Purpose: This study aimed to examine whether the signal-to-noise ratio (SNR) could be increased by combining integrated slice-by-slice shimming (iShim) with a fat suppression (FS) method other than short-tau inversion...Purpose: This study aimed to examine whether the signal-to-noise ratio (SNR) could be increased by combining integrated slice-by-slice shimming (iShim) with a fat suppression (FS) method other than short-tau inversion recovery (STIR) in diffusion-weighted imaging (DWI) and q-space imaging (qsi). Methods: We acquired DWI images (b-values: 0 and nine steps from 400 to 10,000 s/mm2 for six axes) using a prototypical single-shot echo planar imaging sequence by combining two types of shimming (3D Shim and iShim) and two types of FS (STIR and water excitation [WE]) in 10 volunteers. In the DWI study, the SNR for each b-value, FS effect in the b0 image, and distortion in the added image (b0 - b10,000) were evaluated for the above-mentioned four imaging methods. qsi involved original DWI images. In the qsi study, the SNR was evaluated. Results: With regard to both 3D Shim and iShim, the SNRs were significantly higher when using WE than when using STIR in b0 - b900 images (p Conclusion: The combination of iShim and WE has a high SNR on qsi.展开更多
Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently...Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.展开更多
文摘Purpose: This study aimed to examine whether the signal-to-noise ratio (SNR) could be increased by combining integrated slice-by-slice shimming (iShim) with a fat suppression (FS) method other than short-tau inversion recovery (STIR) in diffusion-weighted imaging (DWI) and q-space imaging (qsi). Methods: We acquired DWI images (b-values: 0 and nine steps from 400 to 10,000 s/mm2 for six axes) using a prototypical single-shot echo planar imaging sequence by combining two types of shimming (3D Shim and iShim) and two types of FS (STIR and water excitation [WE]) in 10 volunteers. In the DWI study, the SNR for each b-value, FS effect in the b0 image, and distortion in the added image (b0 - b10,000) were evaluated for the above-mentioned four imaging methods. qsi involved original DWI images. In the qsi study, the SNR was evaluated. Results: With regard to both 3D Shim and iShim, the SNRs were significantly higher when using WE than when using STIR in b0 - b900 images (p Conclusion: The combination of iShim and WE has a high SNR on qsi.
基金funded by Sweden's Innovation Agency(VINNOVA)ASSIST,Analytic Imaging Diagnostic Arena(AIDA),Swedish Foundation for Strategic Research(RMX18-0056)Linkoping University Center for Industrial Information Technology(CENIIT),LiU Cancer Barncancerfonden,and a research grant(00028384)from VILLUM FONDEN。
文摘Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.
