Spin and Gradient Multiple Overlapping-Echo Detachment Imaging (SAGE-MOLED): Highly Efficient T<sub>2</sub>, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics> </math> , and M<sub>0</sub> Mapping for Simultaneous Perfusion and Permeability Measurements.
Authors
Affiliations (3)
Affiliations (3)
- Department of Electronic Science, Xiamen University, Xiamen, China.
- Department of Radiological Sciences, University of California Irvine, Irvine, California, USA.
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Abstract
Combined spin- and gradient-echo EPI (SAGE-EPI) offers advantages in tissue quantification and dynamic imaging but suffers from low spatial resolution and geometric distortions. This study aims to develop a multiple overlapping-echo detachment-based SAGE acquisition (SAGE-MOLED) to enable efficient, distortion-corrected T<sub>2</sub>, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics> </math> , and M<sub>0</sub> mapping for perfusion MRI. SAGE-MOLED was designed as an optimized MOLED variant by refining echo time sampling and integrating multi-train blip-reversed EPI to enhance distortion correction and temporal SNR, enabling reliable extraction of subject-specific arterial input functions (AIFs). To support dynamic imaging, a steady-state Bloch simulation-based synthetic data framework was developed to simultaneously model T<sub>1</sub>, T<sub>2</sub>, and <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics> </math> -related effects, providing training data for an end-to-end deep learning model that enables efficient multiparametric quantification. In addition, a signal-to-concentration model tailored for dynamic MOLED signals was formulated for accurate estimation of permeability and leakage-corrected perfusion parameters. The proposed technique was validated in water phantom experiments, healthy volunteers, and a pilot clinical study. Single-shot SAGE-MOLED demonstrated high consistency with standard methods in both phantom and in vivo experiments, with Pearson correlation coefficient = 0.991 for T<sub>2</sub> and 0.988 for <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics> </math> mapping in the brain. Compared to conventional SAGE-EPI, SAGE-MOLED mitigated geometric distortions and intravoxel dephasing-related signal loss. In perfusion MRI, dynamic SAGE-MOLED enabled simultaneous permeability and leakage-corrected perfusion parameter estimation with a single-dose contrast injection. SAGE-MOLED overcomes key limitations of SAGE-EPI, providing high-fidelity, distortion-corrected T<sub>2</sub>, <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup> </mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics> </math> , and M<sub>0</sub> maps for simultaneous quantification of permeability and perfusion parameters.