Abstract
PurposeTo develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging.MethodsThe proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects.ResultsThe unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature.ConclusionThis study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.
Highlights
Magnetic resonance imaging (MRI) offers many different contrast mechanisms
By comparing the P and R masks for the simulated data and the first and second regularization terms, and for the purpose of bringing the two terms to the same order, we set λ1 = 0.005λ2. This is further reviewed in the Discussion section. Based on this assumption and simulations in the human brain model, λ2 = {6.81, 1.47, 3.16, 1.00} × 10−3 provided the best results in terms of residual errors for the four different scans (FALTE1, FAHTE1, FALTE2, and FAHTE2), respectively
morphology enabled dipole inversion (MEDI) does an excellent job (Figures 3P–T) as does structurally constrained Susceptibility Weighted Imaging and Mapping” (scSWIM) (Figures 3U–Y) in reproducing the model with minimal artifacts and noise. In both these last two reconstructions, the streaking artifact is highly reduced compared to both Thresholded K-space Division (TKD) and iterative Susceptibility Weighted Imaging and mapping (iSWIM) and the images look much better in terms of signalto-noise ratio (SNR)
Summary
Magnetic resonance imaging (MRI) offers many different contrast mechanisms. Today, it is possible to obtain magnetic susceptibility maps, χ (r), of the human brain (and other parts of the body) that show the underlying tissue susceptibility distribution. An alternative approach referred to as iterative Susceptibility Weighted Imaging and mapping (iSWIM) has been used to fill in the missing parts of k-space to overcome these artifacts (Tang et al, 2013). This was accomplished by constraining the susceptibility values in regions with high susceptibility. A better approach, in theory, but one that requires multiple scans, is the Calculation Of Susceptibility through Multiple Orientation Sampling (COSMOS) (Liu et al, 2009) This method utilizes the phase images from multiple orientations to stabilize the inversion process and remove the singularities by weighted linear least squares. This method is usually used as a gold standard in the evaluation of any QSM reconstruction method
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