As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation therapy, targeted biological therapy, immunotherapy) need better tumor characterization and prognostic information along with the accurate anatomy. Magnetic resonance imaging (MRI) has become the gold standard in head and neck cancer evaluation not only for staging but also for assessing tumor response, posttreatment status and complications, as well as for finding residual or recurrent tumor. Multiparametric anatomical and functional MRI (MP-MRI) is a true cancer imaging biomarker providing, in addition to high resolution tumor anatomy, more molecular and functional, qualitative and quantitative data using diffusion- weighted MRI (DW-MRI) and perfusion-dynamic contrast enhanced MRI (P-DCE-MRI), can improve the assessment of biological target volume and determine treatment response. DW-MRI provides information at the cellular level about the cell density and the integrity of the plasma membrane, based on water movement. P-DCE-MRI provides useful hemodynamic information about tissue vascularity and vascular permeability. Recent studies have shown promising results using radiomics features, MP-MRI has opened new perspectives in oncologic imaging with better realization of the latest technological advances with the help of artificial intelligence.

Cone beam computed tomography (CBCT) has revolutionized dental imaging due to its high spatial resolution and ability to provide detailed three-dimensional reconstructions of dental structures. This study introduces an innovative CBCT image processing method using an oriented object detection approach integrated with a Region of Interest (RoI) Transformer. This study addresses the challenge of accurate tooth detection and classification in PAN derived from CBCT, introducing an innovative oriented object detection approach, which has not been previously applied in dental imaging. This method better aligns with the natural growth patterns of teeth, allowing for more accurate detection and classification of molars, premolars, canines, and incisors. By integrating RoI transformer, the model demonstrates relatively acceptable performance metrics compared to conventional horizontal detection methods, while also offering enhanced visualization capabilities. Furthermore, post-processing techniques, including distance and grayscale value constraints, are employed to correct classification errors and reduce false positives, especially in areas with missing teeth. The experimental results indicate that the proposed method achieves an accuracy of 98.48%, a recall of 97.21%, an F1 score of 97.21%, and an mAP of 98.12% in tooth detection. The proposed method enhances the accuracy of tooth detection in CBCT-derived PAN by reducing background interference and improving the visualization of tooth orientation.

Nasal bone fractures represent the most common facial skeletal injury, challenging both function and aesthetics. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based review analyzed 23 studies published within the past 5 years, selected from 998 records retrieved from PubMed, Embase, and Web of Science. Data from 1780 participants were extracted, focusing on diagnostic methods, surgical techniques, anesthesia protocols, and long-term outcomes. Ultrasound and artificial intelligence-based algorithms improved diagnostic accuracy, while telephone triage streamlined necessary encounters. Navigation-assisted reduction, ballooning, and septal reduction with polydioxanone plates improved outcomes. Anesthetic approaches ranged from local nerve blocks to general anesthesia with intraoperative administration of lidocaine, alongside techniques to manage pain from nasal pack removal postoperatively. Long-term follow-up demonstrated improved quality of life, breathing function, and aesthetic satisfaction with timely and individualized treatment. This review highlights the trend toward personalized, technology-assisted approaches in nasal fracture management, highlighting areas for future research.

Although supervised deep learning methods have made significant advances in low-dose computed tomography (LDCT) image denoising, these approaches typically require pairs of low-dose and normal-dose CT images for training, which are often unavailable in clinical settings. Self-supervised deep learning (SSDL) has great potential to cast off the dependence on paired training datasets. However, existing SSDL methods are limited by the neighboring noise independence assumptions, making them ineffective for handling spatially correlated noises in LDCT images. To address this issue, this paper introduces a novel SSDL approach, named, Noise-Aware Blind Spot Network (NA-BSN), for high-quality LDCT imaging, while mitigating the dependence on the assumption of neighboring noise independence. NA-BSN achieves high-quality image reconstruction without referencing clean data through its explicit noise-aware constraint mechanism during the self-supervised learning process. Specifically, it is experimentally observed and theoretical proven that the l1 norm value of CT images in a downsampled space follows a certain descend trend with increasing of the radiation dose, which is then used to construct the explicit noise-aware constraint in the architecture of BSN for self-supervised LDCT image denoising. Various clinical datasets are adopted to validate the performance of the presented NA-BSN method. Experimental results reveal that NA-BSN significantly reduces the spatially correlated CT noises and retains crucial image details in various complex scenarios, such as different types of scanning machines, scanning positions, dose-level settings, and reconstruction kernels.

In medical image segmentation, convolutional neural networks (CNNs) and transformers are dominant. For CNNs, given the local receptive fields of convolutional layers, long-range spatial correlations are captured through consecutive convolutions and pooling. However, as the computational cost and memory footprint can be prohibitively large, 3D models can only afford fewer layers than 2D models with reduced receptive fields and abstract levels. For transformers, although long-range correlations can be captured by multi-head attention, its quadratic complexity with respect to input size is computationally demanding. Therefore, either model may require input size reduction to allow more filters and layers for better segmentation. Nevertheless, given their discrete nature, models trained with patch-wise training or image downsampling may produce suboptimal results when applied on higher resolutions. To address this issue, here we propose the resolution-robust HNOSeg-XS architecture. We model image segmentation by learnable partial differential equations through the Fourier neural operator which has the zero-shot super-resolution property. By replacing the Fourier transform by the Hartley transform and reformulating the problem in the frequency domain, we created the HNOSeg-XS model, which is resolution robust, fast, memory efficient, and extremely parameter efficient. When tested on the BraTS'23, KiTS'23, and MVSeg'23 datasets with a Tesla V100 GPU, HNOSeg-XS showed its superior resolution robustness with fewer than 34.7k model parameters. It also achieved the overall best inference time (< 0.24 s) and memory efficiency (< 1.8 GiB) compared to the tested CNN and transformer models.

To evaluate the diagnostic performance of multiparametric ultrasound (mpUS) and AI-assisted B-mode ultrasound (AI-US), and their potential to reduce unnecessary biopsies to B-mode for solid thyroid nodules. This prospective study enrolled 226 solid thyroid nodules with 145 malignant and 81 benign pathological results from 189 patients (35 men and 154 women; age range, 19-73 years; mean age, 45 years). Each nodule was examined using B-mode, microvascular flow imaging (MVFI), elastography with elasticity contrast index (ECI), and an AI system. Image data were recorded for each modality. Ten readers with different experience levels independently evaluated the B-mode images of each nodule to make a "benign" or "malignant" diagnosis in both an unblinded and blinded manner to the AI reports. The most accurate ECI value and MVFI mode were selected and combined with the dichotomous prediction of all readers. Descriptive statistics and AUCs were used to evaluate the diagnostic performances of mpUS and AI-US. Triple mpUS with B-mode, MVFI, and ECI exhibited the highest diagnostic performance (average AUC = 0.811 vs. 0.677 for B-mode, p = 0.001), followed by AI-US (average AUC = 0.718, p = 0.315). Triple mpUS significantly reduced the unnecessary biopsy rate by up to 12% (p = 0.007). AUC and specificity were significantly higher for triple mpUS than for AI-US mode (both p < 0.05). Compared to AI-US, triple mpUS (B-mode, MVFI, and ECI) exhibited better diagnostic performance for thyroid cancer diagnosis, and resulted in a significant reduction in unnecessary biopsy rate. AI systems are expected to take advantage of multi-modal information to facilitate diagnoses.

To develop and evaluate a hierarchical deep learning system that detects orbital fractures on computed tomography (CT) images and classifies them as depressed or trap-door types. A retrospective diagnostic accuracy study analyzing CT images from patients with confirmed orbital fractures. We collected CT images from 686 patients with orbital fractures treated at a single institution (2010-2025), resulting in 46,013 orbital CT slices. After preprocessing, 7809 slices were selected as regions of interest and partitioned into training (6508 slices) and test (1301 slices) datasets. Our hierarchical approach consisted of a first-stage classifier (YOLOv8) for fracture detection and a second-stage classifier (Vision Transformer) for distinguishing depressed from trap-door fractures. Performance was evaluated at both slice and patient levels, focusing on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) at both slice and patient levels. For fracture detection, YOLOv8 achieved a slice-level sensitivity of 80.4 % and specificity of 79.2 %, with patient-level performance improving to 94.7 % sensitivity and 90.0 % specificity. For fracture classification, Vision Transformer demonstrated a slice-level sensitivity of 91.5 % and specificity of 83.5 % for trap-door and depressed fractures, with patient-level metrics of 100 % sensitivity and 88.9 % specificity. The complete system correctly identified 18/20 no-fracture cases, 35/40 depressed fracture cases, and 15/17 trap-door fracture cases. Our hierarchical deep learning system effectively detects orbital fractures and distinguishes between depressed and trap-door types with high accuracy. This approach could aid in the timely identification of trap-door fractures requiring urgent surgical intervention, particularly in settings lacking specialized expertise.

Compressive imaging (CI) reconstruction, such as snapshot compressive imaging (SCI) and compressive sensing magnetic resonance imaging (MRI), aims to recover high-dimensional images from low-dimensional compressed measurements. This process critically relies on learning an accurate representation of the underlying high-dimensional image. However, existing unsupervised representations may struggle to achieve a desired balance between representation ability and efficiency. To overcome this limitation, we propose Tensor Decomposed multi-resolution Grid encoding (GridTD), an unsupervised continuous representation framework for CI reconstruction. GridTD optimizes a lightweight neural network and the input tensor decomposition model whose parameters are learned via multi-resolution hash grid encoding. It inherently enjoys the hierarchical modeling ability of multi-resolution grid encoding and the compactness of tensor decomposition, enabling effective and efficient reconstruction of high-dimensional images. Theoretical analyses for the algorithm's Lipschitz property, generalization error bound, and fixed-point convergence reveal the intrinsic superiority of GridTD as compared with existing continuous representation models. Extensive experiments across diverse CI tasks, including video SCI, spectral SCI, and compressive dynamic MRI reconstruction, consistently demonstrate the superiority of GridTD over existing methods, positioning GridTD as a versatile and state-of-the-art CI reconstruction method.

Medical image segmentation is vital for accurate diagnosis. While U-Net-based models are effective, they struggle to capture long-range dependencies in complex anatomy. We propose GH-UNet, a Group-wise Hybrid Convolution-ViT model within the U-Net framework, to address this limitation. GH-UNet integrates a hybrid convolution-Transformer encoder for both local detail and global context modeling, a Group-wise Dynamic Gating (GDG) module for adaptive feature weighting, and a cascaded decoder for multi-scale integration. Both the encoder and GDG are modular, enabling compatibility with various CNN or ViT backbones. Extensive experiments on five public and one private dataset show GH-UNet consistently achieves superior performance. On ISIC2016, it surpasses H2Former with 1.37% and 1.94% gains in DICE and IOU, respectively, using only 38% of the parameters and 49.61% of the FLOPs. The code is freely accessible via: https://github.com/xiachashuanghua/GH-UNet .

Quantifying diagnostic image quality (IQ) is not straightforward but essential for optimizing the balance between IQ and radiation dose, and for ensuring consistent high-quality images in CT imaging. This review provides a comprehensive overview of advanced objective reference-free IQ assessment methods for CT scans, beyond standard approaches. A literature search was performed in PubMed and Web of Science up to June 2024 to identify studies using advanced objective image quality methods on clinical CT scans. Only reference-free methods, which do not require a predefined reference image, were included. Traditional methods relying on the standard deviation of the Hounsfield units, the signal-to-noise ratio or contrast-to-noise ratio, all within a manually selected region-of-interest, were excluded. Eligible results were categorized by IQ metric (i.e., noise, contrast, spatial resolution and other) and assessment method (manual, automated, and artificial intelligence (AI)-based). Thirty-five studies were included that proposed or employed reference-free IQ methods, identifying 12 noise assessment methods, 4 contrast assessment methods, 14 spatial resolution assessment methods and 7 others, based on manual, automated or AI-based approaches. This review emphasizes the transition from manual to fully automated approaches for IQ assessment, including the potential of AI-based methods, and it provides a reference tool for researchers and radiologists who need to make a well-considered choice in how to evaluate IQ in CT imaging. This review examines the challenge of quantifying diagnostic CT image quality, essential for optimization studies and ensuring consistent high-quality images, by providing an overview of objective reference-free diagnostic image quality assessment methods beyond standard methods. Quantifying diagnostic CT image quality remains a key challenge. This review summarizes objective diagnostic image quality assessment techniques beyond standard metrics. A decision tree is provided to help select optimal image quality assessment techniques.