Other Reconstruction Methods
This module provides alternative methods for reconstructing images from measurements, which are not well described by other modules in the library.
Learned Filtered Back-Projection
The simplest method for reconstructing an image from a measurements is to first map the measurements to the image domain via a non-learned mapping, and then apply a deep network to the obtain the final reconstruction. This idea was introduced by Jin et al. “Deep Convolutional Neural Network for Inverse Problems in Imaging” for tomographic reconstruction.
Artifact removal architecture \(\phi(A^{\top}y)\). |
Networks for time-varying data
When using time-varying (i.e. dynamic) data of 5D shape (B,C,T,H,W), the reconstruction network must be adapted. To adapt any existing network to take dynamic data as independent time-slices, create a time-agnostic wrapper that flattens the time dimension into the batch dimension.
Time-agnostic network wrapper. |
|
Time-averaging network wrapper. |