Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe artifacts in the sinogram data. The proposed method leverages implicit neural representation (INR) to correct defective pixel response values using implicit continuous functions and simultaneously learns stripe features in the angular direction of the sinogram data. These two components are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in removing ring artifacts while maintaining the clarity of CT images.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging
Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging that aims to acquire high-quality CT images based on sparsely-sampled measurements. Recent works use Implicit Neural Repres…
Implicit neural representations for end-to-end PET reconstruction
Implicit neural representations (INRs) have demonstrated strong capabilities in various medical imaging tasks, such as denoising, registration, and segmentation, by representing images as continuous functions, allowing c…
DenoisingImage ReconstructionDeep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to…
Computed Tomography (CT)Image GenerationMedical DiagnosisMetal Artifact ReductionDuDoNet: Dual Domain Network for CT Metal Artifact Reduction
Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the pr…
Computed Tomography (CT)Medical DiagnosisMetal Artifact ReductionMulti-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting
Recent CT Metal Artifacts Reduction (MAR) methods are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves. In this paper, we are exploring the capabiliti…