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Related Experiment Video

Updated: May 13, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

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Published on: August 19, 2021

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in

Jaya Prakash¹, Phaneendra K Yalavarthy

¹Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.

Medical Physics

|March 8, 2013

Summary

A new, computationally efficient method using the least-squares QR (LSQR)-type approach optimizes regularization parameters for diffuse optical tomography (DOT). This technique offers similar image quality to existing methods but is significantly faster for real-time applications.

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Area of Science:

Medical Imaging
Computational Science
Biomedical Engineering

Background:

Diffuse Optical Tomography (DOT) is a powerful imaging technique.
Choosing the optimal regularization parameter is crucial for accurate DOT reconstruction.
Existing methods for parameter selection can be computationally intensive.

Purpose of the Study:

To develop a computationally efficient automated method for selecting the regularization parameter in DOT.
To improve the speed of DOT reconstruction without compromising image quality.

Main Methods:

Implemented a least-squares QR (LSQR)-type method with Lanczos bidiagonalization.
Utilized a simplex optimization procedure to find the optimal regularization parameter.
Compared the LSQR-type method against L-curve, generalized cross-validation (GCV), and minimal residual method (MRM) using phantom data.

Main Results:

The LSQR-type method demonstrated comparable image reconstruction quality to the MRM-based method.
LSQR-type and MRM-based methods outperformed L-curve and GCV methods.
The proposed LSQR-type method exhibited at least five times lower computational complexity than the MRM-based method.

Conclusions:

The LSQR-type method provides an efficient and automated solution for regularization parameter selection in DOT.
This method overcomes the computational expense of MRM, making it suitable for real-time DOT imaging.