{'fraction_non_alphanumeric': 0.023460171950855688, 'fraction_numerical': 0.008841637058935207, 'mean_word_length': 4.792020106817468, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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 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.

Separating the decay signal from diffusion-weighted scans into two or more compo- 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 The decay signal from diffusion-weighted scans has been intensively studied and, consequently, the field has come up with a plethora of models to describe it, ranging from complex models based on physical descriptions of diffusing water molecules to relatively simple mathematical descriptions of the measured signal. 1,2 A common task is to separate the measured signal into components decaying at different speeds, such as fitting the intravoxel incoherent motion (IVIM) model or even more complex models that divide the diffusion signal into multiple water pools. 3,4 Recent works have shown that the use of IVIM model fitting is valuable for brain tumor characterization or when discerning brain tumor progression from pseudoprogression or radionecrosis. [5][6][7][8] Separating decay signals into two or more components can be challenging, especially in the presence of noise. Vergeldt et al applied so-called phasor representation and subsequent signal separation to an in vivo diffusion dataset of the human brain. 9 The phasor representation is generated by taking the Fourier transform of a signal and subsequently plotting the imaginary versus the real component at a single frequency, usually the lowest nonzero harmonic. This approach achieved promising results for fitting a three-component model with fixed diffusivities (dubbed "unmixing").
The fraction maps generated with this approach may be valuable in a clinical setting or as a starting point for complex biophysical models.
The phasor representation shows the position of an exponential decay curve in phasor space, which is determined by its lifetime, or a combination of lifetimes. Monoexponential decay curves are represented on a semicircle, with components possessing a short lifetime on the right and components with a long lifetime on the left of this semicircle. Inversely, from the position in phasor space, the lifetime of components can be estimated Figure 1.
Multiexponential decay curves, which can be regarded as mixtures of monoexponential decay curves, are represented in the area enclosed by the single-exponential semicircle in phasor space. Bi-exponential decay curves are found on the line segment connecting their two base components; the position on this line is determined by the weighting between the components. Triple-exponential decay curves are found inside the triangle defined by their three base components, etc. These mixed signals can be unmixed (separated) from the sum signal into fractional contributions of up to a maximum of three components when fixing the lifetimes for those components across the dataset. 10 A different approach with fixed lifetimes has been shown to have superior fit quality when compared with conventional fitting approaches. 11 Although the phasor approach to unmixing of diffusion signals is simple and fast, it only allows for the unmixing of, at the most, three signals with known diffusivities. The same fraction maps can be obtained by a simple linear system inversion, but since the phasor transform effectively involves a noise reduction step by selecting the lowest nonzero frequency, the generated maps are expected to have a higher fit quality. Currently, the robustness of both phasor and linear inversion methods against noise and misestimation of the fixed diffusivities is unknown.
Transforming a dataset into phasor space has the additional benefit of producing a global visualization of all decay rates present in the data.
By presenting a human brain diffusion dataset in phasor space using a 2D histogram, Vergeldt et al have extrapolated three major diffusivities: 3.1, 0.65 and <0.3 μm 2 /s (figure 5a in 9 ). Although this separation into three diffusivities is a simplification of the full diffusivity spectrum present in the human brain, 3 the underlying 2D histogram in phasor space represents a visualization of the full spectrum. This global visualization is potentially useful in detecting or diagnosing pathology in an efficient manner.
The phasor space may also be of use when fitting a more complex model, such as the IVIM model. The estimation of this model is often challenging since different combinations of parameters can yield model fits with comparable residuals. 12 A popular approach to circumvent this instability is a two-stage linear approach, often referred to as segmented fitting. 13 This method, however, is prone to bias in parameter estimates. 14 Since the IVIM model is bi-exponential, the phasor transform might be of use in fitting this model, more explicitly by exploiting the property that bi-exponential curves end up on the line connecting the two base components. This allows a reduction of the number of free parameters by two, which might yield more stable fitting results.
The present work investigates the usefulness of the phasor transform for both a global visualization of the decay rates in a diffusion dataset and fitting decay models. We test the hypothesis that using the phasor transform yields more stable fraction and parameter maps in both fixeddiffusivity unmixing and IVIM model fitting. The influence of noise, component displacement and sampling on the accuracy of phasor unmixing is tested in a digital phantom. The performance of phasor unmixing is compared with that of linear unmixing as a benchmark. A phasor-based IVIM fitting routine is developed and compared with both classical nonlinear fitting and linear segmented fitting in terms of accuracy and precision. The results of these simulations are compared with those obtained in a healthy volunteer. As a pilot study, global visualization is tested in five patients with biopsy-proven glioblastoma.

| Phasor transform and decay rate estimation
In this section, we will provide a brief overview of the phasor representation. Both phasor transform and phasor unmixing have been presented in the field of optical microscopy for the separation of fluorescent dyes with different lifetimes. [15][16][17] The existing phasor toolkit allows input signals that are equidistantly sampled over time. Since nonequidistant sampling across b-values is very common in diffusion datasets, we will add an analytical description of phasor unmixing in case of nonequidistant sampling. This improves the general applicability of the phasor toolkit.
To maintain compatibility with existing literature on the phasor transform, this section describes single-exponential signals with s t ð Þ = e − t = τ , with time t and lifetime τ. In diffusion literature, the description s b ð Þ = e −bD , with b-value b and diffusivity D, is common. The translation from one framework to the other is easily achieved by substituting t with b and τ with 1/D in all of the equations below.
F I G U R E 1 A, Simulated signal curves for three diffusivity values D, acquired using 21 equally spaced b-values between 0 and 2500 s/mm 2 . B, Schematic phasor plot that indicates the position in phasor space of each of the curves in A using the same color scheme. The influence of diffusion kurtosis K was added using numerical simulations. C, Schematic phasor plot that shows the principle of two-component mixing. . If the input is an exponential function of time t with lifetime τ, s t ð Þ = e − t = τ , the phasor transform at frequency ω is the Lorentz function: S ω ð Þ= 1 1 + jωτ , with j the unit imaginary number. Plotting the real part of this function against the imaginary part for a fixed frequency ω for all possible lifetimes τ produces a semicircle with radius 0.5 and center (0.5, 0). Inversely, the decay rate of a signal can be estimated from its position in phasor space by inverting the Lorentz function: τ = j S−1 ð Þ Sω . In the case of discrete equidistant sampling, the sampling rate will influence the appearance of the single-exponential semicircle, deforming it into a semi-ellipse. This phenomenon has been described by Fereidouni et al for retrospectively binned exponential decay in the context of optical microscopy. 18 The authors provide an analytical description of this ellipse as a function of frequency, ω, and the number of measured samples or bins, U: with u the bin index, n the harmonic number and T the total sampling duration. The authors also describe a rotation of the deformed semicircle with decreasing U, which is due to their choice of the zero time point. For their case, each bin runs from t = (n-1)T to t = nT, which is a useful choice since photons are sampled continuously and retrospectively binned. In diffusion MRI, diffusion decay is only sampled at the chosen bvalues and not in between. Consequently, we simply consider a discrete Fourier transform of a discretely sampled exponential decay divided by the discrete sum of the same signal, that is, the discrete phasor transform: S ω, U ð Þ= This describes a semi-ellipse such as in the work of Fereidouni et al, but the rotation is no longer observed. An example of ellipse deformation due to discrete sampling can be seen in Figure 1B, which schematically shows the phasor plot of the single-exponential discretely sampled signals shown in Figure 1A. Note that in this example and all the following analyses, we have chosen the lowest harmonic (n = 1), representing the lowest nonzero frequency after Fourier transformation. This is a sensible choice since the Fourier transform of a decaying signal has the highest magnitude there. 18 This leads to the largest possible reference semicircle and the highest stability when unmixing. Additionally, Figure 1B shows the influence of diffusion kurtosis on the representation of a signal phasor space. 19 Since signals that have nonzero kurtosis are not single exponentials, they are not represented on the semi-ellipse. Instead, signals with positive kurtosis are represented in the area enclosed by the semi-ellipse, while signals with negative kurtosis are represented above it.
When sampling is discrete and nonequidistant, as is often the case for b-values in diffusion datasets, the single-exponential semi-ellipse will deform in an irregular way defined by the sampling pattern. This is not of concern when transforming data into phasor space or when determining the position of a decay rate on the circle since one can simply apply the phasor transform as defined. Although estimating the decay rate of a nonequidistantly sampled signal based on its position in phasor space has become nontrivial, this can be accounted for numerically.

| Signal mixing in phasor space
Define a mixture m of single exponentials: with τ p the lifetime of the p-th component contributing to the total signal with fraction a p . After applying the continuous phasor transform, the transformed signal can be described as a combination of the phasor transform of every single exponential: with M the phasor transform of the multiexponential signal m and α p the weighting of the p-th component in phasor space. α p depends on the weights of all components in the time domain, a, and their associated decay rates, τ: Graphically, this means that a mixed signal containing two components (ie, bi-exponential decay) is located on a line connecting the locations of the two pure components in phasor space. 20 The location of the signal on the line is determined by the weight α p . Analogously, mixtures of more components are located inside a polygon defined by the pure component vertices. An example of this process is shown in Figure 1C,D for two and three components, respectively. Note that this useful property of phasor space enables unmixing into the original signal fractions.
In the case of discrete sampling the mixing properties above still hold, but the weights of the components in phasor space now depend on the sampling pattern: with u the sample index, U the total number of samples, t u the time at which the u-th sample was measured, τ p the lifetime of the p-th component, a p the signal fraction of the p-th component and P the total number of single-exponential components. This is a very general description that holds for all possible sampling strategies.

| Phasor unmixing
Mixtures of signals containing up to three components can be unmixed using phasor unmixing. 10 Treating the real and imaginary axes separately and enforcing that the sum of all components is 1, the unmixing of three components can be achieved by first solving: with S p the phasor transform (ie, phasor coordinates) of signal s p with corresponding phasor fraction α p , and M the phasor transform of the mixed signal. The solution of Equation 7 can be written in the form: which provides the phasor fraction, α p , for each component. To solve for signal fractions, a p , we have to invert Equation 6 for three components (P = 3): with E p defined as: Note that although the sum of all components is set to be 1 in Equation 7, individual components are not restricted. Consequently, solutions for both a p and α p outside the range [0, 1] can be returned.
van RIJSSEL ET AL.

| Phasor-based IVIM fitting
The geometric properties of phasor space can be exploited to reduce the number of free parameters when fitting an IVIM model. The IVIM model assumes a sum of two single-exponential signal components 4 : with S the measured signal, b the b-value, S 0 the baseline MR signal, D the diffusion coefficient, D* the pseudodiffusion coefficient and f its associated fraction. In a classical nonlinear least-squares fitting strategy, one has to estimate values for all four parameters of the model: S 0 , D, D* and f.
By exploiting the geometric properties of bi-exponential signals in phasor space, the number of free parameters can be reduced to one, either D or D*. As outlined at the beginning of this section, estimating S 0 is bypassed by transforming the data into phasor space since the phasor transform is normalized by definition. As is shown in Figure 1C Note that section 3.3 describes unmixing for three components. As IVIM only has two components, α 3 = 0, a 3 = 0, and Equation 8 simplifies to: : Also, Equation 9 simplifies to: : The resulting spectrum was divided by the sum signal for normalization. The phasor coordinates of each voxel were then determined by the real and imaginary components of the result at the lowest positive and nonzero frequency (ie, the lowest harmonic).

| Phasor unmixing
As outlined in the Theory section, the fractions of each pure component determine a voxel's coordinates in phasor space. Given the coordinates of these base components (component vertices) in phasor space, the relative signal fractions of up to three components can be estimated using phasor unmixing. A phasor unmixing routine was implemented in MATLAB that provides estimates for each fraction on a voxel-wise basis, by taking each voxel's coordinates in phasor space and the coordinates of the component vertices and applying Equations 8-10 (see also Figure 1D).

| Linear unmixing
Phasor unmixing was compared with the benchmark of linear unmixing. In linear unmixing, the fractions are calculated by directly solving Equation 3 for all fractions a p , while keeping all associated exponentials fixed. This is performed on the measured data in normal space. The linear system was solved using MATLAB's mldivide function.

| IVIM model fitting
Three different algorithms to fit the IVIM model (Equation 11) parameters D, D* and f were compared: nonlinear fitting, segmented fitting and phasor-based fitting. The nonlinear fitting was performed twice: once with and once without constraints on the fraction f.

| Nonlinear fitting
Nonlinear IVIM fitting was implemented using MATLAB's implementation of a trust-region-reflective least squares method. S 0 , D, D* and f were estimated using this solver and initialized at 0.1 μm 2 /s, 15 μm 2 /s and 0.5. D was constrained to be nonnegative, D* was constrained to be larger than or equal to 2.5 μm 2 /s, and f was constrained to be between 0 and 1. Additionally, D was constrained to be smaller than D*; the values of the parameters were switched in case the algorithm returned a result where D > D* and the value for f was adjusted accordingly.

| Segmented fitting
In segmented fitting, estimation was performed in two steps. 13 In both steps, the fitted function was linearized by applying a log transform to the measured data. First, a single exponential was fitted to all data points from b-values higher than 300 s/mm 2 . The perfusion component was considered to be negligible at b-values higher than this threshold, so D was estimated directly from this fit. Next, the fraction f was estimated from the difference between the measured signal and the fit at b = 0, sidestepping the need to fit S 0 . Finally, the fit from the previous step was subtracted from the entire measured signal (using all data points) and D* was estimated by fitting a single exponential to the result.

| Phasor-based fitting
Phasor-based IVIM fitting was implemented using the projection technique described in section 3.4. D* was estimated iteratively on the measured data in normal space, using MATLAB's implementation of a trust-region-reflective least squares method. 21,22 D was deduced from phasor-based symmetry and projection, as outlined in section 3.4. An estimate for f was obtained by phasor unmixing. D* was initialized at 15 μm 2 /s and constrained to be nonnegative. As in the nonlinear fitting method, D was constrained to be smaller than D*. A penalty term was added to the cost function to favor low fractions f (ie, monoexponential fits) for points close to the semicircle. Therefore, the cost function that was minimized is: where SNR is defined as average over standard deviation in the b = 0 s/mm 2 image. Subsequently, the magnitude of the signal was taken, leading to a Rician noise distribution. 24 The distribution of all simulated data in phasor space was visualized by creating two-dimensional histograms. These histograms divided phasor space into square bins of 1/300 by 1/300 mm 4 /s 2 and a color code was used to indicate the number of voxels present in each bin. To improve visibility, a logarithmic color code scale was used.
To investigate the dependency on noise, phasor unmixing was performed and the generated reconstructions were compared with the ground truth fractions. To investigate the influence of displacement of base components on the semicircle, the base components were systematically dis- The performances of the nonlinear, segmented and phasor-based fitting algorithms described above were compared using the simulated data.
For every simulated fraction, the median error as well as the 25% and 75% percentiles in the estimated parameters were calculated. Additionally, to facilitate comparison with our in vivo data, kernel density-estimated probability density functions (PDFs) were calculated for parameters D and fat fraction levels closest to the levels that were found in the in vivo data: 0.07 for gray matter and 0.05 for white matter. These PDFs indicate the distribution of parameter estimates around the true value.

| Scanning protocol and initial processing
All experiments were conducted after approval by the local ethical committee was obtained (NL53099.041.15 for the healthy volunteer, NL59820.041.17 for patient volunteers) and, before the exams, informed consent was obtained from all volunteers. A brain scan was obtained from one healthy male volunteer (aged 29 years) using a 3 T whole-body MR system (Ingenia; Philips, Best, the Netherlands). The scan protocol contained a T 1 w ultrafast gradient echo as well as a diffusion scan with three orthogonal diffusion-encoding directions and 21 b-values ranging from 0 to 2500 s/mm 2 . The total acquisition time for the diffusion scan was $ 10 minutes. A comparable diffusion-weighted protocol with three orthogonal directions was added to the standard clinical protocol for five biopsy-proven glioblastoma patients (aged 40-83 years) in the context of imaging for radiotherapy planning, but with reduced feet-head coverage of 30 2.5 mm slices around the tumor to limit the scan time to $ 5 minutes. Details for each patient can be found in Table 1. Eight baseline scans with no diffusion weighting were acquired at regular intervals in between diffusion-weighted scans to allow signal drift correction. 26 Scan parameters per sequence are reported in Table 2 downsampled to the resolution of the diffusion-weighted scans. 28 A brain mask was derived from the label image by selecting all voxels with a label corresponding to either CSF, white matter or gray matter. The brain mask was applied to all diffusion images before further analysis. Masks of each tissue type were created from the probability maps by selecting voxels with a probability higher than 0.99. Finally, geometric averaging was applied to all diffusion scans.

| Data analysis of the healthy volunteer
The nonlinear, segmented and phasor-based IVIM fitting algorithms described in section 3.2 were applied to the acquired data of the healthy vol-

| Data analysis of the patient volunteers
Phasor analysis was performed on the data acquired from the glioblastoma patients, both on a reduced dataset containing all 15 b-values of 1000 s/mm 2 and below (c.f. healthy volunteer in section 2.4.2) and on the full dataset. On the phasor plot of the first patient created using the full range of acquired b-values (0-2500 s/mm 2 ), a cluster of points was visible that was not seen on similar plots of healthy volunteers. This cluster was crudely delineated using a polygon and the corresponding voxels were obtained. For the four additional patients, the same analysis was performed using the polygon as defined in the phasor space of the first patient.

| Unmixing
The phasor representation of the phantom data for the four simulated noise levels is shown in Figure S1

| IVIM fitting
The runtimes of all tested fitting algorithms were compared: the phasor-based fit used 2.38 x and the segmented fit used 0.0023 x the computation time of the NLLS IVIM fit. A comparison of the nonlinear, segmented and phasor-based IVIM fitting methods on simulated data is presented in Figure 3 and the left panel of Figure 4. Figure 3 shows that for fractions f below 0.25 the phasor-based method had the highest precision for estimates of f and D: the interquartile range was roughly halved compared with that of the segmented method, which was second best. Figure S2 shows the same data as Figure 3, but for the full range of f (0-1). Accurately and precisely estimating D* at low fractions f proved difficult for all methods. The phasor method showed the best accuracy, but the worst precision. Table S1 reports mean and median error (accuracy) and interquartile range (precision) for all tested cases.

| In vivo results
The in vivo results in Figure 4 show a similar distribution as the simulation results, although all distributions were slightly wider, most likely due to physiological variations in the imaged tissue included by the masks. The accuracy of the methods in vivo is hard to assess since no gold standard is available, but similar shifts in peak positions between different fitting strategies were observed for the in vivo results compared with the simulations. The phasor estimates had the lowest spread for all parameters, except for D in gray matter. and only six b-values contained a higher amount of variation than the same map calculated with the phasor method. Figure 6 shows the results obtained from the first glioblastoma patient. In the phasor space corresponding to the full dataset (0-2500 s/mm 2 ), a cluster of points, encircled in green, was observed that was not seen in healthy volunteers. For comparison, Figure 7 shows the phasor space of the healthy volunteer, both for the full brain and for segmented tissues. The pixels belonging to the discovered cluster are indicated in green in  Since phasor unmixing showed slightly worse accuracy and precision compared with linear unmixing for all tested cases, we can conclude that there is no intrinsic increase in fit quality when applying the phasor transform. The performance of phasor unmixing for multi-component separation does not exceed that of linear unmixing. This leads us to conclude that the fit quality of fraction estimates obtained using phasor unmixing is entirely because the diffusivities are fixed. 11 The transform itself, and its inherent noise reduction, do not contribute to an increase in fit quality. The PDFs of estimated IVIM parameters f and D are largely similar between the simulated and measured in vivo data when comparing the distributions in Figure 4. Since all variance in parameter estimation in the simulation can only stem from the added Rician noise, and given the observation that the distributions in vivo are only slightly wider, we conclude that noise is the dominant factor causing variance in IVIM parameter estimation in this in vivo brain experiment.

| IVIM fitting
The parameter maps of the healthy volunteer in Figure 5 show that the most plausible D parameter maps are generated by the segmented and phasor-based method; the maps generated by the nonlinear method contain many areas with implausibly low values. At those locations, the nonlinear fits converge to a local minimum with a very low D* value and a very high f. This tendency can also be observed in Figure 4, where the small peak for an estimated f around 0.7 corresponds with a peak for estimated D at very low values. Both phasor-based and segmented methods avoid this local minimum. Based on our experiment with either 15 or six b-values, the D maps generated by the phasor and nonlinear methods seem to be more robust against a low number of sampling points. The likely cause is that the phasor-based and nonlinear fits use all data points to estimate D, while the segmented fit only includes data points above the threshold in this estimate. A lower number of required b-values may be a valuable advantage in the clinic since acquiring fewer b-values saves scan time.
All methods struggled to estimate D* accurately and precisely. Since the segmented method is linear, the segmented method is also the only method that is unconstrained for D* and frequently returns negative values, even if the value for D is positive. This is likely due to noise and the fact that D* is very hard to estimate. Additionally, the data points that are used for estimating D are more heavily influenced by the Rician nature of the noise since these are the data points corresponding to the highest b-values and consequently the lowest amount of signal. An error in the estimate for D will propagate into the estimate for D*, which is estimated in the last step of the segmented method. This might be an explanation for the negative D* values found by the segmented method.
All methods, except the constrained nonlinear fit, returned negative values in the f parameter maps. These values were more frequent in central areas of the brain, mostly in white matter, where the fractions are expected to be lower. Furthermore, in these central areas, the SNR of the MR signal is lower due to a lower radiofrequency receive efficiency in these areas. Therefore, these slightly negative values most likely arise due to noise.

| Visualization
The phasor plots of patients 1 and 2 (Figures 7 and S6) for b-values up to 2500 s/mm 2 contain a cluster of voxels that roughly corresponds to a hyperintense region on their FLAIR images. The cluster appears to correspond to the FLAIR-enhanced edge region of the tumor and not to the core visible on LGE. On occasion, voxels are included that belong to CSF or the edge of the brain. More careful delineation of the cluster in phasor space might reduce these false positives. However, the locations of these voxels are known to be prone to movement and partial voluming, which negatively impacts analyses.
The cluster was not observed for patients 3-5 ( Figures S7-S9). Patients 4 and 5 underwent surgery before their MRI, during which the LGEvisible tumor was (partially) removed, which may explain the absence of the cluster. The reason for the absence of the cluster in patient 3 is not fully understood, but multiple explanations can be offered. First, patient 3 was diagnosed with a radiologically diffuse glioma, but with World Health Organization grade IV glioblastoma genotype. This type of tumor differs from typical glioblastomas, due to differences in its cellular structure. 29 Second, patient 3 used corticosteroids (4 mg dexamethasone) at the time of the MRI, which may affect the diffusion properties of the tumor. 30 Of note: patient 1 used the same medication at the time of the MRI and the effect of corticosteroids on diffusion properties may differ per tumor type. 31 Future studies might include a larger cohort of patients to determine which clinical and biological factors contribute to the detectability of the cluster.
Since the cluster is not visible in the plot of b-values up to 1000 s/mm 2 and could not be separated using conventional IVIM parameters ( Figure S4), this separation might indicate a change in diffusion kurtosis. The cluster plot of Figure S5 indeed suggests kurtosis is lower in the tumor cluster. This is in agreement with recent studies that have shown that gliomas have altered kurtosis parameters compared with normalappearing white matter and that measurements of mean kurtosis can distinguish low-grade from high-grade gliomas. 32,33 Including FLAIR hyperintensities in the CTV for radiotherapy planning is a matter of debate, since these regions can represent either residual tumor or edema. 34 Current guidelines still recommend segmenting the GTV using a T 1 w contrast-enhanced image and adding an isotropic 20 mm margin to define the CTV. A more refined definition of radiotherapy target volumes may lead to better local control and fewer side effects. Future studies might focus on whether phasor plots or kurtosis fits can aid in achieving a more direct estimation of the CTV volume by distinguishing between edema and residual tumor.

| Study limitations
A limitation of this study is that the IVIM fit was tested only on simulated data, data of one healthy volunteer, and five glioblastoma patients. Therefore, the data distinguishing healthy subjects from patients using the observed cluster remains lacking in size. Consequently, the adoption of a "tumor cluster" in phasor space based on segmentation of the first patient is purely illustrative and a larger population consisting of both patients and healthy subjects would be needed to segment confidently in this way. A limited set of model parameters was tested and the simulated data contains only two components, while in in vivo data multiple components can be present. Most notably, free water was omitted from the simulations. The influence of three or more components on the IVIM fitting techniques was not investigated. However, when comparing the IVIM results of a healthy volunteer (right panel of Figure 4) to those of a two-component simulation (left panel of Figure 4), there is no evidence to suggest that the phasor technique is differently affected than other techniques by the extra components present in the in vivo data. The influence of a wider range of brain pathologies on the calculated parameter maps might be of interest in a clinical setting. The aim of the present work, however, was to assess the added value of the phasor transformation in component separation techniques. The presented phasor IVIM fitting technique serves as an example of the possible benefit to be gained from this transformation. Future work may need to focus on improvements of phasor-based fitting and rigorous evaluation of such a technique in a wide range of clinical cases, to validate the use of phasor-based information in calculating parameter maps and to assess their clinical usefulness.
Since the observed benefit of phasor-based IVIM model fitting is small, a conventional method with slightly different settings may still outperform the phasor method as presented. On the other hand, given the increase in performance observed by applying phasor-based regularization to the problem, phasor information might be a valuable addition to new or existing methods. For example, it may be beneficial to estimate D* by a conventional least-squares fit and use that estimate as a fixed D* in the phasor approach. With the experiments performed in this work, the benefit obtained by phasor-based regularization cannot be separated from the benefit obtained by phasor-based fitting.
Future research may be conducted to separate those contributions and to identify the best way to incorporate phasor information in solving this or other fitting problems.

| CONCLUSION
This paper aimed to evaluate the usefulness of the phasor transform for both visualization of decay rates and for fitting decay models. The phasor representation allows visualization and separation of tissues based on their diffusion properties and improves upon separation using conventional IVIM and kurtosis parameter maps. This has been demonstrated in a clinical setting in two glioblastoma patients. 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. A phasor-based IVIM fitting routine yielded parameter maps with slightly higher precision than standard nonlinear methods.
The usefulness of the phasor transform in unmixing signals when fixing the diffusivities is limited: a linear least-squares routine slightly but consistently outperformed phasor-based unmixing.
van RIJSSEL ET AL.

SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section at the end of this article.