Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model.

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data.

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET.

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners.

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

Purpose: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images.

Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior.

For simultaneous positron-emission-tomography and magnetic-resonance-imaging (PET-MRI) systems, while early methods relied on independently reconstructing PET and MRI images, recent works have demonstrated improvement in image reconstructions of both PET and MRI using joint reconstruction methods. The current state-of-the-art joint reconstruction priors rely on fine-scale PET-MRI dependencies through the image gradients at corresponding spatial locations in the PET and MRI images. In the general context of image restoration, compared to gradient-based models, patch-based models (e.