2 research outputs found
Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo
Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality
combining the advantages of deep ultrasound penetration and high optical
contrast. Image reconstruction is an essential topic in PAI, which is
unfortunately an ill-posed problem due to the complex and unknown
optical/acoustic parameters in tissue. Conventional algorithms used in PAI
(e.g., delay-and-sum) provide a fast solution while many artifacts remain,
especially for linear array probe with limited-view issue. Convolutional neural
network (CNN) has shown state-of-the-art results in computer vision, and more
and more work based on CNN has been studied in medical image processing
recently. In this paper, we present a non-iterative scheme filling the gap
between existing direct-processing and post-processing methods, and propose a
new framework Y-Net: a CNN architecture to reconstruct the PA image by
optimizing both raw data and beamformed images once. The network connected two
encoders with one decoder path, which optimally utilizes more information from
raw data and beamformed image. The results of the test set showed good
performance compared with conventional reconstruction algorithms and other deep
learning methods. Our method is also validated with experiments both in-vitro
and in vivo, which still performs better than other existing methods. The
proposed Y-Net architecture also has high potential in medical image
reconstruction for other imaging modalities beyond PAI.Comment: submitted the journal versio
Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning
Photoacoustic tomography (PAT) is a nonionizing imaging modality capable of
acquiring high contrast and resolution images of optical absorption at depths
greater than traditional optical imaging techniques. Practical considerations
with instrumentation and geometry limit the number of available acoustic
sensors and their view of the imaging target, which result in significant image
reconstruction artifacts degrading image quality. Iterative reconstruction
methods can be used to reduce artifacts but are computationally expensive. In
this work, we propose a novel deep learning approach termed pixelwise deep
learning (PixelDL) that first employs pixelwise interpolation governed by the
physics of photoacoustic wave propagation and then uses a convolution neural
network to directly reconstruct an image. Simulated photoacoustic data from
synthetic vasculature phantom and mouse-brain vasculature were used for
training and testing, respectively. Results demonstrated that PixelDL achieved
comparable performance to iterative methods and outperformed other CNN-based
approaches for correcting artifacts. PixelDL is a computationally efficient
approach that enables for realtime PAT rendering and for improved image
quality, quantification, and interpretation