Report
Fast deep learning based reconstruction for limited angle tomography
| العنوان: | Fast deep learning based reconstruction for limited angle tomography |
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| المؤلفون: | Salomonsson, Knut, Oldgren, Eric, Ström, Emanuel, Öktem, Ozan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Mathematics - Numerical Analysis |
| الوصف: | A major challenge in computed tomography is reconstructing objects from incomplete data. An increasingly popular solution for these problems is to incorporate deep learning models into reconstruction algorithms. This study introduces a novel approach by integrating a Fourier neural operator (FNO) into the Filtered Backprojection (FBP) reconstruction method, yielding the FNO back projection (FNO-BP) network. We employ moment conditions for sinogram extrapolation to assist the model in mitigating artefacts from limited data. Notably, our deep learning architecture maintains a runtime comparable to classical filtered back projection (FBP) reconstructions, ensuring swift performance during both inference and training. We assess our reconstruction method in the context of the Helsinki Tomography Challenge 2022 and also compare it against regular FBP methods. Comment: 11 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.12141 |
| رقم الانضمام: | edsarx.2402.12141 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |