Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography

Zhang, Huijuan; Bo, Wei; Wang, Depeng; DiSpirito, Anthony; Huang, Chuqin; Nyayapathi, Nikhila; Zheng, Emily; Vu, Tri; Gong, Yiyang; Yao, Junjie; Xu, Wenyao; Xia, Jun

doi:10.1109/tmi.2021.3137060

Cited by 20 publications

(15 citation statements)

References 46 publications

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“…Because we only simulated data along the axial-elevation plane, we do not need to consider reconstruction along the lateral direction. In the Deep-E study, we simply stacked envelop-detected A-line signals along the elevation direction to mimic 2D stack-reconstructed images in the axial-elevation plane [ 17 ]. This approach cannot mimic 3D focal-line reconstruction.…”

Section: Methodsmentioning

confidence: 99%

“…These cross-sectional vascular data were simulated into PA raw data and reconstructed using either the 2D stack or focal-point (line) algorithms mentioned in the section above. The 2D stack data was trained using the Deep-E network mentioned in reference [ 17 ]. The focal-point data was trained using a similar fully-dense UNet (FD-UNet).…”

Section: Methodsmentioning

confidence: 99%

“…Details of the FD-UNet were mentioned in [ 17 ]. Briefly speaking, the FD-UNet is based on a CNN-based residual architecture by adding a skip connection between the input and the output [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, very few have attempted to improve the elevation resolution of linear-array-based PACT, due to the complexity of 3D simulation and training. Our group recently introduced Deep-E, a UNet-based method for improving the elevation resolution [ 17 ]. In that study, we converted the 3D problem into 2D space by training data only on the axial-elevation plane.…”

Section: Introductionmentioning

confidence: 99%

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Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging

Zheng¹,

Zhang²,

Huang³

et al. 2022

Sensors

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Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging

Zheng¹,

Zhang²,

Huang³

et al. 2022

Sensors

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“…Learning-based methods were also introduced to PAT image deconvolution. For example, Deep-E, which is a fully dense neural network, provides improved elevational resolution by only using the 2D slices in the axial and elevational plane [35]. For network training, Deep-E uses the 2D images generated via K-Wave [67] as input data, and ground truth vascular images are generated using the Insight Segmentation and Registration Toolkit (ITK) [34].…”

Section: Deconvolutionmentioning

confidence: 99%

Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review

et al. 2023

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Photoacoustic tomography (PAT) is a promising imaging technique that utilizes the detection of light-induced acoustic waves for both morphological and functional biomedical imaging. However, producing high-quality images using PAT is still challenging and requires further research. Besides improving image reconstruction, which turns the raw photoacoustic signal into a PAT image, an alternative way to address this issue is through image post-processing, which can enhance and optimize the reconstructed PAT image. Image post-processing methods have rapidly emerged in PAT and are proven to be essential in improving image quality in recent research. In this review, we investigate the need for image post-processing in PAT imaging. We conduct a thorough literature review on the latest PAT image post-processing articles, including both general and PAT-specific post-processing techniques. In contrast to previous reviews, our analysis focuses specifically on advanced image post-processing rather than image reconstruction methods. By highlighting their potential applications, we hope to encourage further research and development in PAT image post-processing technology.

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Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

Zheng,

Zhang,

Huang

et al. 2023

Advanced Science

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The development of high‐performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully‐dense (3DFD) U‐net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U‐net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

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Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography

Cited by 20 publications

References 46 publications

Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging

Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging

Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human

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