Susceptibility difference weighted imaging in vertical-field MRI.

To realize susceptibility-weighted imaging in vertical-field magnetic resonance imaging (MRI), we developed an image-processing method called "susceptibility difference weighted imaging" (SDWI). In SDWI, contrasts are enhanced using a susceptibility map calculated by using a weighted least-square algorithm with a small iteration number. Experiments were performed on human volunteers to compare image contrast obtained from the conventional method (SWI) and SDWI. In horizontal-field MRI, SDWI results show that veins and deep-gray-matter nuclei were visualized as well as those with SWI. In vertical-field MRI, SDWI visualized veins and deep-gray-matter nuclei without severe streaking artifacts, while SWI did not. In our experiments, the time taken to calculate the susceptibility map in SDWI was less than 10 s. The results indicate that susceptibility-weighted imaging is feasible in vertical-field MRI using SDWI.

Quantitative Susceptibility Mapping Using the Multiple Dipole-inversion Combination with k-space Segmentation Method.

Quantitative susceptibility mapping (QSM) is a new magnetic resonance imaging (MRI) technique for noninvasively estimating the magnetic susceptibility of biological tissue. Several methods for QSM have been proposed. One of these methods can estimate susceptibility with high accuracy in tissues whose contrast is consistent between magnitude images and susceptibility maps, such as deep gray-matter nuclei. However, the susceptibility of small veins is underestimated and not well depicted by using the above approach, because the contrast of small veins is inconsistent between a magnitude image and a susceptibility map. In order to improve the estimation accuracy and visibility of small veins without streaking artifacts, a method with multiple dipole-inversion combination with k-space segmentation (MUDICK) has been proposed. In the proposed method, k-space was divided into three domains (low-frequency, magic-angle, and high-frequency). The k-space data in low-frequency and magic-angle domains were obtained by L1-norm regularization using structural information of a pre-estimated susceptibility map. The k-space data in high-frequency domain were obtained from the pre-estimated susceptibility map in order to preserve small-vein contrasts. Using numerical simulation and human brain study at 3 Tesla, streaking artifacts and small-vein susceptibility were compared between MUDICK and conventional methods (MEDI and TKD). The numerical simulation and human brain study showed that MUDICK and MEDI had no severe streaking artifacts and MUDICK showed higher contrast and accuracy of susceptibility in small-veins compared to MEDI. These results suggest that MUDICK can improve the accuracy and visibility of susceptibility in small-veins without severe streaking artifacts.