257 citations · 635 across the 15 of their papers we have counts for
16 papers
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model
Dayang Wang, Srivathsa Pasumarthi, Greg Zaharchuk +1
Deep learning (DL) based contrast dose reduction and elimination in MRI imaging is gaining traction, given the detrimental effects of Gadolinium-based Contrast Agents (GBCAs). Thes…
Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks
Ramy Hussein, David Shin, Moss Zhao +4
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with…
Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists
Sophie Ostmeier, Brian Axelrod, Benjamin F. J. Verhaaren +7
To determine if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contras…
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
Jiang Liu, Srivathsa Pasumarthi, Ben Duffy +3
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging c…
Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation
Ramy Hussein, Moss Zhao, David Shin +6
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and st…
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Ke Wang, Enhao Gong, Yuxin Zhang +3
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinical…
Self-Supervised Longitudinal Neighbourhood Embedding
Jiahong Ouyang, Qingyu Zhao, Ehsan Adeli +4
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine lear…
Representation Disentanglement for Multi-modal brain MR Analysis
Jiahong Ouyang, Ehsan Adeli, Kilian M. Pohl +2
Multi-modal MRIs are widely used in neuroimaging applications since different MR sequences provide complementary information about brain structures. Recent works have suggested tha…
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
Darvin Yi, Endre Grøvik, Michael Iv +3
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our data…
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Darvin Yi, Endre Grøvik, Michael Iv +3
Deep learning has proven to be an essential tool for medical image analysis. However, the need for accurately labeled input data, often requiring time- and labor-intensive annotati…
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
Endre Grøvik, Darvin Yi, Michael Iv +9
The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI seque…
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
Darvin Yi, Endre Grøvik, Michael Iv +9
Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep learning methods have been applied to segmentation tasks in medica…
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
Endre Grøvik, Darvin Yi, Michael Iv +3
Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study dem…
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech +10
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction. In this work, an MRI reconstruction algorithm, which is referred to as qua…
200x Low-dose PET Reconstruction using Deep Learning
Junshen Xu, Enhao Gong, John Pauly +1
Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in…
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng +8
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it a…