A wavelet-guided diffusion method for brain magnetic resonance parameter mapping using MOLED-based datasets: proof-of-principle

Source abstract

Abstract Objective. Quantitative magnetic resonance imaging (qMRI) can noninvasively acquire parametric maps, providing quantitative biomarkers for clinical assessment. Parametric maps, such as T 2 , T 2 * , and apparent diffusion coefficient (ADC), characterize distinct biophysical processes in tissue in an interpretable, numerical manner. Owing to their distinct physical origins, various parametric maps differ in physical units, numerical magnitudes, and dynamic ranges. Even for the same type of parametric map, inter-subject differences may still exist in anatomical morphology, tissue characteristics, and the local distributions of parameter values. However, deep learning methods trained in image-domain directly learn pixel-level parameter values, and variations in numerical scales and subject-specific parameter distributions may result in anatomical detail loss and localized artifacts in certain tasks. To address this challenge and to deliver dependable quantitative references for clinical assessment, we propose a wavelet-guided diffusion method. Approach. We extend the discrete convolution wavelet transform (DCWT) to two dimensions and combine it with a conditional denoising diffusion method to develop a diffusion-based DCWT (Diff-DCWT) for parameter mapping. Diff-DCWT achieves accurate parameter mapping under the guidance of wavelet coefficient maps, thereby reducing reliance on image domain learning. The performance of Diff-DCWT is evaluated through simulation and in vivo experiments across T 2 , T 2 * , and ADC mapping tasks. Main Results. Both simulation and healthy volunteer experiments demonstrate that T 2 , T 2 * , and ADC maps obtained from Diff-DCWT maintain excellent visual quality and quantitative accuracy. Clinical case studies suggest the potential utility of Diff-DCWT-derived T 2 and ADC maps for stroke staging and help distinguish lesional from normal brain regions in glioma. Significance. Diff-DCWT establishes a high-fidelity framework for parameter mapping and exhibits preliminary clinical value in stroke staging and glioma characterization. Moreover, this work demonstrates the potential of wavelet-domain prior guidance for deep learning-based qMRI applications.