Signal to noise and b-value analysis for optimal intra-voxel incoherent motion imaging in the brain

Abstract

Intravoxel incoherent motion (IVIM) is a method that can provide quantitative information about perfusion in the human body, in vivo, and without contrast agent. Unfortunately, the IVIM perfusion parameter maps are known to be relatively noisy in the brain, in particular for the pseudo-diffusion coefficient, which might hinder its potential broader use in clinical applications. Therefore, we studied the conditions to produce optimal IVIM perfusion images in the brain. IVIM imaging was performed on a 3-Tesla clinical system in four healthy volunteers, with 16 b values 0, 10, 20, 40, 80, 110, 140, 170, 200, 300, 400, 500, 600, 700, 800, 900 s/mm2, repeated 20 times. We analyzed the noise characteristics of the trace images as a function of b-value, and the homogeneity of the IVIM parameter maps across number of averages and sub-sets of the acquired b values. We found two peaks of noise of the trace images as function of b value, one due to thermal noise at high b-value, and one due to physiological noise at low b-value. The selection of b value distribution was found to have higher impact on the homogeneity of the IVIM parameter maps than the number of averages. Based on evaluations, we suggest an optimal b value acquisition scheme for a 12 min scan as 0 (7), 20 (4), 140 (19), 300 (9), 500 (19), 700 (1), 800 (4), 900 (1) s/mm2.

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

“…Nevertheless, we analyzed the signal variability by means of the Coefficient of Variation (CoV) over white and gray matter for each volunteer (refer to Figure S8). We observed that CoV decreased for higher b-values, and that increasing the NEX number can contribute to lower CoVs, especially at low b-values, as also suggested by Merisaari and colleagues 15 . As an increase in NEX corresponds to a further increase in acquisition time, we fixed NEX = 2 for our analysis, also considering that we observed limited variations when increasing NEX to 3 (Figures S8-S10).…”

“…The optimization strategy aimed at minimizing the total relative error of the estimated IVIM parameters computed with a segmented fitting approach, similarly to Lemke et al 23 Such procedure was adopted to account for all IVIM parameters' accuracy, without relying on costly in vivo acquisitions with empirically-defined b-values. 15 Alternative optimization strategies such as that based on Cramér-Rao Lower Bound theory 21,34 can be also considered in the future.…”

“… 23 Such procedure was adopted to account for all IVIM parameters’ accuracy, without relying on costly in vivo acquisitions with empirically‐defined b ‐values. 15 Alternative optimization strategies such as that based on Cramér‐Rao Lower Bound theory 21 , 34 can be also considered in the future.…”

“…Diffusion gradients characteristics are summarized in the b ‐value (s/mm 2 ), which depends on the diffusion gradient waveform, the time duration of the gradients and the interval between them. Different studies in the literature investigated the effect of selecting different b ‐values on DWI acquisitions, 15,16 with some of them implementing optimization frameworks to automatically derive the optimal set. Specifically, optimal choices of b ‐values have been widely studied for the mono‐exponential diffusion model, 17,18 and some studies have implemented similar optimization strategies on the IVIM model for applications in kidney, liver, and breast 19–22 .…”

“…This results in dispersed reference values of IVIM parameters’ estimates, which are possibly mingled with uncertainties and inaccuracies, 13 thus hindering the comparison of results reported in the literature. The optimization of acquisition parameters has the potential to reduce errors in parameters’ estimation 14 , 15 and thereby either to reduce scan time or to improve the quality of IVIM parametric maps. Specifically, the computation of diffusion and perfusion parameters in IVIM relies on the possibility to encode the effects of molecular diffusion by playing gradients with different lengths (or amplitudes) during image acquisitions.…”