Magnetic resonance in medicine · 2026
A Modular Hybrid Framework for CEST MRI Development and Clinical Translation Using pTx-Pulseq Preparation With Open- and Closed-Source Readouts and ONNX Mapping.
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Freudensprung Martin, Rajput Junaid R, Liebig Patrick, Weinmüller Simon, Singer Ludwig, Vladimirov Nikita, Perlman Or, Nagel Armin Michael, Doerfler Arnd, Zaiss Moritz
Аннотация
PURPOSE: Chemical exchange saturation transfer (CEST) MRI offers unique metabolic insights for imaging of cancer, stroke, or neurodegeneration. However, the rapid expansion of CEST preparation schemes and evaluation techniques creates a bottleneck; novel methods often remain complex prototypes requiring offline processing, preventing clinical adoption.
METHODS: To bridge this gap, we propose a hybrid sequence and reconstruction framework. This approach combines a flexible, pTx-enabled preparation block using the open-source Pulseq standard with a closed-source snapshot readout, and an interchangeable ONNX-based neural network for online post-processing.
RESULTS: We validated this framework at 7 T using three distinct applications: deepWASABI for B0 and B1 prediction, a protein CEST approach for B1-corrected CEST mapping, and semi-solid MT MRF for quantifying magnetization transfer rates and semi-solid proton volume fractions. By utilizing closed-source readouts, we leverage advanced proprietary image reconstructions essential for high-resolution whole-brain coverage. Simultaneously, the integrated ONNX network processes contrast-weighted images directly at the scanner to generate parameter maps online.
CONCLUSION: This hybrid architecture eliminates complex compilation steps, allowing deployment by simply copying Pulseq and ONNX files. By enabling push-button execution and immediate clinical data storage, this platform significantly accelerates the development, evaluation, and dissemination of advanced CEST MRI methods and forms a blueprint for scaling any magnetization-prepared MRI approach at any system.
METHODS: To bridge this gap, we propose a hybrid sequence and reconstruction framework. This approach combines a flexible, pTx-enabled preparation block using the open-source Pulseq standard with a closed-source snapshot readout, and an interchangeable ONNX-based neural network for online post-processing.
RESULTS: We validated this framework at 7 T using three distinct applications: deepWASABI for B0 and B1 prediction, a protein CEST approach for B1-corrected CEST mapping, and semi-solid MT MRF for quantifying magnetization transfer rates and semi-solid proton volume fractions. By utilizing closed-source readouts, we leverage advanced proprietary image reconstructions essential for high-resolution whole-brain coverage. Simultaneously, the integrated ONNX network processes contrast-weighted images directly at the scanner to generate parameter maps online.
CONCLUSION: This hybrid architecture eliminates complex compilation steps, allowing deployment by simply copying Pulseq and ONNX files. By enabling push-button execution and immediate clinical data storage, this platform significantly accelerates the development, evaluation, and dissemination of advanced CEST MRI methods and forms a blueprint for scaling any magnetization-prepared MRI approach at any system.