Evaluation of 3D Counterfactual Brain MRI Generation

TL;DR

This paper evaluates six 3D generative models for creating realistic, anatomically consistent counterfactual brain MRIs, highlighting their strengths and limitations in clinical and research applications.

Contribution

It introduces an anatomy-guided framework for converting existing models into 3D counterfactual generators and provides a comprehensive evaluation protocol.

Findings

Anatomically grounded conditioning effectively modifies targeted regions.

Models show limitations in preserving non-targeted structures.

Benchmark highlights the need for architectures capturing anatomical interdependencies.

Abstract

Counterfactual generation offers a principled framework for simulating hypothetical changes in medical imaging, with potential applications in understanding disease mechanisms and generating physiologically plausible data. However, generating realistic structural 3D brain MRIs that respect anatomical and causal constraints remains challenging due to data scarcity, structural complexity, and the lack of standardized evaluation protocols. In this work, we convert six generative models into 3D counterfactual approaches by incorporating an anatomy-guided framework based on a causal graph, in which regional brain volumes serve as direct conditioning inputs. Each model is evaluated with respect to composition, reversibility, realism, effectiveness and minimality on T1-weighted brain MRIs (T1w MRIs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, we test the generalizability of each model with respect to T1w MRIs of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). Our results indicate that anatomically grounded conditioning successfully modifies the targeted anatomical regions; however, it exhibits limitations in preserving non-targeted structures. Beyond laying the groundwork for more interpretable and clinically relevant generative modeling of brain MRIs, this benchmark highlights the need for novel architectures that more accurately capture anatomical interdependencies.