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Purpose Current attenuation correction (AC) of myocardial perfusion (MP) positron emission tomography (PET) remains challenging in routine clinical practice due to the propagation of CT-based artifacts and potential mismatch between PET and CT. The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET (AC PET) images from non-attenuation-corrected PET (NAC PET) images in the reconstruction domain for [13N]ammonia MP PET based on a generative adversarial network (GAN). Methods We recruited 60 patients who underwent rest [13N]ammonia cardiac PET/CT examinations. One static frame and twenty-one dynamic frames were acquired for each patient with both NAC PET and CT-based AC (CTAC) PET images. Paired 3D static or dynamic NAC and CTAC PET images were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) MP PET, respectively. In addition, the pre-trained S-DLAC network was fine-tuned by 3D paired dynamic NAC and CTAC PET frames for then AC in the dynamic PET images (D-DLAC-FT). Qualitative and quantitative assessments were implemented using CTAC PET as reference. Results The proposed S-DLAC, D-DLAC and D-DLAC-FT methods were qualitatively and quantitatively consistent with clinical CTAC. The S-DLAC showed a higher correlation with the reference static CTAC (S-CTAC) as compared to static NAC. The estimated kinetic parameters and blood volume fraction images from D-DLAC and D-DLAC-FT methods showed comparable performances with the reference dynamic CTAC (D-CTAC). D-DLAC-FT was slightly better than D-DLAC in terms of various physical and clinical indices. Conclusion The proposed S-DLAC, D-DLAC and D-DLAC-FT methods reduced attenuation artifacts significantly and achieved comparable performance with clinical CTAC for static and dynamic cardiac PET. The use of transfer learning is effective for the dynamic MP PET AC purpose.

Dynamic positron emission tomography (PET) imaging usually suffers from high statistical noise due to low counts of the short frames. This study aims to improve the image quality of the short frames by utilizing information from other modality. We develop a deep learning-based joint filtering framework for simultaneously incorporating information from longer acquisition PET frames and high-resolution magnetic resonance (MR) images into the short frames. The network inputs are noisy PET images and corresponding MR images while the outputs are linear coefficients of spatially variant linear representation model. The composite of all dynamic frames is used as training label in each sample, and it is down-sampled to 1/10th of counts as the training input. L1-norm combined with two gradient-based regularizations constitute the loss function during training. Ten realistic dynamic PET/MR phantoms based on BrainWeb are used for pre-training and eleven clinical subjects from Alzheimer’s Disease Neuroimaging Initiative further for fine-tuning. Simulation results show that the proposed method can reduce the statistical noise while preserving image details and achieve quantitative enhancements compared with Gaussian, guided filter, and convolutional neural network trained with the mean squared error. The clinical results perform better than others in terms of the mean activity and standard deviation. All of the results indicate that the proposed deep learning-based joint filtering framework is of great potential for dynamic PET image denoising.

Background. To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD). Methods. Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to 13N-NH3 PET/CT myocardial perfusion imaging (MPI) and 18F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results. In univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816–3.042 vs. 2.138–2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.833, 0.875, and 0.949 vs. 0.775, 0.606, 0.713, and 0.744; p=0.041, 0.005, 0.034 0.003, respectively). Conclusions. The joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.

The surface modification using cold plasma technique was introduced to hydroxyapatite(HA). The methods adopted in the study included the formation of bone-like apatite in simulated body fluid and the use of SEM, XPS and XRD. The results showed that the formation of bone-like apatite on HA modified by cold plasma was easier than that without modification. The active mechanism involves the impact of the particles with high energy and high activity against HA, which roughens and etches the surface of HA, heads to the distortion of HA crystal, and thus increases the dissolvability of HA and the local concentration of the Ca and P ions. This approach is helpful to the formation of bone-like appetite. The data demonstrate that the surface modification using cold plasma technique can increase the activity of HA.

The phase constitution, microstructure and properties of a new type of machinable glass-ceramics containing fluorophlogopite-type (FPT) Ca-mica for used in restorative dentistry were investigated. According to the results of X-ray diffraction (XRD) and energy-dispersive spectrometry(EDS), its main crystalline phases were FPT Ca-mica and t-ZrO2, together with few KxCa(1-x)/2Mg2Si4O10F2, m-ZrO2. The flexible strength was 235 MPa, which was nearly two times larger than that of the present mica-based dental materials, and the highest fracture toughness was 2.17 MPa.m1/2. The microstructure had a great effect on properties, the glass-ceramics contained a large volume, and the fine crystals showed higher strength. The material possessed typical microstructure of machinable glass-ceramics and displayed excellent machinability during drilling test and CAD/CAM.