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Untangling the diffusion signal using the phasor transform

Separating the decay signal from diffusion‐weighted scans into two or more components can be challenging. The phasor technique is well established in the field of optical microscopy for visualization and separation of fluorescent dyes with different lifetimes. The use of the phasor technique for sep...

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Autores principales: van Rijssel, Michael J., Froeling, Martijn, van Lier, Astrid L.H.M.W., Verhoeff, Joost J.C., Pluim, Josien P.W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685171/
https://www.ncbi.nlm.nih.gov/pubmed/32701224
http://dx.doi.org/10.1002/nbm.4372
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author van Rijssel, Michael J.
Froeling, Martijn
van Lier, Astrid L.H.M.W.
Verhoeff, Joost J.C.
Pluim, Josien P.W.
author_facet van Rijssel, Michael J.
Froeling, Martijn
van Lier, Astrid L.H.M.W.
Verhoeff, Joost J.C.
Pluim, Josien P.W.
author_sort van Rijssel, Michael J.
collection PubMed
description Separating the decay signal from diffusion‐weighted scans into two or more components can be challenging. The phasor technique is well established in the field of optical microscopy for visualization and separation of fluorescent dyes with different lifetimes. The use of the phasor technique for separation of diffusion‐weighted decay signals was recently proposed. In this study, we investigate the added value of this technique for fitting decay models and visualization of decay rates. Phasor visualization was performed in five glioblastoma patients. Using simulations, the influence of incorrect diffusivity values and of the number of b‐values on fitting a three‐component model with fixed diffusivities (dubbed “unmixing”) was investigated for both a phasor‐based fit and a linear least squares (LLS) fit. Phasor‐based intravoxel incoherent motion (IVIM) fitting was compared with nonlinear least squares (NLLS) and segmented fitting (SF) methods in terms of accuracy and precision. The distributions of the parameter estimates of simulated data were compared with those obtained in a healthy volunteer. In the phasor visualizations of two glioblastoma patients, a cluster of points was observed that was not seen in healthy volunteers. The identified cluster roughly corresponded to the enhanced edge region of the tumor of two glioblastoma patients visible on fluid‐attenuated inversion recovery (FLAIR) images. For fitting decay models the usefulness of the phasor transform is less pronounced, but the additional knowledge gained from the geometrical configuration of phasor space can aid fitting routines. This has led to slightly improved fitting results for the IVIM model: phasor‐based fitting yielded parameter maps with higher precision than the NLLS and SF methods for parameters f and D (interquartile range [IQR] for f: NLLS 27, SF 12, phasor 5.7%; IQR for D: NLLS 0.28, SF 0.18, phasor 0.10 μm(2)/s). For unmixing, LLS fitting slightly but consistently outperformed phasor‐based fitting in all of the tested scenarios.
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spelling pubmed-76851712020-12-03 Untangling the diffusion signal using the phasor transform van Rijssel, Michael J. Froeling, Martijn van Lier, Astrid L.H.M.W. Verhoeff, Joost J.C. Pluim, Josien P.W. NMR Biomed Special Issue Research Articles Separating the decay signal from diffusion‐weighted scans into two or more components can be challenging. The phasor technique is well established in the field of optical microscopy for visualization and separation of fluorescent dyes with different lifetimes. The use of the phasor technique for separation of diffusion‐weighted decay signals was recently proposed. In this study, we investigate the added value of this technique for fitting decay models and visualization of decay rates. Phasor visualization was performed in five glioblastoma patients. Using simulations, the influence of incorrect diffusivity values and of the number of b‐values on fitting a three‐component model with fixed diffusivities (dubbed “unmixing”) was investigated for both a phasor‐based fit and a linear least squares (LLS) fit. Phasor‐based intravoxel incoherent motion (IVIM) fitting was compared with nonlinear least squares (NLLS) and segmented fitting (SF) methods in terms of accuracy and precision. The distributions of the parameter estimates of simulated data were compared with those obtained in a healthy volunteer. In the phasor visualizations of two glioblastoma patients, a cluster of points was observed that was not seen in healthy volunteers. The identified cluster roughly corresponded to the enhanced edge region of the tumor of two glioblastoma patients visible on fluid‐attenuated inversion recovery (FLAIR) images. For fitting decay models the usefulness of the phasor transform is less pronounced, but the additional knowledge gained from the geometrical configuration of phasor space can aid fitting routines. This has led to slightly improved fitting results for the IVIM model: phasor‐based fitting yielded parameter maps with higher precision than the NLLS and SF methods for parameters f and D (interquartile range [IQR] for f: NLLS 27, SF 12, phasor 5.7%; IQR for D: NLLS 0.28, SF 0.18, phasor 0.10 μm(2)/s). For unmixing, LLS fitting slightly but consistently outperformed phasor‐based fitting in all of the tested scenarios. John Wiley and Sons Inc. 2020-07-23 2020-12 /pmc/articles/PMC7685171/ /pubmed/32701224 http://dx.doi.org/10.1002/nbm.4372 Text en © 2020 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd 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 Special Issue Research Articles
van Rijssel, Michael J.
Froeling, Martijn
van Lier, Astrid L.H.M.W.
Verhoeff, Joost J.C.
Pluim, Josien P.W.
Untangling the diffusion signal using the phasor transform
title Untangling the diffusion signal using the phasor transform
title_full Untangling the diffusion signal using the phasor transform
title_fullStr Untangling the diffusion signal using the phasor transform
title_full_unstemmed Untangling the diffusion signal using the phasor transform
title_short Untangling the diffusion signal using the phasor transform
title_sort untangling the diffusion signal using the phasor transform
topic Special Issue Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685171/
https://www.ncbi.nlm.nih.gov/pubmed/32701224
http://dx.doi.org/10.1002/nbm.4372
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