Comparative Efficacy of Non-contrast vs. Contrast-enhanced CT Radiomics in Predicting Coronary Artery Plaques Among Patients with Low Agatston Scores.

Rationale And Objectives: Patients with a low Agatston score often present with clinical signs and symptoms suggestive of coronary artery disease, despite having minimal calcium deposits. This study aimed to compare the efficacy of low-dose non-contrast cardiac CT with coronary computed tomography angiography (CCTA) in pericoronary adipose tissue (PCAT) radiomics for predicting coronary artery plaques, using CCTA as the reference standard.

Materials And Methods: This retrospective study analyzed 459 patients with suspected coronary artery disease and a coronary artery calcium score < 100 Agatston units, who were treated between June 2021 and December 2023 at a tertiary hospital.

[LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients].

Objective: The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition.

Methods: Using 8 patients' data as samples, the RR intervals of patients were predicted by long short-term memory network (LSTM) and gradient lift tree (XGBoost), and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction.

Results: Compared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients.

Iodine concentration, HU accuracy, and metal artifacts evaluation on second-generation dual-layer spectral detector CT images with metal implants: a phantom study.

Background: Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy.

Purpose: To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts.

Material And Methods: An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed.

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET.

Background: Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

Improved [Ga]Ga-PSMA-11 image qualities reconstructed by total variation regularized expectation maximization on total-body PET/CT.

Background: Total variation regularized expectation maximization (TVREM) reconstruction algorithm on the image quality of gallium (GA) prostate-specific membrane antigen-11 ([Ga]Ga-PSMA-11) total-body positron emission tomography/computed tomography (PET/CT).

Methods: Images of a phantom with small hot sphere inserts and the total-body PET/CT scans of 51 prostate cancer patients undergoing [Ga]Ga-PSMA-11 were reconstructed using TVREM with 5 different penalization factors between 0.09 and 0.

Deep Generalized Learning Model for PET Image Reconstruction.

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising.

Motion correction and its impact on quantification in dynamic total-body 18F-fluorodeoxyglucose PET.

Background: The total-body positron emission tomography (PET) scanner provides an unprecedented opportunity to scan the whole body simultaneously, thanks to its long axial field of view and ultrahigh temporal resolution. To fully utilize this potential in clinical settings, a dynamic scan would be necessary to obtain the desired kinetic information from scan data. However, in a long dynamic acquisition, patient movement can degrade image quality and quantification accuracy.

Evaluation of pediatric malignancies using total-body PET/CT with half-dose [F]-FDG.

Purpose: To explore the impact of a true half dose of [F]-FDG on image quality in pediatric oncological patients undergoing total-body PET/CT and investigate short acquisition times with half-dose injected activity.

Methods: One hundred pediatric oncological patients who underwent total-body PET/CT using the uEXPLORER scanner after receiving a true half dose of [F]-FDG (1.85 MBq/kg) were retrospectively enrolled.

[Incorporation of TV regularization in deconvolution for partial volume correction in PET imaging].

We propose a method using total variation (TV) regularization in deconvolution for partial volume correction in PET imaging. In the degraded image model, we used TV regularization procedure in Van Cittert (VC) and Richardson-Lucy (RL) deconvolution algorithms. These methods were tested in simulated NCAT images and images of NEMA NU4-2008 IQ phantom and tumor-bearing mouse scanned by Simens Invoen microPET.

[Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging].

Objective: To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.

Methods: Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.

Results: The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.

Threshold choices of Huber regularization using global- and local-edge-detecting operators for X-ray computed tomographic reconstruction.

Statistical iterative reconstruction (SIR) approaches have shown great potential in x-ray computed tomographic (CT) reconstruction in the case of low-dose protocol. For yielding high quality image, an edge-preserving regularization should be incorporated into the objective function of SIR approaches. A typical example is the Huber regularization with an edge-preserving non-quadratic potential function which increases less rapidly than the quadratic potential function for sufficiently large arguments.