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Updated: Nov 12, 2025

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

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Published on: May 27, 2012

Multi-target Detection with an Arbitrary Spacing Distribution.

Ti-Yen Lan¹, Tamir Bendory¹, Nicolas Boumal¹

¹Program in Applied and Computational Mathematics and the Mathematics Department, Princeton University, Princeton, NJ 08544, USA.

IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society

|March 22, 2021

Summary

This summary is machine-generated.

This study introduces novel methods for reconstructing signals in noisy data, inspired by cryo-electron microscopy. The techniques enable accurate signal reconstruction even when direct detection is impossible due to high noise levels.

Keywords:

autocorrelation analysis blind deconvolution cryo-EM expectation maximization frequency marching

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Area of Science:

Signal processing
Computational imaging
Biophysics

Background:

Single-particle cryo-electron microscopy (cryo-EM) faces challenges in structure reconstruction due to noise.
The multi-target detection model involves multiple signal occurrences in noisy measurements.
High noise levels impede traditional signal detection and averaging methods.

Purpose of the Study:

To develop methods for signal reconstruction in the presence of high additive Gaussian noise.
To address the limitations of direct signal detection in low signal-to-noise ratio (SNR) environments.
To reconstruct signals without prior knowledge of signal occurrence locations.

Main Methods:

Autocorrelation analysis for signal feature extraction.
An approximate expectation-maximization (EM) algorithm for iterative signal estimation.
Methods designed to handle arbitrary spacing distributions of signal occurrences.

Main Results:

Successful signal reconstruction demonstrated using synthetic data.
Empirical evidence shows sample complexity scales as SNR^-3 in the low SNR regime.
The proposed methods effectively reconstruct signals without explicit target detection.

Conclusions:

Autocorrelation analysis and approximate EM are effective for signal reconstruction in high noise.
These methods overcome the limitations of detection-based approaches in low SNR scenarios.
The findings have implications for structure determination in cryo-EM and similar fields.