Adaptive Sequential Bayesian Iterative Learning for Myocardial Motion Estimation on Cardiac Image Sequences.

Motion estimation of left ventricle myocardium on the cardiac image sequence is crucial for assessing cardiac function. However, the intensity variation of cardiac image sequences brings the challenge of uncertain interference to myocardial motion estimation. Such imaging-related uncertain interference appears in different cardiac imaging modalities.

A Meta-Learning Approach for Multicenter and Small-Data Single-Cell Image Analysis.

The application of algorithm-based single-cell imaging techniques can visualize and analyze cellular heterogeneity. However, algorithm-based single-cell imaging techniques are severely limited by the high workload required to label single-cell images and the high variation of cells from different sources. Herein, we propose a meta-learning approach for multicenter and small-data single-cell image analysis.

Energy loss minimization-based side branch flow model for FFR calculation based on intracoronary images.

Background: Fractional flow reserve (FFR) based on intracoronary images, referred to as iFFR, is considered an important functional assessment index for diagnosing coronary stenosis. However, intracoronary images lack side branch information, making accurate side branch flow compensation highly challenging. Existing methods using bifurcation fractal law or supplementary side branch geometry fail to achieve fast and accurate side branch flow compensation.

An orchestration learning framework for ultrasound imaging: Prompt-Guided Hyper-Perception and Attention-Matching Downstream Synchronization.

Ultrasound imaging is pivotal in clinical diagnostics due to its affordability, portability, safety, real-time capability, and non-invasive nature. It is widely utilized for examining various organs, such as the breast, thyroid, ovary, cardiac, and more. However, the manual interpretation and annotation of ultrasound images are time-consuming and prone to variability among physicians.

Causal recurrent intervention for cross-modal cardiac image segmentation.

Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities. For instance, cardiovascular pathologies such as myocardial infarction and congenital heart defects require precise cross-modal characterization to guide clinical decisions.

Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography.

The coronary angiography-derived fractional flow reserve (FFR) curve, referred to as the Angio-FFR curve, is crucial for guiding percutaneous coronary intervention (PCI). The invasive FFR is the diagnostic gold standard for determining functional significance and is recommended to complement coronary angiography. The invasive FFR curve can quantitatively define disease patterns.

Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

Rationale And Objectives: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This study aimed to develop deep learning (DL) and radiomics models using coronary computed tomography angiography (CCTA) to differentiate CTO from STO lesions and compare their performance with that of the conventional method.

Revisiting medical image retrieval via knowledge consolidation.

As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical component of clinical data management, playing a vital role in decision-making and safeguarding patient information. Existing methods usually learn hash functions using bottleneck features, which fail to produce representative hash codes from blended embeddings.

FedBM: Stealing knowledge from pre-trained language models for heterogeneous federated learning.

Federated learning (FL) has shown great potential in medical image computing since it provides a decentralized learning paradigm that allows multiple clients to train a model collaboratively without privacy leakage. However, current studies have shown that data heterogeneity incurs local learning bias in classifiers and feature extractors of client models during local training, leading to the performance degradation of a federation system. To address these issues, we propose a novel framework called Federated Bias eliMinating (FedBM) to get rid of local learning bias in heterogeneous federated learning (FL), which mainly consists of two modules, i.

Bridging multi-level gaps: Bidirectional reciprocal cycle framework for text-guided label-efficient segmentation in echocardiography.

Text-guided visual understanding is a potential solution for downstream task learning in echocardiography. It can reduce reliance on labeled large datasets and facilitate learning clinical tasks. This is because the text can embed highly condensed clinical information into predictions for visual tasks.

Study-level cross-modal retrieval of chest x-ray images and reports with adapter-based fine-tuning.

Cross-modal retrieval is crucial for improving clinical decision-making and report generation. However, current technologies mainly focus on linking single images with reports, ignoring the need to comprehensively observe multiple images in real clinical environments. Additionally, differences in imaging equipment, scanning parameters, geographic regions, and reporting styles in chest x-rays and reports cause inconsistent data distributions, which challenge model reliability and generalization.

Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts.

Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography.

Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. Computer-aided segmentation can reduce the workflow greatly.

Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation.

Background: Percutaneous extracorporeal membrane oxygenation (ECMO) is administered to pediatric patients with cardiogenic shock or cardiac arrest. The traditional method uses focal echocardiography to complete the left ventricular measurement. However, echocardiographic determination of the ejection fraction (EF) by manual tracing of the endocardial borders is time consuming and operator dependent.

Multi-Level Noise Sampling From Single Image for Low-Dose Tomography Reconstruction.

Low-dose digital radiography (DR) and computed tomography (CT) become increasingly popular due to reduced radiation dose. However, they often result in degraded images with lower signal-to-noise ratios, creating an urgent need for effective denoising techniques. The recent advancement of the single-image-based denoising approach provides a promising solution without requirement of pairwise training data, which are scarce in medical imaging.

Design and implementation of corrosion-resistant multitasking cell stainers.

Compared with machine staining, traditional manual staining faces various problems, such as a low preparation success rate, low efficiency, and harm to the human body due to corrosive gases. Therefore, a stainer that is of low cost, has strong corrosion resistance, and is suitable for small-batch preparation should be developed. In this study, by choosing a rotary scheme as the structural basis, a reusable container cover and a master-slave manipulator cooperation scheme are developed, which greatly improve the space utilization rate.

Main Coronary Flow Calculation With the Assistance of Physiological Side Branch Flow.

Objective: Fractional flow reserve (FFR) derived from coronary angiography, referred to as ICA-FFR, is a less-invasive alternative for invasive FFR measurement based on computational fluid dynamics. Blood flow into side branches influences the accuracy of ICA-FFR. However, properly compensating for side branch flow in ICA-FFR analysis is challenging.

Adaptive dynamic inference for few-shot left atrium segmentation.

Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images is crucial for aiding the treatment of patients with atrial fibrillation. Few-shot learning holds significant potential for achieving accurate LA segmentation with low demand on high-cost labeled LGE CMR data and fast generalization across different centers. However, accurate LA segmentation with few-shot learning is a challenging task due to the low-intensity contrast between the LA and other neighboring organs in LGE CMR images.

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors.

Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi-level fusion framework.

The use of multi-modality non-contrast images (i.e., T1FS, T2FS and DWI) for segmenting liver tumors provides a solution by eliminating the use of contrast agents and is crucial for clinical diagnosis.

A coronary CT angiography-derived myocardial radiomics model for predicting adverse outcomes in chronic myocardial infarction.

Background: The prognostic efficacy of a coronary computed tomography angiography (CCTA)-derived myocardial radiomics model in patients with chronic myocardial infarction (MI) is unclear.

Methods: In this retrospective study, a cohort of 236 patients with chronic MI who underwent both CCTA and cardiac magnetic resonance (CMR) examinations within 30 days were enrolled and randomly divided into training and testing datasets at a ratio of 7:3. The clinical endpoints were major adverse cardiovascular events (MACE), defined as all-cause death, myocardial reinfarction and heart failure hospitalization.

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation From Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge.

Non-Invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

Objective: Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography.

Background And Objective: Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value.