Flexible Bayesian Modelling for Nonlinear Image Registration

Brudfors, Mikael; Balbastre, Yaël; Flandin, Guillaume; Nachev, Parashkev; Ashburner, John

doi:10.1007/978-3-030-59716-0_25

Cited by 22 publications

(31 citation statements)

References 31 publications

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“…Although this paper focuses on relatively uninformative spatial regularisers, our method opens the door to the use of informative learned priors. One possibility, inspired by Brudfors, Ashburner, Nachev, Balbastre, 2019 , Brudfors, Balbastre, Flandin, Nachev, Ashburner, 2020 , is to include a multivariate Gaussian mixture model of the log-parameter maps, with learned tissue parameters (means, covariances, tissue probability maps). Alternatively, priors based on learned dictionaries of patches could be used ( Dalca et al., 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Model-based multi-parameter mapping

Balbastre

Brudfors

Azzarito

et al. 2021

Medical Image Analysis

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Section: Discussionmentioning

confidence: 99%

Model-based multi-parameter mapping

Balbastre

Brudfors

Azzarito

et al. 2021

Medical Image Analysis

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“…In the present analysis, we evaluated the degree of atrophy automatically from CT slices to further explore the impact of brain atrophy on clinical outcomes among patients with ABAO after EVT (Adduru et al, 2020;Brudfors et al, 2020). We included consecutive patients in the BASILAR registry who met the following criteria: (1) treated with EVT, (2) accepted noncontrast-enhanced CT (NECT) scanning before the endovascular intervention, and (3) NECT slice thickness ≤ 5 mm (to guarantee the accuracy of the analysis).…”

Section: Study Design and Participantsmentioning

confidence: 99%

“…We estimated brain atrophy automatically based on the CTseg algorithm (https://github.com/WCHN/CTseg), developed by the Ashburner group at the Wellcome Trust Centre for Neuroimaging (Brudfors et al, 2020). This routine spatially normalized brain CT images in the standard brain space at the Montreal Neurological Institute by flexible Bayesian modeling and further segmented the total BPV and CFV after skull stripping.…”

Section: Brain Atrophy Degree Evaluationmentioning

confidence: 99%

“…This routine spatially normalized brain CT images in the standard brain space at the Montreal Neurological Institute by flexible Bayesian modeling and further segmented the total BPV and CFV after skull stripping. Compared with previous methods, CTseg leads to a more robust segmentation that can better handle images with considerable noise and/or large morphological variability (Brudfors et al, 2020). Brain atrophy was then categorized by tertiles in the ratio of BPV-CFV as follows: first tertile (>3.349), mild atrophy; second tertile (3.018-3.349), intermediate atrophy; and third tertile (<3.018), severe atrophy.…”

Section: Brain Atrophy Degree Evaluationmentioning

confidence: 99%

“…Previous methods of evaluating brain atrophy have mainly relied on the subjective visual experience of the neurologists performing the evaluation, which might limit the use of brain atrophy for outcome evaluation in clinical practice (Appleton et al, 2020). Nevertheless, a recently developed automated volumetric algorithm named CTseg has enabled the rapid and objective estimation of the degree of global brain atrophy after mapping to the standard brain map and quantifying brain parenchymal volume (BPV) and cerebrospinal fluid volume (CFV) by CT, which is the most frequent type of brain image used to diagnose ischemic stroke (Adduru et al, 2020;Brudfors et al, 2020). Therefore, based on our previous multicenter BASILAR registry and the CTseg automatic algorithm, this study aims to explore the role of brain atrophy in determining clinical outcomes among patients with ABAO treated with EVT.…”

Section: Introductionmentioning

confidence: 99%

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Severe Brain Atrophy Predicts Poor Clinical Outcome After Endovascular Treatment of Acute Basilar Artery Occlusion: An Automated Volumetric Analysis of a Nationwide Registry

Liu

et al. 2021

Front. Aging Neurosci.

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Background: Brain atrophy globally reflects the effects of preexisting risk factors and biological aging on brain structures and normally predicts poor outcomes in anterior circulation stroke. However, comparing with these patients, acute basilar artery occlusion (ABAO) impairs infratentorial regions frequently and might benefit from brain atrophy due to the resulting residual space to reduce tissue compression and thus improve prognosis, which raises doubts that current understandings for prognostic roles of brain atrophy are also applicable for ABAO. Therefore, this study aims to evaluate brain atrophy automatically from CT images and investigates its impact on outcomes of ABAO following endovascular treatment (EVT).Methods: A total of 231 ABAO who underwent EVT from the BASILAR registry were enrolled. Brain atrophy was quantified as the ratio of brain parenchymal volume to cerebrospinal fluid volume on baseline CT. The primary outcome was the modified Rankin Scale (mRS) score at 3 months.Results: The frequency of favorable outcomes (90-day mRS ≤ 3) was significantly lower in the severe atrophy group (P = 0.014). Adjusted logistic models revealed that severe brain atrophy was significantly negatively associated with favorable outcome incidence (P = 0.006), with no relationship with either in-hospital or 90-day overall mortality (all P > 0.05). Adding a severe atrophy index into the baseline model obviously enhanced its discriminatory ability in predicting the outcome by obviously increasing areas under the receiver operating characteristic curve, net reclassification improvement algorithm, and integrated discrimination improvement algorithm values (all P < 0.05).Conclusion: Severe brain atrophy did not improve in-hospital or overall mortality but impaired the long-term recovery after EVT. This objective and automated marker has the potential to be incorporated into decision-support methods for treating ABAO.

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Voxel‐based dysconnectomic brain morphometry with computed tomography in Down syndrome

Sánchez‐Moreno,

Zhang,

Mateo

et al. 2023

Ann Clin Transl Neurol

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ObjectiveAlzheimer's disease (AD) is a major health concern for aging adults with Down syndrome (DS), but conventional diagnostic techniques are less reliable in those with severe baseline disability. Likewise, acquisition of magnetic resonance imaging to evaluate cerebral atrophy is not straightforward, as prolonged scanning times are less tolerated in this population. Computed tomography (CT) scans can be obtained faster, but poor contrast resolution limits its function for morphometric analysis. We implemented an automated analysis of CT scans to characterize differences across dementia stages in a cross‐sectional study of an adult DS cohort.MethodsCT scans of 98 individuals were analyzed using an automatic algorithm. Voxel‐based correlations with clinical dementia stages and AD plasma biomarkers (phosphorylated tau‐181 and neurofilament light chain) were identified, and their dysconnectomic patterns delineated.ResultsDementia severity was negatively correlated with gray (GM) and white matter (WM) volumes in temporal lobe regions, including parahippocampal gyri. Dysconnectome analysis revealed an association between WM loss and temporal lobe GM volume reduction. AD biomarkers were negatively associated with GM volume in hippocampal and cingulate gyri.InterpretationOur automated algorithm and novel dysconnectomic analysis of CT scans successfully described brain morphometric differences related to AD in adults with DS, providing a new avenue for neuroimaging analysis in populations for whom magnetic resonance imaging is difficult to obtain.

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Flexible Bayesian Modelling for Nonlinear Image Registration

Cited by 22 publications

References 31 publications

Model-based multi-parameter mapping

Model-based multi-parameter mapping

Severe Brain Atrophy Predicts Poor Clinical Outcome After Endovascular Treatment of Acute Basilar Artery Occlusion: An Automated Volumetric Analysis of a Nationwide Registry

Voxel‐based dysconnectomic brain morphometry with computed tomography in Down syndrome

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