Optimizing MR Scan Design for Model-Based ${T}_{1}$ , ${T}_{2}$ Estimation From Steady-State Sequences.

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Physics
• Neuroimaging

Background:

• Accurate quantification of MR relaxation parameters T1 and T2 is crucial for clinical applications.
• Steady-state MRI sequences like Spoiled Gradient-Recalled Echo (SPGR) and Dual-Echo Steady-State (DESS) are sensitive to T1 and T2 variations but typically require multiple scans with varied parameters for estimation.
• Optimizing scan parameters is essential for robust and precise relaxometry.

Purpose of the Study:

• To develop a systematic framework for selecting and optimizing MRI scan types and parameters for T1 and T2 relaxometry.
• To apply this framework to optimize combinations of SPGR and DESS scans for brain white matter (WM) and grey matter (GM) at 3T.
• To evaluate the accuracy and precision of the optimized scan combinations.

Main Methods:

• A Cramér-Rao Bound (CRB)-inspired min-max optimization framework was used to select scan types and optimize acquisition parameters (e.g., flip angles, repetition times).
• The framework was applied to optimize combinations of SPGR and DESS scans for T1 and T2 relaxometry in human brain WM and GM at 3T.
• Phantom accuracy and precision experiments, along with in vivo experiments, were conducted to validate the optimized methods.

Main Results:

• Optimized SPGR/DESS scan combinations demonstrated excellent agreement with reference measurements in phantom accuracy experiments.
• Phantom precision experiments showed that T1 and T2 pooled sample standard deviations aligned with CRB-based predictions.
• In vivo experiments revealed that optimized DESS scans alone provided T1 and T2 estimates with precision comparable to optimized SPGR/DESS combinations in WM and GM.

Conclusions:

• The developed scan optimization framework enables robust and precise T1 and T2 estimation in the human brain.
• Optimized DESS acquisitions alone can yield precise T1 and T2 maps, a novel finding.
• Scan optimization strategies can potentially uncover new parameter mapping techniques using established MRI pulse sequences.