In magnetic resonance imaging (MRI), anisotropic volumes with low through-plane resolution are typically acquired. Recently, diffusion models have shown strong performance in anisotropic MRI super-resolution (SR). However, iterative sam-pling limits the clinical applicability of diffusion models. To address this problem, we propose a One-Step Denoising Diffusion Probabilistic Model (OS-DDPM) for anisotropic MRI SR, which can generate isotropic High-Resolution (HR) data in a single sampling step. Firstly, we construct a Dual-Domain One-Step Generator (DDOS-Generator) comprising a student network for initial reconstruction in the spatial domain and a wavelet denoising module for noise suppression and detail refinement in the wavelet domain. Secondly, variational score distillation is applied to distill the generative prior from the pre-trained multi-step DDPM (teacher) to OS-DDPM, which can produce high-quality one-step SR data comparable to multi-step SR data. Finally, diffusion-based noise-aware contrastive learning is designed to bridge the distribution mismatch between one-step SR data and HR data. Extensive experiments on two public datasets demonstrate that the proposed method offers a fast inference speed and outperforms existing anisotropic SR methods and its teacher diffusion model in most metrics.

Parkinson's Disease (PD) significantly impacts driving abilities, often leading to early driving cessation or accidents due to reduced CHI '25, Yokohama, Japan

Recent advancements in large language models (LLMs) have demonstrated their potential to significantly impact finance trading, particularly through sentiment analysis. The cryptocurrency market, known for its volatility and unpredictability, often renders price-based trading approaches inadequate. This necessitates the adoption of more sophisticated techniques such as market sentiment analysis, which can benefit from the insights provided by LLMs. This study introduces an innovative method that integrates sentiment analysis derived from five distinct LLMs with deep reinforcement learning to devise a cryptocurrency trading strategy. Recognizing that LLM outputs cannot be guaranteed to be infallibly accurate, which contributing to the LLM hallucinations, this paper details the implementation of a stringent outlier detection and removal process. By adopting a “Trust-The-Majority” strategy, the research aims to ensure that trading decisions are informed by reliable sentiment data. In addition, sentiment scores are traditionally timestamped to the publication of news or social media posts. To more accurately reflect the actual impact of such information on market sentiment, this study applies the Ebbinghaus Forgetting Curve to model the waning influence of information over time. This allows for a more nuanced understanding of how news affects market dynamics. The enhanced sentiment scores, in conjunction with traditional market data such as OHLCV (Open, High, Low, Close, Volume), are utilized by a deep reinforcement learning model to make trading decisions. Experimental results demonstrate that the proposed multi-LLM sentiment-driven framework improves trading performance in the fast-paced cryptocurrency market. The methodology outlined in this paper offers a solid foundation for incorporating real-time market sentiment analysis into financial applications.

Intracranial aneurysms (IAs) pose a significant risk due to their potential to rupture, leading to severe clinical outcomes. Accurate quantification of aneurysm morphology and assessment of rupture risk are crucial for timely intervention and treatment. In this study, we present an approach for the geometric quantification of IAs and clinical rupture risk evaluation based on the relationship between these measurements. Our approach includes a comprehensive set of geometric characteristics, such as height, width, neck width, arterial diameter, area, and volume. We applied these measurements to the public IntrA dataset, and to our knowledge, this is the first geometric analysis conducted on this dataset. By integrating these morphological characteristics with expert assessments, we developed a predictive model for IA rupture risk. Our findings reveal a strong correlation between aneurysm depth and rupture risk, with even stronger associations observed for higher-order dimension metrics like surface area and volume. This highlights the critical role of 3D automatic quantification in evaluating rupture risk. This research provides a foundation for further geometric analysis of IAs and offers potential advancements in automated diagnostics and precision medicine.

Accurate segmentation of cerebral aneurysms from 3D vessel meshes is an essential yet challenging task, complicated by diverse imaging modalities that produce multi-resolution representations. Existing methods often struggle to handle meshes of varying granularity and orientations while maintaining segmentation accuracy. In this paper, we propose an end-to-end multi-scale graph-based segmentation framework that incorporates SE(3)-equivariance and an uncertainty-aware loss function. Our approach constructs a multi-scale graph representation on the 3D mesh and leverages a self-attention mechanism over graph edges to achieve adaptive neighborhood awareness, enabling the network to effectively handle multiple mesh resolutions simultaneously. By introducing an SE(3)-equivariant backbone, rotational variations in aneurysm orientation are naturally accommodated, ensuring relevant and effective feature learning. Furthermore, we develop an uncertainty-aware loss that adaptively emphasizes ambiguous regions, improving segmentation quality and confidence. Experimental results on the public datasets IntrA demonstrate that our method outperforms existing techniques, offering improved accuracy, consistency and stability across different mesh resolutions for 3D aneurysm segmentation. Code is available on https://github.com/Dolphin4mi/se3meshseg.

A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by proposing a two-stage diffusion model based on the representation of 3D shapes using signed distance function (SDF). Specifically, we design a diffusion model conditioned on partial shapes, we compress the 3D shape into a compact latent representation using the encoder in the vector quantized variational autoencoder (VQ-VAE) and learn the diffusion model based on this compressed discrete representation. Encoding the latent space with the autoencoder can achieve high-quality 3D cranial shape completion. In order to accurately capture local and fine-grained shape details, the training data is geometrically encoded from a compactly learned code-book. The two-stage diffusion generator with a coarse-to-fine approach possesses precise and expressive structural modeling capabilities to ensure the supplementation of detailed geometric information. Experimental results verified sufficient expressiveness of our model with generating high-fidelity results with fine-grained local details, outperforming the state-of-the-art methods.

Automated, accurate and robust segmentation of CT scans on abdominal organs remains a formidable challenge due to the blurred boundaries and subtle features of adjacent structures of abdominal organs. MedSAM, the fully fine-tuned model based on the Segment Anything Model (SAM) for medical image, fails to perform well relying solely on box prompt. Besides, utilizing the contextual information in the 3D volume data is tricky. To address above issues, we develop a MedSAM-based model for abdominal multi-organ CT segmentation. A feature selection network is introduced to extract entropy and curvature information. Together with the image embedding from the image encoder of MedSAM, the extracted information is processed during the inference. Low-Rank Adaptation (LoRA) is also applied to the image encoder and mask decoder of MedSAM for efficient fine-tuning on the FLARE2022 dataset. During the inference, mask prompts from segmentation results of last adjacent structure together with the expanded box prompt are used to improve the performance in continued context, thereby achieving an automated workflow. The experimental results suggest that the proposed method demonstrates a state-of-the-art performance and a potential clinical promise.

Automatic detecting intracranial aneurysms (IAs) poses significant challenges due to their diversity, varying locations, and complex classifications by size, shape, and phenotype. Current shape-based IAs detection methods, while promising, often neglect the topological connectivity of IAs vertices and the variable traits of the aneurysm's neck, leading to fragmented detections. To address these issues, we present a mesh convolutional neural network based on gauge equivariant convolution to leverage the topological and geometric features of 3D mesh models. Our network comprises four key components: anisotropic message passing (AMP) on mesh surfaces, gauge equivariant convolution (GEC), vector-aware feature reconstruction (VFR), and a pooling-free convolutional architecture. AMP ensures accurate detection of IAs from surrounding vessels by utilizing topological connectivity and anisotropic relationships between mesh vertices. GEC offers rotational equivariance for consistently learning geometric features, improving feature learning stability and efficiency. VFR preserves the geometric and directional integrity of the vector features, enriching the representational capacity of the network. The pooling-free convolutional architecture captures local and global geometric nuances of 3D meshes, achieving precise IAs detection and producing sharper IAs boundaries. Tests on the IntrA dataset show our method outperforms the current best by 1.83% and 1.02% in mIoU and mDSC, respectively.

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise.

Segmenting the cerebral vessels precisely from the time-of-flight magnetic resonance angiography (TOF-MRA) images is important for the diagnosis and therapy of the cerebrovascular diseases. Since the complex structures of cerebral vessels, the current cerebrovascular segmentation algorithms based on statistical model have less accuracy for stenotic vessels and are quite time-consuming. In this paper, we propose a novel automatic cerebrovascular segmentation algorithm based on focused Multi-Gaussians (FMG) model and weighted 3D Markov Random Field. As far as our knowledge, this is the first time to adopt multi-Gaussians distributions as vascular model with the purpose of modeling the vascular tissue more accurately. Furthermore, the fitting range is narrowed to local region related to vessels in order to make the model focus on the vascular tissue and simplify the finite mixture model. To incorporate precise local character of images to the model, we design a new weighted 3D MRF by a weighted neighborhood system (W-NBS). Finally, the particle swarm optimization (PSO) algorithm of parameter estimation has been implemented parallelly based on GPUs and the execution speed was improved by about 70 times. The experimental results show that the algorithm can produce detailed segmentation results especially for stenotic vessels.

Parkinson's disease (PD) is a chronic neurological disorder causing progressive disability that severely affects patients' quality of life. Although early interventions can provide significant benefits, PD diagnosis is often delayed due to both the mildness of early signs and the high requirements imposed by traditional screening and diagnosis methods. In this paper, we explore the feasibility and accuracy of detecting motor impairment in early PD via sensing and analyzing users' common touch gestural interactions on smartphones. We investigate four types of common gestures, including flick, drag, pinch, and handwriting gestures, and propose a set of features to capture PD motor signs. Through a 102-subject (35 early PD subjects and 67 age-matched controls) study, our approach achieved an AUC of 0.95 and 0.89/0.88 sensitivity/specificity in discriminating early PD subjects from healthy controls. Our work constitutes an important step towards unobtrusive, implicit, and convenient early PD detection from routine smartphone interactions.

Many smallholder coffee farmers in Latin America join cooperatives for increased access to global markets. This requires them to understand their costs relative to a complex sustainable coffee production process. To that end, we designed CalcuCafé, a web-based application for cooperative technicians and coffee farmers to calculate a farmer's costs of coffee production. We iteratively developed and evaluated CalcuCafé's design with members of two coffee cooperatives in Peru. Our research uncovered different expectations about the application between technicians and farmers, stemming from differing backgrounds, goals, and perspectives. Learning to use the application in a group setting helped overcome these differences and facilitated collaboration, resulting in a strong buy-in for the application. Our paper contributes a research and design effort to support smallholder coffee farmers, an underrecognized group at the intersection of HCI for sustainable agriculture and HCI for development.

Cerebral small vessel disease (SVD) is common in the elderly and is associated with loss of functional independence, institutionalization, and death. In this paper, we propose a coarse-to-fine feature selection method for accurate SVD detection and timely implementation of interventions. The proposed method first uses an Iterative Random Forest based Feature Selection (IRFFS) method to obtain the most representative features from a feature set that includes gait, balance, and agility performance features extracted from 17 predefined clinical actions. The method then uses the Feature Incremental Extreme Learning Machine (FIELM) model to further verify the discriminant ability of each kind of selected features. Our results demonstrate that the proposed method can effectively select the most significant features for SVD detection, which include gait and agility performance features. Our method achieves up to 91.44% classification accuracy, outperforming other state-of-the-art feature selection methods. Our findings also verify clinical observations indicating that the fine motor pattern features of upper and lower limbs are helpful for high-accuracy SVD detection.