Characterizing Peritumoral Tissue Using DTI-Based Free Water Elimination

2019 ◽  
pp. 123-131 ◽  
Author(s):  
Abdol Aziz Ould Ismail ◽  
Drew Parker ◽  
Moises Hernandez-Fernandez ◽  
Steven Brem ◽  
Simon Alexander ◽  
...  
Keyword(s):  
Free Water ◽  
Water Elimination ◽  
Peritumoral Tissue

scholarly journals Dedicated diffusion phantoms for the investigation of free water elimination and mapping: insights into the influence of T 2 relaxation properties

2020 ◽  
Vol 33 (4) ◽  
Author(s):  
Ezequiel Farrher ◽  
Farida Grinberg ◽  
Li‐Wei Kuo ◽  
Kuan‐Hung Cho ◽  
Richard P. Buschbeck ◽  
...  
Keyword(s):  
Free Water ◽  
Relaxation Properties ◽  
Water Elimination

scholarly journals Diffusion kurtosis imaging with free water elimination: A bayesian estimation approach

2018 ◽  
Vol 80 (2) ◽  
pp. 802-813 ◽  
Author(s):  
Quinten Collier ◽  
Jelle Veraart ◽  
Ben Jeurissen ◽  
Floris Vanhevel ◽  
Pim Pullens ◽  
...  
Keyword(s):  
Bayesian Estimation ◽  
Free Water ◽  
Diffusion Kurtosis Imaging ◽  
Water Elimination ◽  
Diffusion Kurtosis

scholarly journals A diffusion model‐free framework with echo time dependence for free‐water elimination and brain tissue microstructure characterization

2018 ◽  
Vol 80 (5) ◽  
pp. 2155-2172 ◽  
Author(s):  
Miguel Molina‐Romero ◽  
Pedro A. Gómez ◽  
Jonathan I. Sperl ◽  
Michael Czisch ◽  
Philipp G. Sämann ◽  
...  
Keyword(s):  
Time Dependence ◽  
Brain Tissue ◽  
Diffusion Model ◽  
Free Water ◽  
Microstructure Characterization ◽  
Tissue Microstructure ◽  
Model Free ◽  
Water Elimination ◽  
Echo Time

scholarly journals Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zahra Riahi Samani ◽  
Drew Parker ◽  
Ronald Wolf ◽  
Wes Hodges ◽  
Steven Brem ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Free Water ◽  
Majority Voting ◽  
Water Volume ◽  
Extracellular Water ◽  
Peritumoral Tissue ◽  
Water Volume Fraction

AbstractTumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

scholarly journals Opposing white matter microstructure abnormalities in 22q11.2 deletion and duplication carriers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johanna Seitz-Holland ◽  
Monica Lyons ◽  
Leila Kushan ◽  
Amy Lin ◽  
Julio E. Villalon-Reina ◽  
...  
Keyword(s):  
White Matter ◽  
Psychiatric Morbidity ◽  
Free Water ◽  
Water Model ◽  
22Q11.2 Deletion ◽  
Tissue Properties ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted ◽  
Water Elimination ◽  
Number Variation

AbstractDeletions and duplications at the 22q11.2 locus are associated with significant neurodevelopmental and psychiatric morbidity. Previous diffusion-weighted magnetic resonance imaging (MRI) studies in 22q11.2 deletion carriers (22q-del) found nonspecific white matter (WM) abnormalities, characterized by higher fractional anisotropy. Here, utilizing novel imaging and processing methods that allow separation of signal contribution from different tissue properties, we investigate whether higher anisotropy is driven by (1) extracellular changes, (2) selective degeneration of secondary fibers, or (3) volumetric differences. We further, for the first time, investigate WM microstructure in 22q11.2 duplication carriers (22q-dup). Multi-shell diffusion-weighted images were acquired from 26 22q-del, 19 22q-dup, and 18 healthy individuals (HC). Images were fitted with the free-water model to estimate anisotropy following extracellular free-water elimination and with the novel BedpostX model to estimate fractional volumes of primary and secondary fiber populations. Outcome measures were compared between groups, with and without correction for WM and cerebrospinal fluid (CSF) volumes. In 22q-del, anisotropy following free-water elimination remained significantly higher compared with controls. BedpostX did not identify selective secondary fiber degeneration. Higher anisotropy diminished when correcting for the higher CSF and lower WM volumes. In contrast, 22q-dup had lower anisotropy and greater extracellular space than HC, not influenced by macrostructural volumes. Our findings demonstrate opposing effects of reciprocal 22q11.2 copy-number variation on WM, which may arise from distinct pathologies. In 22q-del, microstructural abnormalities may be secondary to enlarged CSF space and more densely packed WM. In 22q-dup, we see evidence for demyelination similar to what is commonly observed in neuropsychiatric disorders.

Free water elimination and mapping from diffusion MRI

2009 ◽  
Vol 62 (3) ◽  
pp. 717-730 ◽  
Author(s):  
Ofer Pasternak ◽  
Nir Sochen ◽  
Yaniv Gur ◽  
Nathan Intrator ◽  
Yaniv Assaf
Keyword(s):  
Diffusion Mri ◽  
Free Water ◽  
Water Elimination

scholarly journals Distinct Tumor Signatures using Deep Learning-based Characterization of the Peritumoral Microenvironment in Glioblastomas and Brain Metastases

2021 ◽  
Author(s):  
Zahra Riahi Samani ◽  
Drew Parker ◽  
Ronald Wolf ◽  
Wes Hodges ◽  
Steven Brem ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Vasogenic Edema ◽  
Mean Diffusivity ◽  
Free Water ◽  
Water Volume ◽  
Extracellular Water ◽  
Peritumoral Tissue ◽  
Water Volume Fraction

Abstract Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction map of the peritumoral regions of 54 metastases and 89 glioblastoma patients. We obtained 93% accuracy in discriminating metastases from glioblastomas. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

scholarly journals Spatiotemporal characterisation of ischaemic lesions in transient stroke animal models using diffusion free water elimination and mapping MRI with echo time dependence

NeuroImage ◽  
2021 ◽  
pp. 118605
Author(s):  
Ezequiel Farrher ◽  
Chia-Wen Chiang ◽  
Kuan-Hung Cho ◽  
Farida Grinberg ◽  
Richard P. Buschbeck ◽  
...  
Keyword(s):  
Animal Models ◽  
Time Dependence ◽  
Free Water ◽  
Water Elimination ◽  
Echo Time
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