Improving Image Quality and Diagnostic Performance of CCTA in Patients with Challenging Heart Rate Conditions using a Deep Learning-based Motion Correction Algorithm.

Objective: Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA.

Materials And Methods: This retrospective study included 240 patients (mean HR: 88.

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

Objective: This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA).

Methods: A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA.

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Purpose: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological and functional evaluation.

Materials And Methods: The acquired image data of 53 CCTA cases, where the patient heart rate (HR) was ≥75 bpm, were reconstructed at 0, ±2, ±4, ±6, and ±8% deviations from each optimal systolic phase, with and without the MCA, yielding a total of 954 images (53 cases × 9 phases × 2 reconstructions). The overall image quality and diagnostic confidence were graded by two radiologists using a 5-point scale, with scores ≥3 being deemed clinically interpretable.

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors.

Purpose: To develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs).

Methods: This retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared.

Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision.

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions.

[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography].

Objective: To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (CTA).

Methods: A total of 33 patients who underwent aortic CTA were prospectively enrolled. Based on the time of their follow-up examinations, the imaging data were divided into Group A and Group B, with Group A being the control group (100 kV, 0.

Image quality assessment of artificial intelligence iterative reconstruction for low dose aortic CTA: A feasibility study of 70 kVp and reduced contrast medium volume.

Purpose: To investigate the image quality and feasibility of a novel artificial intelligence iterative reconstruction (AIIR) algorithm for aortic computer tomography angiography (CTA) with a low radiation dose and contrast material (CM) dosage protocol in comparison with hybrid iterative reconstruction (HIR) algorithm for standard-of-care aortic CTA.

Methods: Fifty consecutive patients (mean age 58 ± 14 years, mean BMI 24.5 ± 4.

Diagnostic performance of corrected FFR metrics to predict hemodynamically significant coronary artery stenosis.

Objectives: To determine the diagnostic performance of the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA) (FFR) difference across the lesion (ΔFFR) or the vessel (ΔFFR) and the gradient of FFR for the identification of hemodynamically significant coronary stenosis.

Methods: From June 2016 to December 2018, 73 patients suspected of having coronary artery disease who underwent CCTA followed invasive coronary angiography (ICA) within 1 month were retrospectively included. ΔFFR, ΔFFR, and FFR gradient were calculated.

Realization of Quasi-Omnidirectional Solar Cells with Superior Electrical Performance by All-Solution-Processed Si Nanopyramids.

Large-scale (156 mm × 156 mm) quasi-omnidirectional solar cells are successfully realized and featured by keeping high cell performance over broad incident angles (θ), via employing Si nanopyramids (SiNPs) as surface texture. SiNPs are produced by the proposed metal-assisted alkaline etching method, which is an all-solution-processed method and highly simple together with cost-effective. Interestingly, compared to the conventional Si micropyramids (SiMPs)-textured solar cells, the SiNPs-textured solar cells possess lower carrier recombination and thus superior electrical performances, showing notable distinctions from other Si nanostructures-textured solar cells.

Controllable nanoscale inverted pyramids for highly efficient quasi-omnidirectional crystalline silicon solar cells.

Nanoscale inverted pyramid structures (NIPs) have always been regarded as one of the paramount light management schemes to achieve extraordinary performance in various devices, especially in solar cells, due to their outstanding antireflection ability with relative lower surface enhancement ratio. However, current approaches to fabricating NIPs are complicated and not cost-effective for massive cell production in the photovoltaic industry. Here, controllable NIPs are fabricated on crystalline silicon (c-Si) wafers by Ag-catalyzed chemical etching and alkaline modification, which is a preferable all-solution-processed method.

Superior broadband antireflection from buried Mie resonator arrays for high-efficiency photovoltaics.

Establishing reliable and efficient antireflection structures is of crucial importance for realizing high-performance optoelectronic devices such as solar cells. In this study, we provide a design guideline for buried Mie resonator arrays, which is composed of silicon nanostructures atop a silicon substrate and buried by a dielectric film, to attain a superior antireflection effect over a broadband spectral range by gaining entirely new discoveries of their antireflection behaviors. We find that the buried Mie resonator arrays mainly play a role as a transparent antireflection structure and their antireflection effect is insensitive to the nanostructure height when higher than 150 nm, which are of prominent significance for photovoltaic applications in the reduction of photoexcited carrier recombination.

A simple strategy for synthesizing highly luminescent carbon nanodots and application as effective down-shifting layers.

We propose a novel strategy to prepare highly luminescent carbon nanodots (C-dots) by employing a hydrothermal method with citric acid as the carbon source and ethylenediamine as the nitrogen source, together with adding moderate ammonia water (AW) to achieve both appropriate inner structure and excellent N passivation. The effect of pH value and AW amount on the luminescence properties has been thoroughly investigated. The photoluminescence quantum yield of the resultant C-dots reaches as high as 84.

High-efficiency nanostructured silicon solar cells on a large scale realized through the suppression of recombination channels.

Nanostructured silicon solar cells show great potential for new-generation photovoltaics due to their ability to approach ideal light-trapping. However, the nanofeatured morphology that brings about the optical benefits also introduces new recombination channels, and severe deterioration in the electrical performance even outweighs the gain in optics in most attempts. This Research News article aims to review the recent progress in the suppression of carrier recombination in silicon nanostructures, with the emphasis on the optimization of surface morphology and controllable nanostructure height and emitter doping concentration, as well as application of dielectric passivation coatings, providing design rules to realize high-efficiency nanostructured silicon solar cells on a large scale.