort, data quality and scan costs.…”

Section: Agreement Between Csa From Mprage and Synt 1 -Wmentioning

confidence: 99%