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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Updated: Jun 24, 2026

Ultrasonic Assessment of Myocardial Microstructure
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Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

A reduced complexity estimation algorithm for ultrasound images de-blurring.

Alessandro Palladini1, Nicola Testoni, Luca De Marchi

  • 1ARCES/DEIS - University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy. apalladini@arces.unibo.it

Computer Methods and Programs in Biomedicine
|April 14, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a Maximum Likelihood (ML) deconvolution technique to enhance ultrasound image quality by reducing intersymbol interference and noise. The method improves image clarity using a Viterbi algorithm for efficient processing.

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Quantifying Intermembrane Distances with Serial Image Dilations
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Last Updated: Jun 24, 2026

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07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Ultrasound Technology

Background:

  • Ultrasound images are often degraded by Intersymbol Interference (ISI) and Additive White Gaussian (AWG) noise.
  • Deconvolution techniques are crucial for improving the resolution and clarity of ultrasound images.
  • Existing methods may face computational challenges or limitations in accurately modeling the ultrasound channel.

Purpose of the Study:

  • To propose and evaluate a novel deconvolution technique for ultrasound images.
  • To enhance image quality by addressing ISI and AWG noise using Maximum Likelihood (ML) estimation.
  • To optimize the computational efficiency of the deconvolution process.

Main Methods:

  • A Maximum Likelihood (ML) estimation procedure is employed for deconvolution.
  • The ultrasonic radio-frequency (RF) signal is modeled as a sequence affected by ISI and AWG noise.
  • A reduced-state Viterbi algorithm is utilized to decrease computational cost.
  • Channel effects are estimated through experimental transducer response measurement or blind homomorphic techniques.
  • Extensive tests were conducted to determine the optimal quantization alphabet for performance.

Main Results:

  • The proposed deconvolution technique demonstrated an enhancement in ultrasound image quality across various metrics.
  • The use of a reduced-state Viterbi algorithm effectively managed computational complexity.
  • Both experimental and blind estimation methods for channel effects yielded improved results.
  • Performance was optimized by identifying the best quantization alphabet.

Conclusions:

  • The ML-based deconvolution technique offers a significant improvement in ultrasound image quality.
  • The Viterbi algorithm provides an efficient approach for processing complex RF signals.
  • The method is robust and adaptable, with channel effects estimated via distinct approaches.
  • This work contributes to advancing ultrasound imaging capabilities through enhanced signal processing.