Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT

Objective To evaluate the diagnostic effectiveness of dual source dual energy computed tomography (DS-DECT) for benign and malignant thyroid nodules. Methods Between January 2019 and December 2021, 60 patients with surgically and pathologically verified thyroid nodules treated at our institution were recruited. DS-DECT was administered to all patients. The iodine content of lesioned and normal tissues, the normalized iodine concentration (NIC) and standardized CT values of benign and malignant nodules, the consistency of examination results and pathological findings, and diagnostic effectiveness were all investigated. Results The diagnosis accuracy was the same as that of surgical pathology, producing a 100% accuracy for the 60 patients with thyroid nodules (42 were benign and 18 were malignant). The iodine content of lesioned solid tissue differed significantly from that of normal tissue, as did the iodine content of malignant and benign nodules (P < 0.05). In the arterial phase, no significant difference was found in NIC and standardized CT values between benign and malignant nodules (P > 0.05). The optimal critical NIC for differentiating benign and malignant nodules in the venous phase was 0.74 and the standardized CT value was 0.79 HU according to the receiver operating characteristics (ROC) curve. Malignant nodules were diagnosed when the NIC was <0.74 and the standardized CT value was <0.79 HU, with AUC values of 0.89 and 0.93, respectively, where the sensitivity and specificity of the differential diagnosis of NIC were 90.48% (38/42) and 88.89% (16/18), respectively, and those of the differential diagnosis of standardized CT value were 92.86% (39/ 42) and 94.44% (17/18), respectively. The diagnosis accuracy of DS-DECT was 100%, and the diagnostic results of morphological characteristics and pathological testing were consistent. The sensitivity and specificity of the NIC values and standardized CT values in the venous phase differential diagnosis of benign and malignant nodules were compatible with the morphological differential diagnosis. Conclusion DS-DECT is highly accurate in determining the benignity and malignancy of thyroid nodules and has a strong potential for clinical promotion to allow for prompt treatment.

/pdf/diagnostic-effectiveness-of-dual-source-dual-energy-computed-d2912p8v.pdf

Diagnostic Effectiveness of Dual Source Dual Energy Computed Tomography for Benign and Malignant Thyroid Nodules

Abstract Background To assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT). Methods This study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity. Results The four reconstructions maintained the IC values unchanged in nine anatomical sites (all p &gt; 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p &lt; 0.001) and presented lower noise and noise peak (both p &lt; 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p &lt; 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p &lt; 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P &lt; 0.001). Conclusions DLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity. 

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC).This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined.RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram.CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization.Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen. 

/pdf/virtual-biopsy-using-ct-radiomics-for-evaluation-of-1kwzcc1v.pdf

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study

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).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).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.The majority of radiomics features were non-reproducible within and between SECT and DECT.• 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. 

/pdf/robustness-of-ct-radiomics-features-consistency-within-and-2ycr9qjk.pdf

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT

Background: Studies comparing accuracy of quantification by dual-energy CT (DECT) scanners have been limited by small numbers of scanners evaluated and narrow ranges of scanning conditions. Objective: To compare DECT scanners of varying vendors, technologies, and generations in terms of the accuracy of iodine concentration and attenuation measurements. Methods: A DECT quality-control phantom was designed to contain 7 inserts of varying iodine concentrations as well as soft-tissue and fat inserts. The phantom underwent DECT using 12 different scanner configurations based on 7 different DECT scanners from 3 vendors, with additional variation in tube voltage settings. Technologies included rapid-switching, dual-source, and dual-layer detector DECT. Scans also used three radiation dose levels (10, 20, and 30 mGy) and multiple reconstruction algorithms [filtered back projection, medium and high iterative reconstruction, deep-learning image reconstruction (DLIR)]. The mean absolute percentage error (MAPE, representing the absolute ratio of measured error to nominal values on average; lower values indicate better accuracy) was calculated for iodine concentration on iodine maps (MAPEiodine) and attenuation on virtual monochromatic images (VMI) using 40, 70, 100, and 140 keV (MAPEHU). Linear mixed models were used to explore factors affecting quantification accuracy. Results: MAPEiodine and MAPEHU ranged from 4.62%-28.55% and 10.21%-26.33%, respectively, across scanner configurations. Accuracies of iodine concentration and attenuation measurements were higher for 3rd-generation rapid-switching and dual-source scanners in comparison with respective earlier-generation scanners and with the single evaluated dual-layer detector scanner. Among all configurations, the 3rd-generation rapid-switching scanner using DLIR had highest quantification accuracy for iodine concentration (MAPEiodine=4.62±3.87%) and attenuation (MAPEHU=10.21±11.43%). Overall, MAPEiodine was significantly affected by scanner configuration (F=450.0, p<.001) and iodine concentration (F=211.0, p<.001). Overall, MAPEHU was significantly affected by scanner configuration (F=233.5, p<.001), radiation dose (F=14.9, p<.001), VMI energy level (F=1959.4, p<.001), and material density (F=411.5, p<.001); radiation dose was significantly associated with MAPEHU for 5 of 12 individual configurations. Conclusion: Quantification accuracy varied among DECT configurations of varying vendors, platforms, and generations, and was also affected by acquisition and reconstruction parameters. DLIR may improve quantification accuracy. Clinical Impact: The interscanner differences in DECT-based measurements should be recognized when performing quantitative evaluation by DECT in clinical practice.

Multivendor Comparison of Quantification Accuracy of Iodine Concentration and Attenuation Measurements by Dual-Energy CT: A Phantom Study.

Emotional contagion has been a focus problem in the current fields of psychology and organizational behavior. Based on the theoretical analysis of the emotional contagion mechanisms and probabilistic theory, a cellular automaton (CA) model has been proposed to simulate the process of emotional contagion. And with the help of this CA model, we study the gross features of employees’ positive emotions in the evolution of emotional contagion and explore the effects of employees’ ability to transport emotion susceptibility and intimacy on the reaction process. The results indicate that employees’ ability to transport positive emotion susceptibility and intimacy are positive related to the emotional contagion between employees.Copyright © 2014 by ASME

A Cellular Automaton Model and its Application on Emotional Infections

Impact of a novel whole-heart motion-correction algorithm on non-contrast cardiac CT images and coronary artery calcium scores

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jing Fan

Author Tools

Chat about Author

Papers

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT

Multivendor Comparison of Quantification Accuracy of Iodine Concentration and Attenuation Measurements by Dual-Energy CT: A Phantom Study.

A Cellular Automaton Model and its Application on Emotional Infections

Impact of a novel whole-heart motion-correction algorithm on non-contrast cardiac CT images and coronary artery calcium scores