Recommendations for Processing Head CT Data

Muschelli, John

doi:10.3389/fninf.2019.00061

Cited by 41 publications

(24 citation statements)

References 44 publications

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“…The pipeline includes five containerized modules, including neural network-based labeling of image acquisition type ( Szegedy et al, 2015 ; Mohammadian Foroushani et al, 2020a ), DICOM to NIfTI conversion ( Li, 2016 ), FSL-based preregistration for skull stripping ( Jenkinson, 2011 ), ANTS-based registration of longitudinal brain masks ( Avants et al, 2009 ), U-Net-based segmentation of CSF ( Chen et al, 2016 ), and its volumetric calculation ( Dhar et al, 2020 ). This parallels the recent recommendations for processing head CT data ( Muschelli, 2019 ). After finishing the process, each executed container uses XNAT’s web-based application programming interface (API) to store its output back into SNIPR.…”

Section: Methodssupporting

confidence: 82%

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

Foroushani

Dhar

Chen

et al. 2021

Front. Neuroinform.

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Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on the open source XNAT imaging informatics platform. The aims of this repository are to: (i) store, manage, process, and facilitate sharing of high-value stroke imaging data sets, (ii) implement containerized automated computational methods to extract image characteristics and disease-specific features from contributed images, (iii) facilitate integration of imaging, genomic, and clinical data to perform large-scale analysis of complications after stroke; and (iv) develop SNIPR as a collaborative platform aimed at both data scientists and clinical investigators. Currently, SNIPR hosts research projects encompassing ischemic and hemorrhagic stroke, with data from 2,246 subjects, and 6,149 imaging sessions from Washington University’s clinical image archive as well as contributions from collaborators in different countries, including Finland, Poland, and Spain. Moreover, we have extended the XNAT data model to include relevant clinical features, including subject demographics, stroke severity (NIH Stroke Scale), stroke subtype (using TOAST classification), and outcome [modified Rankin Scale (mRS)]. Image processing pipelines are deployed on SNIPR using containerized modules, which facilitate replicability at a large scale. The first such pipeline identifies axial brain CT scans from DICOM header data and image data using a meta deep learning scan classifier, registers serial scans to an atlas, segments tissue compartments, and calculates CSF volume. The resulting volume can be used to quantify the progression of cerebral edema after ischemic stroke. SNIPR thus enables the development and validation of pipelines to automatically extract imaging phenotypes and couple them with clinical data with the overarching aim of enabling a broad understanding of stroke progression and outcomes.

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Section: Methodssupporting

confidence: 82%

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

Foroushani

Dhar

Chen

et al. 2021

Front. Neuroinform.

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“…The procedures used to process the brain CT images analyzed in this study followed those described previously. 17 , 18 , 19 ) The CT data stored in DICOM format were converted into NIFTI (Neuroimaging Informatics Technology Initiative) format using dcm2niix software (https://github.com/rordenlab/dcm2niix, accessed July 31, 2021). 20 ) The brain image analysis package FSL 21 ) version 6.0.4 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki, accessed April 30, 2021) was used for further analyses.…”

Section: Methodsmentioning

confidence: 99%

“…As recommended in previous reports, 17 , 18 , 19 ) CT images in NIFTI format were further processed by thresholding Hounsfield units (HU) in the range 0–100 using FSLUTILS. By using FLIRT, 22 , 23 ) the processed CT data were then spatially normalized in reference to a previously reported standard CT template.…”

Section: Methodsmentioning

confidence: 99%

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Uchiyama

Domen

Koyama

2021

PRM

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Objective: This study investigated the potential utility of computed tomography for outcome prediction in patients with intracerebral hemorrhage. Methods: Patients with putaminal and/or thalamic hemorrhage for whom computed tomography images were acquired in our hospital emergency room soon after onset were retrospectively enrolled. Outcome measurements were obtained at discharge from the convalescent rehabilitation ward of our affiliated hospital. Hemiparesis was evaluated using the total score of the motor component of the Stroke Impairment Assessment Set (SIAS-motor; null to full, 0 to 25), the motor component of the Functional Independence Measure (FIM-motor; null to full, 13 to 91), and the total length of hospital stay. After registration of the computed tomography images to the standard brain, the volumes of the hematoma lesions located in the corticospinal tract were calculated. The correlation between the corticospinal tract lesion volumes and the outcome measurements was assessed using Spearman’s rank correlation test. Results: Thirty patients were entered into the final analytical database. Corticospinal tract lesion volumes ranged from 0.002 to 4.302 ml (median, 1.478). SIAS-motor scores ranged from 0 to 25 (median, 20), FIM-motor scores ranged from 15 to 91 (median, 80.5), and the total length of hospital stay ranged from 31 to 194 days (median, 106.5). All correlation tests were statistically significant (P <0.01). The strongest correlation was for SIAS-motor total (R=–0.710), followed by FIM-motor (R=–0.604) and LOS (R=0.493). Conclusions: These findings suggest that conventional computed tomography images may be useful for outcome prediction in patients with intracerebral hemorrhage.

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“…In gantry-tilted scans, Elastix affine-registration was applied. 11 For better scan co-registration, the scans were re-sliced to obtain a 2.5 mm slice thickness if the slice thickness was larger than 2.5 mm. Poor quality scans were defined as scans with movement artefacts, incomplete field of view, severe beam hardening artefacts, and/or metal artefacts.…”

Section: Thrombus Imaging Characteristicsmentioning

confidence: 99%

Early recanalization in large-vessel occlusion stroke patients transferred for endovascular treatment

Terreros

Bruggeman

Swijnenburg

et al. 2021

J NeuroIntervent Surg

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BackgroundWe performed an exploratory analysis to identify patient and thrombus characteristics associated with early recanalization in large-vessel occlusion (LVO) stroke patients transferred for endovascular treatment (EVT) from a primary (PSC) to a comprehensive stroke center (CSC).MethodsWe included patients with an LVO stroke of the anterior circulation who were transferred to our hospital for EVT and underwent repeated imaging between January 2016 and June 2019. We compared patient characteristics, workflow time metrics, functional outcome (modified Rankin Scale at 90 days), and baseline thrombus imaging characteristics, which included: occlusion location, thrombus length, attenuation, perviousness, distance from terminus of intracranial carotid artery to the thrombus (DT), and clot burden score (CBS), between early-recanalized LVO (ER-LVO), and non-early-recanalized LVO (NER-LVO) patients.ResultsOne hundred and forty-nine patients were included in the analysis. Early recanalization occurred in 32% of patients. ER-LVO patients less often had a medical history of hypertension (31% vs 49%, P=0.04), and more often had clinical improvement between PSC and CSC (ΔNIHSS −5 vs 3, P<0.01), compared with NER-LVO patients. Thrombolysis administration was similar in both groups (88% vs 78%, P=0.18). ER-LVO patients had no ICA occlusions (0% vs 27%, P<0.01), more often an M2 occlusion (35% vs 17%, P=0.01), longer DT (27 mm vs 12 mm, P<0.01), shorter thrombi (17 mm vs 27 mm, P<0.01), and higher CBS (8 vs 6, P<0.01) at baseline imaging. ER-LVO patients had lower mRS scores (1 vs 3, P=0.02).ConclusionsEarly recanalization is associated with clinical improvement between PSC and CSC admission, more distal occlusions and shorter thrombi at baseline imaging, and better functional outcome.

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Recommendations for Processing Head CT Data

Cited by 41 publications

References 44 publications

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research

Outcome Prediction of Patients with Intracerebral Hemorrhage by Measurement of Lesion Volume in the Corticospinal Tract on Computed Tomography

Early recanalization in large-vessel occlusion stroke patients transferred for endovascular treatment

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