Imaging for aortic dissections and other acute aortic syndromes relies heavily on computed tomography (CT) scans. There is an ongoing need to educate providers and imaging specialists regarding the different protocols for CT scans and the heightened value of aortic protocol scans for acute aortic syndromes. Current dissection guidelines recommend the treatment for patients with acute aortic syndromes be performed at a high-volume center by a multidisciplinary team that includes an imaging specialist. MRI and echocardiography can provide additional information and possibly at lower radiation exposure compared to CT scans. All imaging modalities are evolving with new and future uses and capabilities.

Recent investigations into Low-Dose Computed Tomography (LDCT) reconstruction methods have brought Model-Based Data-Driven (MBDD) approaches to the forefront. One prominent architecture within MBDD entails the integration of Model-Based Iterative Reconstruction (MBIR) with Deep Learning (DL). While this approach offers the advantage of harnessing information from sinogram and image domains, it also reveals several deficiencies. First and foremost, the efficacy of DL methods within the realm of MBDD necessitates meticulous enhancement, as it directly impacts the computational cost and the quality of reconstructed images. Next, high computational costs and a high number of iterations limit the development of MBDD methods. Last but not least, CT reconstruction is sensitive to pixel accuracy, and the role of loss functions within DL methods is crucial for meeting this requirement.

This paper advances MBDD methods through three principal contributions. Firstly, we introduce an innovative Frequency Adjustment Network (FAN) that effectively adjusts both high and low-frequency components during the inference phase, resulting in substantial enhancements in reconstruction performance. Second, we develop the Momentum-based Frequency Adjustment Network (MFAN), which leverages momentum terms as an extrapolation strategy to facilitate the amplification of changes throughout successive iterations, culminating in a rapid convergence framework. Lastly, we delve into the visual properties of CT images and present a unique loss function named Focal Detail Loss (FDL). The FDL function preserves fine details throughout the training phase, significantly improving reconstruction quality.

Through a series of experiments validation on the AAPM-Mayo public dataset and real-world piglet datasets, the aforementioned three contributions demonstrated superior performance. MFAN achieved convergence in 10 iterations as an iteration method, faster than other methods. Ablation studies further highlight the advanced performance of each contribution.

This paper presents an MBDD-based LDCT reconstruction method using a momentum-based frequency adjustment network with a focal detail loss function. This approach significantly reduces the number of iterations required for convergence while achieving superior reconstruction results in visual and numerical analyses.

Since the quality of low dose CT (LDCT) images is often severely affected by noise and artifacts, it is very important to maintain high quality CT images while effectively reducing the radiation dose.

In recent years, the representation of diffusion models to produce high quality images and stable trainability has attracted wide attention. With the extension of the cold diffusion model to the classical diffusion model, its application has greater flexibility. Inspired by the cold diffusion model, we proposes a low dose CT image denoising method, called CECDM, based on the combination of edge enhancement and cold diffusion model. The LDCT image is taken as the end point (forward) of the diffusion process and the starting point (reverse) of the sampling process. Improved sobel operator and Convolution Block Attention Module are added to the network, and compound loss function is adopted.

The experimental results show that CECDM can effectively remove noise and artifacts from LDCT images while the inference time of a single image is reduced to 0.41 s.

Compared with the existing LDCT image post-processing methods, CECDM has a significant improvement in all indexes.

We evaluated the feasibility of reducing contrast agent and radiation dose in pediatric computed tomography pulmonary angiography (CTPA) while ensuring image quality.

In this prospective study, two readers assessed the computed tomography (CT) image quality (using a 5-point scale (1: undiagnosable and 5: excellent) and objective evaluation criteria (measuring CT and noise values of the left atrium and pulmonary trunk) of 116 patients who underwent pulmonary artery computed tomography angiography (CTA) from January 2023 to April 2024. independent sample t-test and Chi-square test were used to analyze and evaluate group differences.

Fifty-eight participants were enrolled in the study group (mean age, 6.86 years ± 2.74, 30 males) and fifty-eight participants were enrolled in the control group (mean age, 6.71 years ± 2.59, 22 males). The radiation dose was significantly decreased in the study group (study group, 3.01 ± 0.24 mGy, control group 3.77 ± 1.06 mGy, p < 0.001). Overall quality was higher in control group, but displaying ability of pulmonary artery trunk and branch was higher in study group (p < 0.001).

This study proved that a low-dose, low-contrast CTPA strategy could reduce radiation dosage by 50% and contrast agent by 20% while maintaining a satisfying image quality.

The aim of this study was to assess the number of foot/ankle computed tomography (CT) exams ordered per encounter for patients presenting to the emergency department (ED) with foot and ankle trauma over a 5-year period. Secondary aims included evaluating the positivity rate of foot/ankle CT exams and identifying factors associated with receiving a CT foot/ankle. This retrospective study analyzed data from a large urban Level-1 trauma center between 2016 and 2021. Patients were identified by charted chief complaints related to foot and ankle trauma. The primary outcome was the number of CT foot/ankle exams ordered per patient in a given period. A univariate chi-square analysis was conducted to evaluate differences in patient presentations and imaging rates across the study period. Over the 5-year span, there were 9,845 patient encounters, with a significant increase in CT foot/ankle orders from 2.4 % to 6.6 % (p < 0.001). The CT positivity rate, defined as CTs with positive findings, declined from 95.2 % in 2016 to 84.1 % in 2021 (p < 0.001). Black patients had lower odds of receiving CT scans compared to White patients, as did Medicare recipients compared to Medicaid recipients (p < 0.001). Factors such as age (OR: 1.02 per year), year of visit (OR: 2.66 for 2021), time of day (OR: 1.62 for evening arrivals), and arrival by EMS (OR: 5.60) were significantly associated with higher CT order rates. This study highlights a marked increase in CT utilization for foot and ankle trauma with a corresponding decline in the rate of positive findings. Further research is necessary to explore the reasons behind this trend and to identify potential workflow or protocol adjustments to improve imaging efficacy.

Photon-counting detector computed tomography (PCD-CT) can reduce radiation dose in paediatric lung imaging.

The aim of this study was to determine the lowest radiation dose maintaining adequate image quality for high-pitch lung imaging using a PCD-CT in a chest phantom replicating the characteristics of a 5-year-old child.

The phantom was imaged on a dual-source PCD-CT with five different volume CT dose indices (CTDIvol): 0.45 mGy, 0.30 mGy, 0.15 mGy, 0.07 mGy, and 0.01 mGy. Scans were acquired with Sn100 kV in standard and ultra-high resolution modes. Polychromatic images were reconstructed with a 1-mm slice thickness, lung kernel Bl60, without quantum iterative reconstruction and with quantum iterative reconstruction at strengths 2 and 4. Two paediatric radiologists rated reconstructions subjectively, defining adequate image quality as the visibility of small peripheral structures. Objective evaluation included global noise index and global signal-to-noise ratio index.

Exposure times were 0.42 s and 0.84 s for standard and ultra-high resolution modes, respectively. Subjective assessments showed no significant differences across scan modes or quantum iterative reconstruction strengths for both readers at all doses (all, P > 0.05). Scans at 0.07 mGy with quantum iterative reconstruction 4 were deemed to maintain adequate image quality at the lowest dose. Global noise index was always lower and global signal-to-noise ratio index always higher in ultra-high resolution compared with standard mode, underscoring noise reduction achieved via ultra-high resolution mode's small pixel effect.

PCD-CT enables high-pitch lung imaging while maintaining adequate image quality at a radiation dose as low as 0.07 mGy, with quantum iterative reconstruction 4, in a paediatric phantom representing a 5-year-old child.

Neural radiance field (NeRF) models have garnered significant attention for their impressive ability to synthesize high-quality novel scene views from posed 2D images. Recently, the MedNeRF algorithm was developed to render complete computed tomography (CT) projections from a single or a few x-ray images further. Despite this advancement, MedNeRF struggles with accurate pose reconstruction, crucial for radiologists during image analysis, leading to blurry geometry in the generated outputs.

Motivated by these challenges, our research aims to address MedNeRF's limitations in pose accuracy and image clarity. Specifically, we seek to improve the pose accuracy of reconstructed images and enhance the generated output's anatomical detail and quality.

We propose a novel pose-aware discriminator that estimates pose differences between generated and real patches, ensuring accurate poses and deeper anatomical structures in generated images. We enhance volumetric rendering from single-view x-rays by introducing a customized distortion adaptive loss function and present the HTDataset, a new dataset pair that better mimics machine-generated x-rays, offering clearer anatomical depictions with reduced noise.

Our method successfully renders images with correct poses and high fidelity, outperforming existing state-of-the-art methods. The results demonstrate superior performance in both qualitative and quantitative metrics.

The proposed approach addresses the pose reconstruction challenge in MedNeRF, enhances the anatomical detail, and reduces noise in generated images. The use of HTDataset and the innovative discriminator structure lead to significant improvements in the accuracy and quality of the rendered images, setting a new benchmark in the field.

Given the widespread use of medical imaging, evaluating the effectiveness of interventions to improve appropriateness is crucial for optimizing health care resources and patient outcomes.

To assess the effects of implementing a clinical decision support system (CDSS), the European Society of Radiology iGuide, on the appropriateness of the medical imaging ordering behavior of physicians.

A cluster randomized clinical trial with 26 departments at 3 German university hospitals acting as clusters, incorporating a before and after discontinued design. All imaging requests originating from physicians in the participating departments over a 2.5-year period were included (between December 2021 and June 2024).

All departments started without a CDSS and required structured clinical indication data entry and tracking of requested imaging. After randomization, 13 clusters (departments at hospitals) received the CDSS intervention (intervention clusters) and 13 clusters did not (control clusters). The CDSS intervention provided ordering physicians with information as to whether their imaging requests were appropriate, appropriate under certain conditions, or inappropriate; in addition, alternative diagnostic tests, including the corresponding appropriateness score, were suggested by the CDSS, after which physicians could choose to modify their imaging requests.

The primary outcome measure was the proportion of inappropriate imaging requests made per department. A difference-in-differences analysis was used to investigate changes in the proportion of inappropriate imaging requests between departments with vs those without the CDSS.

A total of 65 764 imaging requests were scored using the CDSS; 50.1% of imaging requests were for female patients and the mean patient age was 64 years (SD, 17.1 years). Prior to implementation of the CDSS, there were 21 625 imaging requests from the control clusters, 1367 (6.3%) of which were categorized as inappropriate; and there were 13 338 imaging requests from the intervention clusters, 1007 (7.6%) of which were categorized as inappropriate. After implementation of the CDSS, there were 10 055 imaging requests from the control clusters, 518 (5.2%) of which were categorized as inappropriate; and there were 7206 imaging requests from the intervention clusters, 461 (6.4%) of which were categorized as inappropriate. The intervention clusters showed a similar reduction (mean difference, -0.5% [99% CI, -2.4% to 0.4%]) in inappropriate imaging requests compared with the control clusters (mean difference, -1.8% [99% CI, -4.3% to -0.4%]) and there was a difference-in-differences value of 1.3 percentage points (99% CI, -2.0 to 1.8 percentage points; P = .69), which was not statistically significant.

The CDSS did not reduce the number of inappropriate imaging requests ordered by physicians in academic hospital settings.

ClinicalTrials.gov Identifier: NCT05490290.

Introduction and Objective: The laser direct alignment radiation reduction technique (DARRT) presents a novel approach integrating pulsed low-dose fluoroscopy, ultrasound, direct endoscopic visualization, and laser targeting to minimize fluoroscopy exposure during percutaneous nephrolithotomy (PCNL). This study aims to evaluate the safety and efficacy of laser DARRT in comparison with traditional fluoroscopic PCNL access. Methods: A retrospective analysis was conducted on patients who underwent PCNL. Patients with pre-existing nephrostomy tubes for access and those who underwent solely ultrasound-guided access were excluded from the study. The primary outcomes of the study were comparing fluoroscopy time used for renal access and total fluoroscopy time between the two groups. Secondary outcomes included relative stone-free rates (SFR) defined as ≤4 mm fragments on postoperative computed tomography scan and complication rates. Continuous variables were compared using an independent-sample t test, whereas categorical variables were compared using the chi-square test, with significance set at p < 0.05. Results: A total of 292 patients were eligible for the study. The laser DARRT reduced both access fluoroscopy time (10.8 vs 551.7 seconds; p < 0.001) and total fluoroscopy time (21.8 vs 597.7 seconds; p < 0.001). The relative SFR was significantly higher in the laser DARRT group compared with the conventional group (84.1% vs 64.1%; p < 0.001). There was no significant difference in complication rates between the two groups (p > 0.05). Conclusions: The laser DARRT led to a >95% reduction in access and total fluoroscopy times. By combining the advantages of fluoroscopy, ultrasound, endoscopic vision, and laser guidance, this technique represents a promising option for improving outcomes and minimizing radiation-related risk.

The European Cystic Fibrosis Society-Clinical Trials Network (ECFS-CTN) herein proposes guidance for the use of chest CT-scans for the regular monitoring of lung disease in CF. Statements were completed in a 3-step process: the questions were identified via an anonymous online survey, followed by a comprehensive literature search, and a final Delphi process. The guidance recommends the use of ultra-low dose CT scans (effective radiation dose, 0.08 mSv; equivalent to 2 to 4 chest X-rays), tracking of patients' cumulative radiation and effective communication strategies using "de-medicalized" information for shared decision making. Chest CT scans (with lung volume monitoring) are not recommended systematically in both children and adults. Ultimate responsibility for justifying a chest CT scan lies with the individual professionals directly involved, the final decision being influenced by indications, costs, expertise, available material, resources and/or the patient's values, as well as possible impact on treatment modalities.

A clinical prediction rule has been published by the Pediatric Emergency Care Applied Research Network (PECARN) to identify children at very low risk of intra-abdominal injury requiring acute intervention (IAI-intervention) following blunt abdominal trauma in which computed tomography scan of the abdomen could be avoided. This study aims to assess the external validity of the PECARN prediction rule for IAI-intervention and determine its accuracy for identifying all patients with intra-abdominal injuries following blunt abdominal trauma, including those who did not require acute interventions.

Data were collected prospectively from 14 trauma centers for 2,188 children younger than 16 years who presented following blunt abdominal trauma over a 1-year period. We then retrospectively applied the PECARN prediction rule to this new cohort to risk stratify the patients for intra-abdominal injury (IAI) and IAI-intervention.

A total of 2,188 children with a mean (SD) age of 7.8 (4.6) years were included. The PECARN prediction rule identified 60 of 62 patients with IAI-intervention with a sensitivity of 96.8%, specificity of 46.6%, and a negative predictive value (NPV) of 99.8%. The PECARN prediction rule identified 227 of 261 patients with any IAI with a sensitivity of 86.6%, specificity of 49.7%, and an NPV of 96.5%. The most missed injury by the PECARN rule was liver laceration (n = 22). In addition, 34 of 35 patients with an IAI not predicted by the PECARN rule had an abnormal laboratory value or x-ray finding.

Although the PECARN prediction rule has excellent NPV for identifying patients with intra-abdominal injuries needing an acute intervention, it fails to detect some children with clinically impactful intra-abdominal injuries that required additional management. The addition of trauma bay laboratories and chest x-ray should be used to predict patients at very low risk for all IAI.

Therapeutic/Care Management; Level II.

In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plethora of LDCT denoising methods have been proposed. Among them, deep learning (DL) approaches have emerged as the most effective, due to their robust feature extraction capabilities. Yet, the prevalent use of supervised training paradigms is often impractical due to the challenges in acquiring low-dose and normal-dose CT pairs in clinical settings. Consequently, unsupervised and self-supervised deep learning methods have been introduced for LDCT denoising, showing considerable potential for clinical applications. These methods' efficacy hinges on training strategies. Notably, there appears to be no comprehensive reviews of these strategies. Our review aims to address this gap, offering insights and guidance for researchers and practitioners. Based on training strategies, we categorize the LDCT methods into six groups: (i) cycle consistency-based, (ii) score matching-based, (iii) statistical characteristics of noise-based, (iv) similarity-based, (v) LDCT synthesis model-based, and (vi) hybrid methods. For each category, we delve into the theoretical underpinnings, training strategies, strengths, and limitations. In addition, we also summarize the open source codes of the reviewed methods. Finally, the review concludes with a discussion on open issues and future research directions.

Advancements in tomographic medical imaging have revolutionized diagnostics and treatment monitoring by offering detailed 3D visualization of internal structures. Despite the significant value of computed tomography (CT), challenges such as high radiation dosage and cost barriers limit its accessibility, especially in low- and middle-income countries. Recognizing the potential of radiographic imaging in reconstructing CT images, this scoping review aims to explore the emerging field of synthesizing 3D CT-like images from 2D radiographs by examining the current methodologies.

A scoping review was carried out following PRISMA-SR guidelines. Eligibility criteria for the articles included full-text articles published up to September 9, 2024, studying methodologies for the synthesis of 3D CT images from 2D biplanar or four-projection x-ray images. Eligible articles were sourced from PubMed MEDLINE, Embase, and arXiv.

76 studies were included. The majority (50.8 %, n = 30) were published between 2010 and 2020 (38.2 %, n = 29) and from 2020 onwards (36.8 %, n = 28), with European (40.8 %, n = 31), North American (26.3 %, n = 20), and Asian (32.9 %, n = 25) institutions being primary contributors. Anatomical regions varied, with 17.1 % (n = 13) of studies not using clinical data. Further, studies focused on the chest (25 %, n = 19), spine and vertebrae (17.1 %, n = 13), coronary arteries (10.5 %, n = 8), and cranial structures (10.5 %, n = 8), among other anatomical regions. Convolutional neural networks (CNN) (19.7 %, n = 15), generative adversarial networks (21.1 %, n = 16) and statistical shape models (15.8 %, n = 12) emerged as the most applied methodologies. A limited number of studies included explored the use of conditional diffusion models, iterative reconstruction algorithms, statistical shape models, and digital tomosynthesis.

This scoping review summarizes current strategies and challenges in synthetic imaging generation. The development of 3D CT-like imaging from 2D radiographs could reduce radiation risk while simultaneously addressing financial and logistical obstacles that impede global access to CT imaging. Despite initial promising results, the field encounters challenges with varied methodologies and frequent lack of proper validation, requiring further research to define synthetic imaging's clinical role.

Computed tomography (CT) is continuously becoming a valuable diagnostic technique in clinical practice. However, the radiation dose exposure in the CT scanning process is a public health concern. Within medical diagnoses, mitigating the radiation risk to patients can be achieved by reducing the radiation dose through adjustments in tube current and/or the number of projections. Nevertheless, dose reduction introduces additional noise and artifacts, which have extremely detrimental effects on clinical diagnosis and subsequent analysis. In recent years, the feasibility of applying deep learning methods to low-dose CT (LDCT) imaging has been demonstrated, leading to significant achievements. This article proposes a dual-domain joint optimization LDCT imaging framework (termed DDoCT) which uses noisy sparse-view projection to reconstruct high-performance CT images with joint optimization in projection and image domains. The proposed method not only addresses the noise introduced by reducing tube current, but also pays special attention to issues such as streak artifacts caused by a reduction in the number of projections, enhancing the applicability of DDoCT in practical fast LDCT imaging environments. Experimental results have demonstrated that DDoCT has made significant progress in reducing noise and streak artifacts and enhancing the contrast and clarity of the images.

Nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs) are rare tumors with heterogeneous biology. Radiology and serum biomarkers are used for postresection surveillance; however, no universally established protocol exists. Serum chromogranin A (CgA) concentration is elevated in NF-PNET and generally correlates with burden of disease; many CgA studies include mixed gastrointestinal and pancreatic populations. We sought to review the NF-PNET literature with focus on postresection surveillance and hypothesized that CgA is useful for surveillance after NF-PNET resection.

Comprehensive English literature review by PICO criteria (P, human NF-PNET patients; I, pancreatectomy; C, none; O, CgA correlation with disease recurrence).

Four studies yielded granular data for resected NF-PNET patients. From 333 patients, 113 recurred and 110 (97%) had elevated CgA. Additional 7 studies with mixed gastro-entero-pancreatic NET included 269 NF-PNET patients. In these patients, CgA uniformly predicted disease extent.

Serum CgA correlates with disease extent in NF-PNET and is useful for surveillance after resection of NF-PNET.

Sparse-view CT shortens scan time and reduces radiation dose but results in severe streak artifacts due to insufficient sampling data. Deep learning methods can now suppress these artifacts and improve image quality in sparse-view CT reconstruction.

The quality of sparse-view CT reconstructed images can still be improved. Additionally, the interpretability of deep learning-based optimization methods for these reconstruction images is lacking, and the role of different network layers in artifact removal requires further study. Moreover, the optimization capability of these methods for reconstruction images from various sparse views needs enhancement. This study aims to improve the network's optimization ability for sparse-view reconstructed images, enhance interpretability, and boost generalization by establishing multiple network structures and datasets.

In this paper, we developed a sparse-view CT reconstruction images improvement network (SRII-Net) based on U-Net. We added a copy pathway in the network and designed a residual image output block to boost the network's performance. Multiple networks with different connectivity structures were established using SRII-Net to analyze the contribution of each layer to artifact removal, improving the network's interpretability. Additionally, we created multiple datasets with reconstructed images of various sampling views to train and test the proposed network, investigating how these datasets from different sampling views affect the network's generalization ability.

The results show that the proposed method outperforms current networks, with significant improvements in metrics like PSNR and SSIM. Image optimization time is at the millisecond level. By comparing the performance of different network structures, we've identified the impact of various hierarchical structures. The image detail information learned by shallow layers and the high-level abstract feature information learned by deep layers play a crucial role in optimizing sparse-view CT reconstruction images. Training the network with multiple mixed datasets revealed that, under a certain amount of data, selecting the appropriate categories of sampling views and their corresponding samples can effectively enhance the network's optimization ability for reconstructing images with different sampling views.

The network in this paper effectively suppresses artifacts in reconstructed images with different sparse views, improving generalization. We have also created diverse network structures and datasets to deepen the understanding of artifact removal in deep learning networks, offering insights for noise reduction and image enhancement in other imaging methods.

Purpose To evaluate the accuracy of chest ultra-low-dose CT (ULDCT) as compared with normal-dose CT in the evaluation of pneumonia in individuals who are immunocompromised. Materials and Methods This prospective study included 54 adults who were immunocompromised (median age, 62 years [IQR, 47.75-69.25 years]; 34 [63%] male participants) referred for a chest CT scan between September 2020 and December 2022 to evaluate for pneumonia. Each participant underwent two scans: normal-dose CT (120 kVp and automatic current modulation) and ULDCT (100 kVp and constant current of 10 mA). ULDCT images underwent a postprocessing procedure using an artificial intelligence algorithm to reduce image noise. Two radiologists, blinded to all clinical information, examined the images obtained from the three methods (normal-dose CT, ULDCT, and denoised ULDCT) for the presence of pneumonia and associated findings. The normal-dose CT was used as the reference standard, and sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. Results The median effective radiation dose of ULDCT scans (0.12 mSV) was 1.95% of that of the normal-dose CT (6.15 mSV). Ten of the 54 participants were correctly identified as having no pneumonia, with similar accuracy between denoised ULDCT and ULDCT (100% vs 96%-98%, respectively). Both methods allowed for detection of pneumonia and features associated with invasive fungal pneumonia, but accuracy was slightly better with denoised ULDCT (accuracy, 100% vs 91%-98%). Fine details were better visualized in denoised ULDCT images: tree-in-bud pattern (accuracy, 93% vs 78%-80%), interlobular septal thickening (accuracy, 78%-83% vs 61%-67%), and intralobular septal thickening (accuracy, 85%-87% vs 0%). Conclusion Denoised ULDCT imaging showed better accuracy than ULDCT in identifying lungs with or without pneumonia in individuals who were immunocompromised. Keywords: CT, Pulmonary, Lung, Infection, Technology Assessment Supplemental material is available for this article. © RSNA, 2025.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

In pediatric patients, minimizing radiation and contrast media exposure without compromising diagnostic accuracy is paramount. Double low protocol, which utilizes a low dose contrast concentration and low tube voltage, could be a safer alternative. We compare diagnostic efficacy of double low protocol (Group A, 240 mgI/ml + 80 kVp) with conventional protocol (Group B, 350 mgI/ml + 120 kVp) in pediatric patients (< 10 years) presenting with abdominal pain and suspected acute appendicitis.

This retrospective study included 121 pediatric patients who underwent enhanced abdominal CT between January 2019 and February 2023: 62 with Group A and 59 with Group B. We compared radiation dose, iodine load, and quantitative image quality parameters. Two radiologists independently assessed diagnostic image quality on a 5-point scale, visualization of the appendix, and diagnostic performance for acute appendicitis and its complications.

There were no significant differences in mean age (7.6 ± 2.0 vs. 7.6 ± 2.1, p = 0.956), body weight (31.4 ± 11.2 kg vs. 31.7 ± 11.4 kg, p = 0.972), and contrast media volume used (59.3 ± 21.0 ml vs. 65.0 ± 20.0 ml, p = 135) between the two groups. However, effective dose and iodine load used were significantly lower in Group A compared to Group B (2.7 ± 1.1 mSv vs. 4.3 ± 1.5 mSv and vs. 12.7 ± 4.6gI vs.18.6 ± 6.7gI, all p < 0.001). Although diagnostic image quality, noise and signal-to-noise ratio were significantly lower in Group A, visualization of the appendix (p = 0.853) and diagnostic accuracy for appendicitis were comparable between the two groups (98.4% vs. 94.9%, p = 0.284).

The double low protocol offers an effective alternative for evaluating pediatric patients requiring enhanced abdomen CT, achieving comparable diagnostic performance while significantly reducing radiation dose. We believe that our findings support safer CT acquisition practices for pediatric patients requiring enhanced CT imaging.

Nanomaterials offer a promising approach to mitigating radiation-induced oxidative stress by scavenging reactive oxygen species (ROS). However, developing a nanomaterial that provides protection across a wide range of radiation conditions is challenging due to the photoelectric effects linked to the atomic number (Z) of the materials. Quantum dots (QDs) in a composite system, owing to their small size and when used at low concentrations, minimize photoelectric effects and secondary electron generation. In this study, cerium oxide (CeO2) QDs were combined with low-Z yttrium oxide (Y2O3) to create a nanocomposite (NC) (henceforth CeO2 QDs-Y2O3) that exploits the synergistic effects of both materials, providing protection across a broader spectrum of radiation. CeO2 QDs-Y2O3 demonstrated superior ROS scavenging than individual CeO2 and Y2O3 under nonradiative conditions, particularly for hydroxyl radicals (•OH) and hydrogen peroxide (H2O2), two primary ROS generated under radiation. This improved performance, due to increased oxygen vacancies and a higher Ce3+/Ce4+ ratio, indicates that these properties could help protect cells from oxidative stress during radiation exposure. Radioprotection analysis using the linear-quadratic (LQ) model revealed that the NC provided effective protection at both 150 kVp and 10 MV radiation energies. At 150 kVp, the obtained protection enhancement ratio (PER) values at 10% cell survival for CeO2 QDs-Y2O3, Y2O3, and CeO2 were 1.07, 1.16, and 0.89, respectively, suggesting that the radioprotection afforded by Y2O3 in the NC outweighed the radiosensitization of the encrusted CeO2 QDs. Additionally, despite the higher PER of Y2O3, the NC displayed increased biocompatibility toward the human keratinocyte HaCaT cell line in the absence of radiation compared to Y2O3. At 10 MV, where photoelectric effects are minimal, the NC outperformed both individual components, yielding a PER of 1.28, or a 28% dose enhancement compared to 12% for Y2O3 alone and 19% for CeO2. This study highlights the potential of CeO2 QDs-Y2O3 as a broad-spectrum radioprotective agent, offering enhanced biocompatibility and effective protection against radiation-induced oxidative stress across broad-ranging radiation conditions.

To study the evacuation function of calf muscle-venous pump (CMP) in patients with pelvic venous disorder (PeVD).

This single-center cross-sectional study included 170 female patients (120 with PeVD ± chronic venous disease (CVD) and 50 with CVD without PeVD) and 20 healthy volunteers. All subjects underwent duplex ultrasound (DUS) of the pelvic and lower extremity veins, radionuclide venography (RV) of the lower extremities, and single-photon emission computed tomography (SPECT) of the pelvic veins (PVs) with in vivo labeled red blood cells (RBCs). The pelvic venous congestion (PVC) signs were deposition of labeled RBCs in the PVs and the PVC coefficient (CPVC) > 0.5. The CMP evacuation dysfunction was identified during RV as an increase in the average isotope transit time (Tave) in the tendon (Tt) and muscle (Tm) parts of CMP and in the popliteal vein (Tpv).

The CMP dysfunction was detected in 81.6% and 78.3% of patients with symptomatic and asymptomatic PeVD, accordingly, and in 92% of patients with CVD. This condition was characterized by a significant increase in the isotope transit time (Tt 18-30 s, Tm 27-45 s, and Tpv 20-40 s).

The CMP dysfunction is present in about 80% of patients with PeVD, regardless of the clinical course of PeVD and the presence of CVD of the lower extremities.

Bacterial infections remain a global healthcare challenge, requiring precise diagnostic modalities to guide therapeutic interventions. Current molecular imaging agents predominantly detect nonspecific hemodynamic alterations and lack pathogen-specific targeting capabilities for magnetic resonance imaging (MRI). Leveraging the selective bacterial uptake of maltotriose via the maltodextrin transport pathway, we engineered maltotriose-functionalized magnetic nanoparticles (Malt-MNPs) as a novel MRI contrast agent. Basic physicochemical characterization confirmed the nanosystem's colloidal stability, biocompatibility, and superparamagnetism (saturation magnetization > 50 emu/g). In a rat bacterial infection model, intravenously administered Malt-MNPs selectively accumulated at infection sites, inducing a >50% MRI signal change within 24 h while exhibiting minimal off-target retention in sterile inflammatory lesions (<10% signal change). This specificity enabled clear MRI-based differentiation between bacterial infections and noninfectious inflammation. These findings provide a promising strategy for clinical translation in infection imaging and treatment.

Chest X-ray (CXR) is crucial for diagnosing lung diseases, especially lung nodules. Recent studies indicate that bones, such as ribs and clavicles, obscure 82 to 95% of undiagnosed lung cancers. The development of computer-aided detection (CAD) systems with automated bone suppression is vital to improve detection rates and support early clinical decision-making. Current bone suppression methods face challenges: they often depend on manual subtraction of bone-only images from CXRs, leading to inefficiency and poor generalization; there is significant information loss in data compression within deep convolutional end-to-end architectures; and a balance between model efficiency and accuracy has not been sufficiently achieved in existing research. We introduce a novel end-to-end architecture, the mask-guided model, to address these challenges. Leveraging the Pix2Pix framework, our model enhances computational efficiency by reducing parameter count by 92.5%. It features a rib mask-guided module with a mask encoder and cross-attention mechanism, which provides spatial constraints, reduces information loss during encoder compression, and preserves non-relevant areas. An ablation study evaluates the impact of various factors. The model undergoes initial training on digitally reconstructed radiographs (DRRs) derived from CT projections for bone suppression and is fine-tuned on the JSRT dataset to accelerate convergence. The mask-guided model surpasses previous state-of-the-art methods, showing superior bone suppression performance in terms of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and processing speed. It achieves an SSIM of 0.99 ± 0.002 and a PSNR of 36.14 ± 1.13 on the JSRT dataset. This study underscores the proposed model's effectiveness compared to existing methods, showcasing its capability to reduce model size and increase accuracy. This makes it well-suited for deployment in affordable, low-power hardware devices across various clinical settings.

Bi-cruciate retaining (BCR) total knee arthroplasty (TKA) is considered to provide improved clinical function and kinematics compared with conventional TKA, but it is unclear which factors affect clinical outcomes after BCR TKA. This study aimed to investigate whether rotational alignment of the femoral and tibial components and rotational mismatch between the femoral and tibial components affected early clinical outcomes after BCR TKA, according to the 2011 version of the Knee Society Score (2011KSS).

This retrospective cohort study included 39 knees that underwent BCR TKA. Rotational alignment of the femoral and tibial components and rotational mismatch between the components were measured by computed tomography based three-dimensional evaluation software. 2011KSS was obtained at 3, 6, and 12 months postoperatively. The relationship of each of rotational alignment and rotational mismatch with 2011KSS was analyzed.

Rotational alignment of the femoral and tibial components was not correlated with symptoms, patient satisfaction, patient expectations, or functional activities at 3, 6, or 12 months postoperatively. Rotational mismatch was negatively correlated with symptoms, patient satisfaction and functional activities at 3 months; negatively correlated with symptoms and functional activities at 6 months; and negatively correlated with symptoms, patient satisfaction, patient expectations and functional activities at 12 months postoperatively.

Rotational mismatch between the femoral and tibial components was negatively correlated with 2011KSS, whereas no relationship of rotational alignment of the femoral and tibial components with 2011KSS was observed. Excessive external rotation of the tibial component relative to the femoral component resulted in worse early clinical outcomes.

Objective. X-ray computed tomography employing low-dose x-ray source is actively researched to reduce radiation exposure. However, the reduced photon count in low-dose x-ray sources leads to severe noise artifacts in analytic reconstruction methods like filtered backprojection. Recently, deep learning (DL)-based approaches employing uni-domain networks, either in the image-domain or projection-domain, have demonstrated remarkable effectiveness in reducing image noise and Poisson noise caused by low-dose x-ray source. Furthermore, dual-domain networks that integrate image-domain and projection-domain networks are being developed to surpass the performance of uni-domain networks. Despite this advancement, dual-domain networks require twice the computational resources of uni-domain networks, even though their underlying network architectures are not substantially different.Approach. The U-Net architecture, a type of Hourglass network, comprises encoder and decoder modules. The encoder extracts meaningful representations from the input data, while the decoder uses these representations to reconstruct the target data. In dual-domain networks, however, encoders and decoders are redundantly utilized due to the sequential use of two networks, leading to increased computational demands. To address this issue, this study proposes a cross-domain DL approach that leverages analytical domain transfer functions. These functions enable the transfer of features extracted by an encoder trained in input domain to target domain, thereby reducing redundant computations. The target data is then reconstructed using a decoder trained in the corresponding domain, optimizing resource efficiency without compromising performance.Main results. The proposed cross-domain network, comprising a projection-domain encoder and an image-domain decoder, demonstrated effective performance by leveraging the domain transfer function, achieving comparable results with only half the trainable parameters of dual-domain networks. Moreover, the proposed method outperformed conventional iterative reconstruction techniques and existing DL approaches in reconstruction quality.Significance. The proposed network leverages the transfer function to bypass redundant encoder and decoder modules, enabling direct connections between different domains. This approach not only surpasses the performance of dual-domain networks but also significantly reduces the number of required parameters. By facilitating the transfer of primal representations across domains, the method achieves synergistic effects, delivering high quality reconstruction images with reduced radiation doses.

Traumatic Brain Injury (TBI) is a global health concern and a leading cause of trauma-related death worldwide. Computed tomography (CT) scan is the gold standard for screening for intracranial bleeding following TBI. Most cases of TBI are mild, with negative CT scans. Different instruments and guidelines are employed to better predict which patients need a CT scan and to minimise unnecessary radiation exposure and save resources. One such instrument is the Scandinavian Neurotrauma Committee guidelines.

To validate and examine adherence to the Scandinavian Neurotrauma Committee guidelines in Region Örebro County.

We executed a retrospective study with review of patient records and data analysis. Descriptive and comparative statistics were used, along with binary logistic regression analysis to account for confounding factors.

A total of 505 cases were reviewed. Sensitivity of the guidelines was measured at 95% with specificity at 29%. The positive and negative predictive values were 0.77 and 0.69, respectively. A total of 17 false negative cases were found. One case required surgery, during which a chronic subdural hematoma was identified. Adherence to guidelines was 56%, with the lack of analysis of S100B primarily accounting for non-adherence. A total of 54 CT scans were performed outside of guideline indications.

The guidelines can effectively predict which patients need a CT scan. Increased adherence could potentially decrease the number of CT scans, while inclusion of older age limit as an independent rule-in law for CT scans would increase patient safety.

This study aims to evaluate the quality of diagnostic assessment of three-dimensional (3D) T1-weighted fast field echo (FFE) "black bone" magnetic resonance imaging (MRI) sequences in comparison to the gold standard computed tomography (CT) in patients with mandible fractures. All consecutive patients who presented with mandibular fractures between the mandibular and mental foramen were included and received an MRI in addition to the standard CT (reference). The patient collective's epidemiological data were analyzed, and the images of CT and MRI were assessed regarding the degree of mandibular fracture dislocation in all three planes. In total, 28 patients were included and no statistically significant difference between the measured dislocations between the two modalities were registered. The evaluation of mandibular fracture dislocation using a T1-FFE "black bone" sequence was feasible and comparable to standard CT imaging. Non-ionizing imaging methods might become a useful alternative to conventional CT in the assessment of acute traumatic injury of the mandible, providing a valid option to reduce radiation, particularly in young patients.

Due to its diagnostic accuracy, point-of-care ultrasound (POCUS) is becoming more frequently used in the emergency department (ED), but the feasibility of its use by in-training residents and the potential clinical impact have not been assessed.

This study aimed to assess the feasibility of implementing a structured POCUS training program for in-training ED residents, as well as the clinical impact of their use of POCUS in the management of patients in the ED.

IMPULSE (Impact of a Point-of Care Ultrasound Examination) is a before-and-after implementation study evaluating the impact of a structured POCUS training program for ED residents on the management of patients admitted with acute respiratory failure (ARF) and/or circulatory failure (ACF) in a Swiss regional hospital. The training curriculum was organized into 3 steps and consisted of a web-based training course; an 8-hour, practical, hands-on session; and 10 supervised POCUS examinations. ED residents who successfully completed the curriculum participated in the postimplementation phase of the study. Outcomes were time to ED diagnosis, rate and time to correct diagnosis in the ED, time to prescribe appropriate treatment, and in-hospital mortality. Standard statistical analyses were performed using chi-square and Mann-Whitney U tests as appropriate, supplemented by Bayesian analysis, with a Bayes factor (BF)>3 considered significant.

A total of 69 and 54 patients were included before and after implementation of the training program, respectively. The median time to ED diagnosis was 25 (IQR 15-60) minutes after implementation versus 30 (IQR 10-66) minutes before implementation, a difference that was significant in the Bayesian analysis (BF=9.6). The rate of correct diagnosis was higher after implementation (51/54, 94% vs 36/69, 52%; P<.001), with a significantly shorter time to correct diagnosis after implementation (25, IQR 15-60 min vs 43, IQR 11-70 min; BF=5.0). The median time to prescribe the appropriate therapy was shorter after implementation (47, IQR 25-101 min vs 70, IQR 20-120 min; BF=2.0). Finally, there was a significant difference in hospital mortality (9/69, 13% vs 3/54, 6%; BF=15.7).

The IMPULSE study shows that the implementation of a short, structured POCUS training program for ED residents is not only feasible but also has a significant impact on their initial evaluation of patients with ARF and/or ACF, improving diagnostic accuracy, time to correct diagnosis, and rate of prescribing the appropriate therapy and possibly decreasing hospital mortality. These results should be replicated in other settings to provide further evidence that implementation of a short, structured POCUS training curriculum could significantly impact ED management of patients with ARF and/or ACF.

Computed tomography (CT) reconstruction from incomplete projection data is significant for reducing radiation dose or scanning time. In this work, we investigate a special sampling strategy, which performs two limited-angle scans. We call it orthogonal limited-angle sampling. The X-ray source trajectory covers two limited-angle ranges, and the angle bisectors of the two angular ranges are orthogonal. This sampling method avoids rapid switching of tube voltage in few-view sampling, and reduces data correlation of projections in limited-angle sampling. It has the potential to become a practical imaging strategy. Then we propose a new reconstruction model based on anisotropic self-guided image filtering (ASGIF) and present an algorithm to solve this model. We construct adaptive weights to guide image reconstruction using the gradient information of reconstructed image itself. Additionally, since the shading artifacts are related to the scanning angular ranges and distributed in two orthogonal directions, anisotropic image filtering is used to preserve image edges. Experiments on a digital phantom and real CT data demonstrate that ASGIF method can effectively suppress shading artifacts and preserve image edges, outperforming other competing methods.

Children are more sensitive to ionizing radiation than adults. Even though the risk is very low, exposure from radiological examinations can possibly cause them long-term side effects. Recent large epidemiological studies involving children and young adults have added evidence suggesting that even small doses of radiation, such as those from computed tomography scans, might slightly increase the risk of developing cancer later in life. Therefore, even though radiologic studies are essential for an accurate diagnosis and management of various conditions, it is crucial to minimize radiation exposure. This article addresses radiation protection for children in the medical use of ionizing radiation and it is set in the context of the European legislative framework regarding radiation protection. It advocates for a holistic approach to paediatric radiological tests. This approach includes the key principles of radiation protection, such as the justification of imaging procedures supported by referral guidelines, as well as the optimization of techniques (according to the ALARA principle) and effective communication with parents about the benefits and the risks of radiologic procedures. Protecting children from unnecessary radiation is not only a technical challenge, but also a moral obligation and a legal requirement.

Adopting an appropriate noninvasive radiological method is crucial for periodic surveillance of liver metastases in colorectal cancer (CRC) patients after surgery, which is closely related to clinical management and prognosis. This study aimed to prospectively enroll stage II-III CRC patients for the surveillance of liver metastases and compare the diagnostic performance of contrast-enhanced CT (CE-CT) and non-enhanced abbreviated MRI (NE-AMRI) during this process.

587 CRC patients undergoing radical resection of the primary tumor were evaluated by 1 to 3 rounds of surveillance tests, consisting of abdominal CE-CT and contrast-enhanced MRI (CE-MRI) within 7 days at 6-month intervals. Subsequently, images of NE-AMRI were extracted from the CE-MRI examination, and paired CE-CT and NE-AMRI analysis were performed. The lesion-based detection rates between two protocols were compared, and a subgroup analysis was performed in lesions with a size of ≤10 mm. The patient-based sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the areas under the curves (AUCs) of CE-CT and NE-AMRI in each round were evaluated. Finally, the relationship between the diagnostic accuracy of two protocols and characteristics of patients was explored.

The lesion-based detection rates of NE-AMRI in three rounds were all significantly higher than those of CE-CT ( P < 0.001, P < 0.001, P = 0.003, respectively). In the subgroup analysis of lesions ≤ 10 mm, NE-AMRI also performed better than CE-CT ( P < 0.001, P = 0.002, P = 0.005, respectively). The patient-based sensitivities, specificities, NPVs, and PPVs of NE-AMRI were higher than those of CE-CT in three rounds of surveillance. The AUCs for NE-AMRI were all significantly better than those for CE-CT in each round ( P = 0.015, P = 0.045, P = 0.009, respectively). Furthermore, patient BMI and fatty liver disease had impacts on the diagnostic accuracy of the CE-CT protocol, but not on the NE-AMRI protocol.

NE-AMRI may be a promising periodic surveillance tool for CRC patients after surgery to increase diagnostic accuracy of liver metastases, developing personalized clinical management and improving prognosis, simultaneously avoiding side effects associated with ionizing radiation and contrast agents.

This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths.

A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed.

SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29-0.36 at reduced dose and 0.35-0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV.

SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution.

Introduction: Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and without deep learning reconstruction (DLR) and metal artifact reduction (MAR) in the presence of metal hip prostheses. Methods: Ten urinary system combinations with 4 to 6 mm ureteral stones were implanted into a cadaver with bilateral hip prostheses. Each set was scanned under two different radiation doses (conventional dose [CD] = 115 mAs and ultra-low dose [ULD] = 6.0 mAs). Two scans were obtained for each dose as follows: one with and another without DLR and MAR. Two blinded radiologists ranked each image in terms of artifact, image noise, image sharpness, overall quality, and diagnostic confidence. Stone detection accuracy at each setting was calculated. Results: ULD with DLR and MAR improved subjective image quality in all five domains (p < 0.05) compared with ULD. In addition, the subjective image quality for ULD with DLR and MAR was greater than the subjective image quality for CD in all five domains (p < 0.05). Stone detection accuracy of ULD improved with the application of DLR and MAR (p < 0.05). Stone detection accuracy of ULD with DLR and MAR was similar to CD (p > 0.25). Conclusions: DLR with MAR may allow the application of low-dose CT protocols in patients with hip prostheses. Application of DLR and MAR to ULD provided a stone detection accuracy comparable with CD, reduced radiation exposure by 94.8%, and improved subjective image quality.

This study examined the effects of changes in patient positioning on radiation exposure for panoramic and cone beam computed tomography (CBCT) radiographic examinations by measuring effective dose (E) and equivalent doses.

Simulated radiographic examinations with optimal and suboptimal positioning-anterior shift by 1 centimeter (cm), posterior shift by 1 cm, chin lowered by 10 degrees (°), chin elevated by 10°, rotation by 10°, and lateral shift by 1 cm-were conducted using a tissue-equivalent phantom and optically-stimulated luminescent dosimeters. Exposures were made with the RAYSCAN Alpha Plus 160 X-ray unit using the following exposure parameters: panoramic, 80 kVp, 14 mA, and 13.9s; CBCT (10 x 10 cm FOV), 90 kVp, 10 mA, and 14.0s; CBCT (16 x 10 cm FOV), 90 kVp, 11 mA, and 14.0 s.

All suboptimal panoramic conditions except rotation demonstrated significant decreases in E. Chin elevation during CBCT imaging reduced E with the 10 × 10 cm FOV (P = .0003) and 16 × 10 cm FOV (P < .0001), as well as the equivalent dose to the thyroid gland (P < .0001 for both FOVs).

Suboptimal positioning did not significantly increase E for either panoramic or CBCT exposures. Chin elevation decreased E in the CBCT protocols, with notable decreases in equivalent dose to the thyroid gland.

Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging.

This meta-analysis evaluates the effectiveness of AI in enhancing CT image quality and lowering radiation doses.

A thorough literature search was performed across several databases, including PubMed, Embase, Web of Science, Science Direct, and Cochrane Library, with the final update in 2024. We included studies that compared AI-based interventions to conventional CT techniques. The quality of these studies was assessed using the Newcastle-Ottawa Scale. Random effect models were used to pool results, and heterogeneity was measured using the I² statistic. Primary outcomes included image quality, CT dose index, and diagnostic accuracy.

This meta-analysis incorporated 5 clinical validation studies published between 2022 and 2024, totaling 929 participants. Results indicated that AI-based interventions significantly improved image quality (mean difference 0.70, 95% CI 0.43-0.96; P<.001) and showed a positive trend in reducing the CT dose index, though not statistically significant (mean difference 0.47, 95% CI -0.21 to 1.15; P=.18). AI also enhanced image analysis efficiency (odds ratio 1.57, 95% CI 1.08-2.27; P=.02) and demonstrated high accuracy and sensitivity in detecting intracranial aneurysms, with low-dose CT using AI reconstruction showing noninferiority for liver lesion detection.

The findings suggest that AI-based interventions can significantly enhance CT imaging practices by improving image quality and potentially reducing radiation doses, which may lead to better diagnostic accuracy and patient safety. However, these results should be interpreted with caution due to the limited number of studies and the variability in AI algorithms. Further research is needed to clarify AI's impact on radiation reduction and to establish clinical standards.

Radiation literacy, encompassing the understanding of basic principles, applications, risks, and protective measures related to ionizing radiation, is critical for medical personnel working in jobs that involve the use of radioactive materials or medical imaging. In the context of nuclear emergency preparedness, the level of radiation knowledge among healthcare professionals-such as doctors, nurses, and radiographers-directly influences the effectiveness and safety of emergency responses. This study aims to address this gap by evaluating the radiation knowledge of medical personnel and identifying areas for improvement in profession-specific training programs.

A cross-sectional study was conducted using a convenience sampling method. The study included 723 participants attending a medical emergency response exercise and clinical management workshop on radiation injury in Suzhou, China, in November 2023. Data were collected through a structured questionnaire, descriptive statistics and chi-square tests were performed to analyze participants' radiation knowledge and identify variations across different professional groups.

The majority of participants were female (64.73%), married (75.10%), and held an undergraduate degree (69.99%). Nurses (40.11%) and clinical doctors (30.29%) constituted the largest professional groups. Significant disparities in radiation knowledge were observed among healthcare workers. Nurses and management personnel demonstrated a stronger grasp of fundamental radiation concepts, such as radioactive nuclides and absorbed doses, compared to clinical doctors. For instance, 85.52% of nursing personnel and 72.34% of management personnel accurately identified the half-life of iodine-131, while only 49.32% of clinical doctors showed comparable knowledge. Furthermore, substantial differences in radiation emergency response capabilities were noted across professions. These findings emphasize the necessity for tailored, profession-specific training programs in radiation protection and emergency preparedness.

The study reveals a generally insufficient understanding of basic radiation concepts and emergency response principles among medical personnel. Significant variations in radiation knowledge were observed across different professional groups, highlighting the need for specialized training modules. These modules should focus on fundamental radiation concepts, radiation exposure effects, and emergency response protocols, with content customized to address the unique needs of each professional group. By implementing such targeted training, the overall effectiveness and safety of nuclear emergency responses can be significantly enhanced.

Pediatric pulmonary embolism is a rare yet potentially life-threatening condition, presenting significant diagnostic and therapeutic challenges owing to its nonspecific symptoms and diverse underlying risk factors. This systematic review aims to consolidate data from case series and case reports to provide a comprehensive overview of pediatric pulmonary embolism, focusing on clinical characteristics, diagnostic approaches, treatment strategies, and outcomes.

This systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines and the Cochrane Handbook for Systematic Reviews of Interventions, version 6.3. The study protocol was registered with PROSPERO (ID: CRD42024532471). We utilized the Covidence systematic review software for deduplication and screening of search results. The literature search was developed with a subject specialist and included Medical Subject Headings terms and free-text keywords such as "pulmonary embolism," "pediatric," and "case reports." Databases searched included PubMed, Scopus, Web of Science, and the Cochrane Library up to April 2024, limited to English-language publications. Reference lists of relevant articles were also reviewed.

Pulmonary embolism affected males and females with age ranging from 1 to 18 years. Common underlying conditions included malignancies (for example, Wilms tumor), chronic diseases (for example, nephrotic syndrome), and recent surgical interventions. Diagnostic practices primarily relied on computed tomography pulmonary angiography, supplemented by chest X-ray and ultrasound. Treatment typically involved anticoagulation therapy with unfractionated heparin and low-molecular-weight heparin, transitioning to oral anticoagulants for long-term management. Thrombolytic therapy was used in severe cases. Outcomes varied, with many patients recovering well, though complications such as recurrent embolism and pleural effusion were observed. Fatal cases underscored the critical need for early detection and prompt treatment.

This systemic review underscores the rarity and complexity of pediatric pulmonary embolism, highlighting the necessity for increased clinical vigilance given its nonspecific presentation and diverse underlying risk factors. Accurate diagnosis, primarily via computed tomography pulmonary angiography, with the prompt initiation of anticoagulation therapy are essential for optimal outcomes. Despite favorable recovery rates for most patients, the potential for severe complications and fatalities reinforces the value of timely diagnosis and personalized management approaches. Further research is essential to refine diagnostic protocols, optimize treatment approaches, establish evidence-based guidelines, and improve long-term outcomes for children with pulmonary embolism.

Lead-based radiation shielding used usually in radiology departments is being replaced by non-lead shields due to concerns over toxicity, high weight, and the impossibility of applying it in the imaging field. New studies refer to the use of nanomaterials and lightweight polymer-based composites as an alternative that can solve the problems caused by lead shields. The study aims to develop a flexible composite shield for CT scan imaging and examine its effectiveness in reducing radiation exposure. This study involved the construction of 1 mm thick composite shields used in chest CT scan imaging. The first type consisted of 10% bismuth oxide, the second type consisted of 10% tungsten, the third type consisted of 5% bismuth oxide and 5% tungsten, and 90% of the rest of the structure of all shields was silicone. This study examined the morphological test, tensile strength test, attenuation efficiency, and image quality assessment of these shields. The radiation transmission coefficient for 10% bismuth, 10% tungsten and 5% bismuth-5%tungsten shields was obtained as 0.86, 0.80, and 0.83, respectively. Also, the noise increasing percentage for mentioned shields were 6, 4, and 6%, respectively, compared to the case without a shield. The study found that the 10% tungsten-90% silicone shield is the most effective in reducing radiation exposure due to its K-edge and attenuation coefficients. According to the noise assessment, using these shields does not significantly affect the quality of images.

The purpose of the study was to determine the urologists' knowledge of ionizing radiation and the frequency of CT scan utilization for pediatric patients with urolithiasis. The second aim is to examine the factors that affect the choice of the CT imaging method in these patients. We sent a quantitative online questionnaire to urologists and pediatric urologists in various regions and practice settings of Turkey via a web survey database in June 2022. The survey consisted of 22 items in a multiple-choice or yes/no format and was divided into three sections: participant characteristics, knowledge, and current practice. One hundred and fifty-four participants fully completed this questionnaire. Forty-two participants (27.3%) correctly stated the radiation dose of an abdominal CT scan. Thirty of the participants (19.5%) thought that there was no relationship between exposure to radiation and cancer development. In addition, 76 participants (49.4%) were aware of any scientific literature about this relationship. Ninety-eight participants (63.6%) think that they did not receive adequate training on imaging methods for pediatric patients during their residency program. The choice of CT scans as the first imaging method was increased with age. Even if the choice of imaging method is mostly decided according to the guidelines, various factors such as medicolegal causes, long ultrasound appointment dates, poor quality of assessment, and clinical experiences are inevitable facts that guide the choice of computed tomography. Urologists are responsible for knowing the effects of ionizing radiation and the ALARA principle. Our findings emphasize the lack of knowledge about ionizing radiation. Encouragement of the literature, provision of the mandatory curriculum, and supervision of the use of ionizing radiation will contribute to the elimination of the deficiency in this area.

Spectral shaping (also known as spectral filtration) has been utilized in some of the latest computed tomography (CT) systems. This technique involves using tin (Sn) or silver (Ag) filters, which selectively absorb low-energy photons. This review aims to demonstrate the utility of spectral shaping across a wide range of protocols and clinical situations. Spectral-shaped CT protocols using tin filters allow for the acquisition of diagnostic images and greatly reduce the radiation dose, metal artifacts, and photon starvation. These features make spectral shaping suitable for various clinical situations in diagnostic and interventional CT imaging.

Background/Objectives: Dyspnea, the subjective experience of breathing discomfort, accounts for approximately 5% of emergency department (ED) presentations, 10% of general ward admissions, and 20% of intensive care unit (ICU) admissions. Despite its prevalence, dyspnea remains a challenging clinical manifestation for physicians. To the best of our knowledge, there are no international guidelines for the assessment and management of patients with dyspnea coming to the ED. In this study, we aim to evaluate how dyspnea cases are assessed and managed at Cantonal Hospital Baselland in Liestal (KSBL) and to audit these practices. Methods: We conducted a retrospective, observational study of hospital records from KSBL, including all patients presenting to the ED with dyspnea as their primary symptom who were subsequently admitted to the internal medicine ward for at least one night between January and December 2022. Data on assessment and management practices were compared using the medStandards algorithm. Results: A total of 823 cases were included. The median age at admission was 76 years (with a range of 15-99), and 57% of the patients were male. Blood pressure and heart rate were documented in 93.8% of the cases, respiratory rate in 61.4%, oxygen saturation in 96.1%, and body temperature in 86.3%. The patient's subjective dyspnea description was recorded in 14.8% of the cases, while the temporal onset (timing of symptoms) was documented in 98.8%, and the intensity of effort triggering dyspnea was noted in 36.2% of cases. A dyspnea index scale was used in 7.8% and smoking status was documented in 41.1% of the cases. Lung percussion was performed in 2.6% of the cases, while a lung auscultation was performed in 94.4% and a heart auscultation was performed in 85.3% of cases. A complete blood count with a basic metabolic panel and TSH test was collected in 86.9% of the cases, while a blood gas analysis was collected in 34.0% of the cases. An ECG was reported in 87.5% of the cases. From the 337 patients who should have received an emergency ultrasound, 10.1% received one. The three most frequent final diagnoses were decompensated heart failure (28.4%), pneumonia (26.4%), and COVID-19 (17.0%). None of the three patients with a known neuromuscular disease were admitted to the shock room. Conclusions: Our findings reveal that the medStandards algorithm was only partially followed at the ED in KSBL Liestal, highlighting gaps in detailed history taking, respiratory rate measurement, lung percussion, and emergency ultrasound use. Given the frequency of dyspnea-related presentations, systematic improvements in the adherence to assessment protocols are urgently needed to enhance patient outcomes.