McKelvey School of Engineering

Less energy, better quality PAM images with machine learning

On the left is a noisy, low-fluence photoacoustic microscopy image of blood vessels. By using machine learning, represented as a bridge, the team was able to create a denoised image, pictured on the right. (Courtesy: Hu lab)

Photoacoustic microscopy (PAM) allows researchers to see the smallest vessels inside a body, but it can generate some unwanted signals or noise. A team of researchers at the McKelvey School of Engineering at Washington University in St. Louis found a way to significantly reduce the noise and maintain image quality while reducing the laser energy needed to generate images by 80%.

Song Hu, PhD, associate professor of biomedical engineering, and members of his lab devised this new method using a machine-learning-based image processing technique, called sparse coding, to remove the noise from PAM images of vessel structure, oxygen saturation and blood flow in a mouse brain. Results of the work were published online in IEEE Transactions on Medical Imaging Nov. 1.


To acquire such images, the researchers need a dense sampling of data, which requires a high laser pulse repetition rate that may raise safety concerns. Reducing the laser pulse energy, however, leads to impaired image quality and inaccurate measurement of blood oxygenation and flow. That’s where Zhuoying Wang, a doctoral student in Hu’s lab and first author of the paper, brought in sparse coding, a type of machine learning often used in image processing that doesn’t need a ground truth on which to train, to improve the image quality and quantitative accuracy while using low laser doses.

The team applied the technique to images of blood hemoglobin concentration, oxygenation and flow in a mouse brain at both normal and reduced energy levels. Their two-step approach performed very well, significantly reducing the noise and achieving similar image quality that was previously only possible with five times higher laser energy.

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