A Novel Automatic Real-time Motion Tracking Method in MRI-guided Radiotherapy Using Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家癌症临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院）
Background and Purpose: Accurate motion tracking in MRI-guided Radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation. Materials and Methods: We developed a novel MRIgRT motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese model (ICV), named ETLD+ICV. The ETLD framework was upgraded for real-time cine MRI, including advanced image preprocessing, no-reference image quality assessment, an enhanced median-flow tracker, and a refined detector with dynamic search region adjustments. ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with key parameters optimized. The method was tested on 3.5D MRI scans from 10 patients with liver metastases. Results: Evaluation of 106,000 frames across 77 treatment fractions showed sub-millimeter tracking errors of less than 0.8mm, with over 99% precision and 98% recall for all subjects in the Beam Eye View(BEV)/Beam Path View(BPV) orientation. The ETLD+ICV method achieved a dice global score of more than 82% for all subjects, demonstrating the method's extensibility and precise target volume coverage. Conclusion: This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT that significantly outperforms current methods. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments.
DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy
Department of Computer Science and Software Engineering, Concordia University（计算机科学与软件工程系，康科迪亚大学）；Department of Electrical and Computer Engineering, Concordia University（电气与计算机工程系，康科迪亚大学）；Elekta Ltd.（Elekta公司）；Diagnos Medical Systems（Diagnos医疗系统）
University of Utah（犹他大学）；University of Maryland, School of Medicine（马里兰大学医学院）
Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to appr…
Uppsala University（乌普萨拉大学）；Elekta Instrument AB（医科达仪器有限公司）
Our anatomy is in constant motion. With modern MR imaging it is possible to record this motion in real-time during an ongoing radiation therapy session. In this paper we present an image registration method that exploits the sequential nature of 2D MR images to estimate the corresponding displacement field. The method employs several discriminative correlation filters that independently track specific points. Together with a sparse-to-dense interpolation scheme we can then estimate of the displacement field. The discriminative correlation filters are trained online, and our method is modality …
International University of Business Agriculture and Technology（国际商业、农业与技术大学）
Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to recognize anatomical areas of interest such as surgical planning, monitoring therapy, clinical drug trials, image registration, stereotactic neurosurgery, radiotherapy etc. The task of this paper is to implement different unsupervised classification algorithms in ITK and perform tissue classification (white matter, gray matter, cerebrospinal fluid …
Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）
Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations…
Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed metho…
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy
College of Medicine and Biological Information Engineering（医学与生物信息工程学院）；Cancer Hospital of Dalian University of Technology（大连理工大学肿瘤医院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Washington University in St. Louis（圣路易斯华盛顿大学）；Yonsei University College of Medicine（延世大学医学院）
In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow - an approach we term Intentional Deep Overfit Learning (IDOL). Implementing the IDOL framework in any task in radiotherapy consists of two training stages: 1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and 2) intentionally overfitting this general model to a small training…
Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Amsterdam UMC, Department of Radiation Oncology（阿姆斯特丹大学医学中心放射肿瘤科）；Cancer Center Amsterdam, Imaging and Biomarkers（阿姆斯特丹癌症中心影像与生物标志物）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Brain Imaging（阿姆斯特丹大学医学中心、阿姆斯特丹自由大学、阿姆斯特丹神经科学、脑成像）
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Sichuan Cancer Hospital & Institute, Sichuan Cancer Center（四川省肿瘤医院·研究所（四川省癌症防治中心））
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
Sharif University of Technology（谢里夫理工大学）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心人工智能医学研究所）；Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心/LASI）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR)（埃森大学医学中心（公法团体）科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；TU Dortmund University（多特蒙德工业大学）
Magnetic resonance imaging (MRI) is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearity (GNL) limit anatomical accuracy, potentially compromising the quality of tumour treatments. In addition, slow MR acquisition and reconstruction limit the potential for real-time image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for real-time MR-guided radiotherapy applications. We…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；University Hospital Magdeburg（马格德堡大学医院）；German Centre for Neurodegenerative Diseases（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）；MedDigit
Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient's psychological and physical conditions. Single Image S…
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Centre Antoine Lacassagne（安托万·拉卡桑中心）
Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and non-exclusive class…
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Universitätsklinikum Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲分子神经科学网络综合癌症中心）
Anatomically Consistent Cross-Contrast Super-Resolution of Anisotropic Brain T2w MRI
Columbia University（哥伦比亚大学）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；Herbert Irving Comprehensive Cancer Center（赫伯特·欧文综合癌症中心）；University of Cincinnati（辛辛那提大学）
On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy
Faculty of Medicine and Health, The University of Sydney（澳大利亚悉尼大学医学与健康学院）；Ingham Institute for Applied Medical Research（英厄姆应用医学研究所）；A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院A.A.马蒂诺斯生物医学成像中心）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Background and purpose: Radiation-induced erectile dysfunction (RiED) is commonly seen in prostate cancer patients. Clinical trials have been developed in multiple institutions to investigate whether dose-sparing to the internal-pudendal-arteries (IPA) will improve retention of sexual potency. The IPA is usually not considered a conventional organ-at-risk (OAR) due to segmentation difficulty. In this work, we propose a deep learning (DL)-based auto-segmentation model for the IPA that utilizes CT and MRI or CT alone as the input image modality to accommodate variation in clinical practice. Mate…
Manchester Metropolitan University（曼彻斯特城市大学）；NHS（英国国民保健署）；The Leeds Teaching Hospital（利兹教学医院）；The Clatterbridge Cancer Center（克莱特布里奇癌症中心）；Leicester University（莱斯特大学）
Prostate radiotherapy is a well established curative oncology modality, which in future will use Magnetic Resonance Imaging (MRI)-based radiotherapy for daily adaptive radiotherapy target definition. However the time needed to delineate the prostate from MRI data accurately is a time consuming process. Deep learning has been identified as a potential new technology for the delivery of precision radiotherapy in prostate cancer, where accurate prostate segmentation helps in cancer detection and therapy. However, the trained models can be limited in their application to clinical setting due to di…
Université de Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；CGFL（CGFL（第戎乔治·弗朗索瓦·勒克莱尔中心））；University Hospital of Dijon（第戎大学医院）
Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segmentation in multimodal images by embedding prior knowledge via learning-based shape modeling and registration to learn the modality invariant anatomical structure of organs. For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images. In this paper, we present a fully automatic deep generative model-driven m…
The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MR-based 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited by the 3D MR-imaging latency. In this work, we present a method to pe…
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Radiology and Imaging Sciences（放射学与影像科学）；National Institutes of Health Clinical Center（国家卫生研究院临床中心）；Walter Reed National Military Medical Center（沃尔特·里德国家军事医学中心）；Biostatistics and Clinical Epidemiology Services（生物统计与临床流行病学服务）
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
University Hospital Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲肿瘤研究所综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Institute of Neuroradiology, University Hospital Erlangen（埃尔兰根大学医院神经放射学研究所）；Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学埃尔兰根大学医院神经外科）；Universität Würzburg（维尔茨堡大学）
Imperial College London（帝国理工学院）；National University of Defense Technology（国防科技大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a …
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
Institute of Biomedical Engineering（生物医学工程研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学）；Department of Radiation Oncology（放射肿瘤科）；University Medical Center Schleswig-Holstein（什勒斯威-霍斯特大学医院）；Department of Experimental and Clinical Medicine（实验与临床医学系）；Magna Graecia University（马格拉西亚大学）；Department of Information Engineering（信息工程系）；Università Politecnica delle Marche（帕尔米拉理工大学）
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Hyperfine Research Inc.（Hyperfine研究公司）
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help b…
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network architecture and abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipula…
Brightness-Invariant Tracking Estimation in Tagged MRI
Johns Hopkins University（约翰霍普金斯大学）；University of Maryland School of Medicine（马里兰大学医学院）；Uniformed Services University of the Health Sciences（统一服务卫生科学大学）；The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc.（亨利·M·杰克逊军事医学促进基金会）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Washington University（华盛顿大学）；University of South Carolina（南卡罗来纳大学）
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Accurate and robust segmentation of lung cancers from CT, even those located close to mediastinum, is needed to more accurately plan and deliver radiotherapy and to measure treatment response. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby an informative teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation a…
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Southern Methodist University（南卫理公会大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院·研究所）
Radiotherapy is the main treatment modality for nasopharynx cancer. Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for radiotherapy. As manual delineation is time-consuming and laborious, automatic segmentation of GTV has a potential to improve this process. Currently, most of the deep learning-based automatic delineation methods of GTV are mainly performed on medical images like CT images. However, it is challenged by the low contrast between the pathology regions and surrounding soft tissues, small target region, and anisotropic resol…
MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI
School of Computer Science, University of Birmingham（英国伯明翰大学计算机科学学院）；Division of Informatics, Imaging and Data Sciences, University of Manchester（曼彻斯特大学信息学与数据科学系）；Centre for Computational Imaging and Modelling in Medicine, University of Manchester（曼彻斯特大学医学计算成像与建模中心）；Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（广东省计算机视觉与虚拟现实技术重点实验室，深圳先进技术研究院，中国科学院）；MRC Laboratory of Medical Sciences, Imperial College London（伦敦帝国理工学院医学科学实验室）；UKRI CDT in AI for Healthcare, Imperial College London（伦敦帝国理工学院人工智能与医疗健康CDT）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机科学系）
Purpose: To develop and evaluate a free-breathing respiratory motion compensated 4D (3D+respiration) $T_2$-weighted turbo spin echo sequence with application to radiology and MR-guided radiotherapy. Methods: k-space data are continuously acquired using a rewound Cartesian acquisition with spiral profile ordering (rCASPR) to provide matching contrast to the conventional linear phase encode ordering and to sort data into multiple respiratory phases. Low-resolution respiratory-correlated 4D images were reconstructed with compressed sensing and used to estimate non-rigid deformation vector fields,…
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
Dartmouth College（达特茅斯学院）；Dartmouth Health（达特茅斯医疗）；NVIDIA Corporation（英伟达公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Missouri-Columbia（密苏里大学哥伦比亚分校）
