Abstract
Quantitative susceptibility mapping (QSM) aims to evaluate the distribution of magnetic susceptibility from magnetic resonance phase measurements by solving the ill-conditioned dipole inversion problem. Removing the artifacts and preserving the anisotropy of tissue susceptibility simultaneously is still a challenge in QSM. To deal with this issue, a novel k-QSM network is proposed to resolve dipole inversion issues in QSM reconstruction. The k-QSM network converts the results obtained by truncated k-space division (TKD) into the Fourier domain as inputs. After passing through several convolutional and residual blocks, the ill-posed signals of TKD are corrected by making the network output close to the calculation of susceptibility through multiple orientation sampling (COSMOS)-labeled QSM. To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22.31% lower normalized root mean square error, 10.30% higher peak signal-to-noise ratio (PSNR), 33.10% lower high-frequency error norm, and 1.06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients. The proposed k-QSM method learns less—only to fix the ill-posed signals of TKD, but infers more—both COSMOS-like and anisotropy-preserving QSM results. Its generalization ability and great sensitivity to susceptibility changes can make it a potential method for distinguishing some diseases.
Highlights
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that measures magnetic susceptibility values in tissue from MRI phase measurements (Wang and Liu, 2014; Haacke et al, 2015)
truncated k-space division (TKD) and STAR-QSM can measure the details to some degree, the results are accompanied by numerous unacceptable streak artifacts, marked by yellow arrows
The results of k-QSM0.2 were only significantly different (p = 0.0049) in posterior limb of the internal capsule (PLIC). All these results show that the proposed k-QSM0.1 can preserve the magnetic susceptibility anisotropy (MSA) as well as TKD and MoDL-QSM, as the results of these methods display orientation-dependent susceptibility variation in the PLIC and posterior thalamic radiation (PTR) regions of white matter (WM), whereas QSMnet+ loses the MSA during QSM reconstruction and k
Summary
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that measures magnetic susceptibility values in tissue from MRI phase measurements (Wang and Liu, 2014; Haacke et al, 2015). Phase values can reveal the sensitivity of tissues to a static magnetic field. The sensitivity is determined by magnetic susceptibility, whose contributors include biometals and molecules, for example, calcium, iron, myelin, and lipids (Feng et al, 2021). QSM reconstruction is non-trivial and requires several processing steps involving phase unwrapping (Abdul-Rahman et al, 2007), background field removal (Schweser et al, 2011; Zhou et al, 2014), and dipole inversion, which is an ill-conditioned problem and a source of streaking artifacts (Salomir et al, 2003) due to the singularity in the dipole kernel and the limitation of phase measurements in multiple orientations (Deistung et al, 2016)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.