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

TL;DR

This paper introduces SUMI, a method that uses high-quality photon-counting CT to improve routine chest CT images by modeling and reversing realistic acquisition artifacts, validated by radiologists.

Contribution

The study develops a degradation-to-enhancement approach that leverages high-quality PCCT as a reference to improve EICT images, including a large dataset and a diffusion model.

Findings

SUMI outperforms state-of-the-art methods by 15% in SSIM and 20% in PSNR.

Radiologist-validated dataset with over 17,000 enhanced EICTs.

Improved lesion detection sensitivity and F1 score.

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.