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相关概念视频

Computed Tomography01:10

Computed Tomography

8.0K
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...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

280
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...
280
Reducing Line Loss01:18

Reducing Line Loss

353
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
353

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相关实验视频

Updated: Jan 14, 2026

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

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3D-SLARM:通过3D扫描轻量级自回归模型进行实际的无损体积图像压缩.

Kai Wang, Yuanchao Bai, Daxin Li

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 12, 2026
    PubMed
    概括

    我们开发了一种3D扫描轻量级自回归模型 (3D-SLARM),用于高效的无损体积图像压缩. 这个模型通过轻量级架构实现了最先进的压缩比和快速编码速度.

    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 数据压缩数据压缩

    背景情况:

    • 无损压缩对于保持体积图像中的数据完整性至关重要.
    • 现有的学习方法在平衡压缩比,编码速度和模型复杂性方面面临挑战.

    研究的目的:

    • 为无损体积图像压缩提出一种新,轻量级和高效的模型.
    • 解决当前方法在实现高压缩比率与快速编码速度方面的局限性.

    主要方法:

    • 引入了一个3D扫描轻量级自动回归模型 (3D-SLARM).
    • 集成了一个3D平面扫描模块,用于并行voxel编码.
    • 采用轻量级特征提取模块,具有串行重新参数化 (SerRep) 和非中心面罩卷积 (NCMC).
    • 使用轻量级分布参数和自适应范围预测器 (DPARP) 模块进行高效的参数预测和自适应概率范围生成.

    主要成果:

    • 在大多数体积图像数据集上,3D-SLARM实现了最先进的无损压缩性能.
    • 该模型展示了快速的编码速度.
    • 拟议的架构是轻量级和实用的,适用于现实世界的应用.
    • 有效处理8位和高位深度体积图像.

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    Published on: November 27, 2017

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    Photorealistic Learned Landscapes for Augmented Reality
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    07:01

    3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

    Published on: October 24, 2019

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    结论:

    • 3D-SLARM为无损体积图像压缩提供了一个实用的解决方案.
    • 该模型成功地平衡了高压缩比,快速编码速度和轻量级设计.
    • 新的组件,包括3D平面扫描和SerRep/NCMC,有助于其有效性.