Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.
Image registration algorithms can be generally categorized into two groups: non-rigid and rigid. Recently, many deep learning-based algorithms employ a neural net to characterize non-rigid image registration function. However, do they always perform better? In this study, we compare the state-of-art deep learning-based non-rigid registration approach with rigid registration approach. The data is generated from Kaggle Dog vs Cat Competition \url{https://www.kaggle.com/c/dogs-vs-cats/} and we test the algorithms' performance on rigid transformation including translation, rotation, scaling, shear…
Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be done via registration. Registration is desirable when analyzing time-series data for tracking, averaging, or differential diagnoses of diseases. Efficient registration methods exist for rigid (including linear or affine) transformations; however, for non-rigid (also known as non-affine) transformations, current methods are computationally expensive and time-consuming. In this report, I will explor…
The non-rigid registration between CT data and ultrasonic images of liver can facilitate the diagnosis and treatment, which has been widely studied in recent years. To improve the registration accuracy of the Demons model on the non-rigid registration between 3D CT liver data and 2D ultrasonic images, a novel boundary extraction and enhancement method based on radial directional local intuitionistic fuzzy entropy in the polar coordinates has been put forward, and a new registration workflow has been provided. Experiments show that our method can acquire high-accuracy registration results. Expe…
Learning accurate rigid registration for longitudinal brain MRI from synthetic data
1 Division of Biomedical Imaging, KTH Royal Institute of Technology, Huddinge, Sweden 2 Athinoula A.\ Martinos Center for Biomedical Imaging, Charlestown, USA 3 Department of Radiology, Massachusetts General Hospital, Boston, USA 4 Department of Radiology, Harvard Medical School, Boston, USA 5 Computer Science \& Artificial Intelligence Laboratory, MIT, Cambridge, USA
We present Free Point Transformer (FPT) - a deep neural network architecture for non-rigid point-set registration. Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods. FPT is designed to accept unordered and unstructured point-sets with a variable number of points and uses a "model-free" approach without heuristic constraints. Training FPT is flexible and involves minimizing an intuitive …
University of Edinburgh（爱丁堡大学）；Imperial College London（帝国理工学院）；University of Essex（埃塞克斯大学）
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, …
An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University（东南大学江苏省医学信息处理协同创新国际联合实验室）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Shanghai Changhai Hospital, Naval Medical University（海军军医大学上海长海医院）；Dalian University of Technology（大连理工大学）；School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机科学与信息安全学院）；The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital（河北医科大学第四医院暨河北省肿瘤医院）；Erasmus MC, University Medical Center Rotterdam（鹿特丹伊拉斯姆斯大学医学中心）
University College London（伦敦大学学院）；University of Leeds（利兹大学）
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for …
Muroran Institute of Technology（室兰工业大学）；Niigata University of Health and Welfare（新潟医疗福祉大学）；Konica Minolta, Inc.（柯尼卡美能达公司）
Multi-stain whole-slide-image (WSI) registration is an active field of research. It is unclear, however, how the current WSI registration methods would perform on a real-world data set. AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) challenge is held to verify the performance of the current WSI registration methods by using a new dataset that originates from routine diagnostics to assess real-world applicability. In this report, we present our solution for the ACROBAT challenge. We employ a two-step approach including rigid and non-rigid transforms. The experimental results show that…
Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院）；Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application（临床应用生物力学基础研究北京市重点实验室）；Beijing Institute of Heart, Lung & Blood Vessel Diseases（北京市心肺血管疾病研究所）
Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the X-ray and …
We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encountered in ultrasound-guided interventional procedures. FPT is constructed with a global feature extractor which accepts unordered source and target point-sets of variable size. The extracted features are conditioned by a shared multilayer perceptron point transformer module to predict a displacement vector for each source point, transforming it into the target space. The point …
Aberration corrected scanning transmission electron microscopes (STEM) enable to determine local strain fields, composition and bonding states at atomic resolution. The precision to locate atomic columns is often obstructed by scan artifacts limiting the quantitative interpretation of STEM datasets. Here, a novel bias-corrected non-rigid registration approach is presented that compensates for fast and slow scan artifacts in STEM image series. The bias-correction is responsible for the correction of the slow scan artifacts and based on a explicit coupling of the deformations of the individual i…
National Center for Tumor Diseases（国家肿瘤疾病中心）；University Hospital Carl-Gustav-Carus（卡尔·古斯塔夫·卡鲁斯大学医院）；TU Dresden（德累斯顿工业大学）
Non-rigid registration is a key component in soft-tissue navigation. We focus on laparoscopic liver surgery, where we register the organ model obtained from a preoperative CT scan to the intraoperative partial organ surface, reconstructed from the laparoscopic video. This is a challenging task due to sparse and noisy intraoperative data, real-time requirements and many unknowns - such as tissue properties and boundary conditions. Furthermore, establishing correspondences between pre- and intraoperative data can be extremely difficult since the liver usually lacks distinct surface features and …
Britton Chance Center for Biomedical Photonics（布里顿·钱斯生物医学光子学研究中心）；Wuhan National Laboratory for Optoelectronics（武汉光电国家研究中心）；MoE Key Laboratory for Biomedical Photonics（教育部生物医学光子学重点实验室）；Collaborative Innovation Center for Biomedical Engineering（生物医学工程协同创新中心）；School of Engineering Sciences, Huazhong University of Science and Technology（华中科技大学工程科学学院）
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-…
Attention-aware non-rigid image registration for accelerated MR imaging
University Hospital of Tuebingen（蒂宾根大学医院）；Klinikum Rechts der Isar（右岸医院（慕尼黑工业大学附属））；Technical University of Munich（慕尼黑工业大学）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Imperial College London（伦敦帝国学院）
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR…
University of Warwick（华威大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；The Alan Turing Institute（艾伦·图灵研究所）；University Hospitals Coventry & Warwickshire（考文垂与沃里克郡大学医院）
Cross-slide image analysis provides additional information by analysing the expression of different biomarkers as compared to a single slide analysis. These biomarker stained slides are analysed side by side, revealing unknown relations between them. During the slide preparation, a tissue section may be placed at an arbitrary orientation as compared to other sections of the same tissue block. The problem is compounded by the fact that tissue contents are likely to change from one section to the next and there may be unique artefacts on some of the slides. This makes registration of each sectio…
SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation
Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））；RACOON Study Group, Site Essen（RACOON研究组埃森站点）；Department of Physics, Technical University Dortmund（多特蒙德工业大学物理系）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟（DKTK）埃森合作站点）；Medical Faculty and Faculty of Computer Science, University of Duisburg-Essen（杜伊斯堡-埃森大学医学院与计算机科学学院）
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of motion. Additional alignment cues (point-based or geometric features, and other boundary co…
The Wellcome Centre for Human Neuroimaging, UCL（伦敦大学学院惠康人类神经影像中心）
In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algori…
Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis
Radboudumc（拉德堡德大学医学中心）；Fraunhofer MEVIS（弗劳恩霍夫梅维斯研究所）；Universität zu Lübeck（吕贝克大学）；University Medical Center Groningen（格罗宁根大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Norwegian University of Science and Technology（挪威科技大学）
Purpose: The purpose of this paper is to present a method for real-time 2D-3D non-rigid registration using a single fluoroscopic image. Such a method can find applications in surgery, interventional radiology and radiotherapy. By estimating a three-dimensional displacement field from a 2D X-ray image, anatomical structures segmented in the preoperative scan can be projected onto the 2D image, thus providing a mixed reality view. Methods: A dataset composed of displacement fields and 2D projections of the anatomy is generated from the preoperative scan. From this dataset, a neural network is tr…
We present a registration algorithm which jointly estimates motion and the ground truth image from a set of noisy frames under rigid, constant translation. The algorithm is non-iterative and needs no hyperparameter tuning. It requires a fixed number of FFT, multiplication, and downsampling operations for a given input size, enabling fast implementation on embedded platforms like cubesats where on-board image fusion can greatly save on limited downlink bandwidth. The algorithm is optimal in the maximum likelihood sense for additive white Gaussian noise and non-stationary Gaussian approximations…
Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as CBCT, to meet the online demands of plan evaluation and adaptation. Deep learning (DL)-based automatic segmentation has gained great success in segmenting planning CT, but its applications to CBCT yielded inferior results due to the low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT s…
Structured Analytic Mappings for Point Set Registration
College of Electronic Engineering, Ocean University of China（中国海洋大学电子工程学院）；School of Mathematics and Statistics, Shandong Normal University（山东师范大学数学与统计学院）
