Effect of flow‐encoding strength on intravoxel incoherent motion in the liver

Abstract

Purpose To study the impact of variable flow‐encoding strength on intravoxel incoherent motion (IVIM) liver imaging of diffusion and perfusion. Theory Signal attenuation in DWI arises from (1) intravoxel microvascular blood flow, which depends on the flow‐encoding strength α (first gradient moment) of the diffusion‐encoding waveform, and (2) intravoxel spin diffusion, which depends on the b‐value of the diffusion‐encoding gradient waveforms α and b‐value. Both are linked to the diffusion‐encoding gradient waveform and conventionally are not independently controlled. Methods In this work a convex optimization framework was used to generate gradient waveforms with independent α and b‐value. Thirty‐six unique α and b‐value sample points from 5 different gradient waveforms were used to reconstruct perfusion fraction (f), coefficient of diffusion (D), and blood velocity standard deviation (Vb) maps using a recently proposed IVIM model. Faster acquisition strategies were evaluated with 1000 random subsampling strategies of 16, 8, and 4 α and b‐value. Among the subsampled reconstructions, the sampling schemes that minimized the difference with the fully sampled reconstruction were reported. Results Healthy volunteers (N = 9) were imaged on a 3T scanner. Liver perfusion and diffusion estimates using the fully sampled IVIM method were f = 0.19 ± 0.06, D = 1.15 ± 0.15 × 10–3 mm2/s, and Vb = 5.22 ± 3.86 mm/s. No statistical differences were found between the fully sampled and 2‐times undersampled reconstruction (f = 0.2 ± 0.07, D = 1.19 ± 0.15 × 10–3 mm2/s, Vb = 5.79 ± 3.43 mm/s); 4‐times undersampled (f = 0.2 ± 0.06, D = 1.15 ± 0.17 × 10–3 mm2/s, Vb = 4.66 ± 3.61 mm/s), or 8‐times undersampled ( f = 0.2 ± 0.06, D = 1.23 ± 0.22 × 10–3 mm2/s, Vb = 4.99 ± 3.82 mm/s) approaches. Conclusion We demonstrate the IVIM signal’s dependence on the b‐value, the diffusion‐encoding time and the flow‐encoding strength and observe in vivo the ballistic regime signature of microperfusion in the liver. This work also demonstrates that using an IVIM model and sampling scheme matched to the ballistic regime, pixel‐wise IVIM parameter maps are possible when sampling as few as 4 IVIM signals.

MRI of Reperfused Acute Myocardial Infarction Edema: ADC Quantification versus T1 and T2 Mapping

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Intravoxel Incoherent Motion Magnetic Resonance Imaging in Skeletal Muscle: Review and Future Directions

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On probing intravoxel incoherent motion in the heart‐spin‐echo versus stimulated‐echo DWI

“…Alternatively, anisotropy may be modeled using von Mises (also called Fisher‐axial) distributions to describe the spatial orientation of the vasculature around a main direction, that is, the dispersion of the zenith angle of the capillary segments . Further, the stochastic process assumed in the IVIM model was shown to deviate from a Gaussian distribution within a voxel if only a few capillary segments are traversed and it was shown that the usage of gradient shapes that compensate higher‐order moments leads to a reduced signal attenuation due to perfusion …”