ArticleFrontiers in neuroscience2020
Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients.Physics and imaging in radiation oncology · 2026Article
- Systematic comparison of semi-automated VBM pipelines for assessing chemoradiotherapy effects on the tumoural and non-tumoural brain in glioblastoma multiforme.Magma (New York, N.Y.) · 2025Article
- Resection Location and Work Resumption in Patients With Lower-Grade Glioma: A Multicenter Cohort Study.Neurosurgery practice · 2025Article
- Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space.Neuroinformatics · 2024Article
- Three-dimensional histology reveals dissociable human hippocampal long-axis gradients of Alzheimer's pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Fibre orientation atlas guided rapid segmentation of white matter tracts.Human brain mapping · 2024Article
- Three-dimensional histology reveals dissociable human hippocampal long axis gradients of Alzheimer's pathology.bioRxiv : the preprint server for biology · 2023Article
- Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks.Computer methods and programs in biomedicine · 2023Article
- PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI.Algorithms · 2023Article
- Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology.AJNR. American journal of neuroradiology · 2022Review
- Overestimation of grey matter atrophy in glioblastoma patients following radio(chemo)therapy.Magma (New York, N.Y.) · 2022Article
- Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model.Frontiers in neuroscience · 2022Article
- Article
- Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions.Frontiers in oncology · 2021Article
- A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis.Frontiers in neuroanatomy · 2021Article
- Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy.Clinical and translational radiation oncology · 2021Article
- Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries.Neuro-oncology advancesReview
- Impact of resection location on depressive symptoms following glioma surgery.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.
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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.