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At X-ray beamlines of synchrotron light sources, the achievabletime-resolution for 3D tomographic imaging of the interior of an object hasbeen reduced to a fraction of a second, enabling rapidly changing structures tobe examined. The associated data acquisition rates require sizablecomputational resources for reconstruction. Therefore, full 3D reconstructionof the object is usually performed after the scan has completed. Quasi-3Dreconstruction -- where several interactive 2D slices are computed instead of a3D volume -- has been shown to be significantly more efficient, and can enablethe 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, wepropose Noise2Filter, a learned filter method that can be trained using onlythe measured data, and does not require any additional training data. Thismethod combines quasi-3D reconstruction, learned filters, and self-supervisedlearning to derive a tomographic reconstruction method that can be trained inunder a minute and evaluated in real-time. We show limited loss of accuracycompared to training with additional training data, and improved accuracycompared to standard filter-based methods.

Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography