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 complex details to be captured. For image reconstruction problems, INRs can also reduce artifacts typically introduced by conventional reconstruction algorithms. However, to the best of our knowledge, INRs have not been studied in the context of PET reconstruction. In this paper, we propose an unsupervised PET image reconstruction method based on the implicit SIREN neural network architecture using sinusoidal activation functions. Our method incorporates a forward projection model and a loss function adapted to perform PET image reconstruction directly from sinograms, without the need for large training datasets. The performance of the proposed approach was compared with that of conventional penalized likelihood methods and deep image prior (DIP) based reconstruction using brain phantom data and realistically simulated sinograms. The results show that the INR-based approach can reconstruct high-quality images with a simpler, more efficient model, offering improvements in PET image reconstruction, particularly in terms of contrast, activity recovery, and relative bias.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingImage ReconstructionSimilar Papers 제목 키워드 기반
Contextualising Implicit Representations for Semantic Tasks
Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks. In this work, we propose a method that contextualises…
SegmentationSemantic SegmentationDifferentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differenti…
3D ReconstructionMulti-View 3D ReconstructionNeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations
Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, whe…
3D Reconstruction3D Scene ReconstructionTowards Generalising Neural Implicit Representations
Neural implicit representations have shown substantial improvements in efficiently storing 3D data, when compared to conventional formats. However, the focus of existing work has mainly been on storage and subsequent rec…
SegmentationSemantic SegmentationShape As Points: A Differentiable Poisson Solver
In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in slow inferen…
3D ReconstructionGPUSurface Reconstruction