| Autor: |
Zhijiang Tang, Qiang Xiong, Xuantai Wu, Tianyi Xu, Yuxuan Shi, Ximing Xu, Jun Xu, Ruijue Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Insights into Imaging, Vol 15, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1869-4101 |
| DOI: |
10.1186/s13244-024-01620-z |
| Popis: |
Abstract Purpose The aim of this study was to diminish radiation exposure in interventional radiology (IR) imaging while maintaining image quality. This was achieved by decreasing the acquisition frame rate and employing a deep neural network to interpolate the reduced frames. Methods This retrospective study involved the analysis of 1634 IR sequences from 167 pediatric patients (March 2014 to January 2022). The dataset underwent a random split into training and validation subsets (at a 9:1 ratio) for model training and evaluation. Our approach proficiently synthesized absent frames in simulated low-frame-rate sequences by excluding intermediate frames from the validation subset. Accuracy assessments encompassed both objective experiments and subjective evaluations conducted by nine radiologists. Results The deep learning model adeptly interpolated the eliminated frames within IR sequences, demonstrating encouraging peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results. The average PSNR values for angiographic, subtraction, and fluoroscopic modes were 44.94 dB, 34.84 dB, and 33.82 dB, respectively, while the corresponding SSIM values were 0.9840, 0.9194, and 0.7752. Subjective experiments conducted with experienced interventional radiologists revealed minimal discernible differences between interpolated and authentic sequences. Conclusion Our method, which interpolates low-frame-rate IR sequences, has shown the capability to produce high-quality IR images. Additionally, the model exhibits potential for reducing the frame rate during IR image acquisition, consequently mitigating radiation exposure. Critical relevance statement This study presents a critical advancement in clinical radiology by demonstrating the effectiveness of a deep neural network in reducing radiation exposure during pediatric interventional radiology while maintaining image quality, offering a potential solution to enhance patient safety. Key points • Reducing radiation: cutting IR image to reduce radiation. • Accurate frame interpolation: our model effectively interpolates missing frames. • High visual quality in terms of PSNR and SSIM, making IR procedures safer without sacrificing quality. Graphical Abstract |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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