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Found 14 papers, 3 papers with code
Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation
Cardiac image segmentation is an important step in many cardiac image analysis and modeling tasks such as motion tracking or simulations of cardiac mechanics.
A Generalizable 3D Diffusion Framework for Low-Dose and Few-View Cardiac SPECT
This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning.
Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision
Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality.
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82-Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases.
Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT
Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments.
Adaptive Correspondence Scoring for Unsupervised Medical Image Registration
As the unsupervised learning scheme relies on intensity constancy between images to establish correspondence for reconstruction, this introduces spurious error residuals that are not modeled by the typical training objective.
Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
To mitigate this, we propose a framework for heteroscedastic image uncertainty estimation that can adaptively reduce the influence of regions with high uncertainty during unsupervised registration.
TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction
The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable.
Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency
To overcome these challenges, we propose a dual-domain iterative network for end-to-end joint denoising and reconstruction from low-dose and few-angle projections of cardiac SPECT.
Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT
Additionally, computed tomography (CT)-derived attenuation maps ($\mu$-maps) are commonly used for SPECT attenuation correction (AC), but it will cause extra radiation exposure and SPECT-CT misalignments.
Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network
In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation.
Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT
In this paper, we propose a Dual-Branch Squeeze-Fusion-Excitation (DuSFE) module for the registration of cardiac SPECT and CT-derived u-maps.
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation
We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation.
Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis
In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network.
