IEE Transactions on Medical Imaging This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography
Pancreatic cancer is the emperor of all cancer maladies, mainly because there are no characteristic symptoms in the early stages, resulting in the absence of effective screening and early diagnosis methods in clinical practice. Non-contrast computerized tomography (CT) is widely used in routine check-ups and clinical examinations. Therefore, based on the accessibility of non-contrast CT, an automated early diagnosismethod for pancreatic cancer is proposed. Among this, we develop a novel causalitydriven graph neural network to solve the challenges of stability and generalization of early diagnosis, that is, the proposed met...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Fast and Calibrationless Low-Rank Parallel Imaging Reconstruction Through Unrolled Deep Learning Estimation of Multi-Channel Spatial Support Maps
Low-rank technique has emerged as a powerful calibrationless alternative for parallel magnetic resonance (MR) imaging. Calibrationless low-rank reconstruction, such as low-rank modeling of local k-space neighborhoods (LORAKS), implicitly exploits both coil sensitivity modulations and the finite spatial support constraint of MR images through an iterative low-rank matrix recovery process. Although powerful, this slow iteration process is computationally demanding and reconstruction requires empirical rank optimization, hampering its robust applications for high-resolution volume imaging. This paper proposes a fast and calib...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting
Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to construct due to privacy concerns on medical data. Federated learning (FL) provides a cross-site training paradigm and shows great potential to address this problem. In this work, we represent the first effort to formulate federated weakly supervised segmentation (FedWSS) and propose a novel Federated Drift Mitigation (FedDM) framework to learn segmentation models across multi...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Cross-Image Dependency Modeling for Breast Ultrasound Segmentation
We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast ultrasound images, which is a quite daunting task due to (1) the large variation of breast lesions, (2) the ambiguous lesion boundaries, and (3) the existence of speckle noise and artifacts in ultrasound images. Our work is motivated by the fact that most existing methods only focus on modeling the within-image dependencies while neglecting the cross-image dependencies, which are essential for this task under limited training data and noise. We first propos...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Conditional-Based Transformer Network With Learnable Queries for 4D Deformation Forecasting and Tracking
Real-time motion management for image-guided radiation therapy interventions plays an important role for accurate dose delivery. Forecasting future 4D deformations from in-plane image acquisitions is fundamental for accurate dose delivery and tumor targeting. However, anticipating visual representations is challenging and is not exempt from hurdles such as the prediction from limited dynamics, and the high-dimensionality inherent to complex deformations. Also, existing 3D tracking approaches typically need both template and search volumes as inputs, which are not available during real-time treatments. In this work, we prop...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Noise Suppression With Similarity-Based Self-Supervised Deep Learning
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fash...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

A Skewed Loss Function for Correcting Predictive Bias in Brain Age Prediction
In neuroimaging, the difference between predicted brain age and chronological age, known as brain age delta, has shown its potential as a biomarker related to various pathological phenotypes. There is a frequently observed bias when estimating brain age delta using regression models. This bias manifests as an overestimation of brain age for young participants and an underestimation of brain age for older participants. Therefore, the brain age delta is negatively correlated with chronological age, which can be problematic when evaluating relationships between brain age delta and other age-associated variables. This paper pr...
Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

Table of Contents
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Source: IEE Transactions on Medical Imaging - June 1, 2023 Category: Biomedical Engineering Source Type: research

2023 IEEE Nuclear Science Symposium
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Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

Hodge Laplacian of Brain Networks
The closed loops or cycles in a brain network embeds higher order signal transmission paths, which provide fundamental insights into the functioning of the brain. In this work, we propose an efficient algorithm for systematic identification and modeling of cycles using persistent homology and the Hodge Laplacian. Various statistical inference procedures on cycles are developed. We validate the our methods on simulations and apply to brain networks obtained through the resting state functional magnetic resonance imaging. The computer codes for the Hodge Laplacian are given in https://github.com/laplcebeltrami/hodge. (Source...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix
Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unannotated data due to the lack of manual annotations for them. In this paper, we propose an unreliability-diluted consistency training (UDiCT) mechanism to dilute the unreliability in SSL by assembling reliable annotated data into unreliable unannotated data. Specifically, we first propose an uncertainty-based data pairing module to pair annotated data with unannotated data based o...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

Medical Visual Question Answering via Conditional Reasoning and Contrastive Learning
Medical visual question answering (Med-VQA) aims to accurately answer a clinical question presented with a medical image. Despite its enormous potential in healthcare services, the development of this technology is still in the initial stage. On the one hand, Med-VQA tasks are highly challenging due to the massive diversity of clinical questions that require different visual reasoning skills for different types of questions. On the other hand, medical images are complex in nature and very different from natural images, while current Med-VQA datasets are small-scale with a few hundred radiology images, making it difficult t...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

SLfRank: Shinnar-Le-Roux Pulse Design With Reduced Energy and Accurate Phase Profiles Using Rank Factorization
The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the SLR algorithm consists of two steps: (1) an invertible transform between frequency selective pulses and polynomial pairs that represent Cayley-Klein (CK) parameters and (2) the design of the CK polynomial pair to match the desired magnetization profiles. Because the CK polynomial pair is bi-linearly coupled, the original algorithm sequentially solves for each polynomial i...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means++
This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning
A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases us...
Source: IEE Transactions on Medical Imaging - May 1, 2023 Category: Biomedical Engineering Source Type: research