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In this study, a simple calcination route was adopted to prepare hausmannite Mn3O4 nanoparticles using rice powder as soft bio-template. Prepared Mn3O4 was characterized by Fourier Transform Infra-Red Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray microanalysis (EDX), Powder X-Ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Brunauer-Emmett-Teller (BET) and Solid state UV-Vis spectroscopic techniques. Mn-O stretching in tetrahedral site was confirmed by FTIR and Raman spectra. % of Mn and O content supported Mn3O4 formation. The crystallinity and grain size was found to be 68.76% and 16.43 nm, respectively; tetragonal crystal system was also cleared by XRD. TEM clarified the planes of crystal formed which supported the XRD results and BET demonstrated mesoporous nature of prepared Mn3O4 having low pore volume. Low optical band gap of 3.24 eV of prepared Mn3O4 nanoparticles indicated semiconductor property and was used as cathode material to fabricate CR-2032 coin cell of Aqueous Rechargeable Zinc Ion Battery (ARZIB). A reversible cyclic voltammogram (CV) showed good zinc ion storage performance. Low cell resistance was confirmed by Electrochemical Impedance Spectroscopy (EIS). The coin cell delivered high specific discharge capacity of 240.75 mAhg-1 at 0.1 Ag-1 current density. The coulombic efficiency was found to be 99.98%. It also delivered excellent capacity retention 94.45% and 64.81% after 300 and 1000 charge-discharge cycles, respectively. This work offers a facile and cost effective approach for preparing cathode material of ARZIBs.

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Natural plant based fibres are being increasingly used in sustainable fibre reinforced composite applications in order to meet the demand of using environmentally friendly materials for composites. Fibre metal laminates (FMLs) are used in aerospace, automobile, marine and civil engineering applications, due to their excellent mechanical behaviors compared to traditional metals and their alloys. This study describes a novel fabrication of jute fibre reinforced aluminum metal laminates, using different jute fibre architectures (plain and twill fabric structures), wherein jute fibres were used in the skins and aluminum in the core layers. Jute fibres and aluminum sheets were chemically treated to enhance the compatibility and interfacial bonding at fibre-metals-matrix interfaces. FMLs were manufactured by hot pressing technique, after the application of wet lay-up process for the resin impregnation and they were further tested under tensile, flexural and impact loading conditions. While comparing results, the twill architecture showed improved tensile and flexural properties compared to plain fabric based FMLs. Chemical treatments on twill jute fibres and metal sheets further exceptionally enhanced the flexural properties (151 MPa flexural strength and 21.3 GPa modulus and they were increased by 186.5 % and 722.7 % respectively compared to the untreated jute fibre counterparts) of the laminates due to a significant improvement in the adhesion between the jute fibre and aluminum sheet after alkali treatment applied. Therefore, with these enhanced properties, jute based FML laminates can be used as sustainable composite materials in many structural applications.

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In this work, microcrystalline cellulose (MCC) was isolated from jute sticks and sodium carboxymethyl cellulose (Na-CMC) was synthesized from the isolated MCC. Na-CMC is an anionic derivative of microcrystalline cellulose. The microcrystalline cellulose-based hydrogel (MCCH) and Na-CMC-based hydrogel (Na-CMCH) were prepared by using epichlorohydrin (ECH) as a crosslinker by a chemical crosslinking method. The isolated MCC, synthesized Na-CMC, and corresponding hydrogels were characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electronic microscopy (SEM), and energy dispersive spectroscopy (EDS) for functional groups, crystallinity, surface morphology, and composite elemental composition, respectively. Pseudo-first-order, pseudo-second-order, and Elovich models were used to investigate the adsorption kinetics. The pseudo-second-order one is favorable for both hydrogels. Freundlich, Langmuir, and Temkin adsorption isotherm models were investigated. MCCH follows the Freundlich model (R2 = 0.9967), and Na-CMCH follows the Langmuir isotherm model (R2 = 0.9974). The methylene blue (MB) dye adsorption capacities of ionic (Na-CMCH) and nonionic (MCCH) hydrogels in different contact times (up to 600 min), initial concentrations (10-50 ppm), and temperatures (298-318 K) were investigated and compared. The maximum adsorption capacity of MCCH and Na-CMCH was 23.73 and 196.46 mg/g, respectively, and the removal efficiency of MB was determined to be 26.93% for MCCH and 58.73% for Na-CMCH. The Na-CMCH efficiently removed the MB from aqueous solutions as well as spiked industrial wastewater. The Na-CMCH also remarkably efficiently reduced priority metal ions from an industrial effluent. An effort has been made to utilize inexpensive, readily available, and environmentally friendly waste materials (jute sticks) to synthesize valuable adsorbent materials.

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The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.

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Natural-based lignocellulose fibrous materials can be used as a sustainable alternative to conventional fossil-based fibers such as glass fibers, in lightweight fiber-reinforced thermoplastic composites for marine, automotive, aerospace, or other advanced applications. However, one of the main challenges in using natural fiber-based thermoplastic composites is the low mechanical performance of composite structures. This can be improved significantly through the development of an optimized novel fiber architecture with enhanced fiber packing properties, following a low-cost production process. In this context, this study demonstrates a less energy-consuming and cheaper manufacturing process, for developing highly individualized short jute fiber-based dry fiber preform architecture, with an improved fiber packing property. Short jute fibers were chemically treated with alkali and PVA sizing treatments in the processing of new fiber preform architectures, and they were used in manufacturing of ultimate short jute fiber/polypropylene (PP) thermoplastic composites. The newly developed short fiber thermoplastic composites showed a significant improvement in mechanical properties (tensile, flexural, and impact) compared to any other natural fiber architecture-based (woven, knitted, nonwoven, unidirectional, etc.) composites found in the literature. Due to the use of new fiber architecture, the developed composites' fiber content was observed to increase. In addition, the compatibility of jute fibers with the polypropylene matrix was strengthened with the application of chemical treatments on highly individualized jute fibers. These reasons were responsible for the enhancement of mechanical properties of developed composites. Micromechanics of the fibers in composites were evaluated using the modified rule of the mixture and Halpin-Tsai equations for stiffness prediction of the composites in order to develop a theoretical understanding of newly developed composites' mechanics. It is thought that the improved mechanical performance of short jute fiber/PP thermoplastic composites can extend the use of these composites in many load-demanding applications, wherein normally synthetic fiber composites are used.

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OBJECTIVES: The aim of this study was to develop a method to quantify the peel force in an in vitro model simulating repair of ascending aortic dissections with tissue glue (Bioglue). METHODS: This study adapted an adhesive T-Peel test for the determination of the peel strength of adhesives by measuring the peeling force of a T-shaped bonded tissue. Measurements were performed on iatrogenic dissected ascending porcine aorta, which has been repaired with Bioglue using different pressure levels. Four conditions were tested: zero sample pressure according to the manufacturer's recommendation (n = 10), low (504 Pa; n = 11), moderate pressure (1711 Pa; n = 24) and pressure applied by a round shaped vascular 'Borst clamp' (1764 Pa; n = 23). Non-parametric one-way analysis of variance was applied for statistical significance. RESULTS: The median peel force (lower quartile, upper quartile) of aortic samples increased depending on the applied pressure: [no pressure 0.030 N/mm (0.016, 0.057), low pressure 0.040 N/mm (0.032, 0.070) and moderate pressure 0.214 N/mm (0.050, 0.304)]. Samples pressurized with the Borst clamp reached 0.078 N/mm (0.046, 0.152), which was comparable to the peel force of the unpeeled controls [0.107 N/mm (0.087, 0.124)]. Compared to samples without pressure, Bioglue with the application of the Borst clamp (P = 0.021) and with moderate pressure (P = 0.0007) performed significantly better. CONCLUSIONS: The novel T-Peel test offers an attractive method to test tissue glues in defined in vitro environments. Bioglue peel force increased with pressure on the aortic sample in contrast to low or no pressure as per the manufacturer's recommendation. Modifying current recommended use may aid in increasing effectiveness of this approach.

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Two new species of Tumidiclava Girault (Hymenoptera: Trichogrammatidae), T. breviclavata Ikram & Yousuf sp. nov. and T. loharaensis is Ikram & Yousuf sp. nov. are described from India. A key to the Indian species of Tumidiclava Girault and redescription of Tumdiclava longiclavata Yousuf & Shafee are also provided.

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BACKGROUND: Tuberculosis is a major health problem contributing to significant morbidity and mortality. Early diagnosis and treatment is the key for TB control. Sputum microscopy is a rapid and inexpensive test but due to low and variable sensitivity, many cases can be missed. Culture is considered to be the gold standard but is time consuming. Gene Xpert is a novel and rapid cartridge based nucleic acid amplification test (CBNAAT) that can be used for prompt diagnosis. AIM: To compare the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Gene Xpert with culture in diagnosing tuberculosis in sputum smear negative patients. METHODS: The study is a prospective observational study conducted from December 2017 to January 2019 on 189 patients, who were sputum smear negative but had signs and symptoms suggestive of tuberculosis. Their respiratory samples were taken (either sputum or bronchoalveolar lavage) and sent for Gene Xpert. The results were compared with culture, which was taken as the gold standard, and diagnostic accuracy was assessed. RESULT: A total of 189 patients were included in the study. In 25 patients sputum was taken and in 164 patients BAL was taken (which included 22 patients in whom sputum Gene Xpert was negative but there was high clinical suspicion of tuberculosis). The sensitivity, specificity, PPV and NPV of Gene Xpert in diagnosing smear negative pulmonary tuberculosis was found to be 96.3%, 81.3%, 87.5% and 94.2% respectively. CONCLUSION: Gene Xpert can be used as a rapid diagnostic tool in patients who are sputum smear negative but have clinical features highly suggestive of tuberculosis. It additionally helps in detecting rifampicin resistance. But every Gene Xpert positive case does not necessarily mean an active disease, therefore, past history of tuberculosis along with radiological signs of disease activity are to be considered. In case of negative Gene Xpert but high clinico-radiological suspicion of TB, patients should be followed up on regular intervals, while awaiting their culture.

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Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis.

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Computed tomography (CT) radiation dose management tools should be used whenever possible, particularly with the increasing demand for acquiring CT studies. Herein, we aim to assess the advantages and challenges faced with implementing two CT dose management tools. A second aim was to highlight CT examinations exceeding dose notification values (NVs) and define the common set of causes. A total of 13,037 CT examinations collected over a six-month period, were evaluated, using two independent CT dose management tools, a CT Dose Notification prospective-view tool (PVT) following CT Dose Check standards and a retrospective statistical-based view tool (RSVT). Dose NVs were set to twice the Local Diagnostic Reference Levels. There was a significant discrepancy between dose NV counts registered with prospective (4.15%) and retrospective (7.98%) tools using T-Test. A core difference is the dose configuration setup, with PVT and RSVT being dose per series and whole study, respectively. Both prospective and retrospective dose management tools were equally useful despite their technical difference. Configuring the CT prospective dose notification check tool using NVs that is based on DRLs has limitations, and one needs to establish dose NVs per series to overcome this technical hurdle. Technical challenges make the implementation of CT Dose Check standards puzzling.

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In this work, an HB pencil electrode (HBPE) was electrochemically modified by amino acids (AAs) glycine (GLY) and aspartic acid (ASA) and designated as GLY-HB and ASA-HB electrodes. They were used in the detection of dihydroxybenzene isomers (DHBIs) such as hydroquinone (HQ), catechol (CC), and resorcinol (RS), by cyclic voltammetry (CV), and by differential pulse voltammetry. HBPE was characterized by scanning electron microscopy and energy-dispersive X-ray spectroscopy. These three electrodes showed a linear relationship of current with concentration of DHBIs, and the electrochemical processes were diffusion controlled in all cases. In simultaneous detection, the limit of detection, based on signal-to-noise ratio (S/N = 3), for HQ, CC, and RS was 12.473, 16.132, and 25.25 µM, respectively, at bare HBPE; 5.498, 7.119, and 14.794 µM, respectively, at GLY-HB; and 22.459, 25.478, and 38.303 µM, respectively, at ASA-HB. The sensitivity for HQ, CC, and RS was 470.481, 363.781, and 232.416 µA/mM/cm2, respectively, at bare HBPE; 364.785, 282.712, and 135.560 µA/mM/cm2, respectively, at GLY-HB; and 374.483, 330.108, and 219.574, respectively, at ASA-HB. The interference studies clarified the suitability and reliability of the electrodes for the detection of HQ, CC, and RS in an environmental system. Real sample analysis was done using tap water, and the proposed electrodes expressed recovery with high reproducibility. Meanwhile, these three electrodes have excellent sensitivity and selectivity, which can be used as a promising technique for the detection of DHBIs simultaneously.

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Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity, using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset (H-Activity), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.

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The brain tumor is one of the deadliest cancerous diseases and its severity has turned it to the leading cause of cancer related mortality. The treatment procedure of the brain tumor depends on the type, location and size of the tumor. Relying solely on human inspection for precise categorization can lead to inevitably dangerous situation. This manual diagnosis process can be improved and accelerated through an automated Computer Aided Diagnosis (CADx) system. In this article, a novel approach using two-stage feature ensemble of deep Convolutional Neural Networks (CNN) is proposed for precise and automatic classification of brain tumors. Three unique Magnetic Resonance Imaging (MRI) datasets and a dataset merging all the unique datasets are considered. The datasets contain three types of brain tumor (meningioma, glioma, pituitary) and normal brain images. From five pre-trained models and a proposed CNN model, the best models are chosen and concatenated in two stages for feature extraction. The best classifier is also chosen among five different classifiers based on accuracy. From the extracted features, most substantial features are selected using Principal Component Analysis (PCA) and fed into the classifier. The robustness of the proposed two stage ensemble model is analyzed using several performance metrics and three different experiments. Through the prominent performance, the proposed model is able to outperform other existing models attaining an average accuracy of 99.13% by optimization of the developed algorithms. Here, the individual accuracy for Dataset 1, Dataset 2, Dataset 3, and Merged Dataset is 99.67%, 98.16%, 99.76%, and 98.96% respectively. Finally a User Interface (UI) is created using the proposed model for real time validation.

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The fiber architecture can significantly influence the rate of impregnation of a resin in making composites and the load-bearing ability of individual fibers on testing of the loading directions. Moreover, achieving the maximum mechanical performance of a natural fiber composite selection of yarn liner density and optimization of fabric structure and further modification of the composites remains a great challenge for the composite research community. In this study, a number of jute-based woven derivatives (plain, 2/1 twill, 3/1 twill, zigzag based on a 2/2 twill, and diamond based on a 2/2 twill) have been constructed from similar linear densities of yarn. The effect of the fabric architecture and further modification of optimized composites by applying γ-radiation is also explained in this study. The experimental results show a 54% increase in tensile strength, a 75% increase in tensile modulus, a 69% increase in flexural strength, a 124% increase in flexural modulus, and 64% increase in impact strength of twill (3/1) structured jute fiber polyester composites in comparison to other plain and twill structured composites. A further mechanical improvement of around 20-30% is possible for the optimized twill structured composites by applying γ-radiation on the composites. An FTIR, TGA, and SEM study confirms the chemical, thermal, and fractographic changes after applying the modification of composites.

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Lung cancer, also known as pulmonary cancer, is one of the deadliest cancers, but yet curable if detected at the early stage. At present, the ambiguous features of the lung cancer nodule make the computer-aided automatic diagnosis a challenging task. To alleviate this, we present LungNet, a novel hybrid deep-convolutional neural network-based model, trained with CT scan and wearable sensor-based medical IoT (MIoT) data. LungNet consists of a unique 22-layers Convolutional Neural Network (CNN), which combines latent features that are learned from CT scan images and MIoT data to enhance the diagnostic accuracy of the system. Operated from a centralized server, the network has been trained with a balanced dataset having 525,000 images that can classify lung cancer into five classes with high accuracy (96.81%) and low false positive rate (3.35%), outperforming similar CNN-based classifiers. Moreover, it classifies the stage-1 and stage-2 lung cancers into 1A, 1B, 2A and 2B sub-classes with 91.6% accuracy and false positive rate of 7.25%. High predictive capability accompanied with sub-stage classification renders LungNet as a promising prospect in developing CNN-based automatic lung cancer diagnosis systems.

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Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.

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INTRODUCTION: A wide range of study designs have been utilized in evaluations of home telemonitoring and these studies have produced conflicting outcomes over the years. While some of the research has shown that telemonitoring is beneficial in reducing all-cause mortality, hospital admission, length of stay in hospital and emergency room visits, other studies have not shown such benefits. This study, therefore, aims to examine several home telemonitoring study designs and the influence of study design on study outcomes. METHOD: Articles were obtained by searching PubMed database with the term heart failure combined with the following terms: telemonitoring, telehealth, home monitoring, and remote monitoring. Searches were limited to randomized controlled trial conducted between year January 1, 2000 and February 6, 2021. The characteristics of the study designs and study outcomes were extracted and analyzed. RESULT: Our review of 34 randomized controlled trials of heart failure telemonitoring did not show any significant influence of study design on reduction in number of hospitalizations and/or decrease in mortality. Studies that were done outside North America (USA and Canada) and studies that selected patients at high risk of re-hospitalization were more likely to result in decreased hospitalization and/or mortality, though this was not statistically significant. All the studies that met our inclusion criteria were from high-income countries and only one study enrolled patients at high risk of re-hospitalization. CONCLUSION: There is a need for more studies to understand why telemonitoring studies in Europe were more likely to reduce hospital admission and mortality compared to those in North America. There is also a need for more studies on the effect of telemonitoring in patients at high risk of hospital readmission.

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BACKGROUND: The emergence and spread of antimicrobial resistance (AMR) pose a major threat to the effective treatment and control of typhoid fever. The ongoing outbreak of extensively drug-resistant Salmonella Typhi (S. Typhi) in Pakistan has left azithromycin as the only remaining broadly efficacious oral antimicrobial for typhoid in South Asia. Ominously, azithromycin-resistant S. Typhi organisms have been subsequently reported in Bangladesh, Pakistan, and Nepal. METHODS: Here, we aimed to understand the molecular basis of AMR in 66 S. Typhi organisms isolated in a cross-sectional study performed in a suburb of Chandigarh in Northern India using whole-genome sequencing and phylogenetic analysis. RESULTS: We identified 7 S. Typhi organisms with the R717Q mutation in the acrB gene that was recently found to confer resistance to azithromycin in Bangladesh. Six out of the seven azithromycin-resistant S. Typhi isolates also exhibited triple mutations in gyrA (S83F and D87N) and parC (S80I) genes and were resistant to ciprofloxacin. These contemporary ciprofloxacin/azithromycin-resistant isolates were phylogenetically distinct from each other and from those reported from Bangladesh, Pakistan, and Nepal. CONCLUSIONS: The independent emergence of azithromycin-resistant typhoid in Northern India reflects an emerging broader problem across South Asia and illustrates the urgent need for the introduction of typhoid conjugate vaccines in the region.

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BACKGROUND: The use of cardiac computed tomography angiography (CCTA) as a complementary diagnostic modality to echocardiography in patients with congenital heart diseases (CHDs) is expanding in low- and middle-income countries. The adoption of As Low As Reasonably Achievable techniques is not widespread, resulting in significant unintended radiation exposure, especially in children. Simple quality improvement measures geared toward reducing radiation dose can have a impact on patient safety in resource-limited centers in low- and middle-income countries. OBJECTIVES: To determine how a quality improvement initiative can reduce radiation exposure during CCTA in patients with CHD. METHODS: We designed a key driver -based quality initiative to reduce radiation dose during CCTA for CHD using protocol optimization, communication, and training and implementation as the drivers for intervention. Preintervention variables (radiation exposure, scanning protocols, and image quality) were collected from September 2012 to July 2016 and compared with variables in the postimplementation phase (February 2017 to July 2017). We compared quantitative and categorical variables using the chi-square test. Linear regression analysis was used to evaluate the effect of various factors on radiation dose. RESULTS: We documented a reduction in the effective dose in the postintervention versus preintervention phase (mean, 2.0 versus 21 mSv, P < 0.0001, respectively). Linear regression showed that the optimal organizational levels are associated with the same reduction in radiation. This finding shows that the time factor translates a combination of organizational and technical factors that contributed to the reduction in radiations. CONCLUSIONS: Our project showed a reduction in CCTA-associated radiation exposure.