Quantitative proton density mapping with data-driven reception profile correction derived from macromolecular proton fraction
Yarnykh, V. L.
Show abstract
Purpose: Proton density (PD) and macromolecular proton fraction (MPF) are fundamental MR parameters characterizing water and macromolecular content in tissues. Both parameters can be mapped using a time-efficient MRI protocol, though PD maps require additional corrections for the reception profile (RP) non-uniformity and global scaling. Existing PD mapping methods suffer from uncertainties related to PD normalization to reference tissue. This study aimed to develop a data-driven RP correction method for PD mapping based on MPF as an internal scale. Theory and Methods: One-parameter model linking PD and MPF was derived under assumption of proportionality between total macromolecular volume and proton concentration. This model with a uniform parameter value across brain tissues generates a surrogate PD map, which is used to estimate RP from an uncorrected PD map. The proposed algorithm was tested against a common PD mapping method utilizing bias-field correction and normalization to cerebrospinal fluid. Brain MRI data were acquired at 3T from seven healthy individuals. The protocol included 3D multi-echo T1-, PD-, and magnetization-transfer-weighted gradient-echo sequences. Parametric maps were reconstructed using the single-point MPF mapping algorithm with data-driven B1 correction. Results: MPF-based and bias-field corrections yielded similar PD measurements in white and gray matter with no significant bias and remarkably small variability (coefficients of variation <0.6%). Conclusions: MPF-based RP correction enables computationally simple and robust quantitative PD mapping at no additional cost if MPF maps are available. The main advantage of the proposed method is automatic elimination of global scaling that obviates the need for PD normalization.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A Phantom System Designed to Assess the Effects of Membrane Lipids on Water Proton Relaxation 97%
- Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data 97%
- Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T2 decay data 96%
Similar papers in this journal
- Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI using Reduced Field-of-View Echo-Planar Imaging and Enhanced Reverse Polarity Gradient Method 95%
- Test-retest reproducibility of in vivo magnetization transfer ratio and saturation index in mice at 9.4 Tesla 95%
- AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI): a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan 94%
Similar papers in this journal
- Tailored Magnetic Resonance Fingerprinting 96%
- Advancing High-Resolution 7T Diffusion MRI: Evaluating Phase-Encoding Correction Strategies for Distortion Correction from Basic to Four-Way Acquisitions 96%
- Noise decorrelation optimizes SNR of GABA-edited MRS data: A comparison of RF coil combination methods 95%
Similar papers in this journal
- The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain 96%
- Increased Sensitivity and Signal-to-Noise Ratio in Diffusion-Weighted MRI using Multi-Echo Acquisitions 96%
- Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.