Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

鋼盔

DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for?PET Image Reconstructionning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstr

FLINT

Transformer-Based Dual-Domain Network for?Few-View Dedicated Cardiac SPECT Image Reconstructionsis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implem

PAN

Learned Alternating Minimization Algorithm for?Dual-Domain Sparse-View CT Reconstruction a variational model for CT reconstruction with learnable nonsmooth nonconvex regularizers, which are parameterized as composite functions of deep networks in both image and sinogram domains. To minimize the objective of the model, we incorporate the smoothing technique and residual learning archite

極小量

TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinogramscting standard-dose PET (SPET) images from low-dose PET (LPET) sinograms directly. However, current methods often neglect boundaries during sinogram-to-image reconstruction, resulting in high-frequency distortion in the frequency domain and diminished or fuzzy edges in the reconstructed images. Furt