Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling

Junqi Liu; Xinze Zhou; Wenxuan Li; Scott Ye; Arkadiusz Sitek; Xiaofeng Yang; Yucheng Tang; Daguang Xu

cs.CVApr 8, 2026

Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling

Authors: Junqi Liu, Xinze Zhou, Wenxuan Li, Scott Ye, Arkadiusz Sitek, Xiaofeng Yang, Yucheng Tang, Daguang Xu,

Organizations: Johns Hopkins University · University of California, San Francisco · Harvard Medical School · Emory University ·

Abstract

Photon-counting CT (PCCT) provides superior image quality with higher spatial resolution and lower noise compared to conventional energy-integrating CT (EICT), but its limited clinical availability restricts large-scale research and clinical deployment. To bridge this gap, we propose SUMI, a simulated degradation-to-enhancement method that learns to reverse realistic acquisition artifacts in low-quality EICT by leveraging high-quality PCCT as reference. Our central insight is to explicitly model realistic acquisition degradations, transforming PCCT into clinically plausible lower-quality counterparts and learning to invert this process. The simulated degradations were validated for clinical realism by board-certified radiologists, enabling faithful supervision without requiring paired acquisitions at scale. As outcomes of this technical contribution, we: (1) train a latent diffusion model on 1,046 PCCTs, using an autoencoder first pre-trained on both these PCCTs and 405,379 EICTs from 145 hospitals to extract general CT latent features that we release for reuse in other generative medical imaging tasks; (2) construct a large-scale dataset of over 17,316 publicly available EICTs enhanced to PCCT-like quality, with radiologist-validated voxel-wise annotations of airway trees, arteries, veins, lungs, and lobes; and (3) demonstrate substantial improvements: across external data, SUMI outperforms state-of-the-art image translation methods by 15% in SSIM and 20% in PSNR, improves radiologist-rated clinical utility in reader studies, and enhances downstream top-ranking lesion detection performance, increasing sensitivity by up to 15% and F1 score by up to 10%. Our results suggest that emerging imaging advances can be systematically distilled into routine EICT using limited high-quality scans as reference.

Figures & tables

Figure 1: Overview of SUMI. (1.) A continual learning autoencoder is pre-trained on over 400,000 CT volumes from 145 hospitals across 19 countries under pixel-wise loss $\mathcal{L}_{pp}$ and adversarial loss $\mathcal{L}_{dis}$ , making it the largest and most geographically diverse open-source medical CT autoencoder to date, overpassing prior methods (LDM [ 27 ] : 0, MedDiff [ 35 ] : 2, MONAI [ 5 ] : 7, Med3D [ 7 ] : 8, MAISI [ 39 ] : 10). (2.) A clinical verified degradation simulator transforms high-quality PCCT into three realistic low-quality counterparts: sparse-view (reducing 2D projections), low-dose (reducing photon counts), and conventional (reducing resolution while increasing noise and artifacts), covering the primary sources of EICT degradation. (3.) SUMI takes a degraded input $\tilde{x}_{i}$ from step (2.) and passes it through the autoencoder from step (1.) to produce an enhanced output $x_{i}^{\prime}$ . Training uses the original PCCT $x_{i}$ as ground truth under four losses: pixel-wise loss $\mathcal{L}_{pp}$ for structural fidelity, segmentation loss $\mathcal{L}_{seg}$ and HU consistency loss $\mathcal{L}_{HU}$ to preserve organ boundaries and tissue densities, such as pancreas, tumor, vessel, and airway tree using pre-computed masks $SEG(\cdot)$ , and adversarial loss $\mathcal{L}_{dis}$ via discriminator $D(\cdot)$ for image realism. Once trained, SUMI can enhance any EICT scan to PCCT-like quality without retraining.

method	sparse view		low dose		conventional		mixed
method	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR
SR3 [ 28 ]	46.2	8.7	42.0	8.8	44.4	8.6	47.0	8.7
Pix2Pix [ 17 ]	43.6	11.9	60.0	22.6	60.8	18.1	62.9	22.7
Swin2SR [ 9 ]	62.1	26.7	59.9	28.0	29.0	24.9	66.8	26.4
NLM [ 4 ]	73.2	28.4	72.3	30.2	40.9	26.1	76.9	27.0
SRGAN [ 19 ]	56.8	23.2	85.5	33.0	82.8	32.4	63.1	26.5

Table 1: SUMI improves image quality across degradations. On 446 external patients, our method surpasses the second-best method by up to +35.9% SSIM and +19.5% PSNR under sparse-view, low-dose, conventional, and mixed settings. Baselines are ordered by ascending average performance.

Figure 2: SUMI preserves anatomical structure and tissue density. Pearson correlation ( $r$ ) between ground truth and enhanced CT measurements across all organs shows that SUMI maintains anatomical accuracy and HU consistency. Organ masks are obtained with VISTA3D [ 16 ] .

dataset	DSB17 [ 18 ]	LIDC-IDRI [ 1 ]	LNDb19 [ 25 ]	LTRC [ 2 ]
scans ( $N$ )	1,596	1,018	324	1,496
dataset	Luna16 [ 29 ]	MIDRC [ 36 ]	NLST [ 32 ]	RSNA-STR [ 8 ]
scans ( $N$ )	854	10,496	422	1,110

Table 2: Our method processes and enhances a massive, multi-source cohort of 17,316 public chest CT scans. Cohorts are listed in alphabetical order.

Figure 3: SUMI demonstrates superior generalization and enhancement quality. For each example, the left image shows the CT slice and the right shows the airway tree segmentation. (a) Cross-dataset evaluation: compared with autoencoders trained with limited medical data scale (LDM, Med3D, MAISI), SUMI better preserves fine airway topology under domain shift. (b) PCCT enhancement with ground truth: compared with strong baselines (NLM [ 4 ] , SRGAN [ 19 ] , NEED [ 11 ] ), SUMI maintains structural integrity and small airway branches more faithfully.

	Luna16 [ 29 ] (N=222)				LNDb19 [ 25 ] (N=224)				DSB17 [ 18 ] (N=198)
	lung nodule, MONAI 1 [ 5 ]				lung nodule, MONAI 1 [ 5 ]				lung tumor, grt123 2 [ 20 ]
method	Sen.	Spec.	F1	AUC	Sen.	Spec.	F1	AUC	Sen.	Spec.	F1	AUC
baseline	75.8	88.4	81.6	88.0	61.5	86.9	55.2	74.0	78.4	82.1	80.2	87.0
+ SUMI	84.5	88.4	87.2	92.5	75.0	89.2	70.4	84.5	88.2	86.4	87.3	91.8
$\Delta$	+8.7	+0.0	+5.6	+4.5	+13.5	+2.3	+15.2	+10.5	+9.8	+4.3	+7.1	+4.8

Table 3: SUMI improves downstream detection across three independent chest CT cohorts. When integrated into open-source SOTA baselines, our enhancement consistently increases performance on Luna16 [ 29 ] , LNDb19 [ 25 ] , and DSB17 [ 18 ] , achieving up to +15.2% F1 and +10.5% AUC. Baselines are trained on standard-quality CT, while “+ SUMI” denotes the same architectures retrained using our PCCT-like enhanced data.

Figure 4: SUMI improves chest CT quality across datasets. Quality score distributions (by a pretrained scorer, 0–1, higher is better) on Luna16 [ 29 ] , LNDb19 [ 25 ] , DSB17 [ 18 ] , and a private PCCT dataset. On public datasets, enhanced scans consistently shift toward higher scores, indicating robust quality gains. On PCCT, a controlled degradation-to-enhancement benchmark shows that SUMI narrows the gap to real high-quality PCCT standards.

method	sparse view		low dose		conventional		mixed
method	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR
w/o. sparse view	71.9	30.2	90.0	35.2	87.2	35.3	73.2	30.1
w/o. low dose	72.7	30.2	82.6	28.0	84.3	32.9	76.2	31.5
w/o. conventional	75.8	27.0	86.0	33.0	85.3	32.0	81.9	32.4
all degrades	91.6	33.0	92.8	37.3	88.2	35.0	90.2	33.7

Table 4: Ablation of degradation simulations in SUMI. Leave-one-out experiments under identical settings show that removing sparse-view or low-dose simulation degrades performance on an external test set (446 scans), confirming their role in robust generalization.

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CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation

Yousra Nabila Taifour, Marouane Tliba, Zuheng Ming, Marie Luong+8

1Université Sorbonne Paris Nord · University of Dubai · 3Northwestern University+3

Image Quality Assessment Computed Tomography Medical Image Benchmarks

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Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still largely evaluated using image quality metrics with limited perceptual and clinical validity, while existing datasets remain focused on isolated restoration tasks, hindering unified benchmarking across diverse degradation types. We present CT-DegradBench, a dataset and benchmark for CT degradation detection and severity estimation under controlled single- and mixed-artifact settings. CT-DegradBench enables systematic evaluation across multiple degradation families and severity levels within a common experimental framework. We further propose SeSpeCT (Semantic-Spectral CT degradation estimation), a framework that combines semantic priors from medical vision-language models with complementary frequency-domain cues for artifact analysis. SeSpeCT constructs a training-free semantic quality axis in the multimodal embedding space using radiology-informed text prompts, without task-specific fine-tuning, and combines it with spectral features that capture degradation-specific frequency patterns. The resulting representation enables joint prediction of artifact type and severity. Experimental results show that SeSpeCT consistently outperforms the evaluated baselines under both single- and mixed-degradation settings. The framework is available at https://github.com/yousranb/CT-DEGRADBENCH.

Abstract

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