Tang Zhijiang, Xiong Qiang, Wu Xuantai, Xu Tianyi, Shi Yuxuan, Xu Ximing, Xu Jun, Wang Ruijue
School of Statistics and Data Science, Nankai University, Tianjin, China.
Department of Hepatobiliary Surgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Structural Birth Defect and Reconstruction, Children's Hospital of Chongqing Medical University, Chongqing, China.
Insights Imaging. 2024 Feb 14;15(1):42. doi: 10.1186/s13244-024-01620-z.
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.
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.
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.
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.
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.
• 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.
本研究的目的是在介入放射学（IR）成像中减少辐射暴露，同时保持图像质量。这是通过降低采集帧率并采用深度神经网络对减少的帧进行插值来实现的。
这项回顾性研究涉及对167例儿科患者（2014年3月至2022年1月）的1634个IR序列进行分析。数据集被随机分为训练和验证子集（比例为9:1）用于模型训练和评估。我们的方法通过从验证子集中排除中间帧，有效地在模拟低帧率序列中合成缺失的帧。准确性评估包括客观实验和由九位放射科医生进行的主观评估。
深度学习模型能够巧妙地对IR序列中被删除的帧进行插值，显示出令人鼓舞的峰值信噪比（PSNR）和结构相似性指数（SSIM）结果。血管造影、减法和透视模式的平均PSNR值分别为44.94 dB、34.84 dB和33.82 dB，而相应的SSIM值分别为0.9840、0.9194和0.7752。与经验丰富的介入放射科医生进行的主观实验表明，插值序列和真实序列之间几乎没有可察觉的差异。
我们对低帧率IR序列进行插值的方法已显示出产生高质量IR图像的能力。此外，该模型在IR图像采集期间具有降低帧率的潜力，从而减少辐射暴露。
本研究通过证明深度神经网络在儿科介入放射学中减少辐射暴露同时保持图像质量的有效性，在临床放射学方面取得了关键进展，为提高患者安全性提供了潜在解决方案。
• 减少辐射：减少IR图像以降低辐射。• 精确的帧插值：我们的模型有效地对缺失帧进行插值。• 在PSNR和SSIM方面具有高视觉质量，使IR程序在不牺牲质量的情况下更安全。
