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Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity
This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360759/ https://www.ncbi.nlm.nih.gov/pubmed/28381986 http://dx.doi.org/10.3389/fnins.2017.00132 |
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author | Khanal, Bishesh Ayache, Nicholas Pennec, Xavier |
author_facet | Khanal, Bishesh Ayache, Nicholas Pennec, Xavier |
author_sort | Khanal, Bishesh |
collection | PubMed |
description | This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation. |
format | Online Article Text |
id | pubmed-5360759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-53607592017-04-05 Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity Khanal, Bishesh Ayache, Nicholas Pennec, Xavier Front Neurosci Neuroscience This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation. Frontiers Media S.A. 2017-03-22 /pmc/articles/PMC5360759/ /pubmed/28381986 http://dx.doi.org/10.3389/fnins.2017.00132 Text en Copyright © 2017 Khanal, Ayache and Pennec. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Khanal, Bishesh Ayache, Nicholas Pennec, Xavier Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title | Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title_full | Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title_fullStr | Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title_full_unstemmed | Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title_short | Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity |
title_sort | simulating longitudinal brain mris with known volume changes and realistic variations in image intensity |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360759/ https://www.ncbi.nlm.nih.gov/pubmed/28381986 http://dx.doi.org/10.3389/fnins.2017.00132 |
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