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Updated: Mar 28, 2026

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Signal and Contrast Optimization With Predicted Excitations (SCOPE) for Accelerating Large FOV Body Imaging at UHF
Tobey D Haluptzok1, Simon Schmidt1,2, Gregory J Metzger1
1Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, Minnesota, USA.
Purpose:
Large FOV turbo-spin-echo (TSE) imaging at ultra-high field (UHF) remains challenging due to B1 + inhomogeneity and peak specific absorption rate (pSAR) limitations. This work presents a new time-interleaved acquisition of modes (TIAMO) framework called SCOPE (Signal and Contrast Optimization with Predicted Excitations), which overcomes these challenges by enabling reduced repetition time (TR) without compromising image quality.
Methods:
TIAMO is implemented to operate at half the TR of standard static-shim acquisitions, maintaining scan time parity with traditional acquisitions. To mitigate the expected contrast and signal loss from TR reduction, we developed a novel optimization framework that finds spatially exclusive RF modes, ensuring each voxel is predominantly excited by a single mode, thereby restoring the effective TR. To find these modes, the optimization utilizes an extended phase graph (EPG) model formulated to simulate the TSE signal with interleaved RF excitations. This new signal model is incorporated into the SCOPE framework to compute subject-specific optimal shims. Previous TIAMO methods are reformulated using this new signal model to elucidate performance differences.
Results:
SCOPE produced more homogeneous images with improved pSAR efficiency by alternating SAR hotspots between modes. In vivo measurements in the prostate and kidneys strongly correlated to signal prediction and demonstrated superior image quality compared to prior TIAMO methods. The new signal model clarified performance tradeoffs between TIAMO strategies.
Conclusion:
SCOPE enables rapid, contrast-preserving TSE TIAMO imaging with reduced TR, addressing the longstanding scan-time penalty of TIAMO. This work establishes a foundation for real-time, model-driven pTx optimization in large-FOV UHF imaging.
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