VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution

Hairong Ni; Jiahe Huang; Minghao Guo; Wojciech Matusik; Rose Yu

cs.CVOct 14, 2026

VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution

Authors: Hairong Ni, Jiahe Huang, Minghao Guo, Wojciech Matusik, Rose Yu

Organizations: University of Michigan · University of California, San Diego · MIT CSAIL

Abstract

Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-aware diffusion framework that combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guidance, we use an explicit 3D vascular representation that captures structural information beyond individual slices. This representation also simplifies vascular refinement by allowing a lightweight 3D generative refiner to focus on vascular structure rather than complex CT appearance. Second, to guide slice-wise SR, a lightweight anatomy controller injects the refined structure into the diffusion decoder, while iterative feedback updates this guidance from each new SR volume. On re-annotated MSD Task08 and HVM datasets, VascuSR outperforms interpolation and learning-based SR baselines in segmentation accuracy (Dice and clDice) and 3D vascular structure (connectivity and mean centerline distance), while maintaining image quality comparable to its SR backbone. Compared with learning-based baselines, VascuSR reduces class-averaged connected-component counts by over 20% on both datasets and mean centerline distance by over 10% on MSD.

Figures & tables

Figure 1: Overview of VascuSR. Starting from an initial SR estimate, the recovered HV/PV anatomy is refined in three dimensions and fed back through a lightweight anatomy controller to condition the next diffusion-based reconstruction. Repeating this process for $K$ feedback rounds progressively couples image super-resolution with volumetric vascular refinement.

Method	Dice $\uparrow$		clDice $\uparrow$		Image quality
	HV	PV	HV	PV	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
(a) Re-annotated MSD Task08 HepaticVessel
Bicubic	.2618	.2264	.2475	.1956	25.31	.7742	.3872
RCAN	.5465	.5021	.5616	.4274	34.25	.9124	.1317
SwinIR	.5780	.5623	.5862	.4626	34.44	.9164	.1262
HAT	.5720	.5615	.5782	.4551	34.21	.9149	.1270

Table 1: Quantitative results for

\times 8

CT super-resolution on (a) the MSD Task08 HepaticVessel test set and (b) the HVM test set. In this table, VascuSR uses

K=3

on MSD and

K=2

on HVM. The best and second-best results are shown in bold and with dashed underlining, respectively.

	HV			PV
Method	#CC $\downarrow$	Largest-CC frac $\uparrow$	MCD (mm) $\downarrow$	#CC $\downarrow$	Largest-CC frac $\uparrow$	MCD (mm) $\downarrow$
(a) MSD Task08 HepaticVessel
Bicubic	27.6	.5178	18.128	13.0	.6432	18.171
RCAN	31.1	.7773	6.753	29.0	.6801	8.700
SwinIR	31.6	.8377	6.499	28.1	.7874	8.632
HAT	33.1	.8125	6.517	29.0	.7852	8.453

Table 2: Three-dimensional vascular structure on (a) MSD Task08 HepaticVessel test set and (b) HVM test set. Bold marks the best SR result in each column.

Panel of Figure 2 — Figure 2: MSD case 416 at axial slice $z=094$ . Columns: LR, ResShift, VascuSR (round 3), and HR. Top: image intensities; bottom: HV (red) and PV (blue) contour overlays.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Training	Validation	Test
Re-annotated MSD Task08	243	30	30
HVM	223	29	29

Appendix

Table 4: Patient-level dataset partitions.

Arm	Dice $\uparrow$		clDice $\uparrow$		Image quality
Arm	HV	PV	HV	PV	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
ResShift (baseline)	.6526	.5923	.5877	.4460	32.83	.8896	.0660
0-mask	.6587	.5644	.5909	.4286	32.78	.8877	.0650
VascuSR, round 1	.7077	.6334	.6381	.5158	32.77	.8876	.0649
VascuSR, round 2	.7050	.6337	.6431	.5267	32.77	.8876	.0649
VascuSR, round 3	.7072	.6404	.6446	.5286	32.77	.8876	.0649

Appendix

Table 5: Effect of iterative anatomical feedback on the MSD Task08 HepaticVessel test set. The 0-mask setting retains the controller architecture without anatomical conditioning. The best result in each column is shown in bold, including ties.

Arm	Dice $\uparrow$		clDice $\uparrow$		Image quality
Arm	HV	PV	HV	PV	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
ResShift (baseline)	.5977	.6914	.5687	.5302	23.50	.7001	.0743
0-mask (R0)	.5928	.6888	.5637	.5289	23.40	.6990	.0744
VascuSR, round 1	.6191	.7113	.5980	.5689	23.41	.6990	.0744
VascuSR, round 2	.6206	.7117	.6033	.5731	23.40	.6990	.0744
VascuSR, round 3	.6191	.7111	.6028	.5768	23.40	.6990	.0745

Appendix

Table 6: Effect of iterative anatomical feedback on HVM. The 0-mask setting (R0) retains the controller architecture without anatomical conditioning. The best result in each column is shown in bold.

Figure 4: Additional qualitative comparison on held-out MSD case 416 at axial slice $z=114$ . Columns show LR, ResShift, VascuSR after three feedback rounds, and the HR reference (GT). The upper row shows intensity crops; the lower row adds vascular contours to the reconstructed and HR images. Red and blue denote HV and PV, respectively.

Panel of Figure 5 — Figure 5: Additional qualitative comparison on held-out HVM case 109 at axial slices (a) $z=179$ and (b) $z=204$ . Columns show LR, ResShift, VascuSR after two feedback rounds, and the HR reference (GT). The upper row of each panel shows intensity crops; the lower row adds vascular contours to the reconstructed and HR images. Red and blue denote HV and PV, respectively; HR contours are manual annotations.

Figure 6: Three-dimensional qualitative comparison on four held-out HVM cases. Columns show the initial SR segmentation, round-2 SR segmentation, and segmentation of the HR reference. Red and blue denote HV and PV, respectively.

Method	ROI PSNR $\uparrow$	ROI SSIM $\uparrow$	ROI spatial-LPIPS $\downarrow$
ResShift	34.5933	0.815880	0.155418
ResShift+Dice/clDice	33.0302	0.786745	0.160075
VascuSR, round 3	34.4383	0.815435	0.147031

Appendix

Table 7: Image quality within the 3 mm vessel ROI on the MSD test set. Values are patient-macro means; PSNR is in dB. This comparison uses VascuSR R3.

Method	MAE $\downarrow$	RMSE $\downarrow$	Signed bias ( $\to 0$ )
ResShift	21.97	29.61	+1.95
VascuSR, round 3	22.21	29.86	+4.14
ResShift+Dice/clDice	26.49	34.40	+12.59

Appendix

Table 8: Image fidelity within the 3 mm vessel ROI on the MSD test set. Values are in HU; positive bias indicates overestimated intensity.

Method	TS vessel Dice $\uparrow$
ResShift	.2912
VascuSR, round 3	.3183
ResShift+Dice/clDice	.2803

Appendix

Table 9: TS vessel Dice on the MSD test set.

Figure 7: Qualitative comparison on MSD case 416, axial slice $z=094$ . Columns show LR, ResShift, VascuSR after three feedback rounds, ResShift+Dice/clDice, and the HR reference. The upper row shows CT images; the lower row adds vascular contours, with HV in red and PV in blue. HR contours are manual annotations.

	HV		PV
Method	#CC $\downarrow$	Largest-CC frac $\uparrow$	#CC $\downarrow$	Largest-CC frac $\uparrow$
ResShift	31.0	.9077	26.0	.8843
ResShift+Dice/clDice	39.6	.8936	30.4	.8249
VascuSR, round 3	22.7	.9382	22.4	.8931

Appendix

Table 10: MSD vascular continuity for the three relevant methods, reproduced from Table 2 .

Method	HV $\downarrow$	PV $\downarrow$	HV/PV mean $\downarrow$
ResShift	6.0457	8.0877	7.0667
VascuSR, round 3	5.2311	6.6351	5.9331
ResShift+Dice/clDice	5.6521	7.5368	6.5944

Appendix

Table 11: Class-wise MSD MCD values (mm) from Table 2 , with their HV/PV mean additionally reported.

Training stage	Wall time (h)	GPU-hours
VascuSR refiner	$\approx 9.80$	$\approx 24.60$
ResShift+Dice/clDice	29.33	94.93

Appendix

Table 12: Training costs on HVM, excluding validation. The refiner is trained to the selected 50k checkpoint; ResShift+Dice/clDice is trained for 2,000 effective optimizer updates. For VascuSR, we report the training cost of the 3D vascular refiner.

Method	Dice $\uparrow$	clDice $\uparrow$	PSNR (dB) $\uparrow$	SSIM $\uparrow$
RCAN	0.7699	0.8432	31.29	0.8079
SwinIR	0.7702	0.8409	31.41	0.8096
ResShift	0.7658	0.8376	29.94	0.7647
VascuSR (Round 3)	0.7763	0.8530	29.84	0.7629
HR reference	0.8033	0.8610	$\infty$	1.0000

Appendix

Table 13: Coronary CTA results on the ImageCAS test set. Bold marks the best SR result in each column.

Figure 8: Three-dimensional coronary-artery segmentations for ImageCAS cases (a) 750, (b) 986, and (c) 902. Columns show Initial (ResShift), Round 3 (VascuSR), and HR segmentations.

Figure 9: Axial qualitative comparison on ImageCAS case 750 at $z=135$ and $8\times$ in-plane super-resolution. Columns show LR, ResShift, VascuSR after three feedback rounds, and the HR reference. The upper row shows CT images and the lower row adds red coronary contours. HR contours are manual annotations.

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