Volume 15 (2016) Issue 1 Pages 83-93
Purpose: Q-space imaging (QSI) is a diffusion-weighted imaging (DWI) technique that enables investigation of tissue microstructure. However, for sufficient displacement resolution to measure the microstructure, QSI requires high q-values that are usually difficult to achieve with a clinical scanner. The recently introduced “low q-value method” fits the echo attenuation to only low q-values to extract the root mean square displacement. We investigated the clinical feasibility of the low q-value method for estimating the microstructure of the human corpus callosum using a 3.0-tesla clinical scanner within a clinically feasible scan time.
Methods: We performed a simulation to explore the acceptable range of maximum q-values for the low q-value method. We simulated echo attenuations caused by restricted diffusion in the intra-axonal space (IAS) and hindered diffusion in the extra-axonal space (EAS) assuming 100,000 cylinders with various diameters, and we estimated mean axon diameter, IAS volume fraction, and EAS diffusivity by fitting echo attenuations with different maximum q-values. Furthermore, we scanned the corpus callosum of 7 healthy volunteers and estimated the mean axon diameter and IAS volume fraction.
Results: Good agreement between estimated and defined values in the simulation study with maximum q-values of 700 and 800 cm−1 suggested that the maximum q-value used in the in vivo experiment, 737 cm−1, was reasonable. In the in vivo experiment, the mean axon diameter was larger in the body of the corpus callosum and smaller in the genu and splenium, and this anterior-to-posterior trend is consistent with previously reported histology, although our mean axon diameter seems larger in size. On the other hand, we found an opposite anterior-to-posterior trend, with high IAS volume fraction in the genu and splenium and a lower fraction in the body, which is similar to the fiber density reported in the histology study.
Conclusion: The low q-value method may provide insights into tissue microstructure using a 3T clinical scanner within clinically feasible scan time.