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相关概念视频

Computed Tomography01:10

Computed Tomography

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...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...

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相关实验视频

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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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在低剂量CT无声化任务之间转移U-Net:一项具有不同空间分辨率的验证研究.

Xin Zhang1,2, Ting Su1, Yunxin Zhang3

  • 1Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Quantitative imaging in medicine and surgery
|January 15, 2024
PubMed
概括

低剂量计算机断层扫描 (LDCT) 的深度学习模型可以在不同的空间分辨率上传. 然而,通过一个小的数据集重新训练U-Net模型,可以显著减少文物并提高图像质量.

关键词:
低剂量计算机断层扫描 (LDCT)这就是U-Net.计算机断层扫描 (CT) 消毒网络再培训 网络再培训的空间分辨率.

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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:

  • 深度学习,特别是U-Net,越来越多地用于低剂量计算机断层扫描 (LDCT),以在减少辐射剂量时提高图像质量.
  • 验证训练有素的深度学习模型在不同成像系统的各种LDCT数据集中的通用性,对于临床采用至关重要.

研究的目的:

  • 评估预先训练的U-Net在不同空间分辨率的LDCT图像上的拒绝性能的可复制性.
  • 调查空间分辨率差异对U-Net拒绝可转移性的影响,并确定潜在的文物.

主要方法:

  • 在LDCT图像上训练了一个U-Net模型,然后在六个不同的空间分辨率 (62.5625μm) 的数据集中验证.
  • 量化指标包括剩余方差,PSNR,NRMSE和SSIM用于绩效评估.
  • 用数据子集重新训练网络被探索以减轻交叉分辨率文物.

主要成果:

  • 虽然U-Net在不同空间分辨率的LDCT图像中证明了其有效性,但交叉验证引入了图像工件.
  • 文物变得更加明显,空间分辨率的不一致性越来越大,特别是在对象边缘和中心.
  • 使用大约20%的原始数据重新训练U-Net,显著减少了文物 (NRMSE从0.1898下降到0.1263,SSIM从0.7558增加到0.8036).

结论:

  • 将为LDCT训练的U-Net转移到具有不同空间分辨率的数据集可能会导致文物生成.
  • 建议将U-Net与所需空间分辨率的小,有针对性的数据集进行重新训练,以保持最佳的无声化性能并最大限度地减少文物.