SynthMorph: learning contrast-invariant registration without acquired images
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院 Athinoula A. Martinos 生物医学影像中心）；Harvard Medical School（哈佛医学院）；Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）
Malte Hoffmann, Benjamin Billot, Douglas N. Greve, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca
备注：
16 pages, 15 figures, 3 tables, deformable image registration, data independence, deep learning, MRI-contrast invariance, anatomy agnosticism, final published version
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at https://w3id.org/synthmorph.
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths
University of Peshawar（白沙瓦大学）；Danube Private University（多瑙河私立大学）；University of Engineering & Technology, Peshawar（白沙瓦工程与技术大学）；Medical University of Vienna（维也纳医科大学）；Karl Landsteiner Society（卡尔·兰施泰纳学会）；University of Cambridge（剑桥大学）
Quantib-U（Quantib-U公司）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarit…
Tsinghua University（清华大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream "high-to-low, low-to-high" network structure, and can achieve satisfactory overall registration results. However, accurate alignments for some severely deformed local regions, which are crucial for pinpointing surgical targets, are often overlooked. Consequently, these approaches are not sensitive to some hard-to-align regions, e.g., intra-patient registration of de…
Tsinghua University（清华大学）；Shenzhen International Graduate School（深圳国际研究生院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根和妇女医院）
Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estima…
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Medical University Vienna（维也纳医科大学）；Cambridge University Hospitals National Health Service (NHS) Foundation Trust（剑桥大学医院国民保健服务基金会信托）；Eindhoven University of Technology（埃因霍温理工大学）；Leiden University（莱顿大学）；University College London（伦敦大学学院）；Cancer Research UK Cambridge Institute（英国癌症研究中心剑桥研究所）
This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme…
University College London（伦敦大学学院）；InstaDeep；University College London Hospital NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）；University of Oxford（牛津大学）
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acquired at two different time points. Combining intensity-based similarity and gland segmentation as weak supervision, the population-data-trained registration networks significantly lowered the target registration errors (TREs) on holdout patient data…
We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ult…
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, United States Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Germany Department of Imaging Physics, Delft University of Technology, Netherlands TUM School of Computation, Information；Technology, Technische Universität München, Germany Department of Surgery, Taipei Veterans General Hospital, Taiwan
Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive l…
In this paper, a method for Automatic Image Registration (AIR) through histogram is proposed. Automatic image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of previous related images. This new approach combines several segmentations of the pair of images to be registered. A relaxation parameter on the histogram modes delineation is introduced. It is followed by characterization of the extracted objects through the objects area, axis ratio, and pe…
ShanghaiTech University（上海科技大学）；University of Zürich（苏黎世大学）；ETH Zürich（苏黎世联邦理工学院）
Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) that offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of multi-modal images remains challenging, chiefly due to the entirely different image contrast rendered by these modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation …
2D-3D Deformable Image Registration of Histology Slide and Micro-CT with ML-based Initialization
ImFusion GmbH（ImFusion有限公司）；Technical University of Munich（慕尼黑工业大学）；Institute of Pathology, Heinrich Heine University and University Hospital of Düsseldorf（海因里希·海涅大学与杜塞尔多夫大学医院病理研究所）；Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut（弗劳恩霍夫高速动力学研究所（恩斯特·马赫研究所））
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 …
Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss
University of Oxford（牛津大学）；Nuffield Department of Population Health, University of Oxford（牛津大学纳菲尔德人口健康系）；Big Data Institute, University of Oxford（牛津大学大数据研究所）；Oxford University Hospitals NHS Foundation Trust（牛津大学医院 NHS 基金会信托基金）；Oxford Radiology Research Unit（牛津放射学研究单元）
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National Institutes of Health（美国国立卫生研究院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance (MR) and transrectal ultrasound (TRUS) image registration is a critical component in MR-TRUS fusion guided prostate interventions. However, due to the huge difference between the image appearances and the large variation in image correspondence, MR-TRUS image registration is a very challenging problem. In this paper, an adversarial image registration (AIR) framework is proposed. By training two deep neural networks s…
Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration between these datasets may fail to fully profit the potential of both sensors. In this regard, an optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper…
Technical University of Munich (TUM)（慕尼黑工业大学）；Klinikum Rechts der Isar（伊萨尔河右岸医院）；Imperial College London（伦敦帝国理工学院）
Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the same time to preserve the topology and the invertibility of the transformation. Recent research has focused on leveraging deep learning approaches for this task as these have been shown to achieve competitive registration accuracy while being computationally more efficient than traditional iterative registration methods. In this work, we propose a simple yet effective unsupervised deep learning-based {\em multi-modal} …
Institute for Applied Life Sciences, University of Massachusetts（马萨诸塞大学应用生命科学研究所）；University of Massachusetts（马萨诸塞大学）
Advancement of imaging techniques enables consecutive image sequences to be acquired for quality monitoring of manufacturing production lines. Registration for these image sequences is essential for in-line pattern inspection and metrology, e.g., in the printing process of flexible electronics. However, conventional image registration algorithms cannot produce accurate results when the images contain many similar and deformable patterns in the manufacturing process. Such a failure originates from a fact that the conventional algorithms only use the spatial and pixel intensity information for r…
We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into an elastic B-splines-based transformation model and incorporates an inverse inconsistency penalization supporting symmetric registration performance. We introduce several theoretical and algorithmic solutions which provide high computational efficiency and thereby applicability of the proposed framework in a wide range of real scenarios. We show that INSPIRE delivers highly accurate, as well as stable and robust reg…
University College London（伦敦大学学院）；InstaDeep；University College London Hospital NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）
Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the volume, boundary smoothness and their changes are closely monitored on time-series MR image data. In this paper, we describe a new framework for forecasting prostate morphological changes, as the ability to detect such changes earlier than what is currently possible may enable timely treatment or avoiding unnecessary confirmatory biopsies. In this work, an efficient feature-based MR image registration is first developed…
Ryerson University（瑞尔森大学）；University of Toronto（多伦多大学）；Institute for Biomedical Engineering, Science and Technology (iBEST)（生物医学工程、科学与技术研究所（iBEST））
We propose FlowReg, a deep learning-based framework for unsupervised image registration for neuroimaging applications. The system is composed of two architectures that are trained sequentially: FlowReg-A which affinely corrects for gross differences between moving and fixed volumes in 3D followed by FlowReg-O which performs pixel-wise deformations on a slice-by-slice basis for fine tuning in 2D. The affine network regresses the 3D affine matrix based on a correlation loss function that enforces global similarity. The deformable network operates on 2D image slices based on the optical flow netw…
Multispectral and multimodal images are of important usage in the field of multi-source visual information fusion. Due to the alternation or movement of image devices, the acquired multispectral and multimodal images are usually misaligned, and hence image registration is pre-requisite. Different from the registration of common images, the registration of multispectral or multimodal images is a challenging problem due to the nonlinear variation of intensity and gradient. To cope with this challenge, we propose the phase congruency network (PCNet) to enhance the structure similarity of multispe…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
Fuzhou University（福州大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）
Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consiste…
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）
A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer
University of Cambridge（剑桥大学）；Early Cancer Institute（早期癌症研究所）；Technical University of Munich（慕尼黑工业大学）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；University of Salerno（萨勒诺大学）
Volumetric (3d) images are acquired for many scientific and biomedical purposes using imaging methods such as serial section microscopy, CT scans, and MRI. A frequent step in the analysis and reconstruction of such data is the alignment and registration of images that were acquired in succession along a spatial or temporal dimension. For example, in serial section electron microscopy, individual 2d sections are imaged via electron microscopy and then must be aligned to one another in order to produce a coherent 3d volume. State of the art approaches find image correspondences derived from patc…
Otto von Guericke University (OVGU)（奥托·冯·格里克大学（OVGU））
Dynamic MRI is a technique of acquiring a series of images continuously to follow the physiological changes over time. However, such fast imaging results in low resolution images. In this work, abdominal deformation model computed from dynamic low resolution images have been applied to high resolution image, acquired previously, to generate dynamic high resolution MRI. Dynamic low resolution images were simulated into different breathing phases (inhale and exhale). Then, the image registration between breathing time points was performed using the B-spline SyN deformable model and using cross-c…
School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）；Department of Women and Children’s Health, School of Life Course Sciences, King’s College London（伦敦国王学院生命历程科学学院妇幼健康系）
Accurately estimating and correcting the motion artifacts are crucial for 3D image reconstruction of the abdominal and in-utero magnetic resonance imaging (MRI). The state-of-art methods are based on slice-to-volume registration (SVR) where multiple 2D image stacks are acquired in three orthogonal orientations. In this work, we present a novel reconstruction pipeline that only needs one orientation of 2D MRI scans and can reconstruct the full high-resolution image without masking or registration steps. The framework consists of two main stages: the respiratory motion estimation using a self-su…
In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-regist…
Oriented histogram-based vector field embedding for characterizing 4D CT data sets in radiotherapy
Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心应用医学信息学研究所）；Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心计算神经科学研究所）；Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心放射治疗与放射肿瘤学系）
University of Naples Federico II（那不勒斯费德里科二世大学）；Paris Lodron University of Salzburg（萨尔茨堡巴黎洛德隆大学）
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is suitable for a large variety of EO based value adding products and s…
Information Systems Institute, University of Applied Sciences Western Switzerland（西瑞士应用科学大学信息系统学院）；AGH University of Kraków（克拉科夫AGH大学）；University of Padova（帕多瓦大学）；University of Geneva（日内瓦大学）
MIT（麻省理工学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University College London（伦敦大学学院）；Massachusetts General Hospital（麻省总医院）
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from othe…
ImFusion GmbH（ImFusion有限责任公司）；Technical University of Munich（慕尼黑工业大学）
Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We…
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁大学医学院）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Chinese Academy of Sciences（中国科学院）
Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limi…
National University of Singapore（新加坡国立大学）；University of Cambridge（剑桥大学）
Indirect image registration is a promising technique to improve image reconstruction quality by providing a shape prior for the reconstruction task. In this paper, we propose a novel hybrid method that seeks to reconstruct high quality images from few measurements whilst requiring low computational cost. With this purpose, our framework intertwines indirect registration and reconstruction tasks is a single functional. It is based on two major novelties. Firstly, we introduce a model based on deep nets to solve the indirect registration problem, in which the inversion and registration mappings …
International Prevention Research Institute（国际预防研究所）；Swansea University（斯旺西大学）；Diabetic Eye Screening Wales（威尔士糖尿病眼病筛查中心）；University of Strathclyde Institute of Global Public Health（思克莱德大学全球公共卫生学院）
In order to estimate a registration model of eye fundus images made of an affinity and two radial distortions, we introduce an estimation criterion based on an error between the vessels. In [1], we estimated this model by minimising the error between characteristics points. In this paper, the detected vessels are selected using the circle and ellipse equations of the overlap area boundaries deduced from our model. Our method successfully registers 96 % of the 271 pairs in a Public Health dataset acquired mostly with different cameras. This is better than our previous method [1] and better than…
Manteia Tech（Manteia科技）；University of California, Los Angeles（加州大学洛杉矶分校）；Fuzhou University（福州大学）；Xiamen University（厦门大学）；Mayo Clinic Arizona（亚利桑那梅奥诊所）
Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making it difficult to focus only on the spatial aspect of the images and ignore differences in distributions. In this study, we developed a self-supervised approach, Indescribable Multi-model Spatial Evaluator (IMSE), to address multi-modal image registration. IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes re…
Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Vanderbilt University（范德堡大学）；Insitro；Vanderbilt University Medical Center（范德堡大学医学中心）；Department of Veterans Affairs Medical Center（退伍军人事务部医疗中心）；Vanderbilt University Institute of Imaging Science（范德堡大学成像科学研究所）
针对 COPD 患者吸呼气 CT 因重建核差异导致定量分析误差的问题，提出两阶段流程：先用循环 GAN 进行核 harmonization，再进行可变形配准，显著降低肺气肿测量不一致性并提高配准精度。
Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography (CT) or Magnetic Resonance (MR) images thereby provides complementary functional and/or structural information of the targeted region and organs at risk. Merging this information relies on a correct spatial alignment of the observed anatomy between the acquired images. This can be achieved by the means of multi-modal deformable image registration (DIR), demonstrated to be capable of estimating dense and elastic defor…
Motion free reconstruction of compressively sampled cardiac perfusion MR images is a challenging problem. It is due to the aliasing artifacts and the rapid contrast changes in the reconstructed perfusion images. In addition to the reconstruction limitations, many registration algorithms under perform in the presence of the rapid intensity changes. In this paper, we propose a novel motion correction method that reconstructs the motion free image series from the undersampled cardiac perfusion MR data. The motion correction method uses the novel robust principal component analysis based reconstru…
University of Washington（华盛顿大学）；Magic Leap Inc.（Magic Leap公司）
Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this work, we develop an efficient and accurate retinal matching technique that combines dimension reduction and mutual information (MI), called RetinaMatch. The dimension reduction initializes the MI optimization as a coarse localization process, which narrows the optimization domain and avoids local optima. The effectiveness of Reti…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans w…
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
University of Oxford（牛津大学）；University College London（伦敦大学学院）；InstaDeep Ltd.（InstaDeep有限公司）；UCL Centre for Medical Image Computing（伦敦大学学院医学影像计算中心）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Medical Physics and Biomedical Engineering, UCL（伦敦大学学院医疗物理与生物医学工程系）；Wellcome / EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与物理科学研究委员会介入与外科科学中心）；Active Vision Laboratory, Department of Engineering Science, University of Oxford（牛津大学工程科学系主动视觉实验室）
Image registration is an important preprocessing step in neuroimaging which allows for the matching of anatomical and functional information between modalities and subjects. This can be challenging if there are gross differences in image geometry or in signal intensity, such as in the case of some molecular PET radioligands, where control subjects display relative lack of signal relative to noise within intracranial regions, and may have off target binding that may be confused as other regions, and may vary depending on subject. The use of intermediary images or volumes have been shown to aide…
University of Iowa（爱荷华大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Utah（犹他大学）
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registr…
