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Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction

PURPOSE: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). METHODS: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The...

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Autores principales: Lingala, Sajan Goud, Guo, Yi, Bliesener, Yannick, Zhu, Yinghua, Lebel, R. Marc, Law, Meng, Nayak, Krishna S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980286/
https://www.ncbi.nlm.nih.gov/pubmed/31663134
http://dx.doi.org/10.1002/mp.13885
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author Lingala, Sajan Goud
Guo, Yi
Bliesener, Yannick
Zhu, Yinghua
Lebel, R. Marc
Law, Meng
Nayak, Krishna S.
author_facet Lingala, Sajan Goud
Guo, Yi
Bliesener, Yannick
Zhu, Yinghua
Lebel, R. Marc
Law, Meng
Nayak, Krishna S.
author_sort Lingala, Sajan Goud
collection PubMed
description PURPOSE: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). METHODS: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. RESULTS: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. CONCLUSIONS: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters.
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spelling pubmed-69802862020-02-10 Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction Lingala, Sajan Goud Guo, Yi Bliesener, Yannick Zhu, Yinghua Lebel, R. Marc Law, Meng Nayak, Krishna S. Med Phys DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING) PURPOSE: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). METHODS: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. RESULTS: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. CONCLUSIONS: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters. John Wiley and Sons Inc. 2019-11-19 2020-01 /pmc/articles/PMC6980286/ /pubmed/31663134 http://dx.doi.org/10.1002/mp.13885 Text en © 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING)
Lingala, Sajan Goud
Guo, Yi
Bliesener, Yannick
Zhu, Yinghua
Lebel, R. Marc
Law, Meng
Nayak, Krishna S.
Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title_full Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title_fullStr Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title_full_unstemmed Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title_short Tracer kinetic models as temporal constraints during brain tumor DCE‐MRI reconstruction
title_sort tracer kinetic models as temporal constraints during brain tumor dce‐mri reconstruction
topic DIAGNOSTIC IMAGING (IONIZING AND NON‐IONIZING)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980286/
https://www.ncbi.nlm.nih.gov/pubmed/31663134
http://dx.doi.org/10.1002/mp.13885
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