Motion artifacts in Magnetic Resonance Imaging (MRI) arise due to relatively long acquisition times and can compromise the clinical utility of acquired images. Traditional motion correction methods often fail to address severe motion, leading to distorted and unreliable results. Deep Learning (DL) alleviated such pitfalls through generalization with the cost of vanishing structures and hallucinations, making it challenging to apply in the medical field where hallucinated structures can tremendously impact the diagnostic outcome. In this work, we present an instance-wise motion correction pipeline that leverages motion-guided Implicit Neural Representations (INRs) to mitigate the impact of motion artifacts while retaining anatomical structure. Our method is evaluated using the NYU fastMRI dataset with different degrees of simulated motion severity. For the correction alone, we can improve over state-of-the-art image reconstruction methods by $+5\%$ SSIM, $+5\:db$ PSNR, and $+14\%$ HaarPSI. Clinical relevance is demonstrated by a subsequent experiment, where our method improves classification outcomes by at least $+1.5$ accuracy percentage points compared to motion-corrupted images.
Parallel imaging is a widely-used technique to accelerate magnetic resonance imaging (MRI). However, current methods still perform poorly in reconstructing artifact-free MRI images from highly undersampled k-space data. Recently, implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object. In this study, we adopted INR to parallel MRI reconstruction. The MRI image was modeled as a continuous function of spatial coordinates. This function was parameterized by a neural network and learned directly from the measured k-space it…
Purpose: To improve upon Extreme MRI, a recently proposed method by Ong Et al. for reconstructing high spatiotemporal resolution, 3D non-Cartesian acquisitions by incorporating motion compensation into these reconstructions using an approach termed MoCo-MSLR. Methods: Motion compensation is challenging to incorporate into high spatiotemporal resolution reconstruction due to the memory footprint of the motion fields and the potential to lose dynamics by relying on an initial high temporal resolution, low spatial resolution reconstruction. Motivated by the work of Ong Et al. and Huttinga Et al.,…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Millenium Institute for Intelligent Healthcare Engineering（千禧智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；School of Biomedical Engineering and the National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERCAMRT), Shanghai Jiao Tong University（上海交通大学生物医学工程学院和先进磁共振诊断与治疗国家工程研究中心）
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）
Volumetrically Consistent Implicit Atlas Learning via Neural Diffeomorphic Flow for Placenta MRI
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）
From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction
Department of Bioengineering and Imperial-X, Imperial College London（生物工程系和Imperial-X，帝国理工学院伦敦分校）；Department of Bioengineering, Imperial College London（生物工程系，帝国理工学院伦敦分校）
The University of Iowa（爱荷华大学）；University of Wisconsin at Madison（威斯康星大学麦迪逊分校）
We introduce an unsupervised deep manifold learning algorithm for motion-compensated dynamic MRI. We assume that the motion fields in a free-breathing lung MRI dataset live on a manifold. The motion field at each time instant is modeled as the output of a deep generative model, driven by low-dimensional time-varying latent vectors that capture the temporal variability. The images at each time instant are modeled as the deformed version of an image template using the above motion fields. The template, the parameters of the deep generator, and the latent vectors are learned from the k-t space da…
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右岸医院（慕尼黑工业大学附属））；Imperial College London（帝国理工学院）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
SGAM: Shared Gaussian Geometry with Implicit Amplitude Modeling for Scan-Specific 3D Multi-Contrast MRI Reconstruction
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院劳特伯生物医学成像研究中心）
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；MS Center Amsterdam（阿姆斯特丹多发性硬化中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Leiden University（莱顿大学）；Delft University of Technology（代尔夫特理工大学）
Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varyin…
Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所医学人工智能研究中心）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction
Division of Image Processing, Department of Radiology, Leiden University Medical Center（莱顿大学医学中心放射科图像处理部）；C.J. Gorter Center, Department of Radiology, Leiden University Medical Center（莱顿大学医学中心放射科C.J. 戈特中心）；Cardio Vascular Imaging Group, Department of Radiology, Leiden University Medical Center（莱顿大学医学中心放射科心血管成像组）；Philips Innovative Technologies（飞利浦创新技术公司）
Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation
Department of Neuroradiology, University Hospital Heidelberg（神经放射科，海德堡大学医院）；School of Electrical Engineering and Automation, Wuhan University（电气工程与自动化学院，武汉大学）；Momoni AI；Department of Stomatology, Shenzhen People’s Hospital（口腔科，深圳人民医院）；College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；Department of Game Design, Faculty of Arts, Uppsala University（游戏设计系，艺术学院，乌普萨拉大学）；School of Computer Science, University of Sydney（计算机科学学院，悉尼大学）；School of Biomedical Engineering, The University of Sydney（生物医学工程学院，悉尼大学）
提出一种基于广义 INR 的 MRI 并行成像重建框架，利用尺度嵌入编码器和体素先验渲染全采样 k 空间，在 4–6 倍加速下提升质量并降低计算开销。
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction
The Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention（上海医学影像计算与计算机辅助介入重点实验室）；Academy of Engineering and Technology, Fudan University（复旦大学工程与技术学院）
This paper considers the problem of undersampled MRI reconstruction. We propose a novel Transformer-based framework for directly processing signal in k-space, going beyond the limitation of regular grids as ConvNets do. We adopt an implicit representation of k-space spectrogram, treating spatial coordinates as inputs, and dynamically query the sparsely sampled points to reconstruct the spectrogram, i.e. learning the inductive bias in k-space. To strike a balance between computational cost and reconstruction quality, we build the decoder with hierarchical structure to generate low-resolution an…
KP-INR: A Dual-Branch Implicit Neural Representation Model for Cardiac Cine MRI Reconstruction
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands（莱顿大学医学中心放射科）；Cardiologs, Philips, Paris, France（法国巴黎Philips心血管部门）；Faculty of Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands（埃因霍温理工大学计算机科学学院）；Philips Innovative Technologies, Hamburg, Germany（德国汉堡Philips创新技术）
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
Radiation Oncology, University of California, San Francisco, California（加州大学旧金山分校放射肿瘤学系）；Radiology and Biomedical Imaging, University of California, San Francisco, California（加州大学旧金山分校放射学与生物医学成像系）；Siemens Medical Solutions USA, Inc., Cleveland, Ohio（西门子医疗解决方案美国公司，克利夫兰，俄亥俄）；Radiology at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California（洛杉矶儿童医院放射学，美国南加州大学凯克医学院，洛杉矶，加利福尼亚）
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static $q$-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary $q$-space…
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of under-sampled and noisy measurements. Deep learning approaches have been proven to be successful in solving this ill-posed inverse problem and are capable of producing very high quality reconstructions. However, current architectures heavily rely on convolutions, that are content-independent and have difficulties modeling long-range dependencies in images. Recently, Transformers, the workhorse of contemporary natural language processing, have emerged as powerful building blocks for a multitude of vision task…
Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Informatics Institute（信息学院）；QurAI group（QurAI组）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学）；Department of Biomedical Engineering and Physics（生物医学工程与物理系）；Department of Radiology and Nuclear Medicine（放射学与核医学系）；Department of Radiology（放射学系）；Mayo Clinic（梅奥诊所）；Department of Clinical and Experimental Cardiology（临床与实验心脏病学系）；Department of Experimental Cardiology（实验心脏病学系）；Heart Failure & Arrhythmias（心力衰竭与心律失常）
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。
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the outp…
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院医学人工智能研究中心）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；The State University of New York, Buffalo（纽约州立大学布法罗分校）
Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersampled k-space to the fully sampled images. Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac…
FINDER: Zero-Shot Field-Integrated Network for Distortion-free EPI Reconstruction in Diffusion MRI
Sejong University（世宗大学）；Forschungszentrum Jülich（于利希研究中心）；Athinoula A. Martinos Center for Biomedical Imaging（Athinoula A. Martinos生物医学成像中心）；Harvard Medical School（哈佛医学院）；Hongik University（弘益大学）
Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Department of Neurosurgery, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院神经外科）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
The University of Iowa（爱荷华大学）；University of Wisconsin（威斯康星大学）；Cincinnati Children’s Hospital（辛辛那提儿童医院）；University of Cincinnati（辛辛那提大学）
We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary MRI. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space. Specifically, we model the deformation map at each time instant as the output of a CNN-based generator that has the same weight for all time-frames, driven by a low-dimensional latent vector. The time series of latent vectors account for the dynamics in the dataset, including respirato…
Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞州总医院）
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）
Erasmus MC（伊拉斯姆斯医学中心）；Delft University of Technology（代尔夫特理工大学）
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysis could benefit from a tailored framework that jointly optimizes both tasks to fully exploit the information available in the longitudinal data. Most learning-based registration algorithms, including joint optimization approaches, currently suffer from bias due to selection of a fixed reference frame and only support pairwise transformations. We here propose an analytical framework based on an unbiased learning strat…
AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
College of Biomedical Engineering & Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences（中国医学科学院北京医院国家老年医学中心老年医学研究所）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction
Shanghai University（上海大学）；Shanghai Institute of Applied Mathematics and Mechanics（上海应用数学和力学研究所）；School of Mechanics and Engineering Science（机械与工程科学学院）；Xi’an Jiaotong University（西安交通大学）；School of Communication and Information Engineering（通信与信息工程学院）
AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis
Atlantic Technological University（大西洋理工大学）；Belfast Health and Social Care Trust（贝尔法斯特健康和社会关怀信托基金）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务信托基金）；King’s College London（伦敦国王学院）；Queen’s University Belfast（贝尔法斯特女王大学）
School of Electrical Engineering, Tel Aviv University（特拉维夫大学电气工程学院）
Medical image analysis studies usually require medical image datasets for training, testing and validation of algorithms. The need is underscored by the deep learning revolution and the dominance of machine learning in recent medical image analysis research. Nevertheless, due to ethical and legal constraints, commercial conflicts and the dependence on busy medical professionals, medical image analysis researchers have been described as "data starved". Due to the lack of objective criteria for sufficiency of dataset size, the research community implicitly sets ad-hoc standards by means of the p…
Physically Aware Radiomics Without Interpolation: Disentangling Voxel Geometry and Signal Modification in CT and MRI
Complejo Asistencial Universitario de León（莱昂大学附属医院综合体）；Hospital Universitario Rey Juan Carlos（雷·胡安·卡洛斯大学医院）；Health Research Institute of the Jiménez Díaz Foundation（希门尼斯·迪亚斯基金会健康研究所）；Rey Juan Carlos University（雷·胡安·卡洛斯大学）；Universidad de Oviedo（奥维耶多大学）；Universidad de León（莱昂大学）；IFCA-CSIC（西班牙国家研究委员会高级计算与电子科学研究所）；Universidad Autónoma de Chile（智利大学）
École de Technologie Supérieure（高等技术学院）；CHU Sainte-Justine Research Center（圣茹斯特医院研究中心）；Polytechnique Montréal（蒙特利尔理工学院）
Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of pai…
Fast and Explicit: Slice-to-Volume Reconstruction via 3D Gaussian Primitives with Analytic Point Spread Function Modeling
TUM University Hospital, Technical University of Munich（慕尼黑技术大学医院，慕尼黑技术大学）；Institut de Neurosciences de la Timone, Aix-Marseille Université（里莫尼神经科学研究所，艾克斯-马赛大学）；IMT Atlantique（IMT大西洋）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）
