Neural Bloch-McConnell fitting (NBMF): unsupervised test-time learning of 
clinical semisolid MT/CEST MRF reconstruction

Finkelstein, A; Vladimirov, N; Weinmüller, S; Zaiss, M; Perlman, O

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Neural Bloch-McConnell fitting (NBMF): unsupervised test-time learning of clinical semisolid MT/CEST MRF reconstruction

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Zaiss, M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://archive.ismrm.org/2024/4478.html
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Citation

Finkelstein, A., Vladimirov, N., Weinmüller, S., Zaiss, M., & Perlman, O. (2024). Neural Bloch-McConnell fitting (NBMF): unsupervised test-time learning of clinical semisolid MT/CEST MRF reconstruction. In ISMRM & ISMRT Annual Meeting & Exhibition 2024.

Cite as: https://hdl.handle.net/21.11116/0000-0010-41CF-C

Abstract

Motivation: MRF-based quantification of semi-solid MT/CEST proton-exchange requires a computationally demanding dictionary synthesis/matching. Recently reported unsupervised learning alternatives were incompatible with pulsed clinical CEST and multi-pool imaging.
Goal(s): To develop a training-set-free MRF reconstruction method, learning directly from the acquired data via pulsed-saturation-compatible physical modeling.
Approach: A differentiable multi-pool Bloch-McConnel simulator was designed and embedded within a test-time learning framework. Validation was performed using L-arginine phantoms and a human subject at 3T.
Results: The method enabled quantitative MT/CEST reconstruction in ~1 minute. The resulting maps were highly correlated with ground-truth in-vitro (Pearson’s r>0.95). In-vivo, semi-solid volume fractions were in agreement with MRF-based maps (r~0.8).
Impact: A one-stop-shop for semisolid MT and CEST MRF reconstruction was developed, enabling a training-set-free rapid quantification of exchange parameters on clinical scanners. This accessible approach could help a variety of Bloch-fitting applications to benefit from deep learning through differentiable spin-physics.