Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging.

Motion is unavoidable in dynamic [F]-MK6240 Positron Emission Tomography (PET) imaging, especially in Alzheimer's disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: II-MC, which corrects for both inter-frame and intra-frame motion, and IO-MC, which only corrects for inter-frame motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVR) using the multilinear reference tissue model with two parameters (MRTM2) in tau-rich brain regions.

PET mapping of receptor occupancy using joint direct parametric reconstruction.

Unlabelled: Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves.

Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization.

Recent advances in deep learning (DL) have greatly improved the performance of positron emission tomography (PET) denoising performance. However, DL model performance can vary a lot across subjects, due to the large variability of the count levels and spatial distributions. A generalizable DL model that mitigates the subject-wise variations is highly expected toward a reliable and trustworthy system for clinical application.

Cross noise level PET denoising with continuous adversarial domain generalization.

Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images.

Impact of motion correction on [F]-MK6240 tau PET imaging.

. Positron emission tomography (PET) imaging of tau deposition using [F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality.