BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models.
METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification.
RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92.
CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model\'s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat\'s impact on radiomic features in medical imaging.

Objective. In this study, nonlinearly frequency-modulated (NLFM) ultrasound was applied to magneto-acousto-electrical tomography (MAET) to increase the dynamic range of detection.Approach. Generation of NLFM signals using window function method-based on the principle of stationary phase-and piecewise linear frequency modulation method-based on the genetic algorithm-was discussed. The MAET experiment systems using spike, linearly frequency-modulated (LFM), or NLFM pulse stimulation were constructed, and three groups of MAET experiments on saline agar phantom samples were carried out to verify the performance-respectively the sensitivity, the dynamic range, and the longitudinal resolution of detection-of MAET using NLFM ultrasound in comparison to that using LFM ultrasound. Based on the above experiments, a pork sample was imaged by ultrasound imaging method, spike MAET method, LFM MAET method, and NLFM MAET method, to compare the imaging accuracy.Main results. The experiment results showed that, through sacrificing very little main-lobe width of pulse compression or equivalently the longitudinal resolution, the MAET using NLFM ultrasound achieved higher signal-to-interference ratio (and therefore higher detection sensitivity), lower side-lobe levels of pulse compression (and therefore larger dynamic range of detection), and large anti-interference capability, compared to the MAET using LFM ultrasound.Significance. The applicability of the MAET using NLFM ultrasound was proved in circumferences where sensitivity and dynamic range of detection were mostly important and slightly lower longitudinal resolution of detection was acceptable. The study furthered the scheme of using coded ultrasound excitation toward the clinical application of MAET.

OBJECTIVE: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements.
METHODS: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons.
RESULTS: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC).
CONCLUSIONS: The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

Computed tomography (CT) is a non-invasive examination tool that is widely used in medicine. In this study, we explored its value in visualizing and quantifying coconut.
Twelve coconuts were scanned using CT for three months. Axial CT images of the coconuts were obtained using a dual-source CT scanner. In postprocessing process, various three-dimensional models were created by volume rendering (VR), and the plane sections of different angles were obtained through multiplanar reformation (MPR). The morphological parameters and the CT values of the exocarp, mesocarp, endocarp, embryo, bud, solid endosperm, liquid endosperm, and coconut apple were measured. The analysis of variances was used for temporal repeated measures and linear and non-linear regressions were used to analyze the relationship between the data.
The MPR images and VR models provide excellent visualization of the different structures of the coconut. The statistical results showed that the weight of coconut and liquid endosperm volume decreased significantly during the three months, while the CT value of coconut apple decreased slightly. We observed a complete germination of a coconut, its data showed a significant negative correlation between the CT value of the bud and the liquid endosperm volume (y = -2.6955x + 244.91; R2 = 0.9859), and a strong positive correlation between the height and CT value of the bud (y = 1.9576 ln(x) -2.1655; R2 = 0.9691).
CT technology can be used for visualization and quantitative analysis of the internal structure of the coconut, and some morphological changes and composition changes of the coconut during the germination process were observed during the three-month experiment. Therefore, CT is a potential tool for analyzing coconuts.

OBJECTIVE: To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT.
METHODS: A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger\'s test.
RESULTS: Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth.
CONCLUSIONS: The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth.
CONCLUSIONS: • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

BACKGROUND: [18F]FDG imaging on total-body PET/CT (TB PET/CT) scanners, with improved sensitivity, offers new potentials for cancer diagnosis, staging, and radiation treatment planning. This consensus provides the protocols for clinical practices with a goal of paving the way for future studies with the total-body scanners in oncological [18F]FDG TB PET/CT imaging.
METHODS: The consensus was summarized based on the published guidelines and peer-reviewed articles of TB PET/CT in the literature, along with the opinions of the experts from major research institutions with a total of 40,000 cases performed on the TB PET/CT scanners.
RESULTS: This consensus describes the protocols for routine and dynamic [18F]FDG TB PET/CT scanning focusing on the reduction of imaging acquisition time and FDG injected activity, which may serve as a reference for research and clinic oncological PET/CT studies.
CONCLUSIONS: This expert consensus focuses on the reduction of acquisition time and FDG injected activity with a TB PET/CT scanner, which may improve the patient throughput or reduce the radiation exposure in daily clinical oncologic imaging.
CONCLUSIONS: • [18F]FDG-imaging protocols for oncological total-body PET/CT with reduced acquisition time or with different FDG activity levels have been summarized from multicenter studies. • Total-body PET/CT provides better image quality and improved diagnostic insights. • Clinical workflow and patient management have been improved.

BACKGROUND: Several types of head CT classification systems have been developed to prognosticate and stratify TBI patients.
OBJECTIVE: The purpose of our study was to compare the predictive value and accuracy of the different CT scoring systems, including the Marshall, Rotterdam, Stockholm, Helsinki, and NIRIS systems, to inform specific patient management actions, using the ProTECT III population of patients with moderate to severe acute traumatic brain injury (TBI).
METHODS: We used the data collected in the patients with moderate to severe (GCS score of 4-12) TBI enrolled in the ProTECT III clinical trial. ProTECT III was a NIH-funded, prospective, multicenter, randomized, double-blind, placebo-controlled clinical trial designed to determine the efficacy of early administration of IV progesterone. The CT scoring systems listed above were applied to the baseline CT scans obtained in the trial. We assessed the predictive accuracy of these scoring systems with respect to Glasgow Outcome Scale-Extended at 6 months, disability rating scale score, and mortality.
RESULTS: A total of 882 subjects were enrolled in ProTECT III. Worse scores for each head CT scoring systems were highly correlated with unfavorable outcome, disability outcome, and mortality. The NIRIS classification was more strongly correlated than the Stockholm and Rotterdam CT scores, followed by the Helsinki and Marshall CT classification. The highest correlation was observed between NIRIS and mortality (estimated odds ratios of 4.83).
CONCLUSIONS: All scores were highly associated with 6-month unfavorable, disability and mortality outcomes. NIRIS was also accurate in predicting TBI patients\' management and disposition.

OBJECTIVE: To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT).
METHODS: A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).
RESULTS: The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively.
CONCLUSIONS: The majority of radiomics features were non-reproducible within and between SECT and DECT.
CONCLUSIONS: • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

Due to the limited temporal resolution and cardiac motion, coronary computed tomography angiography (CCTA) exam is one of the most challenging CT protocols which may require operating radiologist to apply additional phase adjustment or motion correction for image reconstruction.
To evaluate image quality between automatic and manual CCTA reconstruction in a 0.25 second rotation time, 16 cm coverage, single-beat, CT scanner with automated phase selection and AI-assisted motion correction.
CCTA exams of 535 consecutive patients were included. All exams were first reconstructed with an automatically selected phase. If there was an unacceptable motion artifact, a manual reconstruction process was performed by radiologists. Additionally, automatic image series which consist of auto-phase selection and a follow-up motion correction were reconstructed. For these two manual and automatic image series, a four-point Likert scale rating system was used to evaluate image quality of the coronary artery segment by two experienced radiologists, according to the 18-segment model.
Fifty-one patients (9.5%) did not have satisfactory image quality after auto-phase selection. In these patients, the heart rate during scanning was higher (78.3±18.4 bpm) than in the remaining 484 patients (68.9±13.1 bpm). Overall, 734 out of the 918 vessel segments were identified for quality evaluation among 51 patients. Automatic and manual image series were rated as having average Likert scores of 3.48±0.62 and 3.32±0.67 (P < 0.001), respectively.
Using a 0.25 second rotation speed, 16 cm z-coverage, CT scanner installed with an AI-assisted motion correction algorithm, the automatic image reconstruction with scanner equipped auto-phase-selection and motion correction algorithm outperforms manually controlled image reconstruction by radiologists. This suggests that the traditional CCTA exam reconstruction workflow could be altered allowing less radiologist involvement and becoming more efficient.

OBJECTIVE: To differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity.
METHODS: 73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases\' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features.
RESULTS: 29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93.
CONCLUSIONS: The radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.