Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.

Background: Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods: This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results: Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions: MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

Classification of low- and high-grade gliomas using radiomic analysis of multiple sequences of MRI brain.

Background: Gliomas are frequent tumors of brain parenchyma, which have histology similar to that of glial cells. Accurate glioma grading is required for determining clinical management. The background of this study is to investigate the accuracy of magnetic resonance imaging (MRI)-based radiomic features extracted from multiple MRI sequences in differentiating low and high-grade gliomas. Materials and Methods: This is a retrospective study. It includes two groups. Group A includes patients with confirmed histopathological diagnosis of low (23) and high-grade (58) gliomas from 2012 to 2020 were included. The MRI images were acquired using a Signa HDxt 1.5 Tesla MRI (GE Healthcare, Milwaukee, USA). Group B includes an external test set consisting of low- (20) and high-grade gliomas (20) obtained from The Cancer Genome Atlas (TCGA). The radiomic features were extracted from axial T2, apparent diffusion coefficient map, axial T2 fluid-attenuated inversion recovery, and axial T1 post-contrast sequences for both the groups. The Mann - Whitney U test was performed to assess the significant radiomic features useful for distinguishing the glioma grades for Group A. To determine the accuracy of radiomic features for differentiating gliomas, AUC was calculated from receiver operating characteristic curve analysis for both groups. Results: Our study noticed in Group A, fourteen MRI-based radiomic features from four MRI sequences showed a significant difference (p < 0.001) in differentiating gliomas. In Group A, we noticed T1 post-contrast radiomic features such as first-order variance (FOV) (sensitivity - 94.56%, specificity - 97.51%, AUC - 0.969) and GLRLM long-run gray-level emphasis (sensitivity - 97.54%), specificity - 96.53%, AUC - 0.972) had the highest discriminative power for distinguishing the histological subtypes of gliomas. Our study noticed no statistical significant difference between ROC curves of significant radiomic features for both groups. In Group B, the T1 post-contrast radiomic features such as FOV (AUC-0.933) and GLRLM long-run gray-level emphasis (AUC-0.981) had also shown high discriminative power for distinguishing the gliomas. Conclusion: Our study concludes that MRI-based radiomic features extracted from multiple MRI sequences provide a non-invasive diagnosis of low- and high-grade gliomas and can be implemented in clinical settings for diagnosing the glioma grades.

Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.

Background: Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology. There is scarcity of research that how ST affects variability of RF in LC. The present study helps in identifying the specific RF categories affected by variations in ST and provides valuable insights for researchers and clinicians working with RF in the field of LC.Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors. Methods: This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm STwas reconstructed retrospectively. RF were extracted from the CECT thorax images of both ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin's Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses. Results: Out of 107 RF, 66 (61.6%) exhibited a statistically significant distinction (p<0.05) when comparing two ST and while 41 (38.3%) RF did not show significant distinction (p>0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility. Conclusions: The ST had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.

Correction: Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol.

Correction for 'Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol' by Saikiran Pendem et al., Dalton Trans., 2019, 48, 8791-8802, https://doi.org/10.1039/C9DT01190K.

Diagnostic reference levels for computed tomography examinations in pediatric population - A systematic review.

Computed tomography (CT) has vital role in diagnosis of various pathologies using cross sectional images. Besides the advantages of CT in pediatric radiology, radiation dose has a significant adverse effect as children are more vulnerable than adults. Establishing Diagnostic Reference levels (DRLs) will determine unusual increase in radiation doses and therefore helps in optimizing the radiation dose by maintaining optimum diagnostic image quality. The objective of the review is to explore the literature on DRLs in pediatric CT examinations and techniques that have been used to establish them. Detailed search was done in PubMed-Medline, Scopus CINAHL, Web of Science, and the Cochrane Library databases to find studies that have established DRLs for pediatric CT examinations. The Preferred Reporting Items for Systematic Review and Meta-Analyses methodology was used to assess the relevant articles. The articles which assessed DRLs in pediatric CT examinations were included. A total of 501 articles were identified, of which 21 articles were included after a detailed screening process. Our review showed increased in pediatric patient dose surveys across the world and also increased in awareness for establishing DRLS among pediatric CT examinations. The review also demonstrated wide variation in DRLs and also deviation in the scanning techniques, protocols used and categorization methods used for establishing DRLs. As the pediatric population is more sensitive to radiation, the current review emphasizes the need for optimization of protocols and international standardization for establishing DRLs to facilitate a more feasible way of comparison of dose globally across CT sites.

Metal-organic-framework derived Co-Pd bond is preferred over Fe-Pd for reductive upgrading of furfural to tetrahydrofurfuryl alcohol.

Combined noble-transition metal catalysts have been used to produce a wide range of important non-petroleum-based chemicals from biomass-derived furfural (as a platform molecule) and have garnered colossal research interest due to the urgent demand for sustainable and clean fuels. Herein, we report the palladium-modified metal-organic-framework (MOF) assisted preparation of PdCo3O4 and PdFe3O4 nanoparticles encapsulated in a graphitic N-doped carbon (NC) matrix via facile in situ thermolysis. This provides a change in selectivity with superior catalytic activity for the reductive upgrading of biomass-derived furfural (FA). Under the optimized reaction conditions, the newly designed PdCo3O4@NC catalyst exhibited highly efficient catalytic performance in the hydrogenation of furfural, providing 100% furfural conversion with 95% yield of tetrahydrofurfuryl alcohol (THFAL). In contrast, the as-synthesized Pd-Fe3O4@NC afforded a THFAL yield of 70% after an 8 h reaction with four consecutive recycling tests. Based on different characterization data (XPS, H2-TPR) for nanohybrids, we can conclude that the presence of PdCo-Nx active sites, and the multiple synergistic effects between Co3O4 and Pd(ii), Co3O4 and Pd0, as well as the presence of N in the carbonaceous matrix, are responsible for the superior catalytic activity and improved catalyst stability. Our strategy provides a facile design and synthesis process for a noble-transition metal alloy as a superior biomass refining, robust catalyst via noble metal modified MOFs as precursors.

Interface Engineering of Graphene-Supported Cu Nanoparticles Encapsulated by Mesoporous Silica for Size-Dependent Catalytic Oxidative Coupling of Aromatic Amines.

In this study, graphene nanosheet-supported ultrafine Cu nanoparticles (NPs) encapsulated with thin mesoporous silica (Cu-GO@m-SiO2) materials are fabricated with particle sizes ranging from 60 to 7.8 nm and are systematically investigated for the oxidative coupling of amines to produce biologically and pharmaceutically important imine derivatives. Catalytic activity remarkably increased from 76.5% conversion of benzyl amine for 60 nm NPs to 99.3% conversion and exclusive selectivity of N-benzylidene-1-phenylmethanamine for 7.8 nm NPs. The superior catalytic performance along with the outstanding catalyst stability of newly designed catalysts are attributed to the easy diffusion of organic molecules through the porous channel of mesoporous SiO2 layers, which not only restricts the restacking of the graphene nanosheets but also prevents the sintering and leaching of metal NPs to an extreme extent through the nanoconfinement effect. Density functional theory calculations were performed to shed light on the reaction mechanism and to give insight into the trend of catalytic activity observed. The computed activation barriers of all elementary steps are very high on terrace Cu(111) sites, which dominate the large-sized Cu NPs, but are significantly lower on step sites, which are presented in higher density on smaller-sized Cu NPs and could explain the higher activity of smaller Cu-GO@m-SiO2 samples. In particular, the activation barrier for the elementary coupling reaction is reduced from 139 kJ/mol on flat terrace Cu(111) sites to the feasible value of 94 kJ/mol at step sites, demonstrating the crucial role of the step site in facilitating the formation of secondary imine products.