LiFT: Lifted Inter-slice Feature Trajectories for 3D Image Generation from 2D Generators

2026-05-18

AI Summary

This work addresses the challenges of high-resolution 3D medical image generation, where fully 3D models suffer from prohibitive computational costs and 2D slice-based approaches often produce anatomically inconsistent results. The authors propose an efficient and anatomically coherent 3D generative framework that decomposes volume synthesis into slice-wise generation coupled with inter-slice feature trajectory modeling. They introduce, for the first time in unconditional generation, a triplane drift loss to align the depth-trajectory distributions of real and generated volumes, and design a bidirectional z-context mixer to enhance inter-slice coherence. Built upon a 2D generator architecture, the method achieves superior single-slice image quality on BraTS 2023 and SynthRAD2023, near state-of-the-art performance in missing-modality reconstruction, approximately 135× lower inference cost, and significantly improved inter-slice consistency in MR-to-CT translation tasks.

Abstract

High-resolution 3D medical image generation remains challenging because fully volumetric models are computationally expensive, while efficient 2D slice generators often fail to preserve anatomical consistency across the third dimension. We propose LiFT, a framework for Lifted inter-slice Feature Trajectories that factorizes 3D volume synthesis into per-slice image generation and inter-slice trajectory learning. Rather than modeling the volumetric distribution end-to-end, LiFT treats a volume as an ordered trajectory in feature space, capturing how anatomical structures appear, transform, and disappear across depth. A tri-planar drifting loss aligns the trajectory of generated slices with the trajectories of real volumes, enabling distributional learning over inter-slice progressions in unconditional generation; in paired translation, a bidirectional $z$-context mixer trained against the registered target supplies through-plane coherence while preserving per-slice fidelity. We evaluate LiFT on BraTS 2023 (unconditional and missing-modality MR) and SynthRAD2023 (MR-to-CT). Across these settings, LiFT preserves per-slice quality, approaches the reported cWDM missing-MR reconstruction quality at $\sim$$135\times$ lower inference cost (without formal equivalence testing), and improves through-plane coherence on MR-to-CT relative to a no-mapper ablation, demonstrating that lightweight inter-slice trajectory learning is a viable route to high-resolution 3D medical synthesis.

Problem

• 3D medical image generation
• anatomical consistency
• inter-slice coherence
• high-resolution synthesis
• 2D-to-3D generation

Innovation

• LiFT
• inter-slice feature trajectories
• 3D medical image generation
• feature space trajectory
• z-context mixer