Untangling the diffusion signal using the phasor transform

Michael J. van Rijssel (Corresponding author), Martijn Froeling, Astrid L.H.M.W. van Lier, Joost J.C. Verhoeff, Josien P.W. Pluim

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

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 μm2/s). For unmixing, LLS fitting slightly but consistently outperformed phasor-based fitting in all of the tested scenarios.

Original languageEnglish
Article numbere4372
Number of pages19
JournalNMR in Biomedicine
Volume33
Issue number12
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • diffusion fraction estimation
  • diffusion modeling
  • intravoxel incoherent motion
  • multi-compartment diffusion modeling
  • phasor representation
  • tissue characterization

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