Publications

  • Li H, Chen M, Wang J, Illapani V, 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 (in press).
  • Parikh NA, Sharma P, He L, Li H, Altaye M, Priyanka Illapani VS. (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. J Pediatr. doi:10.1016/j.jpeds.2020.11.058, PMID: 33259857. Pubmed Journal
  • Merhar SL, Kline JE, Braimah A, Kline-Fath BM, Tkach JA, Altaye M, He L, Parikh NA. (2020) Prenatal opioid exposure is associated with smaller brain volumes in multiple regions. Pediatr Res. doi:10.1038/s41390-020-01265-w, PMID: 33177677. Pubmed Journal
  • Kline JE, Sita Priyanka Illapani V, He L, Parikh NA. (2020) Automated brain morphometric biomarkers from MRI at term predict motor development in very preterm infants. Neuroimage Clin; 28:102475. doi:10.1016/j.nicl.2020.102475, PMID: 33395969; PMCID: PMC7649646. Pubmed PMC Journal
  • 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. Journal
  • Parikh NA, He L, Li H, Priyanka Illapani VS, & Klebanoff MA. (2020). Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatr Neurol, 106, 56-62. PMID: 32139164; PMCID: PMC7500641. Pubmed Journal
  • Parikh N.A., Harpster K., He L., Priyanka Illapani VS., Khalid F.C., Klebanoff M.A., O'Shea T.M., Altaye M. (2020) Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Sci Rep 10, 15920. https://doi.org/10.1038/s41598-020-72632-0 Pubmed Journal
  • Parikh NA, He L, Priyanka Illapani VS, Altaye M, Folger AT, & Yeates KO. (2020). Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. J Pediatr, 220, 56-63. doi: 10.1016/j.jpeds.2020.01.034. Epub 2020 Mar 5. PMID: 32147220; PMCID: PMC7583652. Pubmed Journal
  • 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. Pubmed Journal
  • Chen M, Li H, Wang J, Yuan W, Altaye M, Parikh NA, He L, (2020). Early Prediction of Cognitive Deficit in Very Preterm Infants using Brain Structural Connectome with Transfer Learning Enhanced Deep Convolutional Neural Networks, Front Neurosci, 18;14:858. doi: 10.3389/fnins.2020.00858. PMID: 33041749; PMCID: PMC7530168. Journal
  • Li H, He L, Dudley JA, Maloney TC, Somasundaram E, Brady SL, Parikh NA, Dillman JR. (2020) 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. doi:10.1007/s00247-020-04854-3, PMID: 33048183. Pubmed Journal
  • He L, Li H, Wang J, Chen M, Gozdas E, Dillman JR, Parikh NA. A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants. (2020). Sci Rep. 15;10(1):15072. doi: 10.1038/s41598-020-71914-x. PMID: 32934282; PMCID: PMC7492237. PMC Journal
  • Julia E. Kline, Venkata Sita Priyanka Illapani, Hailong Li, Lili He, Nehal A. Parikh, (2020). Diffuse white matter abnormality in very preterm infants reflects reduced brain network efficiency, medRxiv, preprint. Journal
  • Parikh NA, He L, Illapani VSP, Altaye M, Folger AT, Yeates KO., (2020) Objectively-Diagnosed Diffuse White Matter Abnormality at Term is an Independent Predictor of Cognitive and Language Outcomes in Very Preterm. Journal of Pediatrics. In press. PMC Journal
  • Kline JE, Illapani VSP, He L, Altaye M, Logan JW, Parikh NA., (2019) Early cortical maturation predicts neurodevelopment in very preterm infants. Arch Dis Child Fetal Neonatal Ed. Pubmed Journal
  • Kline JE, Illapani VSP, He L, Altaye M, Parikh NA., (2019) Retinopathy of Prematurity and Bronchopulmonary Dysplasia are Independent Antecedents of Cortical Maturational Abnormalities in Very Preterm Infants. Scientific Report. 9(1). PMC Journal
  • Chen M, Li H, Wang J, Dillman JR, Parikh NA, & He L. (2019). A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. Radiol Artif Intell. 11;2(1):e190012. doi: 10.1148/ryai.2019190012. PMID: 32076663; PMCID: PMC6996597. PMC Journal
  • Li H, Parikh NA, Wang J, Merhar S, Chen M, Parikh M, Holland S, & He L. (2019). Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Front Neurosci, 13, 610. PMID: 31275101; PMCID: PMC6591530. PMC Journal
  • He L., Li H, Dudley JA, Maloney TC, Brady SL, Somasundaram E, Trout AT and Dillman JR. (2019) Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data. American Journal of Roentgenology, 1-10. 10.2214/AJR.19.21082. PMID:31120779 Pubmed Journal
  • Parikh MN, Li H, He L. (2019). Enhancing Diagnosis of Autism with Optimized Machine Learning Models and Personal Characteristic Data. Front Comput Neurosci. 15, 13-9. PMID: 30828295; PMCID: PMC6384273. Pubmed Journal
  • Merhar SL, Gozdas E, Tkach JA, Parikh NA, Kline-Fath BM, He L, Yuan W, Altaye M, Leach JL, Holland SK. (2019). Neonatal Functional and Structural Connectivity are Associated with Cerebral Palsy at Two Years of Age. Am J Perinatol. PMID: 30919395. Pubmed Journal
  • Li H, Parikh NA, & He L. (2018). A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes. Front Neurosci, 12, 491. PMID: 30087587; PMCID: PMC6066582. Pubmed Journal
  • He L, Wang J, Lu ZL, Kline-Fath BM, & Parikh NA. (2018). Optimization of magnetization-prepared rapid gradient echo (MP-RAGE) sequence for neonatal brain MRI. Pediatr Radiol, 48(8), 1139-1151. PMID: 29721599; PMCID: PMC6148771. Pubmed Journal
  • He L, Li H, Holland SK, Yuan W, Altaye M, & Parikh NA. (2018). Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework. Neuroimage Clin, 18, 290-297. PMID: 29876249; PMCID: PMC5987842. Pubmed Journal
  • Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, & Holland SK. (2018). Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments? Brain Struct Funct. PMID: 29992470. Pubmed Journal
  • Wang J, He L, Zheng H, & Lu ZL. (2017). Improving structural brain images acquired with the 3D FLASH sequence. Magn Reson Imaging, 38, 224-232. PMID: 28109888. Pubmed Journal
  • He L, & Parikh NA. (2016). Brain functional network connectivity development in very preterm infants: The first six months. Early Hum Dev, 98, 29-35. PMID: 27351350. Pubmed Journal
  • He L, & Parikh NA. (2015). Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities. Pediatr Neurol, 53(4), 330-337. PMID: 26216502. Pubmed Journal
  • Wang J, He L, Zheng H, & Lu ZL. (2014). Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence. PLoS One, 9(5), e96899. PMID: 24879508; PMCID: PMC4039442. Pubmed Journal
  • Kaur S, Powell S, He L, Pierson CR, & Parikh NA. (2014). Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age. PLoS One, 9(1), e85807. PMID: 24475054; PMCID: PMC3901659. PMC Pubmed Journal
  • Parikh NA, He L, Bonfante-Mejia E, Hochhauser L, Wilder PE, Burson K, & Kaur S. (2013). Automatically quantified diffuse excessive high signal intensity on MRI predicts cognitive development in preterm infants. Pediatr Neurol, 49(6), 424-430. PMID: 24138952; PMCID: PMC3957176. PMC Pubmed Journal
  • He L, & Parikh NA. (2013). 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, 8(12), e85475. PMID: 24392012; PMCID: PMC3877364. PMC Pubmed Journal
  • He L, & Parikh NA. (2013). Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants. Neuroimage, 64, 328-340. PMID: 22974556; PMCID: PMC3544934. PMC Pubmed Journal
  • Yang QX, Wang J, Wang J, Collins CM, Wang C, Smith MB. Reducing SAR and Enhancing Cerebral SNR with High Permittivity Padding at 3 T. Magn. Reson. Med. 2011;65:358-62. Pubmed Journal
  • He L, Orten B, Do S, Karl WC, Kambadakone A, Sahani DV, & Pien H. (2010). A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans Med Imaging, 29(5), 1182-1191. PMID: 20378468. Pubmed Journal
  • Qiu M, Maguire RP, Arora J, Planeta-Wilson B, Weinzimmer D, Wang J, Wang Y, Kim H, Nallakkandi R, Huang Y, 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. Magn. Reson. Med. 2010;63:374-84. PMC Journal
  • He L, & Greenshields IR. (2009). A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images. IEEE Trans Med Imaging, 28(2), 165-172. PMID: 19188105. Pubmed Journal
  • He L, & Greenshields IR. (2008). An MRF spatial fuzzy clustering method for fMRI SPMs. Biomedical Signal Processing and Control, 3(4), 327-333. Journal
  • 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;58:413-8. PMC Journal
  • 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;56:463-468. Pubmed Journal
  • 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. Pubmed Journal
  • Yang QX, Mao W, Wang J, Smith MB, X Zhang, Ugurbil K, W Chen. Manipulation of Image Intensity Distribution at 7.0 T: Passive RF Shimming and Focusing with Dielectric Materials. J Magn Reson. Inamging. J Magn Reson Imaging. 2006;24:197-202. Pubmed Journal
  • Wang J, Qiu M, Yang QX, Smith MB, Constable RT. Correction of Transmission and Reception Fields Induced Signal Intensity Nonuniformities In Vivo. Magn. Reson. Med. 2005;53:408-417. Pubmed Journal
  • Wang J, Qiu M, Constable RT. A Method For Rapid And Effective Correction of Signal Intensity Nonuniformities with Phased Array Coils In Vivo. Magn. Reson. Med. 2005;53:666-674. Pubmed Journal
  • Yang QX, Wang J, Collins CM, Smith MB, Zhang X, Ugurbil K, Chen W. Phantom Design Method for High-Field MRI Human Systems. Magn. Reson. Med. 2004; 52:1016-1020. Pubmed Journal
  • Zheng J, Wang J, Rowold F, Gropler RJ, Woodard PK. Relationship of Apparent Myocardial T2 and Oxygenation: Towards Quantification of Myocardial Oxygen Extraction Fraction. J Magn Reson Imaging. 2004;20:233-241. Pubmed Journal
  • Collins CM, Liu WZ, Wang J, Gruetter R, Vaughan JT, Ugurbil K, Smith MB. Temperature and SAR Calculations for a Human Head within Volume and Surface Coils at 64 and 300 Mhz. J Magn Reson Imaging. 2003; 19; 650-656. Pubmed Journal
  • Wang J, Yang QX, Collins CM, Smith MB, Zhang X, Liu H, Adriany G, Zhu XH, Vaughan JT, Ugurbil K, Chen W. Analysis of the RF Field of a Quadrature Surface Coil at 7 T. Magn Reson. Med. 2002;48:362-369.
  • 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.
  • Yang QX, Wang J, Zhang X, Collins CM, Smith MB, Liu H, Zhu XH, Michaeli S, Adriany G, Vaughan JT, Anderson P, Merkle H, Ugurbil K, Chen W. An Analysis of Wave Behavior in Lossy Dielectric Samples at High Field. Magn Reson. Med 2002;47:982-989. Pubmed Journal
  • Collins CM, Yang QX, Wang J, Zhang X, Liu H, Michaeli S, Zhu XH, Adriany G, Vaughan JT, Anderson P, Merkle H, Ugurbil K, Smith MB, Chen W. 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;47:1026-1028. Pubmed Journal
  • Bus SA, Yang QX, Wang J, Smith MB, Wunderlich R, Cavanagh PR. Intrinsic Muscle Atrophy and Its Relationship to Claw Toe Deformity in Diabetic Patients with Neuropathy: an MRI Study. Diabetes Care. 2002;25:1444-50. Journal

  • 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. Journal
  • 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.
  • 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, NA Parikh, 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 N.A., 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.