This study aimed to compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) using model-based iterative reconstruction (MBIR) and high-resolution deep learning reconstruction (HR-DLR) images to detect functionally significant stenosis with invasive FFR as the reference standard. This single-center retrospective study included 79 consecutive patients (mean age, 70 ± 11 [SD] years; 57 male) who underwent coronary CT angiography followed by invasive FFR between February 2022 and March 2024. CT-FFR was calculated using a mesh-free simulation. The cutoff for functionally significant stenosis was defined as FFR ≤ 0.80. CT-FFR was compared with MBIR and HR-DLR using receiver operating characteristic curve analysis. The mean invasive FFR value was 0.81 ± 0.09, and 46 of 98 vessels (47%) had FFR ≤ 0.80. The mean noise of HR-DLR was lower than that of MBIR (14.4 ± 1.7 vs 23.5 ± 3.1, p < 0.001). The area under the receiver operating characteristic curve for the diagnosis of functionally significant stenosis of HR-DLR (0.88; 95% CI: 0.80, 0.95) was higher than that of MBIR (0.76; 95% CI: 0.67, 0.86; p = 0.003). The diagnostic accuracy of HR-DLR (88%; 86 of 98 vessels; 95% CI: 80, 94) was higher than that of MBIR (70%; 69 of 98 vessels; 95% CI: 60, 79; p < 0.001). HR-DLR improves image quality and the diagnostic performance of CT-FFR for the diagnosis of functionally significant stenosis. Question The effect of HR-DLR on the diagnostic performance of CT-FFR has not been investigated. Findings HR-DLR improved the diagnostic performance of CT-FFR over MBIR for the diagnosis of functionally significant stenosis as assessed by invasive FFR. Clinical relevance HR-DLR would further enhance the clinical utility of CT-FFR in diagnosing the functional significance of coronary stenosis.

High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological structures is increasingly used to obtain quantitative data and valuable insights. Segmentation and volumetric analysis could enable more precise diagnosis, treatment planning, and patient monitoring. These guidelines aim to improve segmentation accuracy and consistency, allowing for better decision-making in both research and clinical environments. Practical advice on planning and organization is provided, focusing on quality, precision, and communication among clinical teams. Additionally, tips and strategies for improving segmentation practices in radiology and radiation oncology are discussed, as are potential pitfalls to avoid. KEY POINTS: As AI continues to advance, volumetry will become more integrated into clinical practice, making it essential for radiologists to stay informed about its applications in diagnosis and treatment planning. There is a significant lack of practical guidelines and resources tailored specifically for radiologists on technical topics like segmentation and volumetric analysis. Establishing clear rules and best practices for segmentation can streamline volumetric assessment in clinical settings, making it easier to manage and leading to more accurate decision-making for patient care.

The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the challenge of classifying BA in adolescents. The results of this DL-based classification were then compared and analyzed with those obtained from manual assessment. A retrospective analysis was conducted on 688 hand and wrist X-ray images of adolescents aged 11.00-23.99 years from western China, which were randomly divided into training set, validation set and test set. The BA assessment results were initially analyzed and compared using four DL network models: InceptionV3, InceptionV3 + SE + Sex, InceptionV3 + Bilinear and InceptionV3 + Bilinear. + SE + Sex, to identify the DL model with the best classification performance. Subsequently, the results of the top-performing model were compared with those of manual classification. The study findings revealed that the InceptionV3 + Bilinear + SE + Sex model exhibited the best performance, achieving classification accuracies of 96.15% and 90.48% for the training and test set, respectively. Furthermore, based on the InceptionV3 + Bilinear + SE + Sex model, classification accuracies were calculated for four age groups (< 14.0 years, 14.0 years ≤ age < 16.0 years, 16.0 years ≤ age < 18.0 years, ≥ 18.0 years), with notable accuracies of 100% for the age groups 16.0 years ≤ age < 18.0 years and ≥ 18.0 years. The BA classification, utilizing the feature fusion DL network model, holds significant reference value for determining the age of criminal responsibility of adolescents, particularly at the critical legal age boundaries of 14.0, 16.0, and 18.0 years.

Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based framework for the rapid and non-invasive preoperative assessment of GAE in pediatric patients using magnetic resonance imaging (MRI). In this study, we propose a novel radiomics-based approach that integrates tumor and peritumoral features extracted from preoperative multiparametric MRI scans to accurately and non-invasively predict the occurrence of tumor-related epilepsy in pediatric patients. Our study developed a multimodal MRI radiomics model to predict epilepsy in pLGGs patients, achieving an AUC of 0.969. The integration of multi-sequence MRI data significantly improved predictive performance, with Stochastic Gradient Descent (SGD) classifier showing robust results (sensitivity: 0.882, specificity: 0.956). Our model can accurately predict whether pLGGs patients have tumor-related epilepsy, which could guide surgical decision-making. Future studies should focus on similarly standardized preoperative evaluations in pediatric epilepsy centers to increase training data and enhance the generalizability of the model.

<i>Objective.</i>Magnetic resonance imaging (MRI) is critical in medical diagnosis and treatment by capturing detailed features, such as subtle tissue changes, which help clinicians make precise diagnoses. However, the widely used single diffusion model has limitations in accurately capturing more complex details. This study aims to address these limitations by proposing an efficient method to enhance the reconstruction of detailed features in MRI.<i>Approach.</i>We present a detail-preserving reconstruction method that leverages multiple diffusion models (DP-MDM) to extract structural and detailed features in the k-space domain, which complements the image domain. Since high-frequency information in k-space is more systematically distributed around the periphery compared to the irregular distribution of detailed features in the image domain, this systematic distribution allows for more efficient extraction of detailed features. To further reduce redundancy and enhance model performance, we introduce virtual binary masks with adjustable circular center windows that selectively focus on high-frequency regions. These masks align with the frequency distribution of k-space data, enabling the model to focus more efficiently on high-frequency information. The proposed method employs a cascaded architecture, where the first diffusion model recovers low-frequency structural components, with subsequent models enhancing high-frequency details during the iterative reconstruction stage.<i>Main results.</i>Experimental results demonstrate that DP-MDM achieves superior performance across multiple datasets. On the<i>T1-GE brain</i>dataset with 2D random sampling at<i>R</i>= 15, DP-MDM achieved 35.14 dB peak signal-to-noise ratio (PSNR) and 0.8891 structural similarity (SSIM), outperforming other methods. The proposed method also showed robust performance on the<i>Fast-MRI</i>and<i>Cardiac MR</i>datasets, achieving the highest PSNR and SSIM values.<i>Significance.</i>DP-MDM significantly advances MRI reconstruction by balancing structural integrity and detail preservation. It not only enhances diagnostic accuracy through improved image quality but also offers a versatile framework that can potentially be extended to other imaging modalities, thereby broadening its clinical applicability.

<i>Objective.</i>This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.<i>Approach.</i>We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance. Two datasets are used: a public dataset (OpenKBP) and an in-house clinical dataset. Model performance is primarily evaluated using two metrics: dose score and dose-volume histogram (DVH) score.<i>Main results.</i>High-resolution inputs improve prediction accuracy (dose score and DVH score) by 8.6%-13.5% compared to low resolution. Using a combination of CT, planning target volumes, and organs-at-risk as input significantly enhances accuracy, with improvements of 57.4%-86.8% over using CT alone. Integrating mean absolute error (MAE) loss with value-based and criteria-based DVH loss functions further boosts DVH score by 7.2%-7.5% compared to MAE loss alone. In the robustness analysis, most models show minimal degradation under Poisson noise (0-0.3 Gy) but are more susceptible to adversarial noise (0.2-7.8 Gy). Notably, certain models, such as SwinUNETR, demonstrate superior robustness against adversarial perturbations.<i>Significance.</i>These findings highlight the importance of optimizing deep learning models and provide valuable guidance for achieving more accurate and reliable radiotherapy dose prediction.

As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there is a lack of systematic evidence on the effectiveness of ML. We conducted a thorough search across Embase, PubMed, the Cochrane Library, and Web of Science from inception to December 14th, 2023.A systematic review and meta-analysis was conducted to assess the value of ML for predicting PD-L1 expression in NSCLC. Totally 30 studies with 12,898 NSCLC patients were included. The thresholds of PD-L1 expression level were < 1%, 1-49%, and ≥ 50%. In the validation set, in the binary classification for PD-L1 ≥ 1%, the pooled C-index was 0.646 (95%CI: 0.587-0.705), 0.799 (95%CI: 0.782-0.817), 0.806 (95%CI: 0.753-0.858), and 0.800 (95%CI: 0.717-0.883), respectively, for the clinical feature-, radiomics-, radiomics + clinical feature-, and pathomics-based ML models; in the binary classification for PD-L1 ≥ 50%, the pooled C-index was 0.649 (95%CI: 0.553-0.744), 0.771 (95%CI: 0.728-0.814), and 0.826 (95%CI: 0.783-0.869), respectively, for the clinical feature-, radiomics-, and radiomics + clinical feature-based ML models. At present, radiomics- or pathomics-based ML methods are applied for the prediction of PD-L1 expression in NSCLC, which both achieve satisfactory accuracy. In particular, the radiomics-based ML method seems to have wider clinical applicability as a non-invasive diagnostic tool. Both radiomics and pathomics serve as processing methods for medical images. In the future, we expect to develop medical image-based DL methods for intelligently predicting PD-L1 expression.

To evaluate the variability of fully automated airway quantitative CT (QCT) measures caused by different kernels and the effect of kernel conversion. This retrospective study included 96 patients who underwent non-enhanced chest CT at two centers. CT scans were reconstructed using four kernels (medium soft, medium sharp, sharp, very sharp) from three vendors. Kernel conversion targeting the medium soft kernel as reference was applied to sharp kernel images. Fully automated airway quantification was performed before and after conversion. The effects of kernel type and conversion on airway quantification were evaluated using analysis of variance, paired t-tests, and concordance correlation coefficient (CCC). Airway QCT measures (e.g., Pi10, wall thickness, wall area percentage, lumen diameter) decreased with sharper kernels (all, p < 0.001), with varying degrees of variability across variables and vendors. Kernel conversion substantially reduced variability between medium soft and sharp kernel images for vendors A (pooled CCC: 0.59 vs. 0.92) and B (0.40 vs. 0.91) and lung-dedicated sharp kernels of vendor C (0.26 vs. 0.71). However, it was ineffective for non-lung-dedicated sharp kernels of vendor C (0.81 vs. 0.43) and showed limited improvement in variability of QCT measures at the subsegmental level. Consistent airway segmentation and identical anatomic labeling improved subsegmental airway variability in theoretical tests. Deep learning-based kernel conversion reduced the measurement variability of airway QCT across various kernels and vendors but was less effective for non-lung-dedicated kernels and subsegmental airways. Consistent airway segmentation and precise anatomic labeling can further enhance reproducibility for reliable automated quantification. Question How do different CT reconstruction kernels affect the measurement variability of automated airway measurements, and can deep learning-based kernel conversion reduce this variability? Findings Kernel conversion improved measurement consistency across vendors for lung-dedicated kernels, but showed limited effectiveness for non-lung-dedicated kernels and subsegmental airways. Clinical relevance Understanding kernel-related variability in airway quantification and mitigating it through deep learning enables standardized analysis, but further refinements are needed for robust airway segmentation, particularly for improving measurement variability in subsegmental airways and specific kernels.

Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these architectures primarily model local dependencies, lacking the ability to capture global interactions like pure Transformer. However, using pure Transformer directly causes the network to lose local feature information. To address this limitation, we propose the Generative Adversarial Dilated Attention Convolutional Transformer(GDacFormer). GDacFormer enhances interactions between tumor regions while balancing global and local information through the integration of adversarial learning with an improved transformer module. Specifically, GDacFormer leverages a generative adversarial segmentation network to learn richer and more detailed features. It integrates a novel Transformer module, DacFormer, featuring multi-scale dilated attention and a next convolution block. This module, embedded within the generator, aggregates semantic multi-scale information, efficiently reduces the redundancy in the self-attention mechanism, and enhances local feature representations, thus refining the brain tumor segmentation results. GDacFormer achieves Dice values for whole tumor, core tumor, and enhancing tumor segmentation of 90.9%/90.8%/93.7%, 84.6%/85.7%/93.5%, and 77.9%/79.3%/86.3% on BraTS2019-2021 datasets. Extensive evaluations demonstrate the effectiveness and competitiveness of GDacFormer. The code for GDacFormer will be made publicly available at https://github.com/MuqinZ/GDacFormer.

This research aims to enhance X-ray lung segmentation by addressing boundary distortions in anatomical structures, with the objective of refining segmentation boundaries and improving the morphological shape of segmented objects. The proposed approach combines the K-segment principal curve with Gaussian Process Regression (GPR) to refine segmentation boundaries, evaluated using lung X-ray datasets at varying resolutions. Several state-of-the-art models, including U-Net, SegNet, and TransUnet, were also assessed for comparison. The model employed a custom kernel for GPR, combining Radial Basis Function (RBF) with a cosine similarity term. The effectiveness of the model was evaluated using metrics such as the Dice Coefficient (DC) and Jaccard Index (JC) for segmentation accuracy, along with Average Symmetric Surface Distance (ASSD) and Hausdorff Distance (HD) for boundary alignment. The proposed method achieved superior segmentation performance, particularly at the highest resolution (1024x1024 pixels), with a DC of 95.7% for the left lung and 94.1% for the right lung. Among the different models, TransUnet outperformed others across both the semantic segmentation and boundary refinement stages, showing significant improvements in DC, JC, ASSD, and HD. The results indicate that the proposed boundary refinement approach effectively improves the segmentation quality of lung X-rays, excelling in refining well-defined structures and achieving superior boundary alignment, showcasing its potential for clinical applications. However, limitations exist when dealing with irregular or unpredictable shapes, suggesting areas for future enhancement.