1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Siemens (United States)US19 papers
- Siemens Healthineers (Germany)DE17 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE7 papers
- King's College LondonGB5 papers
- New York UniversityUS4 papers
- Siemens (Germany)DE4 papers
- Chinese Academy of SciencesCN3 papers
- Chinese University of Hong KongHK3 papers
- Imperial College LondonGB3 papers
- Stanford UniversityUS3 papers
- Université Côte d'AzurFR3 papers
- Chinese Academy of Medical Sciences & Peking Union Medical CollegeCN2 papers
60 papers
A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data
Brunnhilde Ponsi, Thomas Carlier, Lara Marteau +5
Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria. Simultaneous PET/MR imaging, combine…
Dynamic Modulated Arc Therapy (DMAT): A Time Aware, Modulation Steered Optimization Framework for Next Generation Radiotherapy Delivery
Taoran Li, Esa Kuusela, Emmi Ruokokoski +10
Background: Conventional VMAT optimization treats delivery time and deliverability as emergent properties of control-point-centric models that ignore finite acceleration and other…
A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)
Harry Robertshaw, Anna Barnes, Phil Blakelock +20
While we are making progress in overcoming infectious diseases and cancer; one of the major medical challenges of the mid-21st century will be the rising prevalence of stroke. Larg…
Cut to the Mix: Simple Data Augmentation Outperforms Elaborate Ones in Limited Organ Segmentation Datasets
Chang Liu, Fuxin Fan, Annette Schwarz +1
Multi-organ segmentation is a widely applied clinical routine and automated organ segmentation tools dramatically improve the pipeline of the radiologists. Recently, deep learning…
Stroke Lesion Segmentation in Clinical Workflows: A Modular, Lightweight, and Deployment-Ready Tool
Yann Kerverdo, Florent Leray, Youwan Mahé +2
Deep learning frameworks such as nnU-Net achieve state-of-the-art performance in brain lesion segmentation but remain difficult to deploy clinically due to heavy dependencies and m…
Dipolar order mapping based on spin-lock magnetic resonance imaging
Zijian Gao, Qianxue Shan, Ziqin Zhou +2
Purpose: Inhomogeneous magnetization transfer (ihMT) effect reflects dipolar order with a dipolar relaxation time (), specific to motion-restricted macromolecules. We aim t…
A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images
Lingjing Chen, Chengxiu Zhang, Yinqiao Yi +9
We propose a deep learning-based approach that integrates MRI sequence parameters to improve the accuracy and generalizability of quantitative image synthesis from clinical weighte…
Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles
Richard Dirauf, Florian Wolz, Dario Zanca +1
Content-based puzzle solvers have been extensively studied, demonstrating significant progress in computational techniques. However, their evaluation often lacks realistic challeng…
Multi-Plane Vision Transformer for Hemorrhage Classification Using Axial and Sagittal MRI Data
Badhan Kumar Das, Gengyan Zhao, Boris Mailhe +4
Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisitions with varying contrasts a…
3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
Weicheng Zhu, Haoxu Huang, Huanze Tang +10
Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebro…
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann +5
To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or…
Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast 3D Medical Imaging
Badhan Kumar Das, Gengyan Zhao, Han Liu +4
The Masked Autoencoder (MAE) has recently demonstrated effectiveness in pre-training Vision Transformers (ViT) for analyzing natural images. By reconstructing complete images from…
3D Single-shot CEST imaging at 3T Based on True FISP Readout
Yupeng Wu, Qifan Pang, Zhichao Wang +9
To simultaneously fit multiple-pool effects, spectrally selective 3D CEST imaging typically requires single-shot readouts to save time. However, to date, FLASH and EPI have been th…
Rapid, High-resolution and Distortion-free Mapping of Fetal Brain using Multi-echo Radial FLASH and Model-based Reconstruction
Xiaoqing Wang, Hongli Fan, Zhengguo Tan +7
Purpose: To develop a rapid, high-resolution and distortion-free technique for simultaneous water-fat separation, and mapping of the fetal brain at 3T. Methods:…
Towards a vision foundation model for comprehensive assessment of Cardiac MRI
Athira J Jacob, Indraneel Borgohain, Teodora Chitiboi +3
Cardiac magnetic resonance imaging (CMR), considered the gold standard for noninvasive cardiac assessment, is a diverse and complex modality requiring a wide variety of image proce…
AI-based, automated chamber volumetry from gated, non-contrast CT
Athira J Jacob, Ola Abdelkarim, Salma Zook +11
Background: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure. Objectives: To validate a new, fully…
C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu
Deep Learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as CFD (Computational F…
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
Florin Condrea, Saikiran Rapaka, Lucian Itu +4
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold st…
Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation
Francesco Santini, Jakob Wasserthal, Abramo Agosti +11
Purpose: To present and evaluate Dafne (deep anatomical federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of ra…
A multi-organ point cloud registration algorithm for abdominal CT registration
Samuel Joutard, Thomas Pheiffer, Chloe Audigier +6
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heter…
Meta model application for consistency management of models for avionic systems design
Jef Stegen, Stefan Dutre, Joe Zhensheng Guo +2
This paper presents the application of a meta model and single underlying model on an applied avionics system design use case. System models, safety assurance cases and safety requ…
An 8 channel parallel transmit system with current sensor feedback for MRI-guided interventional applications
Felipe Godinez, Raphael Tomi-Tricot, Matthias Barthel +5
Background: Parallel transmit (pTx) has introduced many benefits to MRI with regard to decreased specific absorption rates and improved transmit field homogeneity, of particular im…
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
Julian Krebs, Hervé Delingette, Nicholas Ayache +1
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the…