  At X-ray beamlines of synchrotron light sources, the achievable
time-resolution for 3D tomographic imaging of the interior of an object has
been reduced to a fraction of a second, enabling rapidly changing structures to
be examined. The associated data acquisition rates require sizable
computational resources for reconstruction. Therefore, full 3D reconstruction
of the object is usually performed after the scan has completed. Quasi-3D
reconstruction -- where several interactive 2D slices are computed instead of a
3D volume -- has been shown to be significantly more efficient, and can enable
the real-time reconstruction and visualization of the interior. However,
quasi-3D reconstruction relies on filtered backprojection type algorithms,
which are typically sensitive to measurement noise. To overcome this issue, we
propose Noise2Filter, a learned filter method that can be trained using only
the measured data, and does not require any additional training data. This
method combines quasi-3D reconstruction, learned filters, and self-supervised
learning to derive a tomographic reconstruction method that can be trained in
under a minute and evaluated in real-time. We show limited loss of accuracy
compared to training with additional training data, and improved accuracy
compared to standard filter-based methods.
