Guo J, Xiao N, Li H, et al. Transformer-Based High-Frequency Oscillation Signal Detection on Magnetoencephalography From Epileptic Patients. Front Mol Biosci. 2022;9:822810. Published 2022 Mar 4. doi:10.3389/fmolb.2022.822810.
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Kline JE, Illapani VSP, Li H, He L, Yuan W, Parikh NA. Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. Neuroimage Clin. 2021;31:102739. doi:10.1016/j.nicl.2021.102739.
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He L, Li H, Chen M, et al. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Front Neurosci. 2021;15:753033. Published 2021 Oct 5. doi:10.3389/fnins.2021.753033.
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Merhar SL, Jiang W, Parikh NA, et al. Effects of prenatal opioid exposure on functional networks in infancy. Dev Cogn Neurosci. 2021;51:100996. doi:10.1016/j.dcn.2021.100996.
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Parikh MN, Chen M, Braimah A, Kline J, McNally K, Logan JW, Tamm L, Yeates KO, Yuan W, He L, Parikh NA. (2021). Diffusion MRI microstructural abnormalities at term-equivalent age are associated with neurodevelopmental outcomes at 3 years of age in very preterm infants. Dev Cogn Neurosci. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A7135.
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Li H, Chen M, Wang J, Illapani VSP, Parikh NA and He L. (2021). Automatic segmentation of diffuse white matter abnormality on T2-weighted brain MRI using deep learning in very preterm infants. Radiol Artif Intell. 3(3). doi: 10.1148/ryai.2021200166.
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Dillman JR, Somasundaram E, Brady SL, He L. (2021). Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol. doi:10.1007/s00247-021-05057-0.
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Parikh NA, Sharma P, He L, et al. 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. J Pediatr. 2021;233:58-65.e3. doi:10.1016/j.jpeds.2020.11.058.
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Merhar SL, Kline JE, Braimah A, et al. Correction: Prenatal opioid exposure is associated with smaller brain volumes in multiple regions. Pediatr Res. 2021;90(2):493. doi:10.1038/s41390-020-01297-2.
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Kline JE, Sita Priyanka Illapani V, He L, Parikh NA. Automated brain morphometric biomarkers from MRI at term predict motor development in very preterm infants. Neuroimage Clin. 2020;28:102475. doi:10.1016/j.nicl.2020.102475.
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Alom MZ, He L, Taha TM, Asari VK. (2020). Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network. SPIE (11511), Applications of Machine Learning 2020, 115110F.
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Parikh NA, He L, Li H, Priyanka Illapani VS, Klebanoff MA. Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatr Neurol. 2020;106:56-62. doi:10.1016/j.pediatrneurol.2020.01.011.
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Parikh NA, Harpster K, He L, et al. Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Sci Rep. 2020;10(1):15920. Published 2020 Sep 28. doi:10.1038/s41598-020-72632-0.
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Parikh NA, He L, Priyanka Illapani VS, Altaye M, Folger AT, Yeates KO. Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. J Pediatr. 2020;220:56-63. doi:10.1016/j.jpeds.2020.01.034.
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Logan JW, Tan J, Skalak M, Fathi O, He L, Kline J, Klebanoff M, Parikh NA. (2020) Adverse effects of perinatal illness severity on neurodevelopment are partially mediated by early brain abnormalities in infants born very preterm. J Perinatol. doi:10.1038/s41372-020-00854-1, PMID: 33028936.
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Chen M, Li H, Wang J, et al. Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks. Front Neurosci. 2020;14:858. Published 2020 Sep 18. doi:10.3389/fnins.2020.00858.
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Li H, He L, Dudley JA, et al. DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Pediatr Radiol. 2021;51(3):392-402. doi:10.1007/s00247-020-04854-3.
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He L, Li H, Wang J, et al. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. Sci Rep. 2020;10(1):15072. Published 2020 Sep 15. doi:10.1038/s41598-020-71914-x.
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Kline JE, Illapani VSP, He L, Altaye M, Logan JW, Parikh NA. Early cortical maturation predicts neurodevelopment in very preterm infants. Arch Dis Child Fetal Neonatal Ed. 2020;105(5):460-465. doi:10.1136/archdischild-2019-317466.
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Kline JE, Illapani VSP, He L, Altaye M, Parikh NA. Author Correction: Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Sci Rep. 2021;11(1):7055. Published 2021 Mar 23. doi:10.1038/s41598-021-86025-4.
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Chen M, Li H, Wang J, Dillman JR, Parikh NA, He L. A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. Radiol Artif Intell. 2019;2(1):e190012. Published 2019 Dec 11. doi:10.1148/ryai.2019190012.
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Li H, Parikh NA, Wang J, et al. Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Front Neurosci. 2019;13:610. Published 2019 Jun 18. doi:10.3389/fnins.2019.00610.
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He L, Li H, Dudley JA, et al. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. AJR Am J Roentgenol. 2019;213(3):592-601. doi:10.2214/AJR.19.21082.
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Parikh MN, Li H, He L. Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci. 2019;13:9. Published 2019 Feb 15. doi:10.3389/fncom.2019.00009.
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Merhar SL, Gozdas E, Tkach JA, et al. Neonatal Functional and Structural Connectivity Are Associated with Cerebral Palsy at Two Years of Age. Am J Perinatol. 2020;37(2):137-145. doi:10.1055/s-0039-1683874.
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Li H, Parikh NA, He L. A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci. 2018;12:491. Published 2018 Jul 24. doi:10.3389/fnins.2018.00491.
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He L, Wang J, Lu ZL, Kline-Fath BM, Parikh NA. Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. Pediatr Radiol. 2018;48(8):1139-1151. doi:10.1007/s00247-018-4140-x.
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He L, Li H, Holland SK, Yuan W, Altaye M, Parikh NA. Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. Neuroimage Clin. 2018;18:290-297. Published 2018 Jan 31. doi:10.1016/j.nicl.2018.01.032.
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Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, Holland SK. Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?. Brain Struct Funct. 2018;223(8):3665-3680. doi:10.1007/s00429-018-1707-0.
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Wang J, He L, Zheng H, Lu ZL. Improving structural brain images acquired with the 3D FLASH sequence. Magn Reson Imaging. 2017;38:224-232. doi:10.1016/j.mri.2017.01.014.
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He L, Parikh NA. Brain functional network connectivity development in very preterm infants: The first six months. Early Hum Dev. 2016;98:29-35. doi:10.1016/j.earlhumdev.2016.06.002.
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He L, Parikh NA. Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities. Pediatr Neurol. 2015;53(4):330-337. doi:10.1016/j.pediatrneurol.2015.05.001.
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Wang J, He L, Zheng H, Lu ZL. Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence. PLoS One. 2014;9(5):e96899. Published 2014 May 30. doi:10.1371/journal.pone.0096899.
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Kaur S, Powell S, He L, Pierson CR, Parikh NA. Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age. PLoS One. 2014;9(1):e85807. Published 2014 Jan 24. doi:10.1371/journal.pone.0085807.
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Parikh NA, He L, Bonfante-Mejia E, et al. Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants. Pediatr Neurol. 2013;49(6):424-430. doi:10.1016/j.pediatrneurol.2013.08.026.
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He L, Parikh NA. 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 years of age. PLoS One. 2013;8(12):e85475. Published 2013 Dec 31. doi:10.1371/journal.pone.0085475.
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He L, Parikh NA. Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants. Neuroimage. 2013;64:328-340. doi:10.1016/j.neuroimage.2012.08.081.
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Yang QX, Wang J, Wang J, Collins CM, Wang C, Smith MB. Reducing SAR and enhancing cerebral signal-to-noise ratio with high permittivity padding at 3 T [published correction appears in Magn Reson Med. 2012 Mar;67(3):890]. Magn Reson Med. 2011;65(2):358-362. doi:10.1002/mrm.22695.
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He L, Orten B, Do S, et al. A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans Med Imaging. 2010;29(5):1182-1191. doi:10.1109/TMI.2010.2043536.
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Qiu M, Paul Maguire R, Arora J, et al. Arterial transit time effects in pulsed arterial spin labeling CBF mapping: insight from a PET and MR study in normal human subjects. Magn Reson Med. 2010;63(2):374-384. doi:10.1002/mrm.22218.
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He L, Greenshields IR. A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging. 2009;28(2):165-172. doi:10.1109/TMI.2008.927338.
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He L, & Greenshields IR. (2008). An MRF spatial fuzzy clustering method for fMRI SPMs. Biomedical Signal Processing and Control, 3(4), 327-333.
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Kim H, Pinus AB, Wang J, Murphy PS, Constable RT. On the application of chemical shift-based multipoint water-fat separation methods in balanced SSFP imaging. Magn Reson Med. 2007;58(2):413-418. doi:10.1002/mrm.21303.
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Wang J, Mao W, Qiu M, Smith MB, Constable RT. Factors influencing flip angle mapping in MRI: RF pulse shape, slice-select gradients, off-resonance excitation, and B0 inhomogeneities. Magn Reson Med. 2006;56(2):463-468. doi:10.1002/mrm.20947.
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Wang J, Qiu M, Kim H, Constable RT. T1 Measurements Incorporating Flip Angle Calibration and Correction In Vivo. J Magn. Reson. 2006:182:283-292.
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Yang QX, Mao W, Wang J, et al. Manipulation of image intensity distribution at 7.0 T: passive RF shimming and focusing with dielectric materials. J Magn Reson Imaging. 2006;24(1):197-202. doi:10.1002/jmri.20603.
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Wang J, Qiu M, Yang QX, Smith MB, Constable RT. Measurement and correction of transmitter and receiver induced nonuniformities in vivo [published correction appears in Magn Reson Med. 2006 Oct;56(4):944]. Magn Reson Med. 2005;53(2):408-417. doi:10.1002/mrm.20354.
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Wang J, Qiu M, Constable RT. In vivo method for correcting transmit/receive nonuniformities with phased array coils. Magn Reson Med. 2005;53(3):666-674. doi:10.1002/mrm.20377.
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Yang QX, Wang J, Collins CM, et al. Phantom design method for high-field MRI human systems. Magn Reson Med. 2004;52(5):1016-1020. doi:10.1002/mrm.20245.
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Zheng J, Wang J, Rowold FE, Gropler RJ, Woodard PK. Relationship of apparent myocardial T2 and oxygenation: towards quantification of myocardial oxygen extraction fraction. J Magn Reson Imaging. 2004;20(2):233-241. doi:10.1002/jmri.20111.
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Collins CM, Liu W, Wang J, et al. Temperature and SAR calculations for a human head within volume and surface coils at 64 and 300 MHz. J Magn Reson Imaging. 2004;19(5):650-656. doi:10.1002/jmri.20041.
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Wang J, Yang QX, Zhang X, et al. Polarization of the RF field in a human head at high field: a study with a quadrature surface coil at 7.0 T. Magn Reson Med. 2002;48(2):362-369. doi:10.1002/mrm.10197.
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Zheng J, Wang J, Nolte M, Rowold F, Yablonskiy DA, Woodard PK, Li D, Gropler RJ. Myocardial Oxygenation Mapping with T2 Contrast MRI: A Preliminary Study. Magn. Reson. Med. 2004;51:718-726.
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Yang QX, Wang J, Zhang X, et al. Analysis of wave behavior in lossy dielectric samples at high field. Magn Reson Med. 2002;47(5):982-989. doi:10.1002/mrm.10137.
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Collins CM, Yang QX, Wang JH, et al. Different excitation and reception distributions with a single-loop transmit-receive surface coil near a head-sized spherical phantom at 300 MHz. Magn Reson Med. 2002;47(5):1026-1028. doi:10.1002/mrm.10153.
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Bus SA, Yang QX, Wang JH, Smith MB, Wunderlich R, Cavanagh PR. Intrinsic muscle atrophy and toe deformity in the diabetic neuropathic foot: a magnetic resonance imaging study. Diabetes Care. 2002;25(8):1444-1450. doi:10.2337/diacare.25.8.1444.
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Li Z, Li H, Parikh NA, and He L. Early Prediction of Cognitive Deficit using Quantitative Structural MRI in Very Preterm Infants with Ensemble Learning, in Annual Meeting-Radiological Society of North America (RSNA), November, 2021.
Ali R, Li H, Parikh NA, and He L. A Semi-supervised deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome, in Annual Meeting-Radiological Society of North America (RSNA), November, 2021.
Chen M, Li H, Wang J, Parikh NA and He L. A novel multi-filter convolutional neural network for prediction of cognitive deficits using brain structural connectome in very preterm infants, International Society for Magnetic Resonance in Medicine, 2021.
Li H, Chen M, Wang J, Parikh NA and He L. Early prediction of cognitive deficits in very preterm infants using graph convolutional networks with brain structural connectome, International Society for Magnetic Resonance in Medicine, 2021.
Li H, Chen M, Wang J, Parikh NA and He L. A semi-supervised graph convolutional network for early prediction of motor impairments in very preterm infants using brain connectome, International Society for Magnetic Resonance in Medicine, 2021.
He L, Li H, Chen M, Wang J, Illapani, Altaye M, Parikh NA. Integration of multi-modality MRI for early prediction of cognitive deficits using deep learning, Organization for Human Brain Mapping, 2021.
Li H, Chen M, Wang J, Illapani VSP, Parikh NA and He L. Deep Learning for Automated Segmentation of Diffuse White Matter Abnormality in Very Preterm Infants, Organization for Human Brain Mapping, 2021.
Chen M, Li H, Wang J, Dillman JR, Trout AT, Tkach J, Merhar S, Parikh NA, and He L. Image Synthesis in Multi-Contrast MRI using Wasserstein Cycle-Consistent Adversarial Network, Organization for Human Brain Mapping, 2021.
Alom MZ, He L, Taha TM, Asari VK. Fast and accurate Magnetic Resonance Image (MRI) reconstruction with NABLA-N network. Applications of Machine Learning, 2020.
Logan JW, Salvator A, He L, Skalak M, Klebanoff M, Parikh NA. Adverse effects of early illness severity on neurodevelopment are partly mediated through early brain injury and delayed cortical maturation in infants born very preterm. Pediatric Academic Societies, Philadelphia, PA; 2020.
Li H, Chen M, Wang J, Parikh NA, He L. Automated segmentation of diffuse white matter abnormality in very preterm infants using U-Net. Organization for Human Brain Mapping, 26, 2020.
Dillman JR, He L, Li H, Dudley J, Maloney T, Brady SL, Somasundaram E, Trout AT. DeepLiverNet: A Deep transfer learning model for classifying liver stiffness using clinical and T2-weighted MRI data. Society of Abdominal Radiology, 2020.
Kline J, Illapani P, He L, Parikh NA. Diffuse White Matter Abnormality Correlates with Reduced Network Efficiency in the Very Preterm Brain, Pediatric Academic Societies, Philadelphia, PA; 2020.
Chen M, Li H, Wang J, Braimah A, Altaye M, Parikh NA, He L. Deep transfer learning model in early prediction of cognitive deficits using brain structural connectome data in very preterm infants, International Society for Magnetic Resonance in Medicine Annual Meeting. Sydney, Austrilia; 2020.
He L, Li H, Wang J, Chen M, Dillman JR, Parikh NA. Early prediction of neurodevelopmental deficits in very preterm infants using a multi-task deep transfer learning model. International Society for Magnetic Resonance in Medicine Annual Meeting. Sydney, Austrilia; 2020.
Li H, He L, Dudley J, Maloney T, Somasundaram E, Brady SL, Parikh NA, Dillman JR. A Deep Transfer Learning Model for Liver Stiffness Classification using Clinical and T2-Weighted MRI Data. International Society for Magnetic Resonance in Medicine Annual Meeting. Sydney, Austrilia; 2020.
Wang J, Chen M, He L, Li H, Khandwala V, Wang D, Williamson B, Woo D, Vagal A. A Deep Transfer Learning Model to Predict Patient Outcome in ICH using the Fusion of Clinical and Fluid-Attenuated Inversion Recovery Imaging Data. ISMRM Twenty-Eighth Annual meeting, Sydney, Australia, 2020.
Wang J, Chen M, He L, Li H, Khandwala V, Wang D, Williamson B, Woo D, Vagal A. A machine learning model using T2-weighted FLAIR radiomics features to predict patient outcome in ICH. ISMRM Twenty-Eighth Annual meeting, Sydney, Australia, 2020.
Wang J, Gaskill-Shipley M, He L, Zhang B, Lamba M, Lily Wang L, Cecil KM, and Vagal A. Improving tumor-tissue contrast by increased spatial resolution. ISMRM Twenty-Eighth Annual meeting, Sydney, Australia. 2020.
He L, Chen M, Li H, Wang J, Khandwala V, Woo D, Vagal A. Deep Learning Model to Predict Patient Outcome in ICH using Fluid-Attenuated Inversion Recovery Imaging Data. Radiological Society of North America, 2019.
Somasundaram E, Brady S, Li H, He L, Maloney T, Dudley J, Dillman J. Extracting Heterogeneously Formatted Clinical Data From DICOM Secondary Capture Using OCR. Annual Meeting of the American-Association-of-Physicists-in-Medicine (AAPM), 2019.
Li H, Parikh NA, Wang J, Merhar S, Chen M, Parikh M, Holland S, He L. Segmentation of Diffuse White Matter Abnormality in Preterm Infants using Deep Convolutional Neural Networks. Proceedings International Society Magnetic Resonance Medicine, 27, 2019.
Parikh NA, Klebanoff M, Illapani P, Li H, Kline J, He L, Inflammation is a Common Pathway to Development of Diffuse White Matter Abnormality (DWMA) in Very Preterm Infants, Pediatric Academic Societies, 2019.
Li H, Chen, M, He L, Parikh NA, Neonatal Functional Connectome Graph Theory Measures are Predictive of Neurodevelopmental Outcomes in Very Preterm Infants, Pediatric Academic Societies, 2019.
Parikh NA, Illapani P, He L, Cecil K, Very Preterm Infants with Diffuse White Matter Abnormality (DWMA) Exhibit Aberrant Brain Metabolites Soon After Birth, Pediatric Academic Societies, 2019.
Kline J, Illapani P, He L, Li H, Altaye M, Riddle A, Parikh NA, Neonatal Cortical Surface Metrics Predict Cognitive and Language Scores at 2 Years Corrected Age in Very Preterm Infants, Pediatric Academic Societies, 2019.
Parikh NA, He L, Illapani P, Li H, Objectively Defined Diffuse White Matter Abnormality (DWMA) at Term is an Independent Predictor of Neurodevelopmental Outcomes at 2 Years Corrected Age in Very Preterm Infants, Pediatric Academic Societies, 2019.
Dillman JR, He L, Li H, Dudley J, Maloney T, Brady SL, Somasundaram E, Trout AT. Machine Learning Prediction of Liver Stiffness using Clinical Data and T2-weighted MRI Radiomic Data, Society of Abdominal Radiology, 2019.
Wang J, Ding Y, Sica C and Yang QX. Absolute Phase of Radiofrequency Transmit Field for a Dual Transmit Coil System. Proc. ISMRM Twenty-Seventh Annual meeting, Montreal, Canada. (2019). P4505
Parikh M, Li H, He L. Towards objective diagnosis of autism with optimized machine learning models and personal characteristic data, American College of Epidemiology, 2018.
He L, Li H, Parikh NA. Early Identification of Reduced Brain Functional Connectivity in Very Preterm Infants with Motor Impairments, Proceedings International Society Magnetic Resonance Medicine, 26, 2018.
He L, Li H, Parikh NA. Early Identification of Reduced Brain Functional Connectivity in Very Preterm Infants with Motor Impairments, Proceedings International Society Magnetic Resonance Medicine, 26, 2018.
He L, Li H, Parikh NA. Early Prediction of Language Deficits in Very Preterm Infants Using Functional Connectome Data and Machine Learning, Proceedings International Society Magnetic Resonance Medicine, 26, 2018.
He L, Li H, Dudley J, Maloney T, et al. Machine Learning Prediction of Liver Stiffness Using Clinical Data & T2-Weighted MRI Radiomic Data, International Society Magnetic Resonance Medicine Workshop on Machine Learning, 2018.
He L, Li H, Parikh NA. Early Prediction of Motor Impairments in Very Preterm Infants Using Functional Connectome Data and Machine Learning, Pediatric Academic Societies, 2018.
Parikh NA, He L, Merhar S, Li H. Neonatal Functional Connectivity Correlates with Language Development at 2 Years of Age in Very Preterm Infants, Pediatric Academic Societies, 2018.
Li H, He L, Maloney T, Dudley J, Brady SL, Somasundaram E, Dillman J. Support Vector Machine Model for Stratification of Liver Stiffness using Clinical Data, Radiological Society of North America, 2018.
He L and Parikh NA. Early Prediction of Cognitive Deficits in Very Preterm Infants using Machine Learning Algorithms, Proceedings International Society Magnetic Resonance Medicine, 25, 2017.
He L, Li H, Yuan Weihong, Parikh NA. An Artificial Neural Network Framework for Early Prediction of Cognitive Deficits in Very Preterm Infants, Proceedings International Society Magnetic Resonance Medicine, 25, 2017.
He L, Parikh, NA. Reduced Functional Connectivity is Present at Birth in Preterm Infants with Language Delays at Age 2. Organization for Human Brain Mapping, 23, 2017.
He L, Gozdas, E, Holland SK, Parikh NA. Early Identification of Premature Brain Functional Connectome Using Support Vector Machine. Organization for Human Brain Mapping, 23, 2017.
He L, Wang J, Smith M, Lu Z-L, Parikh NA. Improving the Quality of Neonatal Brain Structural MRI with Shorter Acquisition Train Length, Proceedings International Society Magnetic Resonance Medicine, 24, 2016.
Wang J, He L, and Lu Z-L. 3D FLASH Optimization with Improved Contrast Efficiency and Image Inhomogeneity Correction. Proceedings International Society Magnetic Resonance Medicine, 24, 2016.
Wang J, Smith M, and He L. Optimizing Magnetization Prepared Rapid Gradient Echo (MPRAGE) for Brain Tumor Detection. Proceedings International Society Magnetic Resonanc Medicine, 24, 2016.
He L, and Parikh NA. Early detection at birth of reduced sensorimotor functional connectivity in infants with cerebral palsy, The 3rd Whistler Scientific Workshop, Whistler-Blackcomb, Canada, 2016.
Gozdas E, Parikh NA, Tkach J, He L, Holland S. Functional Connectivity and Network Measures are Reduced in Preterm Infants, Organization for Human Brain Mapping, 22, 2016.
Merhar S, Gozdas, E, Parikh NA, Tkach J, He L, Holland S. Preterm Birth Disrupts Functional Brain Network Development, Pediatric Academic Societies Meeting, 2016.
He L, and Parikh NA. Resting State Network Development in Very Preterm Infants, Proceedings International Society Magnetic Resonance Medicine, 23, 2015.
Ding Y and Wang J. Transmit Field Estimation from K-space Data. Proc. ISMRM Twenty-third Annual meeting, Toronto, Canada (2015). P.2376.
He L, Wang J, Smith M, Parikh NA. Optimization of Magnetization-Prepared Rapid Gradient-Echo (MP-RAGE) Sequence for Neonatal Brain MRI, Proceedings International Society Magnetic Resonance Medicine, 23, 2015.
Wang J, He L, and Lu Z-L. A Comparison of MP-RAGE Sequence Optimizations, Proceedings International Society Magnetic Resonance Medicine, 23, 2015.
He L, Kaur S, and Parikh NA. Spontaneous Brain Activity in Very Preterm Infants with White Matter Signal Abnormalities, International Society Magnetic Resonance Medicine on Functional MRI: Emerging Techniques & New Interpretations, 2014.
He L. Kaur S. and Parikh NA. Early Resting State Network Development in Very Preterm Infants, International Society Magnetic Resonance Medicine on Functional MRI: Emerging Techniques & New Interpretations, 2014.
He L and Parikh NA. Probabilistic Atlas‐Guided Detection of White Matter Signal Abnormalities in Very Preterm Infants, Organization for Human Brain Mapping, 19, 2016.
He L and Parikh NA. Automatic Brain MRI Segmentation in Very Preterm Infants, Proceedings International Society Magnetic Resonance Medicine, 20, 2012.
Wang J, Li L, Lu ZL. Evaluation of MR Image Intensity Inhomogeneity Correction Algorithms. ISMRM Twentieth Annual meeting, Melbourne, Australia (2012). P.2439
Wang J, He L and Lu Z-L. Evaluation of MR Image Intensity Inhomogeneity Correction Algorithms, Proceedings International Society Magnetic Resonance Medicine, 20, 2012.
Wang J, He L, and Lu Z-L. Evaluation of MR Bias Field Correction Algorithms, in Organization for Human Brain Mapping, 18, 2012.
He L, Orten B, Do S, Karl W, Kambadakone A, Sahani D, and Pien H. Spatio-temporal Deconvolution of Perfusion CT Data in Rectal Tumor Patients, IEEE International Symposium on Biomedical Imaging, 2009.
Wang J, Watzl J, Qiu M, de Graaf RA, Constable RT. In vivo Receive Sensitivity Measurement. Proc. ISMRM Seventeenth Annual meeting, Hawaii, USA, (2009). P.4564.
Qiu M, Wang J, Jagriti A, Wang Y, Kim H, Nallakkandi R, Planeta-Wilson B, Weinzimmer D, Carson RE, Constable RT. Arterial Transit Time Effects in Pulsed Arterial Spin Labeling CBF Mapping: Insight from a PET and MR Study in Normal Human Subjects. Proc. ISMRM Seventeenth Annual meeting, Hawaii, USA, (2009). P.3630.
Wang J, Kim H, Qiu M, Constable RT. Lipid Fraction Measurement Incorporating T1 and RF Inhomogeneity Correction. Proc. ISMRM Sixteenth Annual meeting, Toronto, Canada, (2008). P.3793.
Kim H, Robson MD, Qiu M, Wang J, Lim JK, Murphy PS, Constable RT. Characterization of Liver Fibrosis Using Fat-Suppressed Ultrashort TE (FUTE) Image and Multipoint Water-Fat Separation MRI in Patients with Hepatitis C Virus (HCV)-Induced Liver Fibrosis. Proc. ISMRM Sixteenth Annual meeting, Toronto, Canada, (2008). P3715.
He L and Greenshields IR. “Rician Noise Reduction in MR Images using Non-local Maximum Likelihood Estimation”, Proceedings International Society Magnetic Resonance Medicine, 15, 2007.
He L and Greenshields IR. “Spatial Fuzzy Clustering of fMRI SPMs via MRFs”, Proceedings International Society Magnetic Resonance Medicine, 15, 2007.
Demurjian S, Rajasekaran S, Ammar R., Greenshields IR, Doan T, and He L. “Applying LSI and Data Reduction to XML for Counter Terrorism”, Proceedings IEEE Aerospace Conf., 2006.
He L. and Greenshields IR. “Empirical determination of lower bounds on RP embedding”, AAAI Symposium on Artificial Intelligence for Homeland Security, 2005.
