IEEE Transactions on Medical Imaging最新文献_第10页

Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis 用于肺结节分析的具有边际排序损失的多任务深度模型

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2934577

Lihao Liu, Q. Dou, Hao Chen, J. Qin, P. Heng

{"title":"Multi-Task Deep Model With Margin Ranking Loss for Lung Nodule Analysis","authors":"Lihao Liu, Q. Dou, Hao Chen, J. Qin, P. Heng","doi":"10.1109/TMI.2019.2934577","DOIUrl":"https://doi.org/10.1109/TMI.2019.2934577","url":null,"abstract":"Lung cancer is the leading cause of cancer deaths worldwide and early diagnosis of lung nodule is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score regression. However, this is quite challenging due to the considerable difficulty of lung nodule heterogeneity modeling and the limited discrimination capability on ambiguous cases. To solve these challenges, we propose a Multi-Task deep model with Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. Compared to existing methods which consider these two tasks separately, the relatedness between lung nodule classification and attribute score regression is explicitly explored in a cause-and-effect manner within our multi-task deep model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a Siamese network with a margin ranking loss is elaborately designed to enhance the discrimination capability on ambiguous nodule cases. To further explore the internal relationship between two tasks and validate the effectiveness of the proposed model, we use the recursive feature elimination method to iteratively rank the most malignancy-related features. We validate the efficacy of our method MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments show that the diagnosis results with internal relationship explicitly explored in our model has met some similar patterns in clinical usage and also demonstrate that our approach can achieve competitive classification performance and more accurate scoring on attributes over the state-of-the-arts. Codes are publicly available at: https://github.com/CaptainWilliam/MTMR-NET.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"718-728"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2934577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47058684","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: 79

Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices. 将动态脑功能连接作为协方差矩阵空间上的轨迹分析

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 Epub Date: 2019-08-02 DOI: 10.1109/TMI.2019.2931708

Mengyu Dai, Zhengwu Zhang, Anuj Srivastava

引用次数: 0

One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures 一次生成对抗学习在颅颌面骨结构MRI分割中的应用

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2935409

Xu Chen, J. Xia, D. Shen, C. Lian, Li Wang, H. Deng, S. Fung, Dong Nie, Kim-Han Thung, P. Yap, J. Gateno

{"title":"One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures","authors":"Xu Chen, J. Xia, D. Shen, C. Lian, Li Wang, H. Deng, S. Fung, Dong Nie, Kim-Han Thung, P. Yap, J. Gateno","doi":"10.1109/TMI.2019.2935409","DOIUrl":"https://doi.org/10.1109/TMI.2019.2935409","url":null,"abstract":"Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"40 4","pages":"787-796"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2935409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41266392","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: 26

Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction 用于多对比度MRI重建的耦合字典学习

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932961

P. Song, L. Weizman, J. Mota, Yonina C. Eldar, M. Rodrigues

{"title":"Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction","authors":"P. Song, L. Weizman, J. Mota, Yonina C. Eldar, M. Rodrigues","doi":"10.1109/TMI.2019.2932961","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932961","url":null,"abstract":"Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"621-633"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47818490","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: 12

Table of contents 目录表

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/tmi.2020.2973610

引用次数: 0

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models 通过学习非线性低维模型进行约束磁共振光谱成像

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2930586

F. Lam, Yahang Li, Xi Peng

{"title":"Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models","authors":"F. Lam, Yahang Li, Xi Peng","doi":"10.1109/TMI.2019.2930586","DOIUrl":"https://doi.org/10.1109/TMI.2019.2930586","url":null,"abstract":"Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"545-555"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2930586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49664595","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: 41

Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging 同时多VENC和同时多切片相位对比磁共振成像

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2934422

Suhyung Park, Liyong Chen, Jennifer Townsend, Hyunyeol Lee, D. Feinberg

{"title":"Simultaneous Multi-VENC and Simultaneous Multi-Slice Phase Contrast Magnetic Resonance Imaging","authors":"Suhyung Park, Liyong Chen, Jennifer Townsend, Hyunyeol Lee, D. Feinberg","doi":"10.1109/TMI.2019.2934422","DOIUrl":"https://doi.org/10.1109/TMI.2019.2934422","url":null,"abstract":"This work develops a novel, simultaneous multi-VENC and simultaneous multi-slice (SMV+SMS) imaging in a single acquisition for robust phase contrast (PC) MRI. To this end, the pulse sequence was designed to permit concurrent acquisition of multiple VENCs as well as multiple slices on a shared frequency encoding gradient, in which each effective echo time for multiple VENCs was controlled by adjusting net gradient area while multiple slices were simultaneously excited by employing multiband resonance frequency (RF) pulses. For VENC and slice separation, RF phase cycling and gradient blip were applied to create both inter-VENC and inter-slice shifts along phase encoding direction, respectively. With an alternating RF phase cycling that generates oscillating steady-state with low and high signal amplitude, the acquired multi-VENC k-space was reformulated into 3D undersampled k-space by generating a virtual dimension along VENC direction for modulation induced artifact reduction. In vivo studies were conducted to validate the feasibility of the proposed method in comparison with conventional PC MRI. The proposed method shows comparable performance to the conventional method in delineating both low and high flow velocities across cardiac phases with high spatial coverage without apparent artifacts. In the presence of high flow velocity that is above the VENC value, the proposed method exhibits clear depiction of flow signals over conventional method, thereby leading to high VNR image with improved velocity dynamic range.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"742-752"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2934422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42567414","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

Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI 子宫内扩散加权MRI相干引导下胼胝体的自动、年龄一致性重建

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932681

D. Hunt, M. Dighe, Chris Gatenby, C. Studholme

{"title":"Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI","authors":"D. Hunt, M. Dighe, Chris Gatenby, C. Studholme","doi":"10.1109/TMI.2019.2932681","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932681","url":null,"abstract":"Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"601-610"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42571471","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: 3

Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging. 电磁脑成像分布式源的鲁棒经验贝叶斯重构

IF 8.9 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 Epub Date: 2019-07-31 DOI: 10.1109/TMI.2019.2932290

Chang Cai, Mithun Diwakar, Dan Chen, Kensuke Sekihara, Srikantan S Nagarajan

{"title":"Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.","authors":"Chang Cai, Mithun Diwakar, Dan Chen, Kensuke Sekihara, Srikantan S Nagarajan","doi":"10.1109/TMI.2019.2932290","DOIUrl":"10.1109/TMI.2019.2932290","url":null,"abstract":"<p><p>Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.</p>","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"567-577"},"PeriodicalIF":8.9,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49319039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An On-Board Spectral-CT/CBCT/SPECT Imaging Configuration for Small-Animal Radiation Therapy Platform: A Monte Carlo Study 用于小动物放射治疗平台的车载光谱CT/CCBT/SPECT成像配置：蒙特卡罗研究

IF 10.6 1区医学

IEEE Transactions on Medical Imaging Pub Date : 2020-03-01 DOI: 10.1109/TMI.2019.2932333

Hui Wang, K. Nie, Y. Kuang

{"title":"An On-Board Spectral-CT/CBCT/SPECT Imaging Configuration for Small-Animal Radiation Therapy Platform: A Monte Carlo Study","authors":"Hui Wang, K. Nie, Y. Kuang","doi":"10.1109/TMI.2019.2932333","DOIUrl":"https://doi.org/10.1109/TMI.2019.2932333","url":null,"abstract":"This study investigated the feasibility of a highly specific multiplexed image-guided small animal radiation therapy (SART) platform based on triple imaging from on-board single-photon emission computed tomography (SPECT), spectral-CT, and cone-beam CT (CBCT) guidance in radiotherapy treatment. As a proof-of-concept, the SART system was built with the capability of triple on-board image guidance by utilizing an x-ray tube and a single cadmium zinc telluride (CZT) semiconductor photon-counting imager via a Monte Carlo simulation study. The x-ray tube can be set at a low tube current for imaging mode and a high tube current for radiation therapy mode, respectively. In the imaging mode, both x-ray and gamma-ray projection data were collected by the imager to reconstruct CBCT, SPECT and spectral CT images of small animals being treated. The modulation transfer function (MTF) of the pixelated CZT imager measured was 8.6 lp/mm. The overall performances of the CBCT and SPECT imaging of the system were evaluated with sufficient spatial resolution and imaging quality to be fitted into the SART platform. The material differentiation and decomposition capacities of spectral CT within the system were verified using K-edge imaging, image-based optimal energy weighted imaging, and image-based linear material decomposition methods. The triple imaging capability of the system was demonstrated using a PMMA phantom containing gadolinium, iodine and radioisotope 99mTc inserts. All the probes were clearly identified in the registered image. The results demonstrated that a novel SART platform with high-quality on-board CBCT, spectral-CT, SPECT image guidance is technically feasible by using a single semiconductor imager, thus affording comprehensive image guidance from anatomical, functional, and molecular levels for radiation treatment beam delivery.","PeriodicalId":13418,"journal":{"name":"IEEE Transactions on Medical Imaging","volume":"39 1","pages":"588-600"},"PeriodicalIF":10.6,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TMI.2019.2932333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49537593","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: 4