Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer

2019 ◽  
Author(s):  
Alexis Cheng ◽  
Younsu Kim ◽  
Emran Mohammad Abu Anas ◽  
Arman Rahmim ◽  
Emad M. Boctor ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Reconstruction Method ◽  
Ultrasound Tomography ◽  
Image Reconstruction Method ◽  
Limited Angle

Anisotropic structure property based image reconstruction method for limited-angle computed tomography

2021 ◽  
pp. 1-24
Author(s):  
Changcheng Gong ◽  
Li Zeng
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Anisotropic Structure ◽  
Structure Property ◽  
Image Reconstruction Method ◽  
Ct Data ◽  
Limited Angle

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

scholarly journals A Lagrange-Newton Method for EIT/UT Dual-Modality Image Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1966 ◽  
Author(s):  
Guanghui Liang ◽  
Shangjie Ren ◽  
Shu Zhao ◽  
Feng Dong
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Newton Method ◽  
Constraint Equation ◽  
Reconstruction Method ◽  
Ultrasound Tomography ◽  
Impedance Tomography ◽  
Dual Modality ◽  
Mesh Model ◽  
Image Reconstruction Method

An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the change in acoustic impedance distribution, the reconstruction targets of EIT and UT are unified to the conductivity difference using the same mesh model. Some background medium distribution information obtained from ultrasound transmission and reflection measurements can be used to construct a hard constraint about the conductivity difference distribution. Then, the EIT/UT dual-modality inverse problem is constructed by an equality constraint equation, and the Lagrange multiplier method combining Newton-Raphson iteration is used to solve the EIT/UT dual-modality inverse problem. The numerical and experimental results show that the proposed dual-modality image reconstruction method has a better performance than the single-modality EIT method and is more robust to the measurement noise.

scholarly journals Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography

2020 ◽  
Author(s):  
Hanene Ben Yedder ◽  
Ben Cardoen ◽  
Ghassan Hamarneh
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Real World ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Multitask Learning ◽  
Infrared Light ◽  
Reconstruction Method ◽  
Limited Angle ◽  
Accurate Reconstruction

<div>Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. </div><div>DOT image reconstruction from limited-angle data acquisition is severely ill-posed due to the highly scattered photons in the medium and the relatively small number of collected projections. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain accurate reconstruction of target objects, e.g., malignant lesions.</div><div>Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications.</div><div>Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction.</div><div>We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods.</div><div>Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models,</div><div>we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning.</div><div>Both quantitative and qualitative results on phantom and real data indicate the superiority of our multitask method in the reconstruction and localization of lesions in tissue compared to state-of-the-art methods.</div><div>The results demonstrate that multitask learning provides sharper and more accurate reconstruction.</div>

scholarly journals Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography

2021 ◽  
Author(s):  
Hanene Ben Yedder ◽  
Ben Cardoen ◽  
Ghassan Hamarneh
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Real World ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Multitask Learning ◽  
Infrared Light ◽  
Reconstruction Method ◽  
Limited Angle ◽  
Accurate Reconstruction

<div>Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. </div><div>DOT image reconstruction from limited-angle data acquisition is severely ill-posed due to the highly scattered photons in the medium and the relatively small number of collected projections. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain accurate reconstruction of target objects, e.g., malignant lesions.</div><div>Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications.</div><div>Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction.</div><div>We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods.</div><div>Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models,</div><div>we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning.</div><div>Both quantitative and qualitative results on phantom and real data indicate the superiority of our multitask method in the reconstruction and localization of lesions in tissue compared to state-of-the-art methods.</div><div>The results demonstrate that multitask learning provides sharper and more accurate reconstruction.</div>

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