718 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS102 papers
- Massachusetts Institute of TechnologyUS20 papers
- Massachusetts General HospitalUS17 papers
- University College LondonGB10 papers
- Brigham and Women's HospitalUS9 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS8 papers
- Boston Children's MuseumUS8 papers
- Northeastern UniversityUS7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Loyola University ChicagoUS6 papers
- University of LausanneCH6 papers
- University of PennsylvaniaUS6 papers
22 papers · 1 filter
Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios
Prabhjot Kaur, Hakim Ouaalam, Sedat Kandemirli +2
Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which are difficult to acquire. This s…
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
Tiago Assis, Colin P. Galvin, Joshua P. Castillo +12
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative…
FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI
Bo Li, Qi Zeng, Simon K. Warfield +1
Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental ch…
Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
Rizhong Lin, Hamza Kebiri, Ali Gholipour +4
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representat…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
Noga Kertes, Yael Zaffrani-Reznikov, Onur Afacan +3
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable im…
Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series
S. Mazdak Abulnaga, Neel Dey, Sean I. Young +6
Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires a…
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
Rizhong Lin, Ali Gholipour, Jean-Philippe Thiran +3
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift ch…
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI
Benjamin Billot, Neel Dey, Daniel Moyer +5
Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional ne…
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Yohan Jun, Yamin Arefeen, Jaejin Cho +7
Purpose: To develop and evaluate methods for 1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-ser…
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
Yohan Jun, Jaejin Cho, Xiaoqing Wang +4
Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an in…
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Kelly Payette, Hongwei Li, Priscille de Dumast +55
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital ste…
Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord
Haykel Snoussi, Julien Cohen-Adad, Benoit Combes +5
Multiple sclerosis is an inflammatory disorder of the central nervous system. Quantitative MRI has huge potential to provide intrinsic and normative values of tissue properties use…
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Dewen Zeng, Weiwen Jiang, Tianchen Wang +6
Real-time cardiac magnetic resonance imaging (MRI) plays an increasingly important role in guiding various cardiac interventions. In order to provide better visual assistance, the…
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
Junshen Xu, Sayeri Lala, Borjan Gagoski +4
Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so imag…
Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years
Lilla Zöllei, Juan Eugenio Iglesias, Yangming Ou +2
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges i…
Physics-Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography
Xiao Wang, Robert D. MacDougall, Peng Chen +2
For single source helical Computed Tomography (CT), both Filtered-Back Projection (FBP) and statistical iterative reconstruction have been investigated. However for dual source CT…
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
Ayush Singh, Seyed Sadegh Mohseni Salehi, Ali Gholipour
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion…
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
Junshen Xu, Molin Zhang, Esra Abaci Turk +5
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable an…
Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge
Yiming Xiao, Hassan Rivaz, Matthieu Chabanas +13
In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets…
Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans
Venkateswararao Cherukuri, Peter Ssenyonga, Benjamin C. Warf +3
Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissu…