Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis
Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Optimization of Bloch-Siegert B1 Mapping Sequence for Maximum Signal to Noise
Adiabatic Bloch-Siegert B1+ mapping method addresses the long TE and high RF power deposition problems of conventional Bloch-Siegert B1+ mapping by introducing short frequency-swept ABS pulses with maximum sensitivity. H…
Machine learning topological energy braiding of non-Bloch bands
Machine learning has been used to identify phase transitions in a variety of physical systems. However, there is still a lack of relevant research on non-Bloch energy braiding in non-Hermitian systems. In this work, we s…
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in q…
Network Interpretationparameter estimationQuantitative MRILearning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To ove…
Magnetic Resonance Fingerprintingparameter estimationA note on the connectedness property of union-free generic sets of partial orders
This short note describes and proves a connectedness property which was introduced in Blocher et al. [2023] in the context of data depth functions for partial orders. The connectedness property gives a structural insight…