Untangling the diffusion signal using the phasor transform

Publication date

2020-12-01

Authors

van Rijssel, Michael J.
Froeling, MartijnORCID 0000-0003-3841-0497
van Lier, Astrid L H M WORCID 0000-0002-2150-9776
Verhoeff, Joost J CORCID 0000-0001-9673-0793ISNI 0000000393929005
Pluim, Josien P WORCID 0000-0001-7327-9178ISNI 000000014097262X

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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.

Keywords

diffusion fraction estimation, diffusion modeling, intravoxel incoherent motion, multi-compartment diffusion modeling, phasor representation, tissue characterization, Molecular Medicine, Radiology Nuclear Medicine and imaging, Spectroscopy, Journal Article

Citation

van Rijssel, M J, Froeling, M, van Lier, A L H M W, Verhoeff, J J C & Pluim, J P W 2020, 'Untangling the diffusion signal using the phasor transform', NMR in Biomedicine, vol. 33, no. 12, e4372, pp. 1-19. https://doi.org/10.1002/nbm.4372