# On-the-Fly Machine Learning for Improving Image Resolution in Tomography  
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1 Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands;  
[EMAIL_ADDRESS] ([PERSON]); [PERSON]] (W.J. the theoretical maximum based on the detector resolution [7]. When using standard reconstruction algorithms, such as the well-known Filtered Backprojection method (FBP) or Algebraic Reconstruction Technique (ART), the resolution of the reconstructed volume is inherently limited by the resolution and signal-to-noise ratio of the acquired projection data [8; 9]. Various tomographic techniques permit zooming in to a specific parts of an object, leading to magnified projection images. At synchrotron light sources, X-ray images are acquired by converting the high energy photons transmitted through the object into visible light using a scintillator. The visible light is converted into digital images by a conventional high-resolution image sensor. Magnification of the region of interest is achieved by magnifying the visible light using optical instruments placed between the scintillator and image sensor [6]. Laboratory CT systems, on the other hand, have a natural zooming ability, since the X-rays emanate from a point source and are projected onto a linear (fan beam) or planar detector (cone beam). Therefore, moving the object closer to the source magnifies the projected image on the detector. In this paper, we will focus on the 3D cone-beam setup, although the proposed approach is applicable to other tomographic techniques as well, including synchrotron tomography. A variety of strategies have been proposed for increasing the resolution of tomographic volumes, either by changing the scanning process, or by changing the reconstruction method. When changing the scanning process, several strategies are commonly used:  
* High-resolution 3D images can be obtained if a small section can be physically extracted from the object and then scanned at a higher magnification. This provides only information on the particular section, and involves the destruction of the full sample [10]. * Region-of-interest tomography focuses the imaging system on a sub-region of the object, obtaining a set of projections in which that region is always visible, but also superimposed on the surrounding structures.