Mobile-Based Image Reconstruction App Can Be Used at Point of Care

A mobile phone application for photoacoustic tomography (PAT) image reconstruction has demonstrated performance comparable to that of applications implemented on laptop computers and workstations.

The first-of-its-kind application was developed by a team from Iowa State University, Nanyang Technological University, and the Stanford University School of Medicine. The mobile-platform-based application will enable low-resource and other clinical settings to reconstruct PAT images at the point of care, using an inexpensive, readily available smartphone.

The application is Android based and was written in the Python programming language. It uses a single-element ultrasound transducer (SUT)-based delay-and-sum (DAS) beamformer algorithm to reconstruct PAT images on Kivy, a cross-platform Python 3.9.5 framework.

A DAS beamformer algorithm back-projects the acquired PA signals from various tissue locations. It is computationally expensive and time-consuming to use, and results in artifacts in the reconstructed images. However, despite these drawbacks, the simplicity and ease of implementation of a DAS beamformer algorithm make it a popular choice for PAT image reconstruction.

Experimental, pulsed laser diode-based in vivo PAT imaging system. Reconstructed PAT images are displayed on the Android-based application. An in vivo, PAT-reconstructed rat brain vasculature image is shown with the original data set and the twofold downsampled data set. Courtesy of Hui et al., DOI: 10.1117/1.JBO.28.4.046009.

A SUT rotates around the sample in a full circle to acquire PA signals at different locations for the PAT imaging. Various reconstruction algorithms can be used to reconstruct the acquired signals into cross-sectional PAT images. Although the researchers focused on building a SUT-based reconstruction algorithm, they said that the Android-based application is broad enough to use with PA data obtained from an array-based transducer.

The researchers verified the algorithm’s performance on different types of mobile phones using simulated and experimental PAT data sets on rat brain vasculature. The simulated data sets were generated on a k-wave MATLAB toolbox. Experimental data sets came from phantom and in vivo imaging.

The resulting images were well constructed, according to the researchers. “The developed application can successfully reconstruct the PAT data into high-quality PAT images with signal-to-noise ratio values above 30 decibels,” said Manojit Pramanik, a professor of electrical and computer engineering at Iowa State University.

The results showed that the mobile application can reconstruct PAT images with no loss of image quality and at a speed comparable to a laptop application. The algorithm can accomplish the image reconstruction of in vivo, small animal brain data sets in 2.4 seconds using the Samsung Galaxy S21+’s advanced processor. The algorithm’s computational time for small data sets on a Huawei P20 mobile phone is comparable to that on a laptop.

The researchers found that twofold downsampling of the original data set reduced the computational time while maintaining image quality, while threefold downsampling caused visible degradation to the PAT images. The team believes that use of a twofold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming, while preserving image quality with minimal degradation.

“This is a considerably reduced running time for image reconstruction and highlights the efficiency of the mobile phone application,” Pramanik said.

In practice, most PAT image reconstructions are carried out on a workstation, desktop, or laptop, but the growing computational power of mobile phones now makes it possible to realize high-quality PAT image reconstruction on a mobile platform. Although mobile phones have been proposed for various microscopy modalities such as bright-field and fluorescence microscopy and for ultrasound imaging, the use of mobile phones for PAT image reconstruction has not been explored until now.

Over the last decade, PAT has shown potential in preclinical and clinical applications due to its scalable resolution, high imaging depth, and high contrast. An Android-based application that achieves high-quality image reconstruction on smartphones instead of using bulky, expensive desktop computers, laptops, and workstations could make PAT systems easier and less costly to operate.

“This first-of-its-kind application provides an opportunity for PAT image reconstruction on inexpensive, portable, and widely available mobile phones,” Brian Pogue, chair of medical physics at the University of Wisconsin-Madison and editor-in-chief at the Journal of Biomedical Optics, said. “Going ahead, the application can make PAT systems more adaptable and extendable to other fields of biomedical imaging, facilitating point-of-care diagnosis.”

Pogue said that the code for the Android-based application is freely available to the biomedical community on GitHub.

The research was published in Journal of Biomedical Optics (www.doi.org/10.1117/1.JBO.28.4.046009).