Introduction
Microvascular blood flow, also known as perfusion, plays an important role in regulating physiology. In the brain, perfusion indexes such as cerebral blood flow (CBF) and cerebral blood volume (CBV) have been shown useful in the diagnosis and prognosis of neurological diseases [
1‐
3].
To date, three major magnetic resonance (MR) imaging techniques are available for cerebral perfusion measurement: dynamic susceptibility contrast (DSC) imaging [
4], arterial spin-labelling (ASL) imaging [
5], and intravoxel incoherent motion (IVIM) imaging [
6]. With DSC imaging, images are repeatedly acquired to trace the signal evolution following the passage of intravenously administered paramagnetic contrast agent. Multiple perfusion indexes (e.g., CBV, CBF, mean transit time, and time to maximum) can be derived from the temporal dynamics [
7‐
9]. Except for being impracticable in subjects with impaired renal function [
10,
11], DSC imaging imposes little invasiveness and has been routinely performed in many hospitals. With ASL imaging, flow contrast is generated by using radiofrequency pulses tailored to label the protons electromagnetically in arterial blood. Most existing ASL methods provide CBF measurement only [
12], although variants have been proposed to estimate arterial CBV [
13] and arterial transit time [
14]. The caveat of ASL imaging is the inherently low signal-to-noise ratio (SNR). First proposed by Le Bihan et al [
6], IVIM imaging measures the signal drop caused by intravoxel incoherent motion at varied magnitudes of diffusion weighting (quantified by b-values). Perfusion indexes are derived by modelling the signal attenuation as a composite outcome of interstitial water diffusion (characterized by diffusion coefficient D) and intravascular capillary blood flow:
$$ \frac{S(b)}{S_0}=\left(1-f\right) \exp \left(-bD\right)+f \exp \left(-bD*\right) $$
(1)
where S
0 is the signal intensity obtained with b = 0. D* is the pseudo-diffusion coefficient to account for the capillary blood flow that takes up a volume fraction f. IVIM imaging has been applied in several body organs [
15‐
18], and was assessed for measurement sensitivity in the rat brain [
19] and recently in the healthy human brain [
20]. Nonetheless, feasibility study remains relatively scarce in the human brain [
6,
21].
As compared with its counterpart techniques, IVIM imaging does not rely on tracer delivery (e.g., transit of arterial protons for ASL imaging and gadolinium chelates for DSC imaging). This is a desirable feature as bolus dispersion and elongated transit time that stem from variable or abnormal flow dynamics or routes can confound tracer-based perfusion imaging. For example, ASL measurement of white matter perfusion has been challenging mainly because of the long transit time [
22]. On the other hand, previous studies suggest that IVIM imaging is SNR demanding [
23,
24] although the relationship between SNR and perfusion that can be reliably measured remains unclear. The purpose of this study was to numerically and experimentally investigate the robustness of IVIM MR imaging in measuring perfusion indexes in the human brain.
Discussion
Of the two IVIM-derived perfusion indexes, blood volume fraction f is notably more robust than pseudo-diffusion coefficient D* (Figs.
1,
2, and
4b). Both f and D* can be better estimated when blood volume or blood flow is sufficiently large. The threshold, however, is dependent upon SNR and for white matter, also upon the fibre orientation with respect to the direction of diffusion encoding. Based on our imaging setting (particularly, the b-values and number of averages), SNR
b1000 is ~30 and with which ~10 % error in f and >100 % overestimation in D* are expected (Fig.
2). On the other hand, diffusion coefficient D can be estimated with high accuracy (error < 5 %) and precision (variance < 10 %) irrespective of gray/white matter. This suggests that perfusion has a negligible contribution to Eq. (
1) when b ≥ 400 s/mm
2 and it is appropriate to use the two-step fitting for the IVIM model. It is worth noting that IVIM-derived indexes are highly variable when D* to D ratio is below 10 (Fig.
1). Given that D can be reliably measured in a wide range of f and D* and that low D*/D ratio is most likely due to low D*, IVIM imaging may not be applicable to organs where blood flow is slow (e.g., the prostate). On the other hand, several recent studies [
30‐
33] managed to differentiate cancer pathologies in various organs by using IVIM-derived perfusion indexes. However, it may not be straightforward to generalize findings from organ to organ considering the different microvasculature and/or flow rate in different organs.
Cross-modal comparison reveals a statistical correlation between f and CBV
DSC in gray matter (r = 0.29 – 0.48,
p < 10
-5), but not in white matter. IVIM model is not robust when CBV is low (CBV
DSC < 0.02). The correlation also drops when CBV
DSC is beyond 0.10 because these voxels are likely to contain large vessels. Large vessels usually do not comply with the assumption of randomly oriented vasculature. Coherent flow leads to phase accumulation (not signal decay) and in its presence, intravascular diffusion will be attributed to D instead of D*. Taken together, f will be subject to misestimate and low precision. Wirestam et al [
21] also reported a correlation between CBV
IVIM and CBV
DSC (r = 0.563,
p < 10
-5), but two major differences should be noted between their study and ours. First, their comparison was based on regions of interest, presumably due to limited SNR (36 b-values, but only one measurement). Second, they pooled the regions of interest in gray/white matter and across subjects for correlation analysis. The inherent difference between gray matter and white matter could have inflated and dominated the computed correlation. By contrast, our comparison was on a per-subject and voxel-wise basis, and was performed for gray matter and white matter separately. Our analysis should more correctly reflect the correlation between modalities, and is more applicable for per-measurement assessment. In addition, our data showed no correlation between fD* and CBF
ASL. This is likely because of the large variance (i.e., low precision) in D* estimate (Figs.
1,
2, and
4b). As mentioned previously, the D* estimate is more SNR-demanding than the f estimate. Based on computer simulation, Pekar et al [
24] also pointed out the SNR issue in IVIM imaging, but did not address the relationship between SNR and f/D*.
IVIM imaging does not rely on tracer delivery, which makes it a potential candidate for measuring slow or delayed perfusion such as in white matter. Unfortunately, our data showed substantial errors and variability in the IVIM-derived perfusion indexes in white matter. In particular, D* can be overestimated by 300 % along with low precision (coefficient of variation ~60 %); f is relatively accurate (~10 % error), but its variability is still large (coefficient of variation ~50 %). A potential application to take advantage of this tracer-free feature might be f measurement in tumours with blood-brain-barrier disruption in which case contrast agent leaks into interstitial space, leading to complex interplay between T1 and T2* effects. CBV
DSC measured under such a circumstance has been known to be subject to error unless the T1 effect can be removed or corrected for [
34].
There are a few limitations in this study. First, the data presented are based on healthy volunteers in their 20s and 30s. Nevertheless, our results provide quantitative assessment that helps parameter adjustment for IVIM imaging. Second, we did not convert f and D* to CBV and CBF quantities. The purpose of this study is to evaluate the robustness of IVIM-derived perfusion indexes. According to [
28], the calculation of CBV and CBF primarily relies on f and D*, whose precision and accuracy can be inferred from our data. Third, in our fitting we did not consider non-Gaussian diffusion [
35] that can be characterized by including a diffusion kurtosis coefficient to Eq. (
1). Given that the kurtosis term contributes at high b-values (usually beyond 1,000 s/mm
2), our IVIM imaging was based on relatively low b-values and should not include notable kurtosis effect. The suitability of our model fitting without the kurtosis term can also be revealed by the high percentage of voxels where R
2 is above 0.9 with the IVIM model (~95 % in gray matter and ~85 % in white matter). The expense of using the extended model is adding an additional degree of freedom to the nonlinear least-squares fitting that is already SNR demanding. It is noteworthy that in addition to kurtosis, non-Gaussian diffusion has also been investigated with two-exponential model (slow vs. fast diffusing compartments or intra- vs. extra-cellular compartments) [
36,
37] and stretched exponential model (multiple compartments and thus continuous distribution of diffusion coefficients) [
38].
In summary, we have numerically and experimentally assessed the robustness of IVIM imaging in measuring cerebral perfusion indexes. A minimum SNRb1000 of 30 is recommended such that reliable f can be obtained as a noninvasive measure of cerebral blood volume. However, D* has limited robustness and should be interpreted with caution.