IEEE Transactions on Medical Imaging最新文献_第6页

Point Cloud Registration in Laparoscopic Liver Surgery Using Keypoint Correspondence Registration Network 利用关键点对应注册网络在腹腔镜肝脏手术中进行点云注册

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-10 DOI: 10.1109/tmi.2024.3457228

Yirui Zhang, Yanni Zou, Peter X. Liu

引用次数: 0

A Tracking prior to Localization workflow for Ultrasound Localization Microscopy 超声定位显微镜定位前的跟踪工作流程

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-09 DOI: 10.1109/tmi.2024.3456676

Alexis Leconte, Jonathan Porée, Brice Rauby, Alice Wu, Nin Ghigo, Paul Xing, Stephen LEE, Chloé Bourquin, Gerardo Ramos-Palacios, Abbas F. Sadikot, Jean Provost

引用次数: 0

Towards Semantically-Consistent Deformable 2D-3D Registration for 3D Craniofacial Structure Estimation from A Single-View Lateral Cephalometric Radiograph 实现语义一致的可变形 2D-3D 注册，根据单视角头侧 X 光片进行三维颅面结构估算

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-09 DOI: 10.1109/tmi.2024.3456251

Yikun Jiang, Yuru Pei, Tianmin Xu, Xiaoru Yuan, Hongbin Zha

引用次数: 0

Full-wave Image Reconstruction in Transcranial Photoacoustic Computed Tomography using a Finite Element Method 使用有限元法重建经颅光声计算机断层扫描的全波图像

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-09-09 DOI: 10.1109/tmi.2024.3456595

Yilin Luo, Hsuan-Kai Huang, Karteekeya Sastry, Peng Hu, Xin Tong, Joseph Kuo, Yousuf Aborahama, Shuai Na, Umberto Villa, Mark A. Anastasio, Lihong V. Wang

引用次数: 0

Attention-Guided Learning with Feature Reconstruction for Skin Lesion Diagnosis using Clinical and Ultrasound Images 利用临床和超声图像进行皮肤病变诊断的注意力引导学习与特征重构

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-08-29 DOI: 10.1109/tmi.2024.3450682

Chunlun Xiao, Anqi Zhu, Chunmei Xia, Zifeng Qiu, Yuanlin Liu, Cheng Zhao, Weiwei Ren, Lifan Wang, Lei Dong, Tianfu Wang, Lehang Guo, Baiying Lei

引用次数: 0

DCDiff: Dual-Granularity Cooperative Diffusion Models for Pathology Image Analysis DCDiff：用于病理图像分析的双粒度协同扩散模型

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-06-28 DOI: 10.1109/tmi.2024.3420804

Jiansong Fan, Tianxu Lv, Pei Wang, Xiaoyan Hong, Yuan Liu, Chunjuan Jiang, Jianming Ni, Lihua Li, Xiang Pan

引用次数: 0

STAR-RL: Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution STAR-RL：用于可解释病理图像超分辨率的时空分层强化学习

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-06-27 DOI: 10.1109/tmi.2024.3419809

Wenting Chen, Jie Liu, Tommy W.S. Chow, Yixuan Yuan

引用次数: 0

Continuous 3D Myocardial Motion Tracking via Echocardiography 通过超声心动图进行连续三维心肌运动跟踪

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2024-06-27 DOI: 10.1109/tmi.2024.3419780

Chengkang Shen, Hao Zhu, You Zhou, Yu Liu, Si Yi, Lili Dong, Weipeng Zhao, David J. Brady, Xun Cao, Zhan Ma, Yi Lin

引用次数: 0

IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI IMJENSE：用于并行磁共振成像中关节线圈灵敏度和图像估计的特定扫描隐式表示法

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2023-11-21 DOI: 10.48550/arXiv.2311.12892

Rui-jun Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, Hongjiang Wei

{"title":"IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI","authors":"Rui-jun Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, Hongjiang Wei","doi":"10.48550/arXiv.2311.12892","DOIUrl":"https://doi.org/10.48550/arXiv.2311.12892","url":null,"abstract":"Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5× and 6× accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"202 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis 基于功能连接的神经疾病诊断的可学习反条件分析框架

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2023-10-06 DOI: 10.48550/arXiv.2310.03964

Eunsong Kang, Da-Woon Heo, Jiwon Lee, Heung-Il Suk

{"title":"A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis","authors":"Eunsong Kang, Da-Woon Heo, Jiwon Lee, Heung-Il Suk","doi":"10.48550/arXiv.2310.03964","DOIUrl":"https://doi.org/10.48550/arXiv.2310.03964","url":null,"abstract":"To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"8 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139322196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0