arXiv subjects

Todd Aguilera

Publications and source records attributed to Todd Aguilera.

3 recordsLinked to original sources

arXiv2026

Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting

Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6\% to 17.7\%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.

physics.med-ph↗

arXiv2026

Investigating the Uncertainty of Cellular Microenvironment Parameter Estimations via Diffusion MRI Cytometry

This study aims to identify cell microenvironment parameters that can be robustly estimated from IMPULSED diffusion MRI signals and to develop a reliable mapping-based estimation framework. Diffusion MRI signals were simulated using the established IMPULSED model with one pulsed gradient spin echo sequence and two oscillating gradient spin echo sequences at different frequencies. Five cellular parameters were considered: cell diameter ($d$), intracellular diffusion coefficient ($D_{in}$), intracellular volume fraction ($V_{in}$), extracellular diffusion coefficient ($D_{ex}$), and the frequency-dependent slope of $D_{ex}$ ($\beta_{ex}$). Parameter uncertainty was quantified using Jacobian-based sensitivity analysis at an SNR of 30, representing clinically achievable conditions on a 1.5T MRI scanner. To enable direct parameter mapping, signals were logarithmically transformed, reduced in dimension using principal component analysis, and then used to estimate parameters with linear regression, fourth-order polynomial regression, and a fully connected four-layer neural network. Model validation was performed in vitro using MC38 cell lines. Uncertainty analysis identified $d$, $V_{in}$, and $D_{ex}$ as robustly derivable parameters, each with relative uncertainty below 1.0. Among the tested models, the four-layer neural network performed best, with mean absolute errors of 1.7 $\mu$m for $d$, 5.06% for $V_{in}$, and 0.28 $\mu$m$^2$/ms for $D_{ex}$. In vitro validation showed a 6.7% error in cell diameter estimation. These results demonstrate that IMPULSED dMRI can support robust estimation of key cell microenvironment parameters and provide a practical framework for noninvasive assessment of tumor microenvironment changes during radiation therapy response monitoring.

physics.med-ph↗

arXiv2026

Optimizing IMPULSED Acquisition Protocols for Clinical 3T Scanners Through Bayesian Experimental Design

To optimize diffusion MRI acquisition protocols for IMPULSED model at clinical 3T scanner using Bayesian experimental design, enabling accurate cellular-scale parameter estimation under realistic scan time and scanner hardware constraints. Expected Information Gain (EIG) was used as the optimization objective to maximize the information content of acquired measurements for IMPULSED model fitting. Bayesian optimization with Gaussian process surrogates efficiently searched the high-dimensional acquisition parameter space, including pulse types (PGSE, OGSEn1, and OGSEn2), diffusion times, and b-values. Optimized protocols were systematically evaluated against a heuristically designed baseline protocol through simulation studies assessing classification accuracy and parameter estimation performance across SNR levels of 5-40. Robustness to optimization assumptions was examined by varying prior distributions and assumed SNR. In-vivo validation was performed using canine tumor data acquired at 3T. The optimized protocol eliminated OGSEn2 acquisitions, concentrated measurements at high b-values, employing concurrently optimized diffusion timing. Compared to the baseline protocol, the optimized design achieved superior classification accuracy for distinguishing cell populations and reduced parameter estimation error across biologically relevant parameter ranges at various SNRs. Performance advantages were consistent across diverse optimization scenarios, demonstrating robustness to prior knowledge and noise assumptions. In-vivo parameter maps showed substantially improved quality and smoothness. Bayesian optimization substantially improves IMPULSED acquisition design for clinical 3T scanners. This principled, algorithm-agnostic framework enables accurate diffusion MRI cytometry under clinical constraints, with potential applications to tumor characterization and treatment monitoring.

physics.med-ph↗