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Updated: May 26, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Prior image constrained compressed sensing: implementation and performance evaluation.

Pascal Thériault Lauzier¹, Jie Tang, Guang-Hong Chen

¹Medical Physics Department, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA.

Medical Physics

|January 10, 2012

Summary

This summary is machine-generated.

Prior image constrained compressed sensing (PICCS) image reconstruction is optimized with nonlinear conjugate gradient methods. Optimal performance is achieved with a prior image parameter near 0.5, maintaining spatial resolution and similar noise texture to the prior image.

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Last Updated: May 26, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

Medical imaging
Image reconstruction
Compressed sensing

Background:

Prior image constrained compressed sensing (PICCS) integrates a prior image into compressed sensing for improved reconstruction.
Optimization procedures are central to PICCS image reconstruction.

Purpose of the Study:

To implement and compare various unconstrained minimization methods for PICCS.
To evaluate the image quality performance of the PICCS objective function.

Main Methods:

Six minimization methods, including steepest descent and conjugate gradient (CG), were investigated.
Backtracking and Newton-Raphson line searches were evaluated for each algorithm.
Reconstruction accuracy was assessed using relative root mean square error in simulated and animal CT data.

Main Results:

The CG method with Fletcher-Reeves formula and Newton-Raphson line search demonstrated the fastest convergence.
PICCS reconstruction accuracy was optimal when the prior image parameter (α) was approximately 0.5.
Spatial resolution remained constant, and noise texture resembled the prior image (filtered backprojection) for optimal α.

Conclusions:

Nonlinear CG with Newton-Raphson line search offers superior convergence speed for PICCS.
Optimal PICCS performance is achieved with a prior image weighting parameter (α) near 0.5.
PICCS preserves spatial resolution and noise characteristics of the prior image, even with undersampled data.