| A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images L He, IR Greenshields IEEE transactions on medical imaging 28 (2), 165-172, 2008 | 209 | 2008 |
| A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes H Li, NA Parikh, L He Frontiers in neuroscience 12, 381775, 2018 | 171 | 2018 |
| Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data MN Parikh, H Li, L He Frontiers in computational neuroscience 13, 9, 2019 | 125 | 2019 |
| Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence J Wang, L He, H Zheng, ZL Lu PloS one 9 (5), e96899, 2014 | 90 | 2014 |
| Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework L He, H Li, SK Holland, W Yuan, M Altaye, NA Parikh NeuroImage: Clinical 18, 290-297, 2018 | 80 | 2018 |
| Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments? E Gozdas, NA Parikh, SL Merhar, JA Tkach, L He, SK Holland Brain Structure and Function 223, 3665-3680, 2018 | 63 | 2018 |
| Early cortical maturation predicts neurodevelopment in very preterm infants JE Kline, VSP Illapani, L He, M Altaye, JW Logan, NA Parikh Archives of Disease in Childhood-Fetal and Neonatal Edition 105 (5), 460-465, 2020 | 49 | 2020 |
| Prenatal opioid exposure is associated with smaller brain volumes in multiple regions SL Merhar, JE Kline, A Braimah, BM Kline-Fath, JA Tkach, M Altaye, L He, ... Pediatric research 90 (2), 397-402, 2021 | 48 | 2021 |
| Machine learning prediction of liver stiffness using clinical and T2-weighted MRI radiomic data L He, H Li, JA Dudley, TC Maloney, SL Brady, E Somasundaram, AT Trout, ... American Journal of Roentgenology 213 (3), 592-601, 2019 | 46 | 2019 |
| A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection M Chen, H Li, J Wang, JR Dillman, NA Parikh, L He Radiology: Artificial Intelligence 2 (1), e190012, 2019 | 45 | 2019 |
| A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants L He, H Li, J Wang, M Chen, E Gozdas, JR Dillman, NA Parikh Scientific Reports 10 (1), 15072, 2020 | 44 | 2020 |
| Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants NA Parikh, L He, E Bonfante-Mejia, L Hochhauser, PE Wilder, K Burson, ... Pediatric neurology 49 (6), 424-430, 2013 | 41 | 2013 |
| Aberrant executive and frontoparietal functional connectivity in very preterm infants with diffuse white matter abnormalities L He, NA Parikh Pediatric neurology 53 (4), 330-337, 2015 | 40 | 2015 |
| Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age S Kaur, S Powell, L He, CR Pierson, NA Parikh PloS one 9 (1), e85807, 2014 | 38 | 2014 |
| A spatio-temporal deconvolution method to improve perfusion CT quantification L He, B Orten, S Do, WC Karl, A Kambadakone, DV Sahani, H Pien IEEE Transactions on Medical Imaging 29 (5), 1182-1191, 2010 | 37 | 2010 |
| Atlas-guided quantification of white matter signal abnormalities on term-equivalent age MRI in very preterm infants: findings predict language and cognitive development at two … L He, NA Parikh PloS one 8 (12), e85475, 2013 | 36 | 2013 |
| Brain functional network connectivity development in very preterm infants: the first six months L He, NA Parikh Early human development 98, 29-35, 2016 | 35 | 2016 |
| Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants L He, NA Parikh Neuroimage 64, 328-340, 2013 | 31 | 2013 |
| Early prediction of cognitive deficit in very preterm infants using brain structural connectome with transfer learning enhanced deep convolutional neural networks M Chen, H Li, J Wang, W Yuan, M Altaye, NA Parikh, L He Frontiers in neuroscience 14, 563097, 2020 | 27 | 2020 |
| Perinatal risk and protective factors in the development of diffuse white matter abnormality on term-equivalent age magnetic resonance imaging in infants born very preterm NA Parikh, P Sharma, L He, H Li, M Altaye, VSP Illapani, A Arnsperger, ... The Journal of pediatrics 233, 58-65. e3, 2021 | 25 | 2021 |
Nehal A. Parikh, DO, MSProfessor of Pediatrics, Cincinnati Children's and University of Cincinnati在 cchmc.org 的电子邮件经过验证
