ArticleFrontiers in neuroinformatics2019
Recommendations for Processing Head CT Data.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Brain and intracranial volumes are both enlarged and serve as potential risk factors in normal pressure hydrocephalus.Scientific reports · 2025Article
- Automated Detection of Normal Pressure Hydrocephalus Using CT Imaging for Calculating the Ventricle-to-Subarachnoid Volume Ratio.AJNR. American journal of neuroradiology · 2025Article
- Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT.NPJ digital medicine · 2024Article
- Deep learning-based defacing tool for CT angiography: CTA-DEFACE.European radiology experimental · 2024Article
- A comparative analysis of different augmentations for brain images.Medical & biological engineering & computing · 2024Article
- Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.Resuscitation · 2024Article
- A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data.Proceedings of machine learning research · 2023Article
- Anonymization and validation of three-dimensional volumetric renderings of computed tomography data using commercially available T1-weighted magnetic resonance imaging-based algorithms.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.Frontiers in oncology · 2023Article
- Preparing computed tomography images for machine learning in forensic and virtual anthropology.Forensic science international. Synergy · 2023Article
- Development of pre-procedure virtual simulation for challenging interventional procedures: an experimental study with clinical application.The Korean journal of pain · 2022Article
- Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.Scientific reports · 2022Article
- X-ray computed microtomography datasets for osteogenic nanofibrous coated titanium implants.Scientific data · 2022Article
- Feasibility of Pediatric Low-Dose Facial CT Reconstructed with Filtered Back Projection Using Adequate Kernels.Taehan Yongsang Uihakhoe chi · 2022Article
- Early recanalization in large-vessel occlusion stroke patients transferred for endovascular treatment.Journal of neurointerventional surgery · 2022Article
- A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.Healthcare (Basel, Switzerland) · 2022Article
- The Stroke Neuro-Imaging Phenotype Repository: An Open Data Science Platform for Stroke Research.Frontiers in neuroinformatics · 2021Article
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Abstract
Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.