English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Photoacoustic tomography (PAT) is a promising imaging technique that canvisualize the distribution of chromophores within biological tissue. However,the accuracy of PAT imaging is compromised by light fluence (LF), which hindersthe quantification of light absorbers. Currently, model-based iterative methodsare used for LF correction, but they require significant computationalresources due to repeated LF estimation based on differential light transportmodels. To improve LF correction efficiency, we propose to use Fourier neuraloperator (FNO), a neural network specially designed for solving differentialequations, to learn the forward projection of light transport in PAT. Trainedusing paired finite-element-based LF simulation data, our FNO model replacesthe traditional computational heavy LF estimator during iterative correction,such that the correction procedure is significantly accelerated. Simulation andexperimental results demonstrate that our method achieves comparable LFcorrection quality to traditional iterative methods while reducing thecorrection time by over 30 times.

Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography