Biology subjects

Adame Gonzalez, W.

Publications and source records attributed to Adame Gonzalez, W..

3 recordsLinked to original sources

bioRxiv2026

Automatic Quality Control and Error Correction in MRI linear registration via a Residual Parameter Prediction Network for T1w MRI

Errors in linear registration can propagate to downstream nonlinear registration and bias volumetric estimations, deformation-based morphometry (DBM) and voxel-based morphometry (VBM) analyses. Subtle linear registration errors are particularly challenging as they are difficult to detect and may not result in obvious failures in nonlinear registration but still affect downstream results. Therefore, accurate identification and correction of these errors are critical. In this study, we present the Residual Affine COefficient Optimization Network (RACOON), a framework designed to identify and correct linear registration errors in T1w MRI scans registered to the MNI-ICBM152 space. RACOON's correction module achieved a residual misalignment RMSE of 0.778 mm on synthetic dataset, comparable to the variability observed among repeated QC-passed registrations using the same pipeline. For the classification module, RACOON achieved a balanced accuracy of 76.8% and a precision of 74.4%, outperforming existing state-of-the-art methods. RACOON is open source and publicly available at https://github.com/ZhaojinChen/RACOON.

neuroscience↗

bioRxiv2026

Spatiotemporal trajectories of formaldehyde fixation effects on quantitative MRI in postmortem human brains

Introduction: Postmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods: 142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. Results: All investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. Conclusion: Formaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

neuroscience↗

bioRxiv2025

RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders

Due to their high inter-tissue contrast, Magnetic resonance images (MRIs) can reflect neuroanatomical changes related to healthy aging and pathological processes. However, standard brain MRI acquisition resolutions hinder the ability to measure the more subtle changes that occur in early disease stages. Increasing the resolution during acquisition poses multiple challenges, including increased noise, higher acquisition times and cost, and discomfort of the scanned individual. In this work, we propose a robust, generalizable single-image super-resolution network for brain MRIs named Resolution Augmentation with Variational auto-Encoder Networks (RAVEN) with generative adversarial networks (GANs). We show RAVEN is capable of upsampling in-vivo and ex-vivo MRIs of diverse modalities (e.g. T1-weighted, T2-weighted, and T2*) and varying field strengths (3T to 7T) to target voxel sizes as small as 0.5mm isotropic using arbitrary upsampling factors. RAVEN achieved state-of-the-art performance against deep learning and non-deep learning methods, best preserving true anatomical information. We have also made RAVEN open access, with the source code as well as training and evaluation scripts available and ready to use at: https://github.com/waadgo/raven.

neuroscience↗