Android based application for photoacoustic tomography image reconstruction

Photoacoustic tomography (PAT) is a hybrid imaging technique that combines optical illumination with ultrasound detection for high-resolution imaging of deep tissue. Utilizing the photoacoustic (PA) effect, PAT provides the distinct advantages of scalable resolution, higher imaging depth, and high contrast imaging. It uses a nanosecond laser pulse to illuminate the tissue of interest, allowing its chromophore to absorb the incident laser energy. This results in a local temperature rise and generates pressure waves that propagate along the tissue boundaries as ultrasound waves. These PA waves are then acquired with the help of an ultrasound transducer and converted into an internal absorption map using a reconstruction algorithm.

The process of generating this initial pressure map can be accomplished by several different image reconstruction algorithms, including a simple delay-and-sum (DAS) beamformer. This algorithm reprojects the signals obtained from various network locations, which are then added at each pixel in the reconstructed image. However, this makes the DAS beamformer computationally expensive and time consuming, and produces artifacts, i.e., anomalies, in the reconstructed image. Despite these drawbacks, its simplicity and ease of implementation make it a popular choice for PAT reconstruction.

Typically, implementing such a reconstruction algorithm requires a workstation, desktop, or laptop with extensive computing resources. But the computing power of mobile phones has grown in recent years. Although mobile phones have been proposed for various microscopy modalities including ultrasound imaging, their usefulness for photoacoustic imaging such as PAT image reconstruction has not been explored.

Leveraging the advanced processing capabilities of mobile phones, researchers from Singapore and the United States have now developed an Android-based application for PAT image reconstruction. The study was led by Manojit Pramanik, Northrop Grumman Associate Professor in the Department of Electrical and Computer Engineering at Iowa State University, and published in Journal of Biomedical Optics (JBO).

Application developed using a single element ultrasound transducer (SUT) based DAS beamformer algorithm for image reconstruction on Kivy—a Python 3.9.5 cross-platform framework.

The researchers verified its performance on different phones using simulated and experimental PAT data sets. While the simulated data set consisted of point targets, triangular shapes, and vessel shapes of rat brains, the experimental data set consisted of point source ghosts, triangular shaped ghosts, and blood vessels in living rat brains.

“The developed application succeeded in reconstructing PAT data into high-quality PAT images with signal-to-noise ratio values ​​above 30 decibels,” commented Pramanik.

Interestingly, the computational time of the algorithm on the Huawei P20 phone is comparable to that of a laptop for small data sets. In addition, the double downsampling of the original dataset reduces computation time while preserving image quality, thereby enabling image reconstruction with speed and quality. In contrast, threefold downsampling markedly degrades the PAT image.

Moreover, the researchers found that with the advanced processor of the Samsung Galaxy S21+, PAT reconstruction can be achieved in just 2.4 seconds. “This is a significantly reduced running time for image reconstruction and highlights the efficiency of mobile applications,” Pramanik noted.

JBO Editor-in-Chief Brian Pogue, Chair of Medical Physics at the University of Wisconsin–Madison, commented, “This first-of-its-kind application provides an opportunity for the reconstruction of PAT images on an inexpensive, portable, and widely available mobile phone. Going forward, these applications could make PAT systems more adaptable and extendable to other fields of biomedical imaging, facilitating point-of-care diagnosis.” He added, “The code for this Android-based application is now available for free on GitHub, making it a key service for the biomedical imaging community.”

Read the article Open Access Gold by Hui et al., “Android mobile platform-based image reconstruction for photoacoustic tomography,” J. Biomed. Choose. 28(4), 046009 (2023), doi 10.1117/1.JBO.28.4.046009.