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IEEE International Symposium on biomedical imaging

作者： Liyun Chen Linlin Yao Qian Wang Zhong Xue School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai United Imaging Intelligence Co. Ltd Shanghai China School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Deep learning models have been successfully applied to lung nodule malignancy analysis but lack interpretability, for example, in linking underlying nodule visual attributes with malignancy prediction. In this paper, we combine visual attribute prediction and malignancy classification tasks for assisted nodule diagnosis while providing imaging characterization. A semi-supervised attribute prediction model is pre-trained first, and then the downstream lung nodule malignancy classification is fine-tuned, maintaining sensitivity to nodule attributes. Using data from 1018 public and 3547 in-house subjects, we demonstrate that the proposed method yields high malignancy classification with a sensitivity of 87.56%, specificity of 81.92%, and an AUC of 92.41%, while also improving model attribute prediction.

关键词： Deep learning Visualization Analytical models Sensitivity Lung Self-supervised learning Predictive models

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MRI Radiomic Features Harmonization: A Multi-center Phantom Study

MRI Radiomic Features Harmonization: A Multi-Center Phantom ...

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2022 IEEE Nuclear Science Symposium, Medical imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Hajianfar, Ghasem Hosseini, Seyyed Ali Amini, Mehdi Shiri, Isaac Zaidi, Habib Iran University of Medical Sciences Rajaie Cardiovascular Medical and Research Center Tehran Iran Tehran University of Medical Sciences Department of Medical Physics and Biomedical Engineering Tehran Iran Geneva University Hospital Division of Nuclear Medicine and Molecular Imaging Geneva1211 Switzerland

ISBN: (纸本)9781665488723

In cancer imaging, radiomic features are a vital tool for diagnosing, quantifying intratumor heterogeneity, and predicting response to therapy and prognosis. However, the lack of a robust evaluation makes applying them across multi-center studies and larger patient populations complex. The main goal of this study is to use an MRI phantom to examine the reproducibility of MRI radiomic features that represent feature variability with ComBat harmonization in a multi-center examination with various acquisition settings and image pre-processing steps. An MRI phantom was used to acquire images using four scanners. Manual segmentation was used for 19 phantom compartments. Wavelet, bin discretization, and Laplacian of Gaussian were employed to pre-process each image. Ninety-two radiomic features were extracted. ComBat harmonization was applied to all feature sets. The Kruskal-(KW) Wallis's and the Intraclass Correlation Coefficient (ICC) tests were utilized to indicate radiomic features' variability. The number of non-significant p-values ranged from 29-74 and 0-5 features after and before harmonization over different image pre-processing. The results showed that the radiomic features are influenced most by various scanners. The impact of ComBat harmonization on the reproducibility of MRI radiomic features is considerable. © 2022 IEEE.

关键词： ComBat harmonization MRI Phantom Radiomic features robustness

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engineering biocompatible TeSex nano-alloys as a versatile theranostic nanoplatform

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National Science Review 2021年第6期8卷 148-159页

作者： Xiang Ling Zhaokui Jin Qi Jiang Xiaotao Wang Bin Wei Zhongchang Wang Yangsen Xu Tianye Cao Jonathan W.Engle Weibo Cai Chenliang Su Qianjun He International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong ProvinceInstitute of Microscale OptoelectronicsShenzhen University Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical UltrasoundSchool of Biomedical EngineeringHealth Science CenterShenzhen University Department of Quantum and Energy Materials International Iberian Nanotechnology Laboratory (INL) Departments of Radiology and Medical Physics University of Wisconsin-Madison

Photothermal nanotheranostics, especially in the near infrared II（NIR-II） region, exhibits a great potential in precision and personalized medicine, owing to high tissue penetration of NIR-II ***-II-photothermal nanoplatforms with high biocompatibility as well as high photothermal effect are urgently needed but rarely reported so far. Te nanomaterials possess high absorbance to NIR-II light but also exhibit high cytotoxicity, impeding their biomedical applications. In this work, the controllable incorporation of biocompatible Se into the lattice of Te nanostructures is proposed to intrinsically tune their inherent cytotoxicity and enhance their biocompatibility, developing TeSexnano-alloys as a new kind of theranostic nanoplatform. We have uncovered that the cytotoxicity of Te nanomaterials primarily derives from irreversible oxidation stress and intracellular imbalance of organization and energy, and can be eliminated by incorporating a moderate proportion of Se（x = 0.43）. We have also discovered that the as-prepared TeSexnano-alloys have extraordinarily high NIR-II-photothermal conversion efficiency（77.2%）,64Cu coordination and computed tomography contrast capabilities, enabling high-efficacy multimodal photothermal/photoacoustic/positron emission tomography/computed tomography imaging-guided NIR-II-photothermal therapy of cancer. The proposed nano-alloying strategy provides a new route to improve the biocompatibility of biomedical nanoplatforms and endow them with versatile theranostic functions.

关键词： nanomedicine nano-alloys photothermal therapy cancer nanotheranostics biomedical imaging

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Dual-Branch Multimodal Fusion Network for Skin Lesions Diagnosis using Clinical and Ultrasound Image

Dual-Branch Multimodal Fusion Network for Skin Lesions Diagn...

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IEEE International Symposium on biomedical imaging

作者： Chunlun Xiao Anqi Zhu Chunmei Xia Yuanlin Liu Zifeng Qiu Qiao Wang Weiwei Ren Dandan Shan Tianfu Wang Lehang Guo Baiying Lei National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Department of Medical Ultrasound Shanghai Tenth People’s Hospital Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment

Skin lesions describe the abnormal skin tissue that may be an indicator of cancer. Early diagnosis of benign and malignant diseases is crucial. The use of deep learning-based methods can reduce the influence of personnel subjectivity and improve the accuracy of diagnosis. This paper studies the benign and malignant skin disease diagnosis using a multi-modal fusion network. Specifically, we design a two-branch learning network, including global branches and local branches, to learn different information respectively. In addition, we design an attention-based feature fusion strategy to focus more on learning the features of the lesion area, which enhance more discriminative feature information of each modality. Note that this method focuses on learning feature information from clinical images (CN) and grayscale ultrasound images (US). Our method achieves the best performance compared to other methods by conducting experiments on self-collected dataset.

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Coronary Atherosclerotic Plaque Characterization with Photon-counting CT: a Simulation-based Feasibility Study

arXiv

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arXiv 2023年

作者： Li, Mengzhou Wu, Mingye Pack, Jed Wu, Pengwei De Man, Bruno Wang, Adam Nieman, Koen Wang, Ge Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Research Department of Biomedical Engineering School of Engineering Rensselaer Polytechnic Institute TroyNY United States GE HealthCare Technology & Innovation Center NiskayunaNY United States Department of Radiology Stanford University CA United States Stanford University CA United States

Recent development of photon-counting CT (PCCT) brings great opportunities for plaque characterization with much-improved spatial resolution and spectral imaging capability. While existing coronary plaque PCCT imaging results are based on detectors made of CZT or CdTe materials, deep-silicon photon-counting detectors have unique performance characteristics and promise distinct imaging capabilities. In this work, we report a systematic simulation study of a deep-silicon PCCT scanner with a new clinically-relevant digital plaque phantom with realistic geometrical parameters and chemical compositions. This work investigates the effects of spatial resolution, noise, motion artifacts, radiation dose, and spectral characterization. Our simulation results suggest that the deep-silicon PCCT design provides adequate spatial resolution for visualizing a necrotic core and quantitation of key plaque features. Advanced denoising techniques and aggressive bowtie filter designs can keep image noise to acceptable levels at this resolution while keeping radiation dose comparable to that of a conventional CT scan. The ultrahigh resolution of PCCT also means an elevated sensitivity to motion artifacts. It is found that a tolerance of less than 0.4 mm residual movement range requires the application of accurate motion correction methods for best plaque imaging quality with PCCT. © 2023, CC BY-NC-SA.

关键词： Silicon

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Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

arXiv

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arXiv 2024年

作者： Wang, Shanshan Wu, Ruoyou Jia, Sen Diakite, Alou Li, Cheng Liu, Qiegen Ying, Leslie Paul C Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China University of Chinese Academy of Sciences Beijing China Department of Biomedical Engineering Department of Electrical Engineering The State University of New York BuffaloNY14260 United States Department of Electronic Information Engineering Nanchang University Nanchang China Paul C. Lauterbur Research Centre for Biomedical Imaging Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors’ choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems. © 2024, CC BY-NC-ND.

关键词： Image reconstruction

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Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data

arXiv

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arXiv 2024年

作者： Pistos, Michalis Li, Gang Lin, Weili Shen, Dinggang Rekik, Islem BASIRA Lab Imperial-X Department of Computing Imperial College London London United Kingdom Department of Radiology Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain’s anatomy, existing deep learning frameworks focused on forecasting the brain evolution trajectory from a single baseline observation. While yielding remarkable results, they suffer from three major limitations. First, they lack the ability to generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or connectivity type (e.g., T1-w MRI). Second, existing models require extensive training datasets to achieve satisfactory performance which are often challenging to obtain. Third, they do not efficiently utilize incomplete time series data. To address these limitations, we introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets. As a result, we enhance the performance of each hospital’s local generative model, while preserving data privacy. The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations. Our comprehensive experimental results showed the outperformance of FedGmTE-Net++ in brain multi-trajectory prediction from a single baseline graph in comparison with benchmark methods. Our source code is availabl

关键词： Forecasting

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Parameter Choices in Haarpsi for IQA with Medical Images 22

Parameter Choices in Haarpsi for IQA with Medical Images

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22nd IEEE International Symposium on biomedical imaging, ISBI 2025

作者： Karner, Clemens Gröhl, Janek Selby, Ian Babar, Judith Beckford, Jake Else, Thomas R. Sadler, Timothy J. Shahipasand, Shahab Thavakumar, Arthikkaa Roberts, Michael Rudd, James H.F. Schönlieb, Carola-Bibiane Weir-Mccall, Jonathan R Breger, Anna Medical University of Vienna Center for Medical Physics and Biomedical Engineering Vienna Austria University of Cambridge Department of Physics Cambridge United Kingdom University of Cambridge Department of Radiology Cambridge United Kingdom Cambridge University Hospitals Department of Radiology Cambridge United Kingdom University of Cambridge Department of Applied Mathematics and Theoretical Physics Cambridge United Kingdom University of Cambridge Heart & Lung Research Institute Cambridge United Kingdom King's College London School of Biomedical Engineering & Imaging Sciences London United Kingdom

ISBN: (纸本)9798331520526

When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-reference IQA (FR-IQA) measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often-overlooked problem regarding suitability. In previous studies, the FR-IQA measure HaarPSI showed promising behavior for natural and medical images. The measure is based on Haar wavelet representations and the framework allows optimization of two parameters. So far, these parameters have been aligned for natural images. Here, we optimize these parameters for two medical image data sets, a photoacoustic and a chest X-ray data set, with IQA expert ratings. We observe that they lead to similar parameter values and are more sensitive to parameter changes compared to natural image data. We denote the novel optimized setting as HaarPSIMED, which improves the performance of the employed medical images significantly (p © 2025 IEEE.

关键词： Damage detection

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Distribution Matching with Subset-K-Space Embedding For Multi-Contrast MRI Reconstruction

SSRN

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SSRN 2025年

作者： Guan, Yu Lu, Yujuan Cheng, Jing Wang, Shanshan Wei, Hongjiang Liu, Qiegen School of Mathematics and Computer Sciences Nanchang University Nanchang330031 China Paul C. Lauterbur Research Center for Biomedical Imaging SIAT Chinese Academy of Sciences Shenzhen518055 China The School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200127 China School of Information Engineering Nanchang University Nanchang330031 China

To reduce the time required for multiple acquisitions in multi-contrast magnetic resonance imaging (MC-MRI), recent research has focused on collecting partial k-space data from a single contrast to reconstruct high-quality images by leveraging the redundancy among different contrasts. Further exploiting relevant information across diverse contrasts presents a more effective solution for accurate reconstruction. This work proposes a novel reconstruction method that integrates the advantages of subset-k-space distribution prior and high-dimensional global prior for MC-MRI reconstruction. Specifically, the first stage involves the individual decomposition of k-space data from different guided contrasts, which are then combined with the measurements to construct a new subset-k-space. Notably, establishing this subset-k-space minimizes the distance between the distribution of the measurements and the target examples. In addition to capitalizing on the novel distribution matching strategy for improved sampling, the second stage incorporates global prior embedding to constrain the diffusion model within the high-dimensional space, using the reconstructed contrast itself as a reference. This global prior further refines the initial reconstruction obtained in the first stage. Empirical evaluations across various datasets compellingly demonstrate the proposed method's excellent capability to preserve details and achieve accurate reconstruction. © 2025, The Authors. All rights reserved.

关键词： Embeddings

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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