MSB Online Seminar: Dual Domain Neural Networks for Sparse X-ray Computed Tomography.

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Topic: Dual Domain Neural Networks for Sparse X-ray Computed Tomography.

Speaker:  Theodor Cheslerean-Boghiu, Lasser Group

Date and Time: Tuesday, 4 May 2021, 1:00 pm

Abstract: Sparse sampling in X-ray Computed Tomography (CT) is an interesting option towards reducing dose. Recently, the success of deep learning techniques in a lot of medical imaging applications has prompted an increased interest in data-driven tomographic reconstruction algorithms. Nevertheless, most of the proposed solutions boil down to denoising steps performed either pre-reconstruction on the sinogram or post-reconstruction on the reconstructed image, with no reconstruction being performed as part of the network model.
In this presentation, I will talk about an improved deep neural network architecture (DNN) which combines a fine-tuned Filtered Backprojection (FBP) operation with a dual domain filtering step applied on both the sinogram and reconstructed image to produce artifact-free reconstructions from sparse data and we present its performance potential in experiments with clinical data.


This semester, the MSB Seminar will take place online (Zoom). Please register via email (katharina.scholz(at) and we are happy to provide the access data.


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