Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Therefore, retrospective slice-level motion correction through slice-to-volume registration (SVR) is crucial. Previous studies have utilized deep learning (DL) based models to address the SVR task; however, they overlooked the uncertainty stemming from the input stack of slices and did not assign weighting or scoring to each slice. In this work, we introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume, incorporating a self-attention mechanism to enhance robustness against input data variations and uncertainties. It utilizes independent slice and volume encoders and a self-attention module to assign pixel-wise scores for each slice. We conducted evaluation experiments on 200 images involving synthetic rigid motion generated from 27 subjects belonging to the test set, from the publicly available Healthy Brain Network (HBN) dataset. Our experimental results demonstrate that our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods (Euclidean distance of $0.93$ [mm] vs. $1.86$ [mm]). Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0.096$ sec. vs. $1.17$ sec.). Our end-to-end SVR model facilitates real-time head motion tracking during fMRI acquisition, ensuring reliability and robustness against uncertainties in inputs. source code, which includes the training and evaluations, will be available soon.
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
University College London（伦敦大学学院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Allen Institute for Brain Science（艾伦脑科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
Temporal Registration in Application to In-utero MRI Time Series
Massachusetts Institute of Technology（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）；Washington University in St. Louis（圣路易斯华盛顿大学）；Shanghai Jiao Tong University（上海交通大学）
MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.（南方医科大学生物医学工程学院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.（广东省医学图像处理重点实验室）；Guangdong Province Engineering Laboratory for Medical Imaging（广东省医学影像与诊断技术工程实验室）；Information Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.（南方医科大学南华医院信息中心）；Division of Spine Surgery, Department of Orthopaedics, Nanfang hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.（南方医科大学南华医院骨科脊柱外科）
NPBDREG: Uncertainty Assessment in Diffeomorphic Brain MRI Registration using a Non-parametric Bayesian Deep-Learning Based Approach
Technion – Israel Institute of Technology（以色列理工学院）
基于深度神经网络（DNN）的图像配准算法中的不确定性量化在用于临床应用的图像配准算法的部署中起着关键作用，所述临床应用例如手术规划、术中引导、疾病进展或治疗功效的纵向监测以及面向研究的处理管道。当前用于基于DNN的图像配准算法中的不确定性估计的可用方法可能导致次优临床决策，这是由于对于配准潜在空间的假定参数分布的配准主干的不确定性的潜在不准确估计。我们引入NPBDREG，这是一个完全非参数贝叶斯框架，用于在基于DNN的可变形图像配准中进行不确定性估计，方法是将Adam优化器与随机梯度Langevin动力学（SGLD）相结合，通过后验采样来表征底层后验分布。因此，它有可能提供与分布外数据高度相关的不确定性估计。我们证明了NPBDREG的附加值，与基线概率VoxelMorph模型（PrVXM）相比，使用来自四个公开数据库的$390$图像对进行脑MRI图像配准：MGH10，CMUC 12，ISBR 18和LPBA40。NPBDREG显示预测不确定性与分布外数据（$r>0.95$ vs. $r<0.5$）的相关性更好，配准精度（Dice评分，$0.74$ vs. $0.69$，$p \ll 0.01$）提高了7.3%，配准平滑度提高了18%（变形场中折叠百分比，0.014 vs. 0.017，$p \ll 0.01$）。最后，NPBDREG证明了一个更好的泛化能力的数据破坏了混合结构噪声（…
BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
Department of Radiology, Weill Cornell Medicine, New York City, NY, USA（韦尔·科恩医学中心放射科）；School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA（康奈尔大学电气与计算机工程学院）；Computer Science and Artificial Intelligence Lab at the Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院计算机科学与人工智能实验室）
本文提出基于 KeyMorph 的 BrainMorph 关键点基础模型，利用超十万个 3D 脑 MRI 体积训练，实现鲁棒、可解释、快速可控的多模态成对与组级刚性、仿射及非线性配准。
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Technische Universität München（慕尼黑工业大学）；Konica Minolta Laboratory Europe（柯尼卡美能达欧洲实验室）；Johns Hopkins University（约翰斯·霍普金斯大学）；Trinity College Dublin（都柏林圣三一学院）
Motion correction of dynamic contrast enhanced MRI of the liver
Center for Image Sciences, University Medical Center Utrecht, the Netherlands（荷兰乌得勒支大学医学中心影像科学中心）；Department of Radiology, University Medical Center Utrecht, the Netherlands（荷兰乌得勒支大学医学中心放射科）
GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration
University of Leeds（利兹大学）；Radboud University Medical Centre（拉德堡德大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿心医疗科技有限公司）
Back to Physics: Operator-Guided Generative Paths for SMS MRI Reconstruction
School of Advanced Manufacturing, Nanchang University（先进制造学院，南昌大学）；College of Computer Science and Software Engineering, Shenzhen University（计算机科学与软件工程学院，深圳大学）
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
LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling
Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Center（拉德堡德大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Macao Polytechnic University（澳门理工大学）
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；The First Affiliated Hospital of China Medical University（中国医科大学附属第一医院）；VoxelCloud, Inc.（体素云公司）
Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion
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）
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Universitat Pompeu Fabra（庞培法布拉大学）；BCNatal；Institución Catalana de Investigación y Estudios Avanzados (ICREA)（加泰罗尼亚高级研究与发展研究院）；Centre for Biomedical Research on Rare Diseases (CIBERER)（罕见疾病生物医学研究中心）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Radboud University Medical Centre（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Macao Polytechnic University（澳门理工大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
Technische Universität München（慕尼黑工业大学）；ScintHealth GmbH（ScintHealth有限公司）；Reply SpA（Reply股份公司）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能研究所）
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
Auckland Bioengineering Institute（奥克兰生物工程研究所）；University of Auckland（奥克兰大学）；Faculty of Medical and Health Sciences（医学与健康科学学院）；MGH/HST Martinos Center for Biomedical Imaging（麻省总医院/哈佛健康科学技术生物医学成像马蒂诺斯中心）；Harvard Medical School（哈佛医学院）；MIT（麻省理工学院）
Pyramid Attention Network for Medical Image Registration
School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Medical UltraSound Image Computing (MUSIC) Lab（医学超声图像计算实验室）；Smart Medical Imaging, Learning and Engineering (SMILE) Lab（智能医学影像、学习与工程实验室）
What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
Department of Neurosurgery, Neurovascular Unit, Río Hortega University Hospital, Valladolid, Spain（西班牙巴利亚多利德里奥·奥尔特加大学医院神经外科神经血管科）；Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain（西班牙巴利亚多利德生物医学研究与计算分析专业组(GEIBAC)，巴利亚多利德生物健康研究所(IBioVALL)）
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis
Technical University of Munich, TUM University Hospital（慕尼黑工业大学，慕尼黑工业大学附属医院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Imperial College London（伦敦帝国理工学院）
Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
University of Electronic Science and Technology of China（电子科技大学）；King’s College London（伦敦国王学院）；University Hospitals KU Leuven（鲁汶大学附属医院）；University College London（伦敦大学学院）；SenseTime Research（商汤科技研究院）
Uncertainty-Aware Cortical Surface Reconstruction and Spherical Registration for Heterogeneous Brain MRI
Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital – Amager and Hvidovre（丹麦磁共振研究中心，放射科与核医学科，哥本哈根大学医院-阿马格和维多弗）；Department of Health Technology, Technical University of Denmark（技术丹麦大学健康技术系）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（阿蒂努拉·A·马丁诺斯生物医学成像中心，麻省总医院）；Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）；Hawkes Institute, University College London（霍克斯研究所，伦敦大学学院）
4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings
Faculty of Computer Science, Otto-von-Guericke University Magdeburg（马格德堡奥托·冯·格里克大学计算机科学学院）；Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg（马格德堡大学医院放射与核医学诊所）
目的：我们的目标是开发一种强大的4D MRI方法，用于大FOV，能够提取不规则的呼吸运动，可与所有MRI机器一起使用，因此适用于支持广泛的介入设置。方法：我们提出了一种4D MRI重建方法来捕获任意数量的呼吸状态。它在导航切片和搜索区域中使用模板更新，以实现快速和稳健的血管横截面跟踪。它以1.82 mm x 1.82 mm x 4 mm的空间分辨率和200 ms的时间分辨率捕获255 mm x 320 mm x 228 mm的FOV。对13例健康受试者的37个4D MRI图像进行了重建，以验证该方法的有效性。对新方法和基线方法的重建率和速度进行了定量评价。此外，进行了一项由10名放射科医师参与的研究，以评估两种方法的主观重建质量。结果如下：我们的研究结果表明，与基线方法相比，平均重建率有所提高（79.4% vs. 45.5%），平均重建时间有所改善（24 s vs. 73 s）。介入放射科医生认为我们的方法的重建质量高于基线（262.5分vs. 217.5分，p=0.02）。结论：模板更新是提高4D MRI重建率和实现更好重建质量的有效方法。搜索区域减少重建时间。这些改进增加了4D MRI作为经皮介入中无缝支持介入图像引导的基础的适用性。
Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
Department of Electrical and Electronic Engineering and I-X, Imperial College London（伦敦帝国理工学院电气与电子工程系及I-X）；Trinity College Institute of Neuroscience, Trinity College Dublin（都柏林圣三一学院神经科学研究所）
CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI
Leiden University Medical Center（莱顿大学医学中心）；University of Sheffield（谢菲尔德大学）；INSIGNEO；Institute for In Silico Medicine（计算医学研究所）；Department of Infection, Immunity and Cardiovascular Disease（感染、免疫与心血管疾病部门）；Sheffield Teaching Hospitals（谢菲尔德教学医院）；Department of Clinical Radiology（临床放射学部门）
