SUVmean ratios of liver/muscle and lung/muscle from 13N-NH3 PET perfusion outperformed traditional myocardial viability parameters in predicting survival after CABG.

PURPOSE: Myocardial viability evaluation in predicting survival after coronary artery bypass graft (CABG) remains debatable. Thus, this study aimed to investigate the role of 13N-NH3/18F-FDG PET myocardial viability scan in predicting treatment outcomes and survival. METHODS: 90 patients with CABG and pre-surgical PET-based myocardial viability scan were retrospectively reviewed. Perfusion-metabolism features, myocardium motion parameters, and patient characteristics were recorded. Additionally, the SUVmean of blood pool, lung, liver, spleen, and muscle were measured and the SUVmean ratios were calculated. Factors associated with treatment outcomes and survival were analyzed by Logistic and Cox regressions. Nomogram models were subsequently established to predict ejection fraction (EF) improvement and survival outcomes. RESULTS: The mean EF of these 90 patients was 38.1 ± 9.5% and 46.0 ± 9.2% before and after CABG surgery, and 35 patients (38.9%) achieved EF improvement ≥ 10%. EF measurements by PET and echocardiogram showed a reasonable linear correlation (R = 0.752). Sex, pre-surgical EF, mismatch of the left ventricle, total perfusion deficit (TPD), and peak ejection rate (PER) were independent predictive factors of EF improvements. Surgery waiting time, valve damage, and SUVmean ratio of Liver/Muscle were independently predictive of event-free survival (EFS), while valve damage, together with SUVmean ratio of either Liver/Muscle or Lung/Muscle, were independently predictive of overall survival (OS). CONCLUSION: Although traditional cardiac parameters from PET-based myocardial viability can effectively predict EF improvements after CABG, SUVmean ratios of liver/muscle and lung/muscle from 13N-NH3 PET perfusion outperformed these parameters in predicting survival.

Implications of Sarcopenia and Glucometabolism Parameters of Muscle Derived From Baseline and End-of-Treatment 18F-FDG PET/CT in Diffuse Large B-Cell Lymphoma.

OBJECTIVE: We previously found that the incidence of sarcopenia increased with declining glucose metabolism of muscle in patients with treatment-naïve diffuse large B-cell lymphoma (DLBCL). This study aimed to investigate the relationship between sarcopenia and muscle glucometabolism using 18F-FDG PET/CT at baseline and end-of-treatment, analyze the changes in these parameters through treatment, and assess their prognostic values. MATERIALS AND METHODS: The records of 103 patients with DLBCL (median 54 years [range, 21-76]; male:female, 50:53) were retrospectively reviewed. Skeletal muscle area at the third lumbar vertebral (L3) level was measured, and skeletal muscle index (SMI) was calculated to determine sarcopenia, defined as SMI < 44.77 cm²/m² and < 32.50 cm²/m² for male and female, respectively. Glucometabolic parameters of the psoas major muscle, including maximum standardized uptake value (SUVmax) and mean standardized uptake value (SUVmean), were measured at L3 as well. Their changes across treatment were also calculated as ΔSMI, ΔSUVmax, and ΔSUVmean; Δbody mass index was also calculated. Associations between SMI and the metabolic parameters were analyzed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified. RESULTS: The incidence of sarcopenia was 29.1% and 36.9% before and after treatment, respectively. SMI (P = 0.004) was lower, and sarcopenia was more frequent (P = 0.011) at end-of-treatment than at baseline. The SUVmax and SUVmean of muscle were lower (P < 0.001) in sarcopenia than in non-sarcopenia at both baseline and end-of-treatment. ΔSMI was positively correlated with ΔSUVmax of muscle (P = 0.022). Multivariable Cox regression analysis showed that sarcopenia at end-of-treatment was independently negatively associated with PFS (adjusted hazard ratio [95% confidence interval], 2.469 [1.022-5.965]), while sarcopenia at baseline was independently negatively associated with OS (5.051 [1.453-17.562]). CONCLUSION: Sarcopenic patients had lower muscle glucometabolism, and the muscular and metabolic changes across treatment were positively correlated. Sarcopenia at baseline and end-of-treatment was negatively associated with the prognosis of DLBCL.

Stress-only versus rest-stress SPECT MPI in the detection and diagnosis of myocardial ischemia and infarction by machine learning.

BACKGROUND: Rest-stress SPECT myocardial perfusion imaging (MPI) is widely used to evaluate coronary artery disease (CAD). We aim to evaluate stress-only versus rest-stress MPI in diagnosing CAD by machine learning (ML). METHODS: A total of 276 patients with suspected CAD were randomly divided into training (184 patients) and validation (92 patients) cohorts. Variables extracted from clinical, physiological, and rest-stress SPECT MPI were screened. Stress-only and rest-stress MPI using ML were established and compared using the training cohort. Then the diagnostic performance of two models in diagnosing myocardial ischemia and infarction was evaluated in the validation cohort. RESULTS: Six ML models based on stress-only MPI selected summed stress score, summed wall thickness score of stress%, and end-diastolic volume of stress as key variables and performed equally good as rest-stress MPI in detecting CAD [area under the curve (AUC): 0.863 versus 0.877, P  = 0.519]. Furthermore, stress-only MPI showed a reasonable prediction of reversible deficit, as shown by rest-stress MPI (AUC: 0.861). Subsequently, nomogram models using the above-stated stress-only MPI variables showed a good prediction of CAD and reversible perfusion deficit in training and validation cohorts. CONCLUSION: Stress-only MPI demonstrated similar diagnostic performance compared with rest-stress MPI using 6 ML algorithms. Stress-only MPI with ML models can diagnose CAD and predict ischemia from scar.

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network.

PURPOSE: The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. METHODS: We recruited 60 subjects for rest-only scans and 14 subjects for rest-stress scans, all of whom underwent [13N]ammonia cardiac PET/CT examinations to acquire static and dynamic frames with both 3D NAC and CT-based AC (CTAC) PET images. We developed a 3D pix2pix deep learning AC (DLAC) framework via a U-net + ResNet-based generator and a convolutional neural network-based discriminator. Paired static or dynamic NAC and CTAC PET images from 60 rest-only subjects were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) training, respectively. The pre-trained S-DLAC network was then fine-tuned by paired dynamic NAC and CTAC PET frames of 60 rest-only subjects to derive an improved D-DLAC-FT for dynamic PET images. The 14 rest-stress subjects were used as an internal testing dataset and separately tested on different network models without training. The proposed methods were evaluated using visual quality and quantitative metrics. RESULTS: The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods were consistent with clinical CTAC in terms of various images and quantitative metrics. The S-DLAC (slope = 0.9423, R2 = 0.947) showed a higher correlation with the reference static CTAC as compared to static NAC (slope = 0.0992, R2 = 0.654). D-DLAC-FT yielded lower myocardial blood flow (MBF) errors in the whole left ventricular myocardium than D-DLAC, but with no significant difference, both for the 60 rest-state subjects (6.63 ± 5.05% vs. 7.00 ± 6.84%, p = 0.7593) and the 14 stress-state subjects (1.97 ± 2.28% vs. 3.21 ± 3.89%, p = 0.8595). CONCLUSION: The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods achieve comparable performance with clinical CTAC. Transfer learning shows promising potential for dynamic MP PET.

An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging.

OBJECTIVES: We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated 13N-NH3 PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment. METHODS: Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth. We developed an automatic segmentation method, which introduces the self-attention mechanism into 3D U-Net to capture global information of images so as to achieve fine segmentation of LV. Three cross-validation tests were performed on each gate (64 vs. 32 for training vs. validation). The effectiveness was validated by quantitative metrics (modified hausdorff distance, MHD; dice ratio, DR; 3D MHD) as well as cardiac functional parameters (end-systolic volume, ESV; end-diastolic volume, EDV; ejection fraction, EF). Furthermore, the feasibility of the proposed method was also evaluated by intra- and inter-observers with DR and 3D-MHD. RESULTS: Compared with backbone network, the proposed approach improved the average DR from 0.905 ± 0.0193 to 0.9202 ± 0.0164, and decreased the average 3D MHD from 0.4611 ± 0.0349 to 0.4304 ± 0.0339. The average relative error of LV volume between proposed method and ground truth is 1.09±3.66%, and the correlation coefficient is 0.992 ± 0.007 (P < 0.001). The EDV, ESV, EF deduced from the proposed approach were highly correlated with ground truth (r ≥ 0.864, P < 0.001), and the correlation with commercial software is fair (r ≥ 0.871, P < 0.001). DR and 3D MHD of contours and myocardium from two observers are higher than 0.899 and less than 0.5194. CONCLUSION: The proposed approach is highly feasible for automatic segmentation of the LV cavity and myocardium, with potential to benefit the precision of LV function assessment.

Evaluation of the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with obstructive coronary artery disease.

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, 0.839, 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.

[On the active mechanism of hydroxyapatite modified by cold plasma].

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.

[Study on high strength mica-based machinable glass-ceramic].

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.