Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to ...
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Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time.
Dynamic imaging is essential for analyzing various biological processes but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition tim...
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Dynamic imaging is essential for analyzing various biological processes but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition times require severe undersampling, leading to data incompleteness. Multiple images may then be compatible with the data, thus requiring special techniques (regularization) to ensure uniqueness of the reconstruction. Computational and memory requirements are particularly burdensome for three-dimensional applications requiring high spatiotemporal resolution. Exploiting redundancies in the object's spatiotemporal features is key to addressing both challenges. This contribution investigates neural fields, or implicit neural representations, to model the sought-after dynamic object. Neural fields are a particular class of neural networks that represent the dynamic object as a continuous function of space and time, thus avoiding the burden of storing a full-resolution image at each time frame. The proposed approach integrates the neural field representation of the object into the imaging model to formulate the dynamic image reconstruction problem as a self-supervised learning problem. Specifically, the network parameters are estimated by minimizing a regularized data discrepancy functional by use of accelerated first-order stochastic optimization algorithms. Once trained, the neural field is evaluated at arbitrary locations in space and time, allowing for high-resolution rendering of the object. Key advantages of the proposed approach are that neural fields automatically learn redundancies in the sought-after object to both regularize the reconstruction and significantly reduce memory requirements. The proposed framework is illustrated with an application to dynamic image reconstruction from severely undersampled circular Radon transform data.
Dynamic imaging systems monitor physiological processes that evolve or change over time. However, image reconstruction from dynamic data is made difficult by data incompleteness and significant computational burden. D...
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ISBN:
(数字)9781510649385
ISBN:
(纸本)9781510649385;9781510649378
Dynamic imaging systems monitor physiological processes that evolve or change over time. However, image reconstruction from dynamic data is made difficult by data incompleteness and significant computational burden. Data incompleteness, in particular, arises from severe undersampling often necessary to increase frame rate by reducing data acquisition time and leads to ill-posedness of the reconstruction problem. Computational cost and memory requirements are particularly burdensome for three-dimensional problems, especially for applications in which high-resolution in space and time is needed. Two main approaches exist for dynamic image reconstruction. Frame-by-frame approaches solve a sequence of image reconstruction problems (one for each frame). Spatiotemporal approaches instead directly reconstruct the dynamic object using data from all imaging frames at once. Although statistically suboptimal, frame-by-frame approaches have often been advocated because of the ease of implementation and lower memory requirements. This work explores a new spatiotemporal dynamic reconstruction approach that uses neural fields, a special class of neural networks, to drastically reduce the computational complexity and memory requirements while exploiting the object's spatiotemporal redundancies. As a feasibility study, a simple dynamic image reconstruction problem whose forward operator is given by the circular Radon transform is considered. Numerical results demonstrate that the proposed approach is more accurate and uses less memory than the classical frame-by-frame approach.
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