5 citations · 10 across the 5 of their papers we have counts for
5 papers
Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps
Jordan Jomsky, Zongyu Li, Kay C. Igwe +8
BrainAGE is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking f…
Detecting Schizophrenia with 3D Structural Brain MRI Using Deep Learning
Junhao Zhang, Vishwanatha M. Rao, Ye Tian +8
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroim…
Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data
Nanyan Zhu, Chen Liu, Xinyang Feng +4
Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD).…
Substituting Gadolinium in Brain MRI Using DeepContrast
Haoran Sun, Xueqing Liu, Xinyang Feng +12
Cerebral blood volume (CBV) is a hemodynamic correlate of oxygen metabolism and reflects brain activity and function. High-resolution CBV maps can be generated using the steady-sta…
Estimating brain age based on a healthy population with deep learning and structural MRI
Xinyang Feng, Zachary C. Lipton, Jie Yang +2
Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive o…