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Related Concept Videos

Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Related Experiment Video

Updated: Mar 12, 2026

Near Infrared Optical Projection Tomography for Assessments of &#946;-cell Mass Distribution in Diabetes Research
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Noise-Weighted FBP Algorithm for Uniformly Attenuated SPECT Projections.

Gengsheng L Zeng

    IEEE Transactions on Nuclear Science
    |November 15, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study extends the noise-weighted filtered backprojection (FBP) algorithm for uniformly attenuated imaging, showing comparable noise control to ML-EM for SPECT. Further development is needed for non-uniform attenuation in clinical settings.

    Keywords:
    Attenuated Radon transformPoisson noiseSPECTanalytical algorithmexponential Radon transformfiltered backprojection algorithm

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    Area of Science:

    • Medical Imaging
    • Nuclear Medicine
    • Image Reconstruction

    Background:

    • Filtered backprojection (FBP) and Bayesian FBP algorithms are established for un-attenuated Radon transforms in X-ray CT.
    • Iterative algorithms like ML-EM are common for emission tomography but can be computationally intensive.

    Purpose of the Study:

    • To extend the noise-weighted FBP algorithm to handle uniformly attenuated Radon transforms.
    • To evaluate the performance of the extended FBP algorithm for uniformly attenuated SPECT imaging.
    • To compare its noise control capabilities with iterative methods.

    Main Methods:

    • Extension of the noise-weighted FBP algorithm for uniform attenuation.
    • Application to uniformly attenuated single photon emission computed tomography (SPECT).
    • Validation through computer simulations and experimental data.

    Main Results:

    • The proposed extended FBP algorithm demonstrates effective noise control in uniformly attenuated SPECT.
    • Its noise control capability is comparable to the iterative Maximum Likelihood Expectation Maximization (ML-EM) algorithm.
    • The algorithm shows potential for clinical application in SPECT with uniform attenuation.

    Conclusions:

    • The extended noise-weighted FBP algorithm is a viable option for attenuation correction in uniformly attenuated SPECT.
    • Further research is required to develop a stable FBP algorithm for non-uniform attenuators for broader clinical use.