Biology subjects

Ver Steeg, G.

Publications and source records attributed to Ver Steeg, G..

3 recordsLinked to original sources

bioRxiv2025

Generating diffusion MRI scalar maps from T1-weighted images using Reversible GANs

Diffusion tensor imaging (DTI) provides valuable insights into brain tissue microstructure, but acquiring high-quality DTI data is time-intensive and not always feasible. To mitigate data scarcity and enhance accessibility, we investigate the generation of synthetic DTI scalar maps--specifically mean diffusivity (MD)--from structural 3D volumetric T1-weighted brain MRI using a reversible generative adversarial network (RevGAN). Unlike conventional pipelines requiring multiple steps, our approach enables a single-step translation from T1 to diffusion-derived measures. We assess the quality and utility of the synthetic maps in two downstream tasks: sex classification and Alzheimers disease classification. Performance comparisons between models trained on real and synthetic DTI maps demonstrate that RevGAN-generated images retain meaningful microstructural features and offer competitive accuracy, underscoring their potential for data augmentation and analysis in neuroimaging workflows. We also examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort).

neuroscience↗

bioRxiv2025

Synthetic Diffusion Tensor Imaging Maps Generated by 2D and 3D Probabilistic Diffusion Models: Evaluation and Applications

Diffusion tensor imaging (DTI) is a key neuroimaging modality for assessing brain tissue microstructure, yet high-quality acquisitions are costly, time-intensive, and prone to artifacts. To address data scarcity and privacy concerns - and to augment the available data for training deep learning methods - synthetic DTI generation has gained interest. Specifically, denoising diffusion probabilistic models (DDPMs) have emerged as a promising approach due to their superior fidelity, diversity, controllability, and stability compared to generative adversarial networks (GANs) and variational autoencoders (VAEs). In this work, we evaluate the quality, fidelity and added value for downstream applications of synthetic DTI mean diffusivity (MD) maps generated by 2D slice-wise and 3D volume-wise DDPMs. We evaluate their computational efficiency and utility for data augmentation in two downstream tasks: sex classification and dementia classification using 2D and 3D convolutional neural networks (CNNs). Our findings show that 3D synthesis outperforms 2D slice-wise generation in downstream tasks. We present a benchmark analysis of synthetic diffusion-weighted imaging approaches, highlighting key trade-offs in image quality, diversity, efficiency, and downstream performance.

neuroscience↗

bioRxiv2021

3D Convolutional Neural Networks for Classification of Alzheimer's and Parkinson's Disease with T1-Weighted Brain MRI

Parkinsons disease (PD) and Alzheimers disease (AD) are progressive neurodegenerative disorders that affect millions of people worldwide. In this work, we propose a deep learning approach to classify these diseases based on 3D T1-weighted brain MRI. We analyzed several datasets including the Parkinsons Progression Markers Initiative (PPMI), an independent dataset from the University of Pennsylvania School of Medicine (UPenn), the Alzheimers Disease Neuroimaging Initiative (ADNI), and the Open Access Series of Imaging Studies (OASIS) dataset. The UPenn and OASIS datasets were used as independent test sets to evaluate the model performance during inference. We also implemented a random forest classifier as a baseline model by extracting key radiomics features from the same T1-weighted MRI scans. The proposed 3D convolutional neural network (CNN) model was trained from scratch for the classification tasks. For AD classification, the 3D CNN model achieved an ROC-AUC of 0.878 on the ADNI test set and an average ROC-AUC of 0.789 on the OASIS dataset. For PD classification, the proposed 3D CNN model achieved an ROC-AUC of 0.667 on the PPMI test set and an average ROC-AUC of 0.743 on the UPenn dataset. Model performance was largely maintained when using only 25% of the training dataset. The 3D CNN outperformed the random forest classifier for both the PD and AD tasks. The 3D CNN also generalized better on unseen MRI data from different imaging centers. These approaches show promise for screening of PD and AD patients using only T1-weighted brain MRI, which is relatively widely available. This model with additional validation could also be used to help differentiate between challenging cases of AD and PD when they present with similarly subtle motor and non-motor symptoms.

bioinformatics↗