Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma

Visser, Maarten R.; Petr, Jan; Müller, Domenique M J; Eijgelaar, Roelant S; Hendriks, Eef J.; Witte, Marnix G.; Barkhof, Frederik; Herk, Marcel van; Mutsaerts, Henk‐Jan; Vrenken, Hugo; Munck, J.C. de; Hamer, Philip C. De Witt

doi:10.3389/fnins.2020.00585

Cited by 30 publications

(23 citation statements)

References 51 publications

(68 reference statements)

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“…The within-subject difference in deep GM volume was calculated by subtracting the baseline and the 1-year follow-up volume. In every subject the deep GM organs included in the PTV were censored from analysis, to avoid spurious volume-dose relations originating from segmentation errors due to damage around the tumour [22] . If the residual damage (e.g.…”

Section: Methodsmentioning

confidence: 99%

Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy

Nagtegaal

Dávid

Philippens

et al. 2021

Clinical and Translational Radiation Oncology

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Section: Methodsmentioning

confidence: 99%

Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy

Nagtegaal

Dávid

Philippens

et al. 2021

Clinical and Translational Radiation Oncology

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“…Image registration refers to the spatial alignment of the various imaging sequences into the same geometric/anatomic space (Fig 2A). 11 Tumor segmentation refers to contour delineation of the volumes of interest (3D) or ROIs (2D) (Fig 3). Manual segmentation or semiautomated and automated segmentation with superimposed manual segmentation is the criterion standard.…”

Section: Technologic Methods and Computational Processmentioning

confidence: 99%

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

Toll

Hein

et al. 2021

AJNR Am J Neuroradiol

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Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

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“…The MRI subject datasets were registered to the atlas using the FMRIB Software Library's FLIRT and FNIRT tools (Analysis Group, Oxford, United Kingdom) (Jenkinson and Smith, 2001;Jenkinson et al, 2002Jenkinson et al, , 2012) by a 12-parameter affine model with the correlation ratio cost function (FLIRT) and the sum-of-squared differences cost-function (FNIRT), which is accepted as sufficient for most brains (Heinen et al, 2016;Bartel et al, 2019;Visser et al, 2020). A manual adjustment was then performed for registration errors that were found (Figure 2).…”

Section: Atlas Evaluation For Magnetic Resonance Imagingmentioning

confidence: 99%

A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis

et al. 2022

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The olive baboon (Papio anubis) is phylogenetically proximal to humans. Investigation into the baboon brain has shed light on the function and organization of the human brain, as well as on the mechanistic insights of neurological disorders such as Alzheimer’s and Parkinson’s. Non-invasive brain imaging, including positron emission tomography (PET) and magnetic resonance imaging (MRI), are the primary outcome measures frequently used in baboon studies. PET functional imaging has long been used to study cerebral metabolic processes, though it lacks clear and reliable anatomical information. In contrast, MRI provides a clear definition of soft tissue with high resolution and contrast to distinguish brain pathology and anatomy, but lacks specific markers of neuroreceptors and/or neurometabolites. There is a need to create a brain atlas that combines the anatomical and functional/neurochemical data independently available from MRI and PET. For this purpose, a three-dimensional atlas of the olive baboon brain was developed to enable multimodal imaging analysis. The atlas was created on a population-representative template encompassing 89 baboon brains. The atlas defines 24 brain regions, including the thalamus, cerebral cortex, putamen, corpus callosum, and insula. The atlas was evaluated with four MRI images and 20 PET images employing the radiotracers for [11C]benzamide, [11C]metergoline, [18F]FAHA, and [11C]rolipram, with and without structural aids like [18F]flurodeoxyglycose images. The atlas-based analysis pipeline includes automated segmentation, registration, quantification of region volume, the volume of distribution, and standardized uptake value. Results showed that, in comparison to PET analysis utilizing the “gold standard” manual quantification by neuroscientists, the performance of the atlas-based analysis was at >80 and >70% agreement for MRI and PET, respectively. The atlas can serve as a foundation for further refinement, and incorporation into a high-throughput workflow of baboon PET and MRI data. The new atlas is freely available on the Figshare online repository (https://doi.org/10.6084/m9.figshare.16663339), and the template images are available from neuroImaging tools & resources collaboratory (NITRC) (https://www.nitrc.org/projects/haiko89/).

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Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma

Cited by 30 publications

References 51 publications

Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy

Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis

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