Scanning Model Research Articles

Are Surface Models Obtained Using Different Methodologies Comparable?

Surface models are used to quantify differences in form in a variety of anatomical analyses. Different methods for obtaining surface models are used across studies and multiple methodologies may be used within a single study. The number of methodologies available for the collection of surface data has multiplied in recent years. Though this presents an opportunity, it also presents a potential problem. Are surface models obtained using different methodologies comparable? Here I quantify surface distances between aligned surface models obtained using a blue light scanner and photogrammetry.Proximal tibiae from ten individuals were mounted to a rotating stand and scanned using an Artec Space Spider; surface models were automatically reconstructed using the real‐time fusion setting in Artec Studio 16. The same tibiae were photographed from 65 angles using a Canon Coolpix camera set to the macro setting. Photographs for each individual were masked and aligned to build a surface mesh using Agisoft Metashape. Artec scanner and photogrammetry surface models for each individual were imported into Avizo Lite. The photogrammetry model for each individual was scaled and aligned to the Artec model by iteratively minimizing surface distance between the models. Once surface alignment reached a stable configuration, surface distances from the Artec model to the photogrammetry model were calculated. Surface distances were also calculated between aligned pairs of individuals obtained using the Artec scanner.Surface distances between different individuals obtained using the same methodology were 73 times larger than those between models of the same individual obtained using different methodologies (p<0.001). Mean surface distance between models of the same individual was 0.020 mm and mean surface distance between models of different individuals was 1.477 mm. While surface distances exceeded 0.5 mm for approximately 75% of vertices in between individual comparisons, surface distances never exceeded 0.5 mm in comparisons of Artec scanner and photogrammetry derived models. These results suggest that surface models obtained using different methodologies are comparable, at least in this case.

CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features.

Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans (n = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar’s test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).

Video-evoked fMRI BOLD responses are highly consistent across different data acquisition sites.

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here, we characterize the consistency of brain responses across independent samples of participants viewing the same videos in functional magnetic resonance imaging (fMRI) scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video‐evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant‐level variability rather than scanner‐ or data‐ related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.

Estimation of Cosmic Effect on Background Nuclear Radiation

The effect of cosmos on background nuclear radiation has been estimated with altitude on Idanre hill using the rad scanner model 500VBR. Five readings were taken in order to ensure accuracy and reliability of the result. The mean absorbed dose rate in air for ground, middle and top of the hill were estimated to be 322.67±35.80, 353.72±37.79 and 457.88 ± 40.30 nGy h-1 respectively. The absorbed dose rate and the effective dose equivalent increases with height which is an indication of nuclear radiation enhancement by the cosmos. The mean outdoor annual effective dose equivalent was obtained as 0.40±0.05, 0.43±0.08 and 0.56±0.06 mSv y-1 respectively. The mean absorbed dose rate and the mean outdoor annual effective dose equivalent from different heights are higher than the world average of 59 nGy h-1 r and 0.07 mSv y-1 respectively and the World Health Organization recommendation of 0.1 mSv y-1, but lower than the International Commission on Radiological Protection recommendation of 1.0 mSv y-1. The estimated excess lifetime cancer risk 1.83 x10-3 is higher than the world average value of 2.9 x 10-4. This implies a highly significant radiation health risk in the area most predominantly as the height increases and the cosmic effect heightened up. Keywords: Cosmos; Background nuclear radiation; Rad scanner; Effective dose; Cancer risk.

Gray matter volumetric correlates of dimensional impulsivity traits in children: Sex differences and heritability.

Previous research investigated the cerebral volumetric correlates of impulsivity largely in moderate‐sized samples and few have examined the distinct correlates of dimensions of impulsivity, sex differences, or heritability of the correlates. Here, we performed voxel‐based morphometry analysis of data (n = 11,474; 5,452 girls, 9–10 years) curated from the Adolescent Brain Cognition Development project. In a linear regression with all five UPPS‐P subscores as regressors and age in months, total intracranial volume, study site, and scanner model as covariates, higher levels of lack of premeditation, and sensation seeking were correlated with larger cortical and subcortical gray matter volumes (GMVs). In contrast, higher positive urgency was correlated with smaller GMVs in many of the same regions. The dimensional impulsivity traits also involved distinct volumetric correlates, with, for instance, sensation seeking and positive urgency specifically implicating bilateral caudate head/mid‐cingulate cortex and bilateral lateral orbitofrontal cortex/left precentral gyrus, respectively. Boys relative to girls scored higher in all impulsivity dimensions. Girls relative to boys showed significantly stronger positive and negative correlations between sensation seeking and insula, putamen, and inferior frontal gyrus (IFG) GMVs and between positive urgency and cingulate cortex, insula, and IFG GMVs, respectively. With a subsample of twins, the dimensional impulsivity traits were weakly to moderately heritable in both girls and boys, and the GMV correlates were highly heritable in girls and boys combined. These findings collectively suggest shared and nonshared as well as sex differences in the cerebral volumetric bases of dimensional impulsivity traits and may facilitate research of externalizing psychopathology in children.

Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient-specific CT dosimetry.

This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation. A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ doseestimation. Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT doseestimation.

Feasibility of a method for low contrast CT image quality assessment using difference detail curves for abdominal scans

This work describes a measurement method for assessing dose-related image-quality of CT scans based on the difference detail curve (DDC) method, and showcases its use in a low contrast setting. The method is based on a phantom consisting of elliptical slices of different sizes into which contrast object modules can be inserted. These modules contain contrast objects based on (synthetic) resin mixtures with sucrose (native) or sodium iodine (contrast medium). Mixing ratios are provided to achieve a range of clinically relevant CT-numbers with these materials. The phantom is characterized in terms of contrast accuracy, energy dependency and long-term drift with satisfying results. Contrast accuracy and energy dependency are similar to that of water or soft tissue. Image quality of 655 scans of the phantom acquired at 30 different clinical institutions and with 16 different CT scanner models from 4 manufacturers was assessed by calculating a difference detail curve (DDC) from evaluation of up to 5 human observers using a custom-made software (RadiVates) described in this work. Based on these measurements, inter-observer variability was quantified using a bootstrap method and was shown to be a large contributor to the overall variability. This work demonstrates that assessment of CT image quality is feasible with the aforementioned phantom and DDC method.

Left-Right Intensity Asymmetries Vary Depending on Scanner Model for FLAIR and T1 Weighted MRI Images.

BackgroundLocalized regions of left–right image intensity asymmetry (LRIA) were incidentally observed on T2‐weighted (T2‐w) and T1‐weighted (T1‐w) diagnostic magnetic resonance imaging (MRI) images. Suspicion of herpes encephalitis resulted in unnecessary follow‐up imaging. A nonbiological imaging artifact that can lead to diagnostic uncertainty was identified.PurposeTo investigate whether systematic LRIA exist for a range of scanner models and to determine if LRIA can introduce diagnostic uncertainty.Study TypeA retrospective study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data base.SubjectsOne thousand seven hundred fifty‐three (median age: 72, males/females: 878/875) unique participants with longitudinal data were included.Field Strength3T.SequencesT1‐w three‐dimensional inversion‐recovery spoiled gradient‐echo (IR‐SPGR) or magnetization‐prepared rapid gradient‐echo (MP‐RAGE) and T2‐w fluid‐attenuated inversion recovery (FLAIR) long tau fast spin echo inversion recovery (LT‐FSE‐IR). Only General Electric, Philips, and Siemens' product sequences were used.AssessmentLRIA was calculated as the left–right percent difference with respect to the mean intensity from automated anatomical atlas segmented regions. Three neuroradiologists with 37 (**), 32 (**), and 3 (**) years of experience rated the clinical impact of 30 T2‐w three‐dimensional FLAIR exams with LRIA to determine the diagnostic uncertainty. Statistical comparisons between retrospective intensity normalized T1m and original T1‐w images were made.Statistical TestsFor each image type, a linear mixed effects model was fit using LRIA scores from all scanners, regions, and participants as the outcome and age and sex as predictors. Statistical significance was defined as having a P‐value <0.05.ResultsLRIA scores were significantly different from zero on most scanners. All clinicians were uncertain or recommended definite diagnostic follow‐up in 62.5% of cases with LRIA >10%. Individuals with acute brain pathology or focal neurologic deficits are not enrolled in ADNI; therefore, focal signal abnormalities were considered false positives.Data ConclusionLRIA is system specific, systematic, creates diagnostic uncertainty, and impacts IR‐SPGR, MP‐RAGE, and LT‐FSE‐IR product sequences.Level of Evidence: 2Technical Efficacy Stage: 3

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.

Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

ObjectivesTo determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. BackgroundCCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. MethodsCCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). ResultsWithin the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370–400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. ConclusionThe diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstractAn AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters

BackgroundWe investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients.MethodsThis study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour.ResultsScanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed.ConclusionsCombining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.

Conversion from dose length product to effective dose for the CT component of whole-body PET/CT.

For dose management of CT, the ratio of effective dose (ED) to dose length product (DLP) is often used to convert DLP to ED. We evaluated this ratio in the CT component of whole-body PET/CT performed under various imaging conditions to determine a practical method for ED estimation applicable to PET/CT. In total, 400 patients who underwent whole-body PET/CT were enrolled. The imaging conditions were variable in terms of the scanner model, setting of automatic exposure control (AEC) setting and arm positioning. The scan range was divided into six anatomical regions. DLP was calculated for each region, and multiplied by the conversion factor for the respective region to determine regional ED. The six regional EDs were summed together to determine ED by the regional DLP method (EDrDLP). Additionally, regional ED was assessed using CT-Expo, software dedicated to CT dose estimation, and the total of six regional EDs were defined as ED by the CT-Expo method (EDCT-Expo). EDrDLP/DLP and EDCT-Expo/DLP were calculated using DLP automatically provided by the scanner. EDrDLP/DLP ranged from 0.0121 to 0.0128mSv/mGy/cm with the arms up and from 0.0127 to 0.0134mSv/mGy/cm with the arms down. Putting the arms down slightly increased EDrDLP/DLP, presumably due to an increased contribution of the chest and abdomen to total radiation exposure. The AEC setting and scanner model also influenced EDrDLP/DLP significantly but slightly. EDCT-Expo/DLP showed apparent scanner dependence, which appeared mainly attributable to differences in the constants used for DLP calculation between the scanner and CT-Expo. Multiplication of scanner-derived DLP by a conversion factor of 0.013mSv/mGy/cm provides acceptable ED estimates.

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies.

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for 18F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30-300s and 1800s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NECphantom), 10-mm sphere contrast (QH,10mm), background variability (N10mm), contrast-to-noise ratio (QH,10mm/N10mm), image noise level (CVBG), and SUVmax and SUVpeak for hot spheres (10-37mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: QH,10mm/N10mm ≥ 2.5 and CVBG ≤ 14.1%. The 10th-90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CVBG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CVBG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.

To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.

Monte Carlo evaluation of hypothetical long axial field-of-view PET scanner using GE Discovery MI PET front-end architecture.

The development of total-body PET scanners is of growing interest in the PET community. Investigation into the imaging properties of a hypothetical extended axial field-of-view (AFOV) GE Healthcare SiPM-based Discovery MI (DMI) system architecture has not yet been performed. In this work, we assessed its potential as a whole-body scanner using Monte Carlo simulations. The aim of this work was to (1) develop and validate a Monte Carlo model of a four-ring scanner and (2) extend its AFOV up to 2m to evaluate performance gain through NEMA-based evaluation. The DMI four-ring geometry and its pulse digitization scheme were modeled within the GATE Monte Carlo platform using published literature. The GATE scanner model was validated by comparing results against published NEMA performance measurements. Following the validation of the four-ring model, the model was extended to simulate 8-, 20-, 30-, and 40-ring systems. Spatial resolution, sensitivity, NECR, and scatter fraction were characterized with modified NEMA NU-2 2018 standards; however, the image quality measurements were not acquired due to computational limitations. Spatial resolutions were simulated for all scanner ring configurations using point sources to examine the effects of parallax errors. NEMA count rates were estimated using a standard 70cm scatter phantom and an extended version of scatter phantom of length 200cm with (1-800) MBq of 18 F for all scanners. Sensitivity was evaluated using NEMA methods with a 70cm standard and a 200cm long line source. The average FWHM of the radial/tangential/axial spatial resolution reconstructed with filtered back-projection at 1 and 10cm from the scanner center were 3.94/4.10/4.41mm and 5.29/4.89/5.90mm for the four-ring scanner. Sensitivity was determined to be 14.86cps/kBq at the center of the FOV for the four-ring scanner using a 70cm line source. Sensitivity enhancement up to 21-fold and 60-fold were observed for 1 and 2m AFOV scanners compared to four-ring scanner using a 200cm long line source. Spatial resolution simulations in a 2m AFOV scanner suggest a maximum degradation of ∼23.8% in the axial resolution compared to the four-ring scanner. However, the transverse resolution was found to be relatively constant when increasing the axial acceptance angle up to ±70°. The peak NECR was 212.92 kcps at 22.70 kBq/ml with a scatter fraction of 38.9% for a four-ring scanner with a 70cm scatter phantom. Comparison of peak NECR using the 200cm long scatter phantom relative to the four-ring scanner resulted in a NECR gain of 15 for the 20-ring and 28 for the 40-ring geometry. Spatial resolution, sensitivity, and scatter fraction showed an agreement within ∼7% compared with published measured values. The four-ring DMI scanner simulation was successfully validated against published NEMA measurements. Sensitivity and NECR performance of extended 1 and 2 m AFOV scanners based upon the DMI architecture were subsequently simulated. Increases in sensitivity and count-rate performance are consistent with prior simulation studies utilizing extensions of the Siemens mCT architecture and published NEMA measurements with the uEXPLORER system.

ИССЛЕДОВАНИЕ РЕЗУЛЬТАТОВ ОБРАБОТКИ ПОЛУЧЕННЫХ РАЗЛИЧНЫМИ МОДЕЛЯМИ НАЗЕМНЫХ ЛАЗЕРНЫХ СКАНЕРОВ ДАННЫХ ДЛЯ КОНТРОЛЯ КАЧЕСТВА РЕМОНТА АВТОМОБИЛЬНЫХ ДОРОГ

One of the tasks solved through the application of terrestrial laser scanning technology is the quali-ty control of road works. With the stated accuracy of measuring distances of the order of several mil-limeters, terrestrial laser scanning allows conducting continuous control of the road construction or repair. Nowadays a large number of different terrestrial laser scanners have been produced, each of which allows identifying most of the roadway defects and determining its flatness due to the ability to perform high-density surveys. The reliability of determining flatness and detecting defects from scan-ning data can vary significantly because of the fact that almost every laser scanner has its own unique technical characteristics. To achieve the highest reliability values, it is necessary to adhere both to techniques of performing the field part of all surveying works and to data processing techniques, which consist in obtaining point clouds and constructing digital road surface models. If the main es-sence of these works does not depend on the used laser scanner model, then the individual stages of the applied techniques may differ. 2 models of laser scanners produced by different manufacturers are discussed to solve the task of asphalt pavement laying quality. Their comparative characteristics and peculiarities of performing all surveying works with their usage are given. The results of digital road surface model generation are analyzed, and peculiarities of processing of data obtained with var-ious laser scanners are discussed. Based on the results of the analysis, it is shown that not all models of laser scanners can be suitable for controlling the quality of road repairs. The technique of prelimi-nary data processing is presented, which makes it possible to increase reliability of solving this task for laser scanners with insufficient quality of obtained point clouds.

Quantification of infarct core signal using CT imaging in acute ischemic stroke

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA).Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS).In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms “NCCT” and “CTA” to refer to them. This pipeline enables us to answer the questions “Does the injection of bolus enhance the infarct core signal?” and “What is the ideal bolus timing to enhance the infarct core signal?” without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core.The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS.In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors.

A NOVEL APPROACH FOR USIND MACHINE LEARNING FORENSIC SCANNER IDENTIFICATION

In this paper, we review recent work in media forensics for digital images, video, audio, and documents. The proposed system uses deep-learning to automatically learn the intrinsic features from various scanned images. Our experimental results show that high accuracy can be achieved for source scanner identification. The proposed system can also generate a reliability map that indicates the manipulated regions in an scanned image. This study is the first to extract brightness variations as a unique characteristic of each scanner model and recognize the potential of brightness variations in source identification and manipulation detection

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

BackgroundMeasurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis.MethodsA fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm (‘machine’) performance was compared to three clinicians (‘human’) and a commercial tool (cvi42, Circle Cardiovascular Imaging).FindingsMachine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint.ConclusionWe present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision.

ON THE ISSUE OF ASSESSING THE DAMAGE CAUSED TO THE ENERGY SYSTEM AS A RESULT OF THE 3-RD ARTSAKH WAR OR THE 44-DAY WAR

The article analyses scenario approaches to the development of the energy system of the world, as well as agreeing on the positive and negative sides, and proposes practical mechanisms to improve the efficiency of the energy system of the NKR. Modern models of energy development are presented in the article, of which 6 are mainly used. The necessity of using the SCANNER model to improve the efficiency of the energy system of the NKR is especially emphasized, because it is as close to reality as possible. The paper calculated the losses of " Artsakhenergo" CJSC and the reasons for the decrease in the level of profitability of CNG plants. In addition, the point of view was distinguished, according to which not all schemes of global development and various integrations are applicable in Armenia and Artsakh, and so far the main developments are due to the rather wide spread of alternative energy receiving stations.