Inline AI: Open-source Deep Learning Inference for Cardiac MR
Hui Xue, Rhodri H Davies, James Howard, Hunain Shiwani, Azaan Rehman, Iain Pierce, Henry Procter, Marianna Fontana, James C Moon, Eylem Levelt, Peter Kellman
Cardiac Magnetic Resonance (CMR) is established as a non-invasive imaging technique for evaluation of heart function, anatomy, and myocardial tissue characterization. Quantitative biomarkers are central for diagnosis and management of heart disease. Deep learning (DL) is playing an ever more important role in extracting these quantitative measures from CMR images. While many researchers have reported promising results in training and evaluating models, model deployment into the imaging workflow is less explored.
A new imaging AI framework, the InlineAI, was developed and open-sourced. The main innovation is to enable the model inference inline as a part of imaging computation, instead of as an offline post-processing step and to allow users to plug in their models. We demonstrate the system capability on three applications: long-axis CMR cine landmark detection, short-axis CMR cine analysis of function and anatomy, and quantitative perfusion mapping.
The InlineAI allowed models to be deployed into imaging workflow in a streaming manner directly on the scanner. The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner. Several biomarkers were extracted from model outputs in the demonstrated applications and reported as curves and tabular values. All processes are full automated. the model inference was completed within 6-45s after the end of imaging data acquisition.
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking…
National Heart, Lung and Blood Institute, National Institutes of Health（美国国立卫生研究院国家心肺血液研究所）；Barts Health NHS Trust（巴特保健国民保健信托基金）；University of Leeds（利兹大学）；Royal Free Hospital（皇家自由医院）
Recent development of quantitative myocardial blood flow (MBF) mapping allows direct evaluation of absolute myocardial perfusion, by computing pixel-wise flow maps. Clinical studies suggest quantitative evaluation would be more desirable for objectivity and efficiency. Objective assessment can be further facilitated by segmenting the myocardium and automatically generating reports following the AHA model. This will free user interaction for analysis and lead to a 'one-click' solution to improve workflow. This paper proposes a deep neural network based computational workflow for inline myocardi…
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Department of Mathematics and Scientific Computing, University of Graz, Austria（格拉茨大学数学与科学计算系，奥地利）；PRIN-PNRR project no. P2022N5ZNP（PRIN-PNRR项目编号P2022N5ZNP）；INdAM-GNCS
Personalized Topology-Informed Localization of Standard 12-Lead ECG Electrode Placement from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins
School of Electronics & Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；School of Biomedical Engineering and Imaging Sciences, Kings College London（伦敦国王学院生物医学工程与影像科学学院）
In this work, we attempt the segmentation of cardiac structures in late gadolinium-enhanced (LGE) magnetic resonance images (MRI) using only minimal supervision in a two-step approach. In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation. Each manually annotated ground truth provides labels of the myocardium and the left ventricle (LV) and right ventricle (RV) cavities, which are used as atlases. After multi-atlas label fusion by majority voting, we possess noisy labels fo…
Department of Bioengineering, Imperial College London（帝国理工学院生物工程系）；Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital（皇家布里托尼医院心血管磁共振单位）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心脏和肺研究所）
Neural Implicit Heart Coordinates: 3D cardiac shape reconstruction from sparse segmentations
School of Biomedical Engineering and Imaging Sciences（生物医学工程与成像科学学院）；William Harvey Research Institute, NIHR Barts Biomedical Research Centre（威廉·哈里森研究机构、NIHR巴茨生物医学研究中心）；Simula Research Laboratory（Simula研究实验室）；Department of Informatics（信息学院）；Department of Bioengineering（生物工程学院）；Barts Heart Centre, Barts Health NHS Trust（巴茨心脏中心、巴茨健康 NHS信托）；Technical University of Munich（慕尼黑技术大学）
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
University of Leeds（利兹大学）；Radboud University Medical Centre（拉德堡德大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿心医疗科技有限公司）
Rochester Institute of Technology（罗切斯特理工学院）；Dalhouse University（达尔豪斯大学）
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiotemporal TMP dynamics. These models are controlled by high-dimensional physical parameters which, if fixed, can introduce model errors and reduce the accuracy of TMP reconstruction. Simultaneous adaptation of these parameters during TMP reconstruction, however, is difficult due to their high dimensionality. We introduce a novel model-constrained inf…
Eindhoven University of Technology（埃因霍温理工大学）；Philips Research Laboratories（飞利浦研究实验室）；Philips Healthcare（飞利浦医疗）
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation techniqu…
National Heart, Lung and Blood Institute, National Institutes of Health（美国国立卫生研究院心肺血液研究所）；Barts Heart Centre, Barts Health NHS Trust（巴特健康国民保健信托巴特心脏中心）；National Amyloidosis Centre, Royal Free Hospital（皇家自由医院国家淀粉样变性病中心）
Purpose: To develop a convolutional neural network (CNN) solution for robust landmark detection in cardiac MR images. Methods: This retrospective study included cine, LGE and T1 mapping scans from two hospitals. The training set included 2,329 patients and 34,019 images. A hold-out test set included 531 patients and 7,723 images. CNN models were developed to detect two mitral valve plane and apical points on long-axis (LAX) images. On short-axis (SAX) images, anterior and posterior RV insertion points and LV center were detected. Model outputs were compared to manual labels by two operators fo…
A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学霍克斯研究所、医学物理与生物医学工程系、伦敦大学学院）；InstaDeep；Department of Computing, Imperial College London（伦敦帝国学院计算机系）；Department of Brain Sciences, Imperial College London（伦敦帝国学院脑科学系）；Institute of Cardiovascular Sciences, University College London（伦敦大学学院心血管科学研究所）；Barts Heart Centre, Barts Health NHS Trust（巴特斯心脏中心、巴特斯健康 NHS信托）
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；St Thomas’ Hospital（圣托马斯医院）
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac stru…
MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）
