Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models tailored to 4D pMRI are still lacking. This study presents the first attempt to model 4D pMRI using a GNN-based spatiotemporal model PerfGAT, integrating spatial information and temporal kinetics to predict Isocitrate DeHydrogenase (IDH) mutation status in glioma patients. Specifically, we propose a graph structure learning approach based on edge attention and negative graphs to optimize temporal correlations modeling. Moreover, we design a dual-attention feature fusion module to integrate spatiotemporal features while addressing tumor-related brain regions. Further, we develop a class-balanced augmentation methods tailored to spatiotemporal data, which could mitigate the common label imbalance issue in clinical datasets. Our experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches, promising to model pMRI effectively for patient characterization.
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategi…
University of Bern（伯尔尼大学）；Department of Neurology, Inselspital, University of Bern（伯尔尼大学 Inselspital 神经科）；Department of Neurology, University of California Los Angeles (UCLA)（加州大学洛杉矶分校（UCLA）神经科）；Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern（伯尔尼大学 Inselspital 诊断与介入神经放射学研究所高级神经影像支持中心）
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The proposed architecture is trained end-to-end and solely relies on raw perfusion data for inference. We used a dataset of 151 acute ischemic stroke cases for evaluation. Our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy.
Technical University of Munich（慕尼黑工业大学）；GE Global Research（通用电气全球研发中心）；Harvard Medical School（哈佛医学院）
Perfusion-weighted magnetic resonance imaging (MRI) is an imaging technique that allows one to measure tissue perfusion in an organ of interest through the injection of an intravascular paramagnetic contrast agent (CA). Due to a preference for high temporal and spatial resolution in many applications, this modality could significantly benefit from accelerated data acquisitions. In this paper, we specifically address the problem of reconstructing perfusion MR image series from a subset of k-space data. Our proposed approach is motivated by the observation that temporal variations (dynamics) in …
University of Minho（米尼奥大学）；University Hospital Inselspital and University of Bern（Inselspital大学医院与伯尔尼大学）；University of Bern（伯尔尼大学）
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the physician towards a better stroke assessment and information about tissue outcome. Typically, automatic methods consider the information of the standard kinetic models of diffusion and p…
Eindhoven University of Technology（埃因霍温理工大学）；King’s College London（伦敦国王学院）；Philips Healthcare（飞利浦医疗）
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perf…
King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）；Eindhoven University of Technology（埃因霍温理工大学）；Philips Healthcare（飞利浦医疗）
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia. However, due to the relatively high noise level and low temporal resolution of the acquired data and the complexity of the tracer-kinetic models, the model fitting can yield unreliable parameter estimates. A solution to this problem is the use of Bayesian inference which can incorporate prior knowledge and improve the reliability of the parameter estimation. This, however, uses Markov chain Monte Carlo sampling to approximate the posterior distr…
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…
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科斯基基辅理工学院”）
Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters using dynamic susceptibility contrast-enhanced magnetic resonance imaging. It is required as input for perfusion quantification and has a great impact on the result of the deconvolution operation. To improve the reproducibility and reliability of arterial input function detection, several semi- or fully automatic methods have been proposed. This study provides an overview of the current state of the field of arterial input function detection. Methods most commonly used for semi- and fu…
Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis
Indiana University School of Medicine（印第安纳大学医学院）；Purdue University（普渡大学）；University of Calgary（卡尔加里大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Duke University（杜克大学）；The Ohio State University（俄亥俄州立大学）；Weill Cornell Medical Center（威尔康奈尔医学中心）；OhioHealth（俄亥俄健康）
University of California San Francisco（加州大学旧金山分校）；University of Pennsylvania（宾夕法尼亚大学）
Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and…
DUN-SRE: Deep Unrolling Network with Spatiotemporal Rotation Equivariance for Dynamic MRI Reconstruction
University of Nottingham Ningbo China（诺丁汉大学宁波校区）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University, Shanghai, China（上海科技大学信息科学与技术学院）；iHuman Institute, Shanghaitech University, Shanghai, China（上海科技大学iHuman研究所）
The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·金梅尔综合癌症中心）；The Johns Hopkins University（约翰斯·霍普金斯大学）
The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging (PWI) methods. Each of these parameters has distinct radiological-pathological meaning. For example, DWI interrogates the movement of water in the tissue and PWI gives an estimate of the blood flow, both are critical me…
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water. ASL is increasingly used in clinical studies due to its noninvasiveness, repeatability and benefits in quantification. However, ASL suffers from an inherently low-signal-to-noise ratio (SNR) requiring repeated measurements of control/spin-labeled (C/L) pairs to achieve a reasonable image quality, which in return increases motion sensitivity. This leads to clinically prolonged scanning times increasing the risk of motion artifacts. Thus, there is an immense need of adv…
Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a combined TNN and Casorati MNN regularizations framework to reconstruct dMRI, which we term as TMNN. The proposed method simultaneously exploits the spatial structure and the temporal correlation of the dynamic MR data. The optimization problem can be efficiently solve…
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，俄亥俄州立大学）；Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG（研究与临床转化，磁共振，西门子医疗有限公司）；Division of Cardiovascular Medicine, The Ohio State University（心血管医学系，俄亥俄州立大学）
University of Bath（巴斯大学）；Imago7 foundation（Imago7基金会）；University of Edinburgh（爱丁堡大学）；GE Healthcare（GE医疗）；Technical University of Munich（慕尼黑工业大学）
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training sa…
Middlesex University（密德萨斯大学）；UCL Institute of Child Health（伦敦大学学院儿童健康研究所）；University of Surrey（萨里大学）
Delineation of the kidney region in dynamic contrast-enhanced magnetic resonance Imaging (DCE-MRI) is required during post-acquisition analysis in order to quantify various aspects of renal function, such as filtration and perfusion or blood flow. However, this can be obfuscated by the Partial Volume Effect (PVE), caused due to the mixing of any single voxel with two or more signal intensities from adjacent regions such as liver region and other tissues. To avoid this problem, firstly, a kidney region of interest (ROI) needs to be defined for the analysis. A clinician may choose to select a re…
University of Bath（巴斯大学）；Technical University of Munich（慕尼黑工业大学）；GE Healthcare（通用电气医疗集团）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neura…
Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion
Dept. of Math and Computer Science, University of Catania（数学与计算机科学系，卡塔尼亚大学）；Centre for Medical Image Computing, University College London（医学影像计算中心，伦敦大学学院）；MIFT Department, University of Messina（MIFT系，梅斯ina大学）
提出 BrLP 时空模型，通过在小型潜在空间运行、整合受试者元数据与疾病动力学先验，并引入 LAS 算法，实现了 3D 脑部 MRI 个体化疾病进展的高精度预测。
University of Oxford（牛津大学）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院国民保健服务基金会信托）；King’s College London（伦敦国王学院）
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints t…
Quantitative perfusion maps using a novelty spatiotemporal convolutional neural network
School of Applied Technology, Shenzhen University（深圳大学应用技术学院）；College of Health Science and Environmental Engineering, Shenzhen Technology University（深圳技术大学健康科学与环境工程学院）；Department of Biomedical Engineering, Columbia University（哥伦比亚大学生物医学工程系）；Zuckerman Institute, Columbia University（哥伦比亚大学扎克曼研究所）；College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine（同济大学医学院附属上海市第四人民医院）
This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised machine learning approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. An habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the High Angiogenic Tumor (HAT) habitat, as the most perfused region of the enhancing tumor; the Low Angiogenic Tumor (LAT) habi…
Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions
Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht（MRI治疗与诊断计算成像组，放射治疗系，乌得勒支大学医学中心）
The University of Iowa（爱荷华大学）；University of Utah（犹他大学）
We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover dynamic magnetic resonance images from undersampled measurements. We introduce a generalized formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low rank structure. Using variable splitting, we decouple the complex optimization problem to simpler and we…
Gabor Primitives for Accelerated Cardiac Cine MRI Reconstruction
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的chair，慕尼黑技术大学（TUM）和慕尼黑技术大学医院，德国慕尼黑）；School of Computation and Information Technology, Technical University of Munich, Munich, Germany（计算与信息科技学院，慕尼黑技术大学，德国慕尼黑）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany（生物医学影像中的机器学习研究所，海德堡慕尼黑，德国纽赫堡）；Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, USA（放射科，波士顿儿童医院和哈佛医学院，美国波士顿）；Munich Center for Machine Learning, Technical University of Munich, Munich, Germany（慕尼黑机器学习中心，慕尼黑技术大学，德国慕尼黑）；GE HealthCare, Munich, Germany（通用电气医疗系统，德国慕尼黑）；School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom（生物医学工程与成像科学学院，伦敦国王学院，英国伦敦）；Department of Computing, Imperial College London, London, United Kingdom（计算学院，伦敦帝国学院，英国伦敦）
本文提出 Gabor 原始要素用于加速心脏 cine MRI 重建，通过调制高斯包络实现灵活频谱支持，提升对光滑结构和锐利边界的表示能力。
University of Bath（巴斯大学）；GE Healthcare（通用电气医疗集团）；Technical University of Munich（慕尼黑工业大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawback of requiring high-fidelity ground truth tissue map training data which is limited. This paper proposes NonLinear Equivariant Imaging (NLEI), a self-supervised learning approach to eliminate the need for ground truth for deep MRF image reconstruction. NLEI extends the recent Equivariant Imaging framework to nonlinear inverse problems such as MRF. Only fast, compressed-sampled MRF scans are used for training. NLEI le…
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. The p…
A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
Swiss Data Science Center, EPFL and ETH Zürich（瑞士数据科学中心，EPFL 和 ETH 苏黎世）；Diagnostic and Interventional Radiology, University Hospital Zürich（诊断与介入放射学，苏黎世大学医院）；Institute for Biomedical Engineering, University and ETH Zürich（生物医学工程研究所，苏黎世大学和 ETH 苏黎世）；Biomedical Informatics Group, ETH Zürich（生物医学信息学小组，ETH 苏黎世）；Division of Preventive Medicine, Brigham and Women’s Hospital / Harvard Medical School（预防医学部，布莱根妇女医院/哈佛医学院）
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Huashan Hospital, Fudan University（复旦大学附属华山医院）；Fudan University（复旦大学）；Jing’an District Centre Hospital of Shanghai（上海市静安区中心医院）
Ischemic stroke lesion segmentation from Computed Tomography Perfusion (CTP) images is important for accurate diagnosis of stroke in acute care units. However, it is challenged by low image contrast and resolution of the perfusion parameter maps, in addition to the complex appearance of the lesion. To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation. Our framework consists of three components based on Convolutional Neural Networks (CNNs) a…
Bridging MRI and PET physiology: Untangling complementarity through orthogonal representations
Dept. of diagnostic and interventional Radiology and Neuroradiology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院诊断与介入放射学及神经放射学系）；Digital Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院数字医学）；Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗流程与增强现实教席）；Bavarian Center for Cancer Research (BZKF) Augsburg, Germany（德国奥格斯堡巴伐利亚癌症研究中心）；Dept. of Nuclear Medicine, University Hospital Augsburg, Germany（德国奥格斯堡大学医院核医学系）；Dept. of Urology, University Hospital Augsburg, Germany（德国奥格斯堡大学医院泌尿外科）；Center for Advanced Analytics and Predictive Sciences, University of Augsburg, Germany（德国奥格斯堡大学高级分析与预测科学中心）
Technische Universität München（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；University of Sheffield（谢菲尔德大学）
Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent. Unfortunately, in many applications, conventional clinical DCE-MRI suffers from low spatiotemporal resolution and insufficient volume coverage. In this paper, we propose a novel deep learning based approach to directly estimate the PK parameters from undersampled DCE-MRI data. Specifically, we design a custom loss function wh…
Cardiac magnetic resonance imaging (CMR) is a noninvasive imaging modality that provides a comprehensive evaluation of the cardiovascular system. The clinical utility of CMR is hampered by long acquisition times, however. In this work, we propose and validate a plug-and-play (PnP) method for CMR reconstruction from undersampled multi-coil data. To fully exploit the rich image structure inherent in CMR, we pair the PnP framework with a deep learning (DL)-based denoiser that is trained using spatiotemporal patches from high-quality, breath-held cardiac cine images. The resulting "PnP-DL" method …
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College, London, UK（埃利扎贝塔·加德纳妇女健康研究所，大学学院，伦敦）；University College London Hospital NHS Foundation Trust, London, UK（伦敦大学学院医院国家健康服务信托基金）；Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK（医学物理与生物医学工程系，伦敦大学学院医院）；Department of Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom（放射科，伦敦大学学院医院国家健康服务信托基金）
Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）
Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix. In the decoding step, the contribution of each sub-bolus can be isolated resulting in dynamic perfusion scans. When acquiring te-ASL both with and without flow-crushing, the ASL-signal in the arteries can be isolated resulting in 4D-angiographic information. However, obtaining multi-timepoint perfusion and angiographic data requires two acquis…
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Department of Electrical and Computer Engineering, UCLA（电气与计算机工程系，加州大学洛杉矶分校）；Medical Informatics, UCLA（医学信息学，加州大学洛杉矶分校）；Department of Radiological Sciences, UCLA（放射科学系，加州大学洛杉矶分校）；Department of Radiology, UC San Francisco（放射科，旧金山大学医学院）；Department of Bioengineering, UCLA（生物工程系，加州大学洛杉矶分校）；Department of Pathology and Laboratory Medicine, UCLA（病理学与实验室医学系，加州大学洛杉矶分校）
Universidad de Valladolid（巴利亚多利德大学）；King’s College London（伦敦国王学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Centre for Marine Sciences - CCMAR（海洋科学中心（CCMAR））
First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternat…
A novel neural network architecture, known as DL-ESPIRiT, is proposed to reconstruct rapidly acquired cardiac MRI data without field-of-view limitations which are present in previously proposed deep learning-based reconstruction frameworks. Additionally, a novel convolutional neural network based on separable 3D convolutions is integrated into DL-ESPIRiT to more efficiently learn spatiotemporal priors for dynamic image reconstruction. The network is trained on fully-sampled 2D cardiac cine datasets collected from eleven healthy volunteers with IRB approval. DL-ESPIRiT is compared against a sta…
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Helmholtz Munich（亥姆霍兹慕尼黑）；Technical University of Munich（慕尼黑工业大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Imperial College London（伦敦帝国理工学院）；King’s College London（伦敦国王学院）
University of Minnesota（明尼苏达大学）；Center for Magnetic Resonance Research（磁共振研究中心）
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging has emerged as a powerful strategy, becoming a part of large-scale studies, such as the Human Connectome Project. However, when SMS imaging is combined with in-plane acceleration for higher acceleration rates, conventional SMS reconstruction methods may suffer from noise amplification and other artifacts. Recently, deep learning (DL) techniques have gained interest…
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Department of Medicine, National University of Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore（新加坡国立心脏中心心内科部）
