Citations

Abstract This article provides an overview of deep-learning–based techniques in CT image reconstruction and processing (referred to as “DLR”), covering technical implementations, performance evaluation, radiation dose reduction, and future perspectives. DLR methods can be categorized into projection-space, projection-to-image-space, image-space, and various hybrid techniques, with applications such as noise reduction, artefact correction, and spatial resolution enhancement. Performance evaluations include phantom-based studies, patient image-based studies, and virtual imaging trials. These evaluation studies demonstrated that DLR can effectively reduce image noise while preserving an image texture like in traditional filtered-backprojection (FBP) images, although the extent of radiation dose reduction varies widely depending on the study and the specific diagnostic task. Challenges remain in low-contrast lesion detection and characterization, where dose reduction may still be less than 50% compared to traditional reconstruction methods. Additionally, the potential for DLR methods to generate false structures or “hallucinations,” especially at low radiation doses, emphasizes the need for effective monitoring and mitigation strategies from both technical and clinical perspectives. Quantitative, accurate, and efficient evaluation techniques, such as virtual image trial-based methods, can be explored to help optimize these algorithms for reducing radiation dose and enhancing diagnostic performance.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT). Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled “study” dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions. Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs. Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Abstract Objectives To develop a volume of interest (VOI) imaging technique in multi-detector-row helical CT to reduce radiation dose or improve image quality within the VOI. Methods A deep-learning method based on a residual U-Net architecture, named VOI-Net, was developed to correct truncation artifacts in VOI helical CT. Three patient cases, a chest CT of interstitial lung disease and 2 abdominopelvic CT of liver tumour, were used for evaluation through simulation. Results VOI-Net effectively corrected truncation artifacts (root mean square error [RMSE] of 5.97 ± 2.98 Hounsfield Units [HU] for chest, 3.12 ± 1.93 HU, and 3.71 ± 1.87 HU for liver). Radiation dose was reduced by 71% without sacrificing image quality within a 10-cm diameter VOI, compared to a full scan field of view (FOV) of 50 cm. With the same total energy deposited as in a full FOV scan, image quality within the VOI matched that at 350% higher radiation dose. A radiologist confirmed improved lesion conspicuity and visibility of small linear reticulations associated with ground-glass opacity and liver tumour. Conclusions Focusing radiation on the VOI and using VOI-Net in a helical scan, total radiation can be reduced or higher image quality equivalent to those at higher doses in standard full FOV scan can be achieved within the VOI. Advances in knowledge A targeted helical VOI imaging technique enabled by a deep-learning-based artifact correction method improves image quality within the VOI without increasing radiation dose.

ABSTRACT Introduction Publication bias (PB) threatens the validity of meta‐analyses by distorting effect size estimates, potentially leading to misleading conclusions. With advanced pattern recognition and multimodal capabilities, large language models (LLMs) may be able to evaluate PB and make the systematic review process more efficient. Methods We evaluated the ability of two state‐of‐the‐art multimodal LLMs, GPT‐4o and Llama 3.2 Vision, to detect PB using funnel plots alone and in combination with quantitative inputs. We simulated meta‐analyses under varying conditions, including the absence of PB, different levels of presence of PB, varying total number of studies within a meta‐analysis, and differing degrees of between‐study heterogeneity. Results Neither GPT‐4o nor Llama 3.2 Vision consistently detected the presence of PB across various settings. Under no‐publication‐bias conditions, GPT‐4o achieved a higher specificity outperforming Llama 3.2 Vision, with the difference most shown in the meta‐analyses with 20 or more studies. The inclusion of quantitative inputs alongside funnel plots did not significantly improve performance. Additionally, between‐study heterogeneity and patterns of non‐reported studies had minimal impact on the models’ assessments. Conclusions The ability of LLMs to detect PB without fine‐tuning is limited at the present time. This study highlights the need for specialized model adaptation before LLMs can be effectively integrated into meta‐analysis workflows. Future research can focus on targeted refinements to enhance LLM performance and utility in evidence synthesis.

ABSTRACT Background and Objectives The relevance of Cochrane systematic reviews depends on timely completion and updates. This study aimed to empirically assess the lifecycles of Cochrane reviews published from 2003 to 2024, including transitions from protocol to review, update patterns, and withdrawals. Methods We extracted data from Cochrane Library publications between 2003 and 2024. Each review topic was identified using a unique six‐digit DOI‐based ID. We recorded protocol publication, review publication, updates, and withdrawals (i.e., removed from the Cochrane Library for editorial or procedural reasons), calculating time intervals between stages and conducting subgroup analyses by review type. Results Of 8137 protocols, 71.9% progressed to reviews (median 25.7 months), 2.4% were updated during the protocol stage, and 10.0% were withdrawn. Among 8477 reviews, 64.3% were never updated by the time of our analysis; for those updated at least once, the median interval between updates was 57.2 months. Withdrawal occurred in 2.5% of reviews (median 67.6 months post‐publication). Subgroup analyses showed variation across review types; diagnostic and qualitative reviews tended to have longer protocol‐to‐review times than other types of reviews. Conclusions Cochrane reviews show long development and update intervals, with variation by review type. Greater use of automation and targeted support may improve review efficiency and timeliness.

Abstract The benefits of photon-counting detector (PCD) CT include improved spatial resolution, increased radiation dose efficiency, and the elimination of electronic noise. Unlike traditional energy-integrating-detector (EID) CT systems, PCDs have inherent spectral capabilities, which support “always-on” dual energy and multi-energy imaging acquisition. PCD-CT directly converts X-ray photons into electrical signals and captures their individual energy levels. Current commercially available PCD-CT scanners, all from one manufacturer, include 2 models that incorporate dual-source geometry. This innovation is particularly advantageous for paediatric imaging by enabling rapid scans that reduce motion artifact and therefore the need for sedation and can now be combined in the current model with multi-energy imaging or ultra-high-resolution acquisition. This review article explores how the technical benefits of PCD CT improve the diagnostic quality of images and opens the field of paediatric radiology up to future opportunities for research and new applications.

Abstract Quantitative computed tomography (CT) enables the measurement of biophysical processes characterized by morphology, composition, flow, and/or motion to aid in clinical diagnosis and intervention. Since its initial application for determining bone mineral density for skeletal fragility assessment, quantitative CT has continued to evolve alongside CT’s technological advancements. A key challenge facing quantitative CT is the lack of standardization pertinent to image acquisition, reconstruction, and image analysis. With the introduction of spectral CT involving dual-energy approaches and photon-counting detectors (PCD), we are now able to obtain detailed information regarding mass densities of endogenous and exogeneous materials. Furthermore, energy-resolved CT yields spectral image types (eg, virtual monoenergetic image) that are, in principle, independent of tube potential and patient size. This paves the way for workflow standardization and helps improve the consistency and reproducibility of CT-derived measurements. In this article, we review clinical applications of quantitative CT, discuss key challenges associated with quantitative CT and its adoption into routine practice, and outline the unique benefits ushered by new CT technologies such as PCD-CT towards improving quantitative imaging.