Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
- Siemens Healthineers, CT collaboration, Shanghai, China.
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
Abstract
To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification. This study retrospectively included patients with chest discomfort who underwent CT myocardial perfusion + CT angiography + LIE from two hospitals. Two DL models, residual dense network (RDN) and conditional generative adversarial network (cGAN), were developed and validated. 423 patients were randomly divided into training (182 patients), tuning (48 patients), internal validation (92 patients) and external validation group (101 patients). LIE<sub>single</sub> (single-stack image), LIE<sub>averaging</sub> (averaging multiple-stack images), LIE<sub>RDN</sub> (single-stack image denoised by RDN) and LIE<sub>GAN</sub> (single-stack image denoised by cGAN) were generated. We compared image quality score, signal-to-noise (SNR) and contrast-to-noise (CNR) of four LIE sets. The identifiability of denoised images for positive LIE and increased ECV (> 30%) was assessed. The image quality of LIE<sub>GAN</sub> (SNR: 13.3 ± 1.9; CNR: 4.5 ± 1.1) and LIE<sub>RDN</sub> (SNR: 20.5 ± 4.7; CNR: 7.5 ± 2.3) images was markedly better than that of LIE<sub>single</sub> (SNR: 4.4 ± 0.7; CNR: 1.6 ± 0.4). At per-segment level, the area under the curve (AUC) of LIE<sub>RDN</sub> images for LIE evaluation was significantly improved compared with those of LIE<sub>GAN</sub> and LIE<sub>single</sub> images (p = 0.040 and p < 0.001, respectively). Meanwhile, the AUC and accuracy of ECV<sub>RDN</sub> were significantly higher than those of ECV<sub>GAN</sub> and ECV<sub>single</sub> at per-segment level (p < 0.001 for all). RDN model generated denoised LIE images with markedly higher SNR and CNR than the cGAN-model and original images, which significantly improved the identifiability of visual analysis. Moreover, using denoised single-stack images led to accurate CT-ECV quantification. Question Can the developed models denoise CT-derived late iodine enhancement high images and improve signal-to-noise ratio? Findings The residual dense network model significantly improved the image quality for late iodine enhancement and enabled accurate CT- extracellular volume quantification. Clinical relevance The residual dense network model generates denoised late iodine enhancement images with the highest signal-to-noise ratio and enables accurate quantification of extracellular volume.