TopoGate: Quality-Aware Topology-Stabilized Gated Fusion for Longitudinal Low-Dose CT New-Lesion Prediction

Seungik Cho

eess.IVFeb 19, 2026

TopoGate: Quality-Aware Topology-Stabilized Gated Fusion for Longitudinal Low-Dose CT New-Lesion Prediction

Authors: Seungik Cho

Organizations: Department of Physics & Astronomy, Rice University, USA

Abstract

Longitudinal low-dose CT follow-ups vary in noise, reconstruction kernels, and registration quality. These differences destabilize subtraction images and can trigger false new lesion alarms. We present TopoGate, a lightweight model that combines the follow-up appearance view with the subtraction view and controls their influence through a learned, quality-aware gate. The gate is driven by three case-specific signals: CT appearance quality, registration consistency, and stability of anatomical topology measured with topological metrics. On the NLST--New-Lesion--LongCT cohort comprising 152 pairs from 122 patients, TopoGate improves discrimination and calibration over single-view baselines, achieving an area under the ROC curve of 0.65 with a standard deviation of 0.05 and a Brier score of 0.14. Removing corrupted or low-quality pairs, identified by the quality scores, further increases the area under the ROC curve from 0.62 to 0.68 and reduces the Brier score from 0.14 to 0.12. The gate responds predictably to degradation, placing more weight on appearance when noise grows, which mirrors radiologist practice. The approach is simple, interpretable, and practical for reliable longitudinal LDCT triage.

Figures & tables

Figure 1: Longitudinal LDCT pair. (Left) Follow-up (FU); (Right) Registered baseline (BL_reg). Differences in reconstruction and residual misalignment can destabilize temporal subtraction $\Delta$ , motivating quality-aware fusion.

Figure 2: TopoGate framework. We deformably register baseline CT to the follow-up to obtain $\mathrm{BL_{reg}}$ , then crop aligned 3D ROIs around each lesion point $\mathbf{p}^{(i)}$ and compute the temporal difference $\Delta=\mathrm{FU}-\mathrm{BL_{reg}}$ . Two 3D encoders extract appearance and difference embeddings $(f_{\mathrm{app}},f_{\Delta})$ . A quality vector $\mathbf{q}=[q_{\text{ct}},q_{\text{reg}},q_{\text{topo}}]\in[0,1]^{3}$ controls a monotonic gate $\alpha=\sigma(w_{1}q_{\text{ct}}+w_{2}q_{\text{topo}}-w_{3}q_{\text{reg}}+b)$ , which adaptively fuses the two view-specific predictions: $s=\alpha\,g_{\mathrm{app}}(f_{\mathrm{app}})+(1-\alpha)\,g_{\Delta}(f_{\Delta})$ , producing the calibrated output $\hat{y}=\sigma(s)$ .

Model	AUROC ( $\pm$ SD)
App-only	0.55 $\pm$ 0.09
$\Delta$ -only	0.57 $\pm$ 0.08
Topology-only	0.61 $\pm$ 0.06
TopoGate	0.65 $\pm$ 0.05
Gate + All features	0.58 $\pm$ 0.06

Table 1: Full-cohort performance.

Figure 3: Gate response vs. quality. Larger $\alpha$ (color) is associated with higher CT quality and topology stability, increasing trust in the appearance branch.

Figure 4: Effect of quality filtering. Removing low-quality pairs increases AUROC and reduces Brier, improving reliability.

Figure 5: Robustness. As simulated noise increases, the mean gate weight $\alpha$ increases monotonically, shifting weight from $\Delta$ to appearance.

Gated Multimodal Fusion

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Mar 21, 2026J/K move · Enter open · S save

TAFG-MAN: Timestep-Adaptive Frequency-Gated Latent Diffusion for Efficient and High-Quality Low-Dose CT Image Denoising

Tangtangfang Fang, Yang Jiao, Xiangjian He, Jingxi Hu+1

University of Nottingham Ningbo China, Ningbo, Zhejiang, China

LDCT Denoising Diffusion Models for Image Restoration Computed Tomography Latent Diffusion Models Conditional Diffusion Models

Open paper AskPDF arXiv

Low-dose computed tomography (LDCT) reduces radiation exposure but also introduces substantial noise and structural degradation, making it difficult to suppress noise without erasing subtle anatomical details. In this paper, we present TAFG-MAN, a latent diffusion framework for efficient and high-quality LDCT image denoising. The framework combines a perceptually optimized autoencoder, conditional latent diffusion restoration in a compact latent space, and a lightweight Timestep-Adaptive Frequency-Gated (TAFG) conditioning design. TAFG decomposes condition features into low- and high-frequency components, predicts timestep-adaptive gates from the current denoising feature and timestep embedding, and progressively releases high-frequency guidance in later denoising stages before cross-attention. In this way, the model relies more on stable structural guidance at early reverse steps and introduces fine details more cautiously as denoising proceeds, improving the balance between noise suppression and detail preservation. Experiments show that TAFG-MAN achieves a favorable quality-efficiency trade-off against representative baselines. Compared with its base variant without TAFG, it further improves detail preservation and perceptual quality while maintaining essentially the same inference cost, and ablation results confirm the effectiveness of the proposed conditioning mechanism.

Abstract

Gated Multimodal Fusion

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