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Found 10 papers, 3 papers with code
ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction
Neural fields can address the twin challenges of data incompleteness and computational burden by exploiting underlying redundancies in these spatiotemporal objects.
Technical Note: An Efficient Implementation of the Spherical Radon Transform with Cylindrical Apertures
The spherical Radon transform (SRT) is an integral transform that maps a function to its integrals over concentric spherical shells centered at specified sensor locations.
Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography
Deep learning-based image-to-image learned reconstruction (IILR) methods are being investigated as scalable and computationally efficient alternatives.
Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers
Aim: The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task.
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
We propose derivative-informed neural operators (DINOs), a general family of neural networks to approximate operators as infinite-dimensional mappings from input function spaces to output function spaces or quantities of interest.
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Computational and memory requirements are particularly burdensome for three-dimensional dynamic imaging applications requiring high resolution in both space and time.
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
In this work, a new empirical sampling method is proposed that computes multiple solutions of a tomographic inverse problem that are consistent with the same acquired measurement data.
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
We use the projection basis vectors in the active subspace as well as the principal output subspace to construct the weights for the first and last layers of the neural network, respectively.
