Jia Guo

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Assistant Professor

University of California, Riverside

United States

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Jia Guo is an Assistant Professor at the University of California Riverside, USA. His research focuses on advanced imaging techniques, particularly in arterial spin labeling and deep learning applications for medical diagnostics. Recent publications include optimization strategies for blood flow measurements and novel methodologies for synthesizing PET imaging from MRI data.

Recent Grants

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Ultra-fast cerebral blood flow imaging for quantifying brain dynamics

Open Date: 2022-08-08

Close Date: 2024-07-31

Articles (10)

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2025

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2024

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2024

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2024

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2023

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2022

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2021

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2021

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2020

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2020

Collaborators (9)

Yuriko Suzuki

University of Oxford

UNITED KINGDOM

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Thomas Liu

Professor

University of California, San Diego

UNITED STATES

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Audrey Fan

Assistant Professor

University of California, Davis

UNITED STATES

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Qin Qin

Associate Professor

Johns Hopkins University

UNITED STATES

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Guido Davidzon

Stanford University

UNITED STATES

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Moss Zhao

Stanford University

UNITED STATES

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María A. Fernández-Seara

Professor

Clinica Universidad de Navarra

SPAIN

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Jan Petr

Amsterdam University Medical Centers

NETHERLANDS

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Luis Hernandez-Garcia

Research Professor

University of Michigan

UNITED STATES

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Jia Guo

Research Interests

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About

Recent Grants

Articles (10)

Optimization of deep learning–based denoising for arterial spin labeling: Effects of averaging and training strategies

A generalized signal model for dual‐module velocity‐selective arterial spin labeling

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks

Optimizing background suppression for dual‐module velocity‐selective arterial spin labeling: Without using additional background‐suppression pulses

Robust dual‐module velocity‐selective arterial spin labeling (dm‐VSASL) with velocity‐selective saturation and inversion

Recent Technical Developments in ASL: A Review of the State of the Art

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit

Comparison of velocity‐selective arterial spin labeling schemes

Predicting PET Cerebrovascular Reserve with Deep Learning by Using Baseline MRI: A Pilot Investigation of a Drug-Free Brain Stress Test

Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias

Collaborators (9)

Yuriko Suzuki

Thomas Liu

Audrey Fan

Qin Qin

Guido Davidzon

Moss Zhao

María A. Fernández-Seara

Jan Petr

Luis Hernandez-Garcia

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