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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Updated: May 28, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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一个基于DICOM的兽医深度学习否定算法可以改善主观和客观的大脑MRI图像质量.

Wilfried Mai1, Silke Hecht2, Matthew Paek3

  • 1Department of Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
|February 13, 2025
PubMed
概括

一个新的深度学习 (DL) 消除噪声的算法显著改善了狗和猫的大脑MRI扫描中的信号与噪声和对比与噪声的比率. 兽医放射科医生观察到图像质量提高,T2W,T2-FLAIR和GRE序列中的噪声减少.

关键词:
人工智能的人工智能是人工智能.降低噪音 减少噪音后期处理 后期处理

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科学领域:

  • 兽医放射学 兽医放射学
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 磁共振成像 (MRI) 对于诊断兽医患者的大脑疾病至关重要.
  • 图像噪声可以降低兽医大脑MRI的诊断质量.
  • 深度学习 (DL) 算法显示了改善医疗图像质量的潜力.

研究的目的:

  • 评估基于DICOM的深度学习 (DL) 对兽医大脑MRI的解密算法的有效性.
  • 将原生MRI扫描与DL算法处理的MRI扫描进行定量和质量比较.
  • 评估DL无声化对狗和猫大脑MRI图像质量指标的影响.

主要方法:

  • 分析横截面方法比较研究涉及30只狗和猫.
  • 在T2W,T2-FLAIR和GRE序列上对信号与噪声比 (SNR) 和对比与噪声比 (CNR) 的定量分析.
  • 由三个盲目兽医放射科医生进行的定性评估,评估粗度,对比度和整体图像质量.

主要成果:

  • 对皮层灰质,皮下白质,深灰质和内部囊的SNR有统计学显著的增加.
  • 在皮质灰色和白质,深灰质和内部囊之间显著更高的CNR.
  • 放射科医生报告说,无色化图像的粗度,对比度和整体质量得分通常更好.

结论:

  • 基于DICOM的DL消噪算法有效地减少了狗和猫的大脑1.5TMRI中的噪声.
  • 显而易见地,DL无声化可以改善定量图像质量指标 (SNR和CNR).
  • 该算法导致兽医放射科医生感知到的图像质量得到改善,有助于诊断.