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

Abstract

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.

Preoperative Cerebral Oxygen Extraction Fraction Imaging Generated from 7T MR Quantitative Susceptibility Mapping Predicts Development of Cerebral Hyperperfusion following Carotid Endarterectomy

“…QSM images were generated from the source images with an in-house program with a multiple dipole-inversion combination with k-space segmentation 25 and regularization-enabled sophisticated harmonic artifact reduction for phase data methods, 26 as described previously. 23 A 2D Gaussian low-pass filter with a kernel size of 60% of the total image power in each section was applied to extract iron deposition in deep nuclei, hemosiderin deposition, dural sinuses, and large venous structures; and a 2D Gaussian high-pass filter of 2% was applied to extract small venous structures.…”

Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution

“…Therefore a direct unfiltered substitution matching the TKD threshold of 0.2 was utilized. This is a substantially different implementation compared to other published k-space substitution approaches in QSM, which have required complex segmentation and combination procedures [47].…”

Quantitative parameter mapping of contrast agent concentration and relaxivity and brain tumor extracellular pH

“…In addition, defines a CA’s molar concentration. In this study, multiple dipole-inversion combination with k-space segmentation (MUDICK) and 326 ppm were used for estimating and , respectively 16 , 17 . The relaxivity r 1 was subsequently estimated with Eq.…”