Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

Proc. Natl. Acad. Sci. U.S.A.

Cordero‐Grande

et al. 2021

Self Cite

During the second and third trimesters of human gestation, rapid neurodevelopment is underpinned by fundamental processes including neuronal migration, cellular organization, cortical layering, and myelination. In this time, white matter growth and maturation lay the foundation for an efficient network of structural connections. Detailed knowledge about this developmental trajectory in the healthy human fetal brain is limited, in part, due to the inherent challenges of acquiring high-quality MRI data from this population. Here, we use state-of-the-art high-resolution multishell motion-corrected diffusion-weighted MRI (dMRI), collected as part of the developing Human Connectome Project (dHCP), to characterize the in utero maturation of white matter microstructure in 113 fetuses aged 22 to 37 wk gestation. We define five major white matter bundles and characterize their microstructural features using both traditional diffusion tensor and multishell multitissue models. We found unique maturational trends in thalamocortical fibers compared with association tracts and identified different maturational trends within specific sections of the corpus callosum. While linear maturational increases in fractional anisotropy were seen in the splenium of the corpus callosum, complex nonlinear trends were seen in the majority of other white matter tracts, with an initial decrease in fractional anisotropy in early gestation followed by a later increase. The latter is of particular interest as it differs markedly from the trends previously described in ex utero preterm infants, suggesting that this normative fetal data can provide significant insights into the abnormalities in connectivity which underlie the neurodevelopmental impairments associated with preterm birth.

Section: Resultsmentioning

confidence: 99%

Development of human white matter pathways in utero over the second and third trimester

Wilson

Proc. Natl. Acad. Sci. U.S.A.

Cordero‐Grande

et al. 2021

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“…For visualisation of single-subject data, the data ‘sub-CC00083XX10/ses-30900’ was used as it has clearly visible stripe patterns. This baby’s postmenstrual age (PMA) at scan is 42 weeks and the image lies in the 59th percentile quality score estimated from the motion and outlier weights (40). To create a population-average template from representative data, we randomly selected 32 subjects from 38 to 42 weeks PMA that have quality assessment scores above the 20th percentile (see figure 10).…”

Section: Methodsmentioning

confidence: 99%

“…The dMRI data was processed using a motion correction and reconstruction algorithm (MCR) with integrated slice-to-volume reconstruction (SVR) (40). In brief, the reconstruction is based on an iterative estimation of a data-driven multi-shell low-rank (model-free) data representation (SHARD), slice outlier estimation, and rigid registration algorithm (41).…”

Section: Methodsmentioning

confidence: 99%

dStripe: slice artefact correction in diffusion MRI via constrained neural network

Christiaens

Hajnal

et al. 2020

Preprint

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MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.

“…Datasets were acquired on a 3T Philips Achieva scanner equipped with a dedicated 32-channel neonatal head coil and baby transportation system (Hughes et al, 2017). The multi-shell HARDI volumes were acquired with four phase-encode directions on four shells with b-values of 0, 400, 1000 and 2600 s/mm 2 with TE 90 ms, TR 3800 ms (Hutter et al, 2018;Tournier et al, 2020) with 1.5 × 1.5 × 3 mm resolution and 1.5 mm slice overlap and reconstructed to 1.5 mm isotropic resolution using the SHARD pipeline (Christiaens et al, 2021). The structural T2w volumes were acquired using a TSE sequence with TR 12 s, TE 156 ms.…”

Section: Cohort Datasets and Preprocessingmentioning

confidence: 99%

Multi-channel 4D parametrized Atlas of Macro- and Microstructural Neonatal Brain Development

Uus

Grigorescu

et al. 2021

Preprint

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Structural and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. The existing models of the developing brain in time include only either structural or diffusion channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work we propose methodology to generate multi-channel (MC) continuous spatio-temporal parametrized atlas of brain development based on MC registration driven by both T2-weighted and orientation distribution functions (ODF) channels along with the Gompertz model (GM) fitting of the signals and spatial transformations in time. We construct a 4D MC atlas of neonatal brain development during 38 to 44 week PMA range from 170 normal term subjects from developing Human Connectomme Project. The resulting atlas consists of fourteen spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. We demonstrate applicability of the atlas for quantitative region-specific comparison of 140 term and 40 preterm subjects scanned at the term-equivalent age. We show multi-parametric microstructural differences in multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.