基于深度神经网络的合成图像数字光学使用数字重建断层扫描
Shinichiro Mori1, Ryusuke Hirai2, Yukinobu Sakata2
1National Institutes for Quantum Science and Technology, Quantum Life and Medical Science Directorate, Institute for Quantum Medical Science, Inage-ku, Chiba, 263-8555, Japan. mori.shinichiro@qst.go.jp.
Physical and engineering sciences in medicine
|June 22, 2023
概括
一个深度神经网络 (DNN) 成功地从数字重建放射图 (DRR) 中生成现实的X射线平面探测器 (FPD) 图像. 这一进步提高了图像质量,并可以简化不同成像模式之间的比较.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 准确的图像采集对于放射治疗规划和质量保证至关重要.
- 比较来自不同模式的图像,例如数字重建放射图 (DRR) 和平板探测器 (FPD) 图像,可能具有挑战性.
- 目前的方法可能需要大量的时间来处理和比较图像.
研究的目的:
- 开发和评估一个深度神经网络 (DNN),用于从DRR图像中合成FPD图像.
- 为了评估DNN生成的FPD图像的图像质量,与地面真相FPD和输入DRR图像相比.
- 确定该技术在医疗成像工作流程效率提高方面的潜力.
主要方法:
- 开发了一个DNN,可以从DRR图像中生成合成FPD图像.
- 来自前列腺和头 (H&N) 恶性瘤的患者数据被用于培训和验证.
- 图像质量通过使用平均绝对误差 (MAE),峰值信号对噪声比 (PSNR) 和结构相似性指数测量 (SSIM) 进行了定量评估.
主要成果:
- DNN成功生成了类似于地面真相的FPD图像的合成FPD图像.
- 对于前列腺癌病例,合成FPD图像显示MAE (0.12 ± 0.02) 和PSNR (16.81 ± 1.54dB) 与DRR图像相比显著改善.
- 对于H&N癌症病例,合成FPD图像也显示出优异的指标:MAE (0.08 ± 0.03),PSNR (19.40 ± 2.83 dB) 和SSIM (0.80 ± 0.04) 与DRR图像相比.
结论:
- 开发的DNN有效地从DRR图像中合成高质量的FPD图像.
- 这种技术为提高图像质量和促进不同成像模式之间的视觉比较提供了潜在的解决方案.
- 该方法在临床工作流程中增加吞吐量是有希望的,在临床工作流程中,图像比较至关重要.
相关概念视频
01:10Computed Tomography
4.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.6K
01:27Imaging Studies III: Computed Tomography
30
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
30