Radiotherapy is a crucial treatment for brain tumor malignancies. To address the limitations of CT-based treatment planning, recent research has explored MR-only radiotherapy, requiring precise MR-to-CT synthesis. This study compares two deep learning approaches, supervised (Pix2Pix) and unsupervised (CycleGAN), for generating pseudo-CT (pCT) images from T1- and T2-weighted MR sequences. 3270 paired T1- and T2-weighted MRI images were collected and registered with corresponding CT images. After preprocessing, a supervised pCT generative model was trained using the Pix2Pix framework, and an unsupervised generative network (CycleGAN) was also trained to enable a comparative assessment of pCT quality relative to the Pix2Pix model. To assess differences between pCT and reference CT images, three key metrics (SSIM, PSNR, and MAE) were used. Additionally, a dosimetric evaluation was performed on selected cases to assess clinical relevance. The average SSIM, PSNR, and MAE for Pix2Pix on T1 images were 0.964 ± 0.03, 32.812 ± 5.21, and 79.681 ± 9.52 HU, respectively. Statistical analysis revealed that Pix2Pix significantly outperformed CycleGAN in generating high-fidelity pCT images (p < 0.05). There was no notable difference in the effectiveness of T1-weighted versus T2-weighted MR images for generating pCT (p > 0.05). Dosimetric evaluation confirmed comparable dose distributions between pCT and reference CT, supporting clinical feasibility. Both supervised and unsupervised methods demonstrated the capability to generate accurate pCT images from conventional T1- and T2-weighted MR sequences. While supervised methods like Pix2Pix achieve higher accuracy, unsupervised approaches such as CycleGAN offer greater flexibility by eliminating the need for paired training data, making them suitable for applications where paired data is unavailable.

This study focuses on determining the effective young modulus (stiffness) of various lattice structures for titanium scaffolds filled with magnesium and without magnesium. For specific patient success of the implant is depends on adequate elastic modulus which helps proper osteointegration. The Mg filled portion in the Ti scaffold is expected to dissolve with time as the bone growth through the Ti scaffold porous cavity is started. The proposed method is based on a general numerical homogenization scheme to determine the effective elastic properties of the lattice scaffold at the macroscopic scale. A large numerical campaign has been conducted on 18 geometries. The 3D scaffold is conceived based on the model generated from the Micro CT data of the prepared sample. The effect of the scaffold local features, e.g., the distribution of porosity, presence of scaffold's surface area to the adjacent bone location, strut diameter of implant, on the effective elastic properties is investigated. Results show that both the relative density and the geometrical features of the scaffold strongly affect the equivalent macroscopic elastic behaviour of the lattice. 6 samples are made (three each Mg filled and three without Mg) The compression test was carried out for each type of samples and the displacement obtained from the test results were in close match with the simulated results from finite element analysis. To predict the unknown required stiffness what would be the ratio between Ti scaffold and filled up Mg have been calculated using the data driven AI model.

Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities. Retraining or fine-tuning these models requires substantial human effort and computational resources. To address this, in-context learning (ICL) has emerged as a promising paradigm, enabling query image segmentation by conditioning on example image-mask pairs provided as prompts. Unlike previous approaches that rely on implicit modeling or non-end-to-end pipelines, we redefine the core interaction mechanism in ICL as an explicit retrieval process, termed E-ICL, benefiting from the emergence of vision foundation models (VFMs). E-ICL captures dense correspondences between queries and prompts at minimal learning cost and leverages them to dynamically weight multi-class prompt masks. Built upon E-ICL, we propose EICSeg, the first end-to-end ICL framework that integrates complementary VFMs for universal medical image segmentation. Specifically, we introduce a lightweight SD-Adapter to bridge the distinct functionalities of the VFMs, enabling more accurate segmentation predictions. To fully exploit the potential of EICSeg, we further design a scalable self-prompt training strategy and an adaptive token-to-image prompt selection mechanism, facilitating both efficient training and inference. EICSeg is trained on 47 datasets covering diverse modalities and segmentation targets. Experiments on nine unseen datasets demonstrate its strong few-shot generalization ability, achieving an average Dice score of 74.0%, outperforming existing in-context and few-shot methods by 4.5%, and reducing the gap to task-specific models to 10.8%. Even with a single prompt, EICSeg achieves a competitive average Dice score of 60.1%. Notably, it performs automatic segmentation without manual prompt engineering, delivering results comparable to interactive models while requiring minimal labeled data. Source code will be available at https://github.com/ zerone-fg/EICSeg.

Graph neural networks (GNNs) have achieved remarkable success in learning graph representations, especially graph Transformers, which have recently shown superior performance on various graph mining tasks. However, the graph Transformer generally treats nodes as tokens, which results in quadratic complexity regarding the number of nodes during self-attention computation. The graph multilayer perceptron (MLP) mixer addresses this challenge using the efficient MLP Mixer technique from computer vision. However, the time-consuming process of extracting graph tokens limits its performance. In this article, we present a novel architecture named ChebMixer, a newly proposed graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens. First, we produce multiscale representations of graph nodes via fast Chebyshev polynomial-based spectral filtering. Next, we consider each node's multiscale representations as a sequence of tokens and refine the node representation with an effective MLP Mixer. Finally, we aggregate the multiscale representations of nodes through Chebyshev interpolation. Owing to the powerful representation capabilities and fast computational properties of the MLP Mixer, we can quickly extract more informative node representations to improve the performance of downstream tasks. The experimental results prove our significant improvements in various scenarios, ranging from homogeneous and heterophilic graph node classification to medical image segmentation. Compared with NAGphormer, the average performance improved by 1.45% on homogeneous graphs and 4.15% on heterophilic graphs. And the average performance improved by 1.39% on medical image segmentation tasks compared with VM-UNet. We will release the source code after this article is accepted.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

Ultrasound examinations, while valuable, are time-consuming and often limited in availability. Consequently, many hospitals implement reservation systems; however, these systems typically lack prioritization for examination purposes. Hence, our hospital uses a waitlist system that prioritizes examination requests based on their clinical value when slots become available due to cancellations. This system, however, requires a manual review of examination purposes, which are recorded in free-form text. We hypothesized that artificial intelligence language models could preliminarily estimate the priority of requests before manual reviews. This study aimed to investigate potential challenges associated with using language models for estimating the priority of medical examination requests and to evaluate the performance of language models in processing Japanese medical texts. We retrospectively collected ultrasound examination requests from the waitlist system at Keio University Hospital, spanning January 2020 to March 2023. Each request comprised an examination purpose documented by the requesting physician and a 6-tier priority level assigned by a radiologist during the clinical workflow. We fine-tuned JMedRoBERTa, Luke, OpenCalm, and LLaMA2 under two conditions: (1) tuning only the final layer and (2) tuning all layers using either standard backpropagation or low-rank adaptation. We had 2335 and 204 requests in the training and test datasets post cleaning. When only the final layers were tuned, JMedRoBERTa outperformed the other models (Kendall coefficient=0.225). With full fine-tuning, JMedRoBERTa continued to perform best (Kendall coefficient=0.254), though with reduced margins compared with the other models. The radiologist's retrospective re-evaluation yielded a Kendall coefficient of 0.221. Language models can estimate the priority of examination requests with accuracy comparable with that of human radiologists. The fine-tuning results indicate that general-purpose language models can be adapted to domain-specific texts (ie, Japanese medical texts) with sufficient fine-tuning. Further research is required to address priority rank ambiguity, expand the dataset across multiple institutions, and explore more recent language models with potentially higher performance or better suitability for this task.

Liver multiphase enhanced computed tomography (MPECT) is vital in clinical practice, but its utility is limited by various factors. We aimed to develop a deep learning network capable of automatically generating MPECT images from standard non-contrast CT scans. Dataset 1 included 374 patients and was divided into three parts: a training set, a validation set and a test set. Dataset 2 included 144 patients with one specific liver disease and was used as an internal test dataset. We further collected another dataset comprising 83 patients for external validation. Then, we propose a Mask-Adaptive Normalization-based Generative Adversarial Network with Cycle-Consistency Loss (MAN-GAN) to achieve non-contrast CT to MPECT translation. To assess the efficiency of MAN-GAN, we conducted a comparative analysis with state-of-the-art methods commonly employed in diverse medical image synthesis tasks. Moreover, two subjective radiologist evaluation studies were performed to verify the clinical usefulness of the generated images. MAN-GAN outperformed the baseline network and other state-of-the-art methods in all generations of the three phases. These results were verified in internal and external datasets. According to radiological evaluation, the image quality of generated three phase images are all above average. Moreover, the similarities between real images and generated images in all three phases are satisfactory. MAN-GAN demonstrates the feasibility of liver MPECT image translation based on non-contrast images and achieves state-of-the-art performance via the subtraction strategy. It has great potential for solving the dilemma of liver CT contrast canning and aiding further liver interaction clinical scenarios.

Foundation models for medical imaging are a prominent research topic, but risks associated with the imaging features they can capture have not been explored. We aimed to assess whether imaging features from foundation models enable patient re-identification and to relate re-identification to demographic features prediction. Our data included Colour Fundus Photos (CFP), Optical Coherence Tomography (OCT) b-scans, and chest x-rays and we reported re-identification rates of 40.3%, 46.3%, and 25.9%, respectively. We reported varying performance on demographic features prediction depending on re-identification status (e.g., AUC-ROC for gender from CFP is 82.1% for re-identified images vs. 76.8% for non-re-identified ones). When training a deep learning model on the re-identification task, we reported performance of 82.3%, 93.9%, and 63.7% at image level on our internal CFP, OCT, and chest x-ray data. We showed that imaging features extracted from foundation models in ophthalmology and radiology include information that can lead to patient re-identification.

Accurate assessments of axial vertebral rotation (AVR) is essential for managing idiopathic scoliosis. The Nash-Moe classification method has been extensively used for AVR assessment; however, its subjective nature can lead to measurement variability. Therefore, herein, we propose an automated deep learning (DL) model for AVR assessment based on posteroanterior spinal radiographs. We develop a two-stage DL framework using the MMRotate toolbox and analyze 1080 posteroanterior spinal radiographs of patients aged 4-18 years. The framework comprises a vertebra detection model (864 training and 216 validation images) and a pedicle detection model (14,608 training and 3652 validation images). We improved the Nash-Moe classification method by implementing a 12-segment division system and width ratio metric for precise pedicle assessment. The vertebra and pedicle detection models achieved mean average precision values of 0.909 and 0.905, respectively. The overall classification accuracy was 0.74, with grade-specific performance between 0.70 and 1.00 for precision and 0.33 and 0.93 for recall across Grades 0-3. The proposed DL framework processed complete posteroanterior radiographs in < 5 s per case compared with conventional manual measurements (114 s per radiograph). The best performance was observed in mild to moderate rotation cases, with performance in severe rotation cases limited by insufficient data. The implementation of DL framework for the automated Nash-Moe classification method exhibited satisfactory accuracy and exceptional efficiency. However, this study is limited by low recall (0.33) for Grade 3 and the inability to classify Grade 4 towing to dataset constraints. Further validation using augmented datasets that include severe rotation cases is necessary.

Magnetic resonance imaging (MRI) involves a trade-off between imaging time, signal-to-noise ratio (SNR), and spatial resolution. Reducing the imaging time often leads to a lower SNR or resolution. Deep-learning-based reconstruction (DLR) methods have been introduced to address these limitations. Image-domain super-resolution DLR enables high resolution without additional image scans. High-quality images can be obtained within a shorter timeframe by appropriately configuring DLR parameters. It is necessary to maximize the performance of super-resolution DLR to enable efficient use in MRI. We evaluated the performance of a vendor-provided super-resolution DLR method (PIQE) on a Canon 3 T MRI scanner using an edge phantom and clinical brain images from eight patients. Quantitative assessment included structural similarity index (SSIM), peak SNR (PSNR), root mean square error (RMSE), and full width at half maximum (FWHM). FWHM was used to quantitatively assess spatial resolution and image sharpness. Visual evaluation using a five-point Likert scale was also performed to assess perceived image quality. Image domain super-resolution DLR reduced scan time by up to 70 % while preserving the structural image quality. Acquisition matrices of 0.87 mm/pixel or finer with a zoom ratio of ×2 yielded SSIM ≥0.80, PSNR ≥35 dB, and non-significant FWHM differences compared to full-resolution references. In contrast, aggressive downsampling (zoom ratio 3 from low-resolution matrices) led to image degradation including truncation artifacts and reduced sharpness. These results clarify the optimal use of PIQE as an image-domain super-resolution method and provide practical guidance for its application in clinical MRI workflows.