# Latent Causal Modeling for 3D Brain MRI Counterfactuals

arXiv: https://arxiv.org/abs/2409.05585 · HTML: https://mlsanity.com/paper/2409.05585/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Diffusion models for missing value imputation in tabular data](https://mlsanity.com/paper/2210.17128/) (2022)
- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN](https://mlsanity.com/paper/2008.01910/) (2020)
- [Neural Causal Models for Counterfactual Identification and Estimation](https://mlsanity.com/paper/2210.00035/) (2022)
- [High Fidelity Image Counterfactuals with Probabilistic Causal Models](https://mlsanity.com/paper/2306.15764/) (2023)
- [Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/2006.06930/) (2020)
- [Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.08034/) (2022)
- [Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs](https://mlsanity.com/paper/2310.04630/) (2023)
- [Anatomically constrained CT image translation for heterogeneous blood vessel segmentation](https://mlsanity.com/paper/2210.01713/) (2022)

## Built on by (most cited first, 7 of 7)

- [Explaining 3D Computed Tomography Classifiers with Counterfactuals](https://mlsanity.com/paper/2502.07156/) (2025)
- [CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomography](https://mlsanity.com/paper/2507.17420/) (2025)
- [Integrating Anatomical Priors into a Causal Diffusion Model](https://mlsanity.com/paper/2509.09054/) (2025)
- [Cycle Diffusion Model for Counterfactual Image Generation](https://mlsanity.com/paper/2509.24267/) (2025)
- [Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models](https://mlsanity.com/paper/2609.24879/) (2026)
- [Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI](https://mlsanity.com/paper/2604.05171/) (2026)
- [Evaluating the Safety of Deep Learning-Based Brain MRI Reconstruction](https://mlsanity.com/paper/2608.28714/) (2026)

## Abstract (arXiv)

The number of samples in structural brain MRI studies is often too small to properly train deep learning models. Generative models show promise in addressing this issue by effectively learning the data distribution and generating high-fidelity MRI. However, they struggle to produce diverse, high-quality data outside the distribution defined by the training data. One way to address this issue is to use causal models developed for 3D volume counterfactuals. However, accurately modeling causality in high-dimensional spaces is challenging, so these models generally generate 3D brain MRIs of lower quality. To address these challenges, we propose a two-stage method that constructs a Structural Causal Model (SCM) within the latent space. In the first stage, we employ a VQ-VAE to learn a compact embedding of the MRI volume. Subsequently, we integrate our causal model into this latent space and execute a three-step counterfactual procedure using a closed-form Generalized Linear Model (GLM). Our experiments conducted on real-world high-resolution MRI data (1 mm) provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) demonstrate that our method can generate high-quality 3D MRI counterfactuals.