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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.
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.
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.
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