A New Deep Learning Network for Mitigating Limited-view and Under-sampling Artifacts in Ring-shaped Photoacoustic Tomography.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
Huijuan ZhangJun Xia

Abstract

Photoacoustic tomography (PAT) is a hybrid technique for high-resolution imaging of optical absorption in tissue. Among various transducer arrays proposed for PAT, the ring-shaped transducer array is widely used in cross-sectional imaging applications. However, due to the high fabrication cost, most ring-shaped transducer arrays have a sparse transducer arrangement, which leads to limited-view problems and under-sampling artifacts. To address these issues, we paired conventional PAT reconstruction with deep learning, which recently achieved a breakthrough in image processing and tomographic reconstruction. In this study, we designed a convolutional neural network (CNN) called a ring-array deep learning network (RADL-net), which can eliminate limited-view and under-sampling artifacts in PAT images. The method was validated on a three-quarter ring transducer array using numerical simulation, phantom imaging, and in vivo imaging. Our results indicate that the proposed RADL-net significantly improves the quality of reconstructed images on a three-quarter ring transducer array. The method is also superior to the conventional compressed sensing (CS) algorithm.

Citations

Dec 19, 2020·Molecular Imaging·Mucong LiJunjie Yao
Dec 29, 2020·Physics in Medicine and Biology·Dhiman DasManojit Pramanik
Jan 12, 2021·Photoacoustics·Changchun YangFei Gao
Mar 16, 2021·Photoacoustics·Janek GröhlLena Maier-Hein
Mar 30, 2021·Experimental Biology and Medicine·Anthony DiSpiritoJunjie Yao

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