Generative Tomography Reconstruction
We propose an end-to-end differentiable architecture for tomography reconstruction that directly maps a noisy sinogram into a denoised reconstruction. Compared to existing approaches our end-to-end architecture produces more accurate reconstructions while using less parameters and time. We also propose a generative model that, given a noisy sinogram, can sample realistic reconstructions. This generative model can be used as prior inside an iterative process that, by taking into consideration the physical model, can reduce artifacts and errors in the reconstructions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction
This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simulta…
Computed Tomography (CT)DenoisingDictionary LearningImage ReconstructionUsing Spatial Diffusions for Optoacoustic Tomography Image Reconstruction
Optoacoustic tomography image reconstruction has been a problem of interest in recent years. By exploiting the exceptional generative power of the recently proposed diffusion models we consider a scheme which is based on…
Image ReconstructionSSIMScore-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their application to Positron Emission Tomogr…
Image ReconstructionPhysics-assisted Generative Adversarial Network for X-Ray Tomography
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction p…
Generative Adversarial NetworkDensePANet: An improved generative adversarial network for photoacoustic tomography image reconstruction from sparse data
Image reconstruction is an essential step of every medical imaging method, including Photoacoustic Tomography (PAT), which is a promising modality of imaging, that unites the benefits of both ultrasound and optical imagi…
Generative Adversarial NetworkImage GenerationImage Reconstruction