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相关概念视频

Deconvolution01:20

Deconvolution

162
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
162
Convolution Properties II01:17

Convolution Properties II

208
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
208
Convolution Properties I01:20

Convolution Properties I

153
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
153
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

264
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
264
Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

676
The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
676
Computed Tomography01:10

Computed Tomography

4.5K
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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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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一维卷积神经网络用于杰科比在扩散光学断层学中.

Huangjian Yi, Ruigang Yang, Xuelei He

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    这项研究引入了一种新的神经网络方法,用于扩散光学断层扫描 (DOT) 的反向问题. 该方法显著减少了雅可比矩阵计算的计算时间,加速了DOT重建.

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    科学领域:

    • 生物医学光学 生物医学光学
    • 计算成像技术的成像
    • 医学物理 医学物理

    背景情况:

    • 分散光学断层扫描 (DOT) 的反向问题是计算密集的.
    • 计算雅可比矩阵是DOT重建中的一个重要瓶.
    • 现有的方法,如非线性最小平方,需要大量的计算时间.

    研究的目的:

    • 开发一种计算效率高的方法来解决DOT反向问题.
    • 为了加速扩散光学断层扫描中的重建过程.
    • 为了减少在DOT中对雅可比矩阵计算所需的时间.

    主要方法:

    • 开发了一种数据驱动的神经网络方法.
    • 单值分解 (SVD) 用于计算更新的Jacob.
    • 一个卷积神经网络 (CNN) 被训练来将边界测量映射到单数值.

    主要成果:

    • 与助理方法相比,拟议的神经网络方法显著减少了计算时间.
    • 重建的吸收系数与使用Adjoint方法获得的可比.
    • 该方法证明了DOT反向问题的计算效率有所提高.

    结论:

    • 数据驱动的神经网络方法为加速DOT重建提供了可行的解决方案.
    • 这种方法有可能提高在临床环境中扩散光学断层扫描的效率.
    • 进一步的研究可能会导致医疗应用中更快,更准确的DOT成像.