Deep learning is a popular and powerful tool in computed tomography (CT) image processing such as organ segmentation, but its requirement of large training datasets remains a challenge. Even though there is a large anatomical variability for children during their growth, the training datasets for pediatric CT scans are especially hard to obtain due to risks of radiation to children. In this paper, we propose a method to conditionally synthesize realistic pediatric CT images using a new auxiliary classifier generative adversarial network (ACGAN) architecture by taking age information into account. The proposed network generated age-conditioned high-resolution CT images to enrich pediatric training datasets.
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Whirlpool（惠而浦公司）
Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present a SR technique for MR images that is based on generative adversarial netwo…
Zhejiang University of Technology（浙江工业大学）；Fourth Military Medical University（第四军医大学）；Utron Technology Co., Ltd.（欧创科技有限公司）
Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields. However, high-quality brain image reconstruction remains challenging since reconstructing images from the measured weak signals is a highly non-linear and ill-conditioned problem. In this work, we propose a generative adversarial network (GAN) enhanced MIT technique, named MITNet, based on a complex convolutional neural network (CNN). The experimental results on the real…
Massachusetts Institute of Technology（麻省理工学院）；Singapore-MIT Alliance for Research and Technology (SMART) Centre（新加坡-麻省理工学院研究与技术联盟中心）；Sandia National Laboratories（桑迪亚国家实验室）；National Institute of Standards and Technology（美国国家标准与技术研究院）
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, …
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
ETH Zurich（苏黎世联邦理工学院）；University of Regensburg（雷根斯堡大学）；Hospital St. Hedwig of the Order of St. John of God（圣约翰慈善会圣海德薇医院）；University Children’s Hospital Regensburg (KUNO)（雷根斯堡大学儿童医院（KUNO））
Zhejiang University（浙江大学）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Hangzhou Universal Medical Imaging Diagnostic Center（杭州全景医学影像诊断中心）
18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients. Reconstructing the low-dose PET (L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective way that both reduces the radiation exposure and remains diagnostic accuracy. In this paper, we propose a resource-efficient deep learning framework for L-PET reconstruction and analysis, referred to as transGAN-S…
Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques
University of Kentucky（肯塔基大学）；Institute for Biomedical Informatics（生物医学信息学研究所）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Conditional Diffusion Model for Electrical Impedance Tomography
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Electrical Engineering and Automation, Tianjin University of Technology（天津理工大学电气工程与自动化学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
School of Computer Science, Sichuan University（四川大学计算机学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）
Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer
AIRLab, Politecnico di Milano（米兰理工学院AIR实验室）；Politecnico di Milano（米兰理工学院）；NEARLab, Politecnico di Milano（米兰理工学院NEAR实验室）；Istituto Europeo di Oncologia（欧洲肿瘤研究所）；Università degli Studi dell’Insubria（意大利北部大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Erasmus MC（伊拉斯姆斯医学中心）；Erasmus MC-Sophia Children Hospital（伊拉斯姆斯医学中心索菲亚儿童医院）；University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital（哥本哈根大学医院）；Herlev-Gentofte Hospital（海莱乌-根措夫特医院）
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: i) a dataset of pediatric patients including subjects with cystic fibrosis, ii) a subset of…
CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model
Johns Hopkins University（约翰斯·霍普金斯大学）；Tsinghua University（清华大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；University of Pennsylvania（宾夕法尼亚大学）
Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video
School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；Manufacturing Industry Chain Collaboration Industrial Software Key Laboratory of Sichuan Province（四川省制造业产业链协同工业软件重点实验室）；School of Medicine, University of Electronic Science and Technology（医学院，电子科技大学）
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）
Washington University in St. Louis（圣路易斯华盛顿大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality image…
Tomographic image reconstruction is relevant for many medical imaging modalities including X-ray, ultrasound (US) computed tomography (CT) and photoacoustics, for which the access to full angular range tomographic projections might be not available in clinical practice due to physical or time constraints. Reconstruction from incomplete data in low signal-to-noise ratio regime is a challenging and ill-posed inverse problem that usually leads to unsatisfactory image quality. While informative image priors may be learned using generic deep neural network architectures, the artefacts caused by an …
CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
Department of Biomedical Data Science, Stanford University School of Medicine（斯坦福大学医学院生物医学数据科学系）；Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University（根特大学数学建模、统计与生物信息学系）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）
MDAA-Diff: CT-Guided Multi-Dose Adaptive Attention Diffusion Model for PET Denoising
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南方医科大学南芳医院PET中心）
A Query-Time Framework for Transient 2D Pore-Scale Flow Prediction and Generative Design
School of Civil Engineering, The University of Sydney（悉尼大学土木工程学院）；Zhejiang University（浙江大学）；College of Civil Engineering and Architecture, Zhejiang University（浙江大学建筑工程学院）；School of Civil Engineering, Southeast University（东南大学土木工程学院）；School of Civil and Environmental Engineering, UNSW Sydney（新南威尔士大学悉尼校区土木与环境工程学院）
Massachusetts Institute of Technology（麻省理工学院）；Sandia National Laboratory（桑迪亚国家实验室）
Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks t…
Low-dose Computed Tomography (LDCT) reconstruction is an important task in medical image analysis. Recent years have seen many deep learning based methods, proved to be effective in this area. However, these methods mostly follow a supervised architecture, which needs paired CT image of full dose and quarter dose, and the solution is highly dependent on specific measurements. In this work, we introduce Denoising Diffusion LDCT Model, dubbed as DDLM, generating noise-free CT image using conditioned sampling. DDLM uses pretrained model, and need no training nor tuning process, thus our proposal …
University of California Davis Health（加州大学戴维斯分校健康中心）
Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical process, which may lead to unsatisfactory performance. In this paper, we describe the equivalence between the kernel representation and a trainable neural network model. A deep kernel method is then prop…
