Variable-density velocity-selective magnetization preparation for non-contrast-enhanced peripheral MR angiography.
Minyoung KimInpyeong HwangSeung Hong ChoiJaeseok ParkTaehoon ShinPublished in: Physical and engineering sciences in medicine (2024)
Velocity-selective (VS) magnetization preparation has shown great promise for non-contrast-enhanced (NCE) magnetic resonance angiography (MRA) with the ability to generate positive angiographic contrast directly using a single 3D acquisition. However, existing VS-MRA methods have an issue of aliased saturation around a certain velocity, known as velocity field-of-view (vFOV), which can cause undesired signal loss in arteries. This study aimed to develop a new version of the VS preparation pulse sequence that overcomes the aliased saturation problem in conventional VS preparation. Utilizing the fact that an excitation profile is the Fourier transform of excitation k-space sampling, we sampled the k-space in a non-uniform fashion by scaling gradient pulses accordingly to have aliased excitation diffused over velocity. The variable density sampling function was numerically optimized to maximize the average of the velocity passband signal while minimizing its variance. The optimized variable density VS magnetization was validated through Bloch simulations and applied to peripheral NCE MRA in healthy subjects. The in-vivo experiments showed that the proposed variable density VS-MRA significantly lowered arterial signal loss observed in conventional VS-MRA, as evidenced by a higher arterial signal-to-noise ratio (58.50 ± 14.29 vs. 55.54 ± 12.32; p < 0.05) and improved artery-to-background contrast-to-noise ratio (22.75 ± 7.57 vs. 20.60 ± 6.51; p < 0.05).
Keyphrases
- contrast enhanced
- magnetic resonance
- computed tomography
- diffusion weighted
- magnetic resonance imaging
- blood flow
- diffusion weighted imaging
- molecularly imprinted
- optical coherence tomography
- blood pressure
- dual energy
- high resolution
- molecular dynamics
- mass spectrometry
- big data
- artificial intelligence
- psychometric properties