3d-cine-magnetic-resonance-imaging-respiratory-motion-mechanically

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats

The pathophysiology of diaphragm dysfunction in mechanically ventilated patients has yet to be fully understood, and adequate animal models are required ...

Amsterdam University Medical Center

Erasmus Medical Center

The conventional gradient-echo steady-state signal model is the basis of various spoiled gradient-echo (SPGR) based quantitative MRI models, including variable flip angle (VFA) MRI and dynamic contrast-enhanced MRI (DCE). However, including preparation pulses, such as fat suppression or saturation bands, disrupts the steady-state and leads to a bias in T and DCE parameter estimates. This work introduces a signal model that improves the accuracy of VFA T-mapping and DCE for interrupted spoiled gradient-echo (I-SPGR) acquisitions. The proposed model was applied to a VFA T-mapping I-SPGR sequence in the Gold Standard T-phantom (3 T), in the brain of four healthy volunteers (3 T), and to an abdominal DCE examination (1.5 T). T-values obtained with the proposed and conventional model were compared to reference T-values. Bland-Altman analysis (phantom) and analysis of variance (in vivo) were used to test whether bias from both methods was significantly different (p = 0.05). The proposed model outperformed the conventional model by decreasing the bias in the phantom with respect to the phantom reference values (mean bias -2 vs. -35% at 3 T) and in vivo with respect to the conventional SPGR (-6 vs. -37% bias in T, p < 0.01). The proposed signal model estimated approximately 48% (depending on baseline T) higher contrast concentrations in vivo, which resulted in decreased DCE pharmacokinetic parameter estimates of up to 35%. The proposed signal model improves the accuracy of quantitative parameter estimation from disrupted steady-state I-SPGR sequences. It therefore provides a flexible method for applying fat suppression, saturation bands, and other preparation pulses in VFA T-mapping and DCE.

A signal model for fat-suppressed T-mapping and dynamic contrast-enhanced MRI with interrupted spoiled gradient-echo readout.

Deep learning has proven to be a suitable alternative to least squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR acquisition protocols. In practice, QMRI acquisition protocols differ substantially between different studies and clinical settings. The lack of generalizability and adoptability of current deep learning approaches for QMRI parameter estimation impedes the implementation of these algorithms in clinical trials and clinical practice. Neural Controlled Differential Equations (NCDEs) allow for the sampling of incomplete and irregularly sampled data with variable length, making them ideal for use in QMRI parameter estimation. In this study, we show that NCDEs can function as a generic tool for the accurate estimation of QMRI parameters, regardless of QMRI sequence length, configuration of independent variables and QMRI forward model (variable flip angle T1-mapping, intravoxel incoherent motion MRI, dynamic contrast-enhanced MRI). NCDEs achieved lower mean squared error than LSQ fitting in low-SNR simulations and in vivo in challenging anatomical regions like the abdomen and leg, but this improvement was no longer evident at high SNR. When NCDEs improve parameter estimation, they tend to do so by reducing the variance in estimation errors. These findings suggest that NCDEs offer a robust approach for reliable QMRI parameter estimation, especially in scenarios with high uncertainty or low image quality. We believe that with NCDEs, we have solved one of the main challenges for using deep learning for QMRI parameter estimation in a broader clinical and research setting.

Article	Year
A signal model for fat-suppressed T-mapping and dynamic contrast-enhanced MRI with interrupted spoiled gradient-echo readout. NMR in biomedicine\| PubMed ID: 39571186	2025
Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations. Medical image analysis\| PubMed ID: 40945171	2025

Myrte Wennen

Biography

JoVE Journal Publications

Other Publications