IEEE transactions on medical imaging最新文献

{"title":"Hierarchical Data Integration With Gaussian Processes: Application to the Characterization of Cardiac Ischemia-Reperfusion Patterns","authors":"Benoit Freiche;Gabriel Bernardino;Romain Deleat-Besson;Patrick Clarysse;Nicolas Duchateau","doi":"10.1109/TMI.2024.3512175","DOIUrl":"10.1109/TMI.2024.3512175","url":null,"abstract":"Cardiac imaging protocols usually result in several types of acquisitions and descriptors extracted from the images. The statistical analysis of such data across a population may be challenging, and can be addressed by fusion techniques within a dimensionality reduction framework. However, directly combining different data types may lead to unfair comparisons (for heterogeneous descriptors) or over-exploitation of information (for strongly correlated modalities). In contrast, physicians progressively consider each type of data based on hierarchies derived from their experience or evidence-based recommendations, an inspiring approach for data fusion strategies. In this paper, we propose a novel methodology for hierarchical data fusion and unsupervised representation learning. It mimics the physicians’ approach by progressively integrating different high-dimensional data descriptors according to a known hierarchy. We model this hierarchy with a Hierarchical Gaussian Process Latent Variable Model (GP-LVM), which links the estimated low-dimensional latent representation and high-dimensional observations at each level in the hierarchy, with additional links between consecutive levels of the hierarchy. We demonstrate the relevance of this approach on a dataset of 1726 magnetic resonance image slices from 123 patients revascularized after acute myocardial infarction (MI) (first level in the hierarchy), some of them undergoing reperfusion injury (microvascular obstruction (MVO), second level in the hierarchy). Our experiments demonstrate that our hierarchical model provides consistent data organization across levels of the hierarchy and according to physiological characteristics of the lesions. This allows more relevant statistical analysis of myocardial lesion patterns, and in particular subtle lesions such as MVO.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1529-1540"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Masked Deformation Modeling for Volumetric Brain MRI Self-Supervised Pre-Training","authors":"Junyan Lyu;Perry F. Bartlett;Fatima A. Nasrallah;Xiaoying Tang","doi":"10.1109/TMI.2024.3510922","DOIUrl":"10.1109/TMI.2024.3510922","url":null,"abstract":"Self-supervised learning (SSL) has been proposed to alleviate neural networks’ reliance on annotated data and to improve downstream tasks’ performance, which has obtained substantial success in several volumetric medical image segmentation tasks. However, most existing approaches are designed and pre-trained on CT or MRI datasets of non-brain organs. The lack of brain prior limits those methods’ performance on brain segmentation, especially on fine-grained brain parcellation. To overcome this limitation, we here propose a novel SSL strategy for MRI of the human brain, named Masked Deformation Modeling (MDM). MDM first conducts atlas-guided patch sampling on individual brain MRI scans (moving volumes) and an MNI152 template (a fixed volume). The sampled moving volumes are randomly masked in a feature-aligned manner, and then sent into a U-Net-based network to extract latent features. An intensity head and a deformation field head are used to decode the latent features, respectively restoring the masked volume and predicting the deformation field from the moving volume to the fixed volume. The proposed MDM is fine-tuned and evaluated on three brain parcellation datasets with different granularities (JHU, Mindboggle-101, CANDI), a brain lesion segmentation dataset (ATLAS2), and a brain tumor segmentation dataset (BraTS21). Results demonstrate that MDM outperforms various state-of-the-art medical SSL methods by considerable margins, and can effectively reduce the annotation effort by at least 40%. Codes and pre-trained weights will be released at <uri>https://github.com/CRazorback/MDM</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1596-1607"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAICN: Graph Attention Iterative Contraction Network for Bioluminescence Tomography","authors":"Heng Zhang;Hongbo Guo;Yuqing Hou;Xiaowei He;Shuangchen Li;Beilei Wang;Jingjing Yu;Yanqiu Liu;Mengxiang Chu;Xuelei He;Huangjian Yi","doi":"10.1109/TMI.2024.3510837","DOIUrl":"10.1109/TMI.2024.3510837","url":null,"abstract":"Bioluminescence tomography (BLT) can provide non-invasive quantitative three-dimensional tumor information which has been widely applied in pre-clinical studies. Meanwhile, in recent years, deep learning methods have significantly improved the reconstruction resolution and speed by establishing a non-linear mapping relationship between surface-measured bioluminescence and light source distribution. However, this mapping relationship only works for specific biological tissues and light transmission processes under fixed wavelengths, resulting in poor stability and generalizability. To meet the requirements of diverse practical scenarios and inspired by more effective sparse regularization and graph representation theory, we propose a novel Graph Attention Iterative Contraction Network (GAICN) to conduct a finite element mesh spatial representation study. In the GAICN framework, two learnable spatial topological transforms based on the graph attention mechanism and an iterative contraction activation function were devised to achieve non-local feature aggregation and dynamic adjustment of weights between first-order neighboring nodes in the mesh. As a deep unrolling method, GAICN naturally inherits the coherence of surface bioluminescence with the light source in Forward-Backward Splitting (FBS), thus enhancing the generalizability, stability and interpretability of the network. Both simulation and in-vivo experiments further indicated that GAICN achieved superior reconstruction performance in terms of spatial location, dual light source resolution, stability, generalizability, as well as in-vivo practicability.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 4","pages":"1659-1670"},"PeriodicalIF":0.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AsyDisNet: Scalable Mammographic Asymmetry and Architectural Distortion Detection With Angle-Based Quadruplet Loss","authors":"Zhenjie Cao;Zhuo Deng;Zhicheng Yang;Jialin Yuan;Jie Ma;Lan Ma","doi":"10.1109/TMI.2024.3508738","DOIUrl":"10.1109/TMI.2024.3508738","url":null,"abstract":"Early asymmetry (AS) and architectural distortion (AD) detection on mammograms are essential in breast cancer diagnosis. However, they are challenging as the prevalence of AS and AD is very low. This paper proposes an efficient AsyDisNet for the AS and AD detection. First, a novel angle-based quadruplet loss is proposed to detect the AS and AD with limited pixel-level labeled mammograms. Second, we scale the AsyDisNet with a novel semi-weakly supervised learning framework to boost the detection performance with a large number of mammograms with image-level labels extracted from medical reports. The validation on the two largest and privately collected datasets shows an average of <inline-formula> <tex-math>$sim ~10$ </tex-math></inline-formula>% improvement over State-of-the-Art baselines in terms of sensitivities under various false-positive-per-image (FPPI). Furthermore, the proposed AsyDisNet is scalable to the current Picture Archiving and Communication System (PACS) with incremental learning ability. The dataset will be made publicly available at <uri>https://github.com/ML-AILab/AsyDisNet</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1518-1528"},"PeriodicalIF":0.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142776428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topicwise Separable Sentence Retrieval for Medical Report Generation","authors":"Junting Zhao;Yang Zhou;Zhihao Chen;Huazhu Fu;Liang Wan","doi":"10.1109/TMI.2024.3507076","DOIUrl":"10.1109/TMI.2024.3507076","url":null,"abstract":"Automated radiology reporting holds immense clinical potential in alleviating the burdensome workload of radiologists and mitigating diagnostic bias. Recently, retrieval-based report generation methods have garnered increasing attention. These methods predefine a set of candidate queries and compose reports by searching for sentences in an off-the-shelf sentence gallery that best match these candidate queries. However, due to the long-tail distribution of the training data, these models tend to learn frequently occurring sentences and topics, overlooking the rare topics. Regrettably, in many cases, the descriptions of rare topics often indicate critical findings that should be mentioned in the report. To address this problem, we introduce a Topicwise Separable Sentence Retrieval (Teaser) for medical report generation. To ensure comprehensive learning of both common and rare topics, we categorize queries into common and rare types to learn differentiated topics, and then propose Topic Contrastive Loss to effectively align topics and queries in the latent space. Moreover, we integrate an Abstractor module following the extraction of visual features, which aids the topic decoder in gaining a deeper understanding of the visual observational intent. Experiments on the MIMIC-CXR and IU X-ray datasets demonstrate that Teaser surpasses state-of-the-art models, while also validating its capability to effectively represent rare topics and establish more dependable correspondences between queries and topics. The code is available at <uri>https://github.com/CindyZJT/Teaser.git</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1505-1517"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LHR-RFL: Linear Hybrid-Reward-Based Reinforced Focal Learning for Automatic Radiology Report Generation","authors":"Xiulong Yi;You Fu;Jianzhi Yu;Ruiqing Liu;Hao Zhang;Rong Hua","doi":"10.1109/TMI.2024.3507073","DOIUrl":"10.1109/TMI.2024.3507073","url":null,"abstract":"Radiology report generation that aims to accurately describe medical findings for given images, is pivotal in contemporary computer-aided diagnosis. Recently, despite considerable progress, current radiology report generation models still struggled to achieve consistent quality across difficult and easy samples, which dramatically impacts their clinical value. To solve this problem, we explore the difficult samples mining in radiology report generation and propose the Linear Hybrid-Reward based Reinforced Focal Learning (LHR-RFL) to effectively guide the model to allocate more attention towards some difficult samples, thereby enhancing its overall performance in both general and intricate scenarios. In implementation, we first propose the Linear Hybrid-Reward (LHR) module to better quantify the learning difficulty, which employs a linear weighting scheme that assigns varying weights to three representative Natural Language Generation (NLG) evaluation metrics. Then, we propose the Reinforced Focal Learning (RFL) to adaptively adjust the contributions of difficult samples during training, thereby augmenting their impact on model optimization. The experimental results demonstrate that our proposed LHR-RFL improves the performance of the base model across all NLG evaluation metrics, achieving an average performance improvement of 20.9% and 13.2% on IU X-ray and MIMIC-CXR datasets, respectively. Further analysis also proves that our LHR-RFL can dramatically improve the quality of reports for difficult samples. The source code will be available at <uri>https://github.com/</uri> SKD-HPC/LHR-RFL.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1494-1504"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Debiased Estimation and Inference for Spatial–Temporal EEG/MEG Source Imaging","authors":"Pei Feng Tong;Haoran Yang;Xinru Ding;Yuchuan Ding;Xiaokun Geng;Shan An;Guoxin Wang;Song Xi Chen","doi":"10.1109/TMI.2024.3506596","DOIUrl":"10.1109/TMI.2024.3506596","url":null,"abstract":"The development of accurate electroencephalography (EEG) and magnetoencephalography (MEG) source imaging algorithm is of great importance for functional brain research and non-invasive presurgical evaluation of epilepsy. In practice, the challenge arises from the fact that the number of measurement channels is far less than the number of candidate source locations, rendering the inverse problem ill-posed. A widely used approach is to introduce a regularization term into the objective function, which inevitably biased the estimated amplitudes towards zero, leading to an inaccurate estimation of the estimator’s variance. This study proposes a novel debiased EEG/MEG source imaging (DeESI) algorithm for detecting sparse brain activities, which corrects the estimation bias in signal amplitude, dipole orientation and depth. The DeESI extends the idea of group Lasso by incorporating both the matrix Frobenius norm and the L1-norm, which guarantees the estimators are only sparse over sources while maintains smoothness in time and orientation. We also derived variance of the debiased estimators for standardization and hypothesis testing. A fast alternating direction method of multipliers (ADMM) algorithm is proposed for solving the matrix form optimization problem directly without the need for vectorization. The proposed algorithm is compared with eleven existing ESI methods using simulations and an open source EEG dataset whose stimulation locations are known precisely. The DeESI exhibits the best performance in peak localization and amplitude reconstruction.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1480-1493"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CGNet: A Correlation-Guided Registration Network for Unsupervised Deformable Image Registration","authors":"Yuan Chang;Zheng Li;Wenzheng Xu","doi":"10.1109/TMI.2024.3505853","DOIUrl":"10.1109/TMI.2024.3505853","url":null,"abstract":"Deformable medical image registration plays a significant role in medical image analysis. With the advancement of deep neural networks, learning-based deformable registration methods have made great strides due to their ability to perform fast end-to-end registration and their competitive performance compared to traditional methods. However, these methods primarily improve registration performance by replacing specific layers of the encoder-decoder architecture designed for segmentation tasks with advanced network structures like Transformers, overlooking the crucial difference between these two tasks, which is feature matching. In this paper, we propose a novel correlation-guided registration network (CGNet) specifically designed for deformable medical image registration tasks, which achieves a reasonable and accurate registration through three main components: dual-stream encoder, correlation learning module, and coarse-to-fine decoder. Specifically, the dual-stream encoder is used to independently extract hierarchical features from a moving image and a fixed image. The correlation learning module is used to calculate correlation maps, enabling explicit feature matching between input image pairs. The coarse-to-fine decoder outputs deformation sub-fields for each decoding layer in a coarse-to-fine manner, facilitating accurate estimation of the final deformation field. Extensive experiments on four 3D brain MRI datasets show that the proposed method achieves state-of-the-art performance on three evaluation metrics compared to twelve learning-based registration methods, demonstrating the potential of our model for deformable medical image registration.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1468-1479"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTLESS: A Scatter-Window Projection and Deep Learning-Based Transmission-Less Attenuation Compensation Method for Myocardial Perfusion SPECT","authors":"Zitong Yu;Md Ashequr Rahman;Craig K. Abbey;Richard Laforest;Nancy A. Obuchowski;Barry A. Siegel;Abhinav K. Jha","doi":"10.1109/TMI.2024.3496870","DOIUrl":"10.1109/TMI.2024.3496870","url":null,"abstract":"Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT), typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis in case of misalignment between SPECT and CT images. To address these issues, we developed a method for <underline>c</u>ardiac SPEC<underline>T</u> AC using deep <underline>l</u>earning and <underline>e</u>mission <underline>s</u>catter-window photon<underline>s</u> without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans. Pre-defined attenuation coefficients are assigned to these regions, yielding the attenuation map used for AC. We objectively evaluated this method in a retrospective study with anonymized clinical SPECT/CT stress MPI images on the clinical task of detecting perfusion defects with an anthropomorphic model observer. CTLESS yielded statistically non-inferior performance compared to a CT-based AC (CTAC) method and significantly outperformed a non-AC (NAC) method on this clinical task. Similar results were observed in stratified analyses with different sexes, defect extents, and defect severities. The method was observed to generalize across two SPECT scanners, each with a different camera. In addition, CTLESS yielded similar performance as CTAC and outperformed NAC method on the fidelity-based figures of merit, namely, root mean squared error (RMSE) and structural similarity index measure (SSIM). Moreover, as we reduced the training dataset size, CTLESS yielded relatively stable AUC values and generally outperformed another DL-based AC method that directly estimated the attenuation coefficient within each voxel. These results demonstrate the capability of the CTLESS method for transmission-less AC in SPECT and motivate further clinical evaluation.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1308-1320"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope","authors":"Maximilian Neidhardt;Sarah Latus;Tim Eixmann;Gereon Hüttmann;Alexander Schlaefer","doi":"10.1109/TMI.2024.3505676","DOIUrl":"10.1109/TMI.2024.3505676","url":null,"abstract":"Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate data and the need for real-time estimates. Our elasticity estimation approach is based on imaging complex and diffuse wave fields that encompass multiple wave frequencies and propagate in various directions. We optimize the probe design to enable different scan patterns. To maximize temporal sampling while maintaining three-dimensional information we define a scan pattern in a conical shape with a temporal frequency of 5.05kHz. To efficiently process the image sequences of complex wave fields we consider a spatio-temporal deep learning network. We train the network in an end-to-end fashion on measurements from phantoms representing multiple elasticities. The network is used to obtain localized and robust elasticity estimates, allowing to create elasticity maps in real-time. For 2D scanning, our approach results in a mean absolute error of 6.31(576)kPa compared to 11.33(1278)kPa for conventional phase tracking. For scanning without estimating the wave direction, the novel 3D method reduces the error to 4.48(363)kPa compared to 19.75(2182)kPa for the conventional 2D method. Finally, we demonstrate feasibility of elasticity estimates in ex-vivo porcine tissue.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 3","pages":"1445-1453"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}