Abstract
.SignificanceOptical properties (absorption coefficient and scattering coefficient) of tissue are the most critical parameters for disease diagnosis-based optical method. In recent years, researchers proposed spatial frequency domain imaging (SFDI) to quantitatively map tissue optical properties in a broad field of contactless imaging. To solve the limitations in wavebands unsuitable for silicon-based sensor technology, a compressed sensing (CS) algorithm is used to reproduce the original signal by a single-pixel detectors. Currently, the existing single-pixel SFDI method mainly uses a random sampling policy to extract and recover signals in the acquisition stage. However, these methods are memory-hungry and time-consuming, and they cannot generate discernible results under low sampling rate. Explorations on high performance and efficiency single-pixel SFDI are of great significance for clinical application.AimFourier single-pixel imaging can reconstruct signals with less time and space costs and has fewer reconstruction errors. We focus on an SFDI algorithm based on Fourier single-pixel imaging and propose our Fourier single-pixel image-based spatial frequency domain imaging method (FSI-SFDI).ApproachFirst, we use Fourier single-pixel imaging algorithm to collect and compress signals and SFDI algorithm to generate optical parameters. Given the basis that the main energy of general image signals is concentrated in the range of low frequency of Fourier frequency domain, our FSI-SFDI uses a circular-sampling scheme to sample data points in the low-frequency region. Then, we reconstruct the image details from these points by optimization-based inverse-FFT method.ResultsOur algorithm is tested on simulated data. Results show that the root mean square error (RMSE) of optical parameters is lower than 5% when the data reduction is 92%, and it can generate discernible optical parameter image with low sampling rate. We can observe that our FSI-SFDI primarily recovers the optical properties while keeping the RMSE under the upper bound of 4.5% when we use an image with resolution as the example for calculation and analysis. Not only that but also our algorithm consumes less space and time for an image with resolution, the signal reconstruction takes only 1.65 ms, and requires less RAM memory. Compared to CS-SFDI method, our FSI-SFDI can reduce the required number of measurements through optimizing algorithm.ConclusionsMoreover, FSI-SFDI is capable of recovering high-quality resolvable images with lower sampling rate, higher-resolution images with less memory and time consumed than previous CS-SFDI method, which is very promising for clinical data collection and medical analysis.
Highlights
Results show that the root mean square error (RMSE) of optical parameters is lower than 5% when the data reduction is 92%, and it can generate discernible optical parameter image with low sampling rate
We can observe that our FSI-spatial frequency domain imaging (SFDI) primarily recovers the optical properties while keeping the RMSE under the upper bound of 4.5% when we use an image with 512 × 512 resolution as the example for calculation and analysis
FSI-SFDI is capable of recovering high-quality resolvable images with lower sampling rate, higher-resolution images with less memory and time consumed than previous compressed sensing theory (CS)-SFDI method, which is very promising for clinical data collection and medical analysis
Summary
The significant issues in SFDI research are as follows: The existing SFDI methods use a camera for data collection, which relies on electronics integration (silicon) and is limited by CCD and CMOS digital technology. Several attempts have been tried to fix the issue (1) by introducing an SFDI system that is based on compressed sensing theory (CS), named CS-SFDI.[7] CS method uses a single-pixel detector to collect the images and reconstructs the images with fewer sampling points. The most frequently used method of CS is to reconstruct the image with optimization-based algorithms.[8,9,10] CS-SFDI replaces the camera with a single-pixel photo detector and collects the measurement matrix of human tissue to reconstruct and demodulate images
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