346 citations
- Xiaofeng Yang3 profiles19 · h 50
- Jinho D. Choi18 · h 34
- Eugene Agichtein14 · h 47
- Carl Yang2 profiles12 · h 41
- E. Weeks11 · h 46
- S. Boettcher11 · h 31
- Stefan Boettcher2 profiles11 · h 7
- S. Urazhdin11 · h 36
- Eric R. Weeks3 profiles9 · h 1
- Li Xiong3 profiles9 · h 47
- Richard L. J. Qiu2 profiles9 · h 15
- Tonghe Wang9 · h 44
- Georgia Institute of TechnologyUS43 papers
- Atlanta University CenterUS13 papers
- University of PennsylvaniaUS12 papers
- University of ChicagoUS10 papers
- Yale UniversityUS10 papers
- California Institute of TechnologyUS8 papers
- Georgia State UniversityUS8 papers
- Princeton UniversityUS8 papers
- Stanford UniversityUS8 papers
- University of Illinois Urbana-ChampaignUS8 papers
- Carnegie Mellon UniversityUS7 papers
- Cornell UniversityUS7 papers
8 papers · 2 filters
TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
Ming Li, Jike Zhong, Shitian Zhao +6
The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as t…
Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction
Mojtaba Safari, Zach Eidex, Richard L. J. Qiu +3
Background: To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion…
Cross-Modal Urban Sensing: Evaluating Sound-Vision Alignment Across Street-Level and Aerial Imagery
Pengyu Chen, Xiao Huang, Teng Fei +1
Environmental soundscapes convey substantial ecological and social information regarding urban environments; however, their potential remains largely untapped in large-scale geogra…
Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI
Mojtaba Safari, Shansong Wang, Qiang Li +5
Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts,…
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reco…
A Physics-Informed Deep Learning Model for MRI Brain Motion Correction
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motio…