Department of Biomedical Engineering, NTU

Journal Papers

1. Ouyang J, Chen KT, Armindo RMD, Davidzon GA, Hawk E, Moradi F, Rosenberg J, Lan E, Zaharchuk G. “Predicting FDG-PET Images from Multi-contrast MRI using Deep Learning in Patients with Brain Neoplasms.” J Magn Reson Imaging. 2024 Mar; 59(3):1010-1020. doi:10.1002/jmri.28837
2. Fettahoglu A, Zhao MY, Khalighi MM, Vossler H, Jovin M, Davidzon GA, Zeineh M, Boada F, Mormino E, Henderson V, Moseley M, Chen KT, Zaharchuk G. “Early Frame Florbetaben PET/MRI for Cerebral Blood Flow Quantification in Patients with Cognitive Impairment: Comparison to an Oxygen-15 Water Gold Standard.” J Nucl Med. 2024 Feb 1; 65(2):306-312. doi:10.2967/jnumed.123.266273
3. Chen KT, Tesfay R, Koran MEI, Ouyang J, Shams S, Young CB, Davidzon GA, Liang T, Khalighi MM, Mormino E, Zaharchuk G. “Ultra-low-dose ¹⁸F-PI-2620 Tau PET/MRI with Generative Adversarial Network-based Enhancement in Aging and Neurodegenerative Populations.” Am J Neuroradiol. 44(9):1012-1019 doi:10.3174/ajnr.A7961
4. Hussein R, Zhao MY, Shin D, Guo J, Chen KT, Armindo RD, Davidzon G, Moseley ME, Zaharchuk G. “Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation.” ICPR 2022.
5. Chen KT, Adeyeri O, Toueg TN, Mormino E, Khalighi MM, Zaharchuk G. “Investigating Simultaneity for Deep Learning-enhanced Actual Ultra-low-dose Amyloid PET/MRI Imaging.” Am J Neuroradiol. 43(3), 354-360 doi:10.3174/ajnr.A7410.
6. Zhao MY, Woodward A, Fan AP, Chen KT, Yu Y, Chen DY, Moseley ME, Zaharchuk G.  “Reproducibility of Cerebrovascular Reactivity Measurements: A Systematic Review of Neuroimaging Techniques.” J Cereb. Blood Flow Metab. 42(5), 700-717.
7. Chen KT, Toueg TN, Koran MEI, Davidzon G, Zeineh M, Holley D, Gandhi H, Halbert K, Boumis A, Kennedy G, Mormino E, Khalighi M, Zaharchuk G.  “True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.” Eur. J Nucl. Med. Mol. Imaging. 2021 48(8):2416-2425. doi: 10.1007/s00259-020-05151-9
8. Chen KT, Schürer M, Ouyang J, Koran ME, Davidzon G, Mormino E, Tiepolt S, Hoffmann K-T, Sabri O, Zaharchuk G, Barthel H.  “Generalization of Deep Learning Models for Ultra-low-count Amyloid PET/MRI using Transfer Learning.” Eur. J Nucl. Med. Mol. Imaging. 2020 47:2998-3007. doi:10.1007/s00259-020-04897-6
9. Ouyang J, Chen KT, Gong E, Pauly JM, Zaharchuk G. “Ultra-low-dose PET Reconstruction using Generative Adversarial Network with Feature Matching and Task-Specific Perceptual Loss.” Medical Physics. 2019 46(8): 3555-3564. doi:10.1002/mp.13626
10. Levine MA, Calabro F, Izquierdo-Garcia D, Chonde DB, Chen KT, Hong I, Price JC, Luna B, Catana C. “Assessment of Motion Bias on the Detection of Dopamine Response to Challenge.” J Cereb. Blood Flow Metab. Submitted.
11. Chen KT, Gong E, Macruz F, Xu J, Boumis A, Khalighi M, Poston KL, Sha SJ, Greicius MD, Mormino E, Pauly JM, Srinivas S, Zaharchuk G. “Ultra-low-dose ¹⁸F-Florbetaben Amyloid PET Imaging using Deep Learning with Multi-contrast MRI Inputs.” Radiology. 2019 290(3): 649–656. doi:10.1148/radiol.2018180940
12. Chen KT, Salcedo S, Gong K, Chonde DB, Izquierdo-Garcia D, Drzezga A, Rosen BR, Qi J, Dickerson BC, Catana C. “An Efficient Approach to Perform MR-assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies.” J Nucl Med. 2019 60(2): 272-278. doi:10.2967/jnumed.117.207142
13. Chen KT, Salcedo S, Chonde DB, Izquierdo-Garcia D, Levine MA, Price JC, Dickerson BC, Catana C. “MR-assisted PET Motion Correction in Simultaneous PET/MRI Studies of Dementia Subjects.” J Magn Reson Imaging. 2018 48(5): 1288-1296. doi: 10.1002/jmri.26000
14. Gong K, Cheng-Liao J, Wang G, Chen KT, Catana C, Qi J. “Direct Patlak Reconstruction from Dynamic PET Data Using Kernel Method with MRI Information Based on Structural Similarity.” IEEE Trans. Med. Imag. 2018 37(4): 955-965. doi:10.1109/TMI.2017.2776324
15. Chen KT, Izquierdo-Garcia D, Poynton CB, Chonde DB, Catana C. “On the Accuracy and Reproducibility of a Novel Probabilistic Atlas-based Generation for Calculation of Head Attenuation Maps in Integrated PET/MR Scanners.” Eur. J Nucl. Med. Mol. Imaging 2017 44(3): 398-407. doi:10.1007/s00259-016-3489-z
16. Hutchcroft W, Wang G, Chen KT, Catana C, Qi J. “Anatomically-Aided PET Reconstruction Using the Kernel Method.” Phys. Med. Biol. 2016 61: 6668-6683.
17. Izquierdo-Garcia D, Hansen AE, Förster S, Benoit D, Schachoff S, Fürst S, Chen KT, Chonde DB, Catana C. "A Novel SPM-based Technique for Attenuation Correction Combining Segmentation and Non-rigid Atlas Formation: Application to Simultaneous PET/MR Brain Imaging." J Nucl. Med. 2014 51(11): 1825-1830.
18. Poynton CB*, Chen KT*, Chonde DB, Izquierdo-Garcia D, Gollub RL, Gerstner E, Batchelor T, Catana C. "Probabilistic Atlas-Based Segmentation of Combined T1-Weighted and DUTE MRI for Calculation of Head Attenuation Maps in Integrated PET/MRI Scanners." Am. J. Nucl. Med. Mol. Imaging 2014; 4(2): 160-171. (*Co-first author)
19. Lu T-P, Chen KT, Tsai M-H, Kuo K-T, Hsiao CK, Lai L-C, Chuang EY. "Identification of Genes with Consistent Methylation Levels across Different Human Tissues." Sci. Rep. 2014; 4:4351.

Conference Papers

1. Kumar A, Kim D, Mormino EC, Chaudhari A, Young CB, Chen KT, Khalighi M, Zaharchuk G. “Deep Learning-based Disambiguation for Multiple AD Radiotracers Using PET/MRI.” Annual Meeting ISMRM 2024, Singapore. (Accepted.)
2. Wang H-W, Chung M-C, Tsai C-H, Chen KT, Shih TTF. “An Accessible Toolbox for MR Spectroscopy Data Extraction and Analysis with Optimization for Diabetes Patients.” Annual Meeting ISMRM 2024, Singapore. (Accepted.)
3. Lin Y-N, Huang S-Y, Gong E, Hsiao I-T, Chen KT. “MRI-styled PET: Dual-modality Fusion for PET Image Enhancement.” 2023 IEEE Nuclear Science Symposium and Medical Imaging Conference, Vancouver, BC, Canada.
4. Kuo S-Y, Lee M-Y, Ko C-L, Chen KT. “The Effect of Multimodal Anatomical Images in Deep Learning-enhanced Low-dose Amyloid PET Imaging.” European Association of Nuclear Medicine 2023, Vienna, Austria. (Oral presentation)
5. Lee M-Y, Kuo S-Y, Ko C-L, Chen KT. “The Effect of Multimodal Anatomical Images in Deep Learning-based Prediction of Amyloid Deposition from Dynamic Amyloid PET.” World Molecular Imaging Congress 2023, Prague, Czechia. (Oral presentation)
6. Fettahoglu A, Zhao MY, Chen KT, Khalighi MM, Zeineh M, Mormino E, Moseley M, Zaharchuk G. “Validation of early-frame 18F-FDG PET for CBF quantification: Comparison to simultaneous eASL measurements in memory patients.” Alzheimer’s Association International Conference 2023, Amsterdam, Netherlands.
7. Ouyang J, Chen KT, Rosenberg J, Zaharchuk G. “Predicting FDG PET from Multi-contrast MRIs using Deep Learning in Patients with Brain Neoplasms.” Annual Meeting ISMRM 2023, Toronto, ON, Canada.
8. Zhao MY, Chen KT, Khalighi MM, Mormino E, Zaharchuk G. “Dual-tracer assessment of CBF and early amyloid uptake of Alzheimer’s disease using ¹⁵O-water and amyloid PET.” Alzheimer’s Association International Conference 2022, San Diego, CA, USA.
9. Chen KT, Tesfay R, Koran MEI, Ouyang J, Khalighi MM, Mormino E, Zaharchuk G. “Generative Adversarial Network-Enhanced Ultra-low-dose [¹⁸F]-PI-2620 Tau PET/MR Imaging.” Alzheimer’s Association International Conference 2022, San Diego, CA, USA.
10. Ouyang J, Chen KT, Zaharchuk G. “Zero-dose FDG-PET Imaging for Patients with Brain Neoplasms Using Deep Learning with Multi-Contrast MRI Inputs.” Brain & BrainPET 2022, Glasgow, UK.
11. Chen KT, Zhao MY, Mormino E, Khalighi MM, Zaharchuk G. “Dual-tracer assessment of CBF and early amyloid uptake of Alzheimer’s disease.” Brain & BrainPET 2022, Glasgow, UK.
12. Chen KT, Tesfay R, Koran MEI, Ouyang J, Khalighi MM, Mormino E, Zaharchuk G. “Diagnostic Ultra-low-dose ¹⁸F-PI-2620 Tau PET/MRI with Generative Adversarial Network-based Enhancement.” Joint ISMRM-ESMRMB Annual Meeting, London, UK.
13. Khalighi MM, Chen KT, Deller T, Jansen F, Zaharchuk G. “Dual Tracer Brain PET Simulation from Two Separate Exams.” SNMMI 2021 Annual Meeting, Washington, DC, USA.
14. Chen KT, Adeyeri O, Toueg TN, Mormino E, Khalighi MM, Zaharchuk G. “Does Simultaneous Morphological Inputs Matter for Deep Learning Enhancement of Ultra-low Amyloid PET/MRI?.” Annual Meeting ISMRM 2021, Vancouver, BC, Canada.
15. Chen KT, Adeyeri O, Toueg TN, Mormino E, Khalighi MM, Zaharchuk G. “Generalizing Ultra-low-dose PET/MRI Networks Across Radiotracers: From Amyloid to Tau.” Annual Meeting ISMRM 2021, Vancouver, BC, Canada.
16. Ouyang J, Chen KT, Zaharchuk G. “Zero-dose FDG PET Brain Imaging.” Annual Meeting ISMRM 2021, Vancouver, BC, Canada.
17. Hussein R, Zhao M, Guo J, Chen KT, Shin D, Moseley M, Zaharchuk G. “Ablation Studies in 3D Encoder-Decoder Networks for Brain MRI-to-PET Transformation.” Annual Meeting ISMRM 2021, Vancouver, BC, Canada.
18. Khalighi MM, Deller T, Chen KT, Toueg TN, Holley D, Halbert K, Mormino E, Zeineh M, Moradi F, Zaharchuk G, Iagaru A. “High resolution PET image denoising using anatomical priors by K-nearest neighborhoodmethod in the feature space.” Annual Meeting ISMRM 2021, Vancouver, BC, Canada.
19. Chen KT, Adeyeri O, Toueg TN, Mormino E, Khalighi MM, Zaharchuk G. “Investigating Simultaneity for Deep Learning-enhanced Actual Ultra-low-dose Amyloid PET/MRI Imaging.” ASNR 59^th Annual Meeting.
20. Chen KT, Toueg TN, Holley D, Halbert K, Koran MEI, Davidzon G, Zeineh M, Mormino E, Khalighi MM, Zaharchuk G. “The Validation and Value of CNN-processed Ultra-low-dose Amyloid PET/MR Imaging.” 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, MA, USA. (Oral presentation)
21. Chen KT, Holley D, Halbert K, Toueg TN, Boumis A, Mormino E, Khalighi MM, Zaharchuk G. “MRI-assisted Deep Learning-enhanced Actual Ultra-low-dose Amyloid PET Acquisitions.” Annual Meeting ISMRM 2020, Paris, France. https://www.ismrm.org/20m/
10. Chen KT, Holley D, Halbert K, Toueg TN, Boumis A, Kennedy G, Mormino E, Khalighi MM, Zaharchuk G. “Quantitative Assessment of Deep Learning-enhanced Actual Ultra-low-dose Amyloid PET/MR Imaging.” J Nucl Med 2020 61(Suppl 1):521. (Oral presentation)
11. Macdonald T, Chen KT, Koran ME, Moseley M, Zaharchuk G. “DualNet: a Deep Neural Network to Predict Individual Tau and Amyloid PET Images from a Combined Dose Image using the Disambiguation of Dual Dose Amyloid-Tau PET Scans Using The ADNI Dataset.” J Nucl Med 2020 61(Suppl 1):3009.
12. Schürer M*, Chen KT*, Jochimsen T, Rullmann M, Patt M, Tiepolt S, Schröter M, Weise C, Saur D, Zaharchuk G*, Sabri O*, Barthel H* “Impact of deep learning artificial intelligence approaches on amyloid PET diagnosis.” EANM 2019 Congress, Barcelona, Spain.
13. Chen KT, Schürer M, Ouyang J, Gong E, Tiepolt S, Sabri O, Zaharchuk G, Barthel H. “The Clinical and Quantitative Value of Ultra-low-dose Amyloid PET/MRI Deep Learning Network Generalization Methods.” 8^th Conference on PET/MR and SPECT/MR, Munich, Germany. (Oral presentation)
14. Chen KT, Schürer M, Ouyang J, Gong E, Tiepolt S, Sabri O, Zaharchuk G, Barthel H. “Investigating the Optimal Method to Generalize an Ultra-low-dose Amyloid PET/MRI Deep Learning Method Across Scanner Models.” Brain & Brain PET 2019, Yokohama, Kanagawa, Japan. (Oral presentation)
15. Fan AP, Chen KT, Azevedo C, Castillo JB, Nadiadwala A, Toueg T, Sha SJ, Greicius MD, Davidzon GA, Chin FT, Zaharchuk G, Mormino EC. “Non-invasive Quantification of Tau Accumulation in Dementia Using Simultaneous ¹⁸F-PI-2620 PET/MRI.” Brain & Brain PET 2019, Yokohama, Kanagawa, Japan.
16. Chen KT, Schürer M, Ouyang J, Gong E, Tiepolt S, Sabri O, Zaharchuk G, Barthel H. “How to Generalize a Deep Learning Model to New Data Lacking Appropriate MR Inputs? An Exploration using Ultra-low-dose Amyloid PET/MRI.” Annual Meeting ISMRM 2019, Montreal, QC, Canada. 
17. Chen KT, Schürer M, Ouyang J, Gong E, Tiepolt S, Sabri O, Zaharchuk G, Barthel H. “Transfer Learning of an Ultra-low-dose Amyloid PET/MRI U-Net Across Scanner Models.” Annual Meeting ISMRM 2019, Montreal, QC, Canada. (Oral presentation)
18. Chen KT, Schürer M, Gong E, Khalighi MM, Sabri O, Zaharchuk G, Barthel H. “A Generalizable Deep Learning Network for Imaging Neuropathology with Ultra-low-dose PET/MRI.” ASNR 57^th Annual Meeting, Boston, MA, USA. (Oral presentation)
19. Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. “Ultra-Low-dose Amyloid PET/MRI Reconstruction by Generative Adversarial Network.” Annual Meeting ISMRM 2019, Montreal, QC, Canada.
20. Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. “Ultra-Low-dose PET Reconstruction Using Generative Adversarial Network with Feature Matching.” SPIE Medical Imaging 2019. San Diego, CA, USA.
21. Chen KT, Schürer M, Ouyang J, Xie Y, Gong E, Tiepolt S, Sabri O, Zaharchuk G, Barthel H. “Handling Missing Contrast for the Generalization of Ultra-low-dose Amyloid PET Reconstruction Across Scanner Models.” ISMRM Workshop on Machine Learning Part 2. Washington, DC, USA.
22. Ouyang J, Chen KT, Gong E, Zaharchuk G, Pauly J. “Ultra-Low-dose Amyloid PET-MR Reconstruction Using 2.5D Generative Adversarial Network with Feature Matching.” ISMRM Workshop on Machine Learning Part 2. Washington, DC, USA.
23. Yu Y, Xie Y, Thamm T, Gong E, Chen KT, Zaharchuk G. “Prediction of Subacute Infarction in Acute Ischemic Stroke Using Baseline Multi-modal MRI and Deep Learning.” Stroke. 2019 50:AWMP19.
24. Ouyang J, Chen KT, Gong E, Zaharchuk G, Pauly J. “Ultra-Low-dose PET Imaging Using Generative Adversarial Network.” 2018 SIIM Conference on Machine Intelligence in Medical Imaging. San Francisco, CA, USA.
25. Chen KT, Macruz F, Gong E, Khalighi MM, Zaharchuk G. “Low-dose Amyloid PET Reconstruction Using a Pre-Trained Multimodal Deep Learning Network.” Alzheimer’s Association International Conference. Chicago, IL, USA.
26. Chen KT, Gong E, Macruz F, Xu J, Khalighi MM, Pauly JM, Srinivas S, Zaharchuk G. “On the Clinical Value of CNN-processed Ultra-low-dose Amyloid PET Reconstructions.” 7^th Conference on PET/MR and SPECT/MR. Elba, Italy.
27. Macruz F, Chen KT, Gong E, Xu J, Khalighi MM, Pauly JM, Zaharchuk G. “Using Deep Learning Technique to Synthesize High Quality Cerebral Full-dose Positron Emission Magnetic Resonance Images (PET-MRI) from Low-dose Ones.” ASNR 56^th Annual Meeting. Vancouver, BC, Canada.
28. Macruz F, Chen KT, Gong E, Xu J, Khalighi MM, Zaharchuk G. “Usando Deep Learning para Reproduzir Imagens Cerebrais de Alta Qualidade de PET-RM a Partir de Imagens de Baixa Qualidade Adquiridas com Apenas um Quarto da Dose de Radiotraçador.” Jornada Paulista de Radiologia 2018. São Paulo, SP, Brazil.
29. Chen KT, Gong E, Macruz F, Xu J, Khalighi MM, Pauly JM, Zaharchuk G. “Ultra-low-dose Amyloid PET Reconstruction using Deep Learning with Multi-contrast MRI Inputs.” Joint Annual Meeting ISMRM-ESMRMB 2018. Paris, France.
30. Gong E, Chen KT, Guo J, Fan AP, Pauly JM, Zaharchuk G. “Mapping Metabolic Activation as FDG-PET/Amyloid-PET using Contrast-free MRI and Deep Learning.” Joint Annual Meeting ISMRM-ESMRMB 2018. Paris, France.
31. Chen KT, Gong E, Macruz F, Xu J, Khalighi MM, Pauly JM, Zaharchuk G. “Using U-Nets with Morphological MR Inputs for Ultra-Low-Dose Amyloid PET Reconstruction.” ISMRM Workshop on Machine Learning. Pacific Grove, CA, USA. (Oral presentation)
32. Levine MA, Chonde DB, Chen KT, Izquierdo-Garcia D, Catana C. “Unification of PET and MR-based Head Motion Estimates for PET Motion Correction.” ISMRM-SNMMI Co-Provided Workshop on PET/MRI 2017. Chicago, IL, USA.
33. Chen KT, Salcedo S, Qi J, Dickerson BC, Catana C. “An efficient approach to obtain optimized metabolic PET and morphological MR data in dementia patients.” American Academy of Neurology Annual Meeting 2017. Boston, MA, USA.
34. Gong K, Wang G, Chen KT, Catana C, Qi J.  “Nonlinear PET Parametric Image Reconstruction with MRI Information Using Kernel Method.” Proc. 2017 SPIE Medical Imaging. Orlando, FL, USA.
35. Gong K, Wang G, Chen KT, Catana C, Qi J.  “Dynamic PET Reconstruction Using the Kernel Method with MRI Information.” Proc. 2016 IEEE NSS/MIC. Strasbourg, France.
36. Chen KT, Hutchcroft W, Salcedo S, Chonde DB, Dickerson BC, Qi J, Catana C. “Improved Quantificaton of PET Data using Temporally and Spatially Correlated MR Data.” Proc. WMIC 2016. New York, NY, USA. (Oral presentation).
37. Catana C, Chonde DB, Chen KT, Izquierdo-Garcia D, Bowen S, Hooker J, Roffman J. “Combined MR-assisted Motion and Partial Volume Effects Corrections – Impact on PET Data Quantification.” EJNMMI Physics 2014 1(Suppl 1):A38.
38. Chonde DB, Izquierdo-Garcia D, Chen KT, Catana C. “Masamune: A Tool for Automatic Dynamic PET Data Processing, Image Reconstruction and Integrated PET/MRI Data Analysis.” EJNMMI Physics 2014 1(Suppl 1):A57.
39. Izquierdo-Garcia D, Chen KT, Hansen AE, Förster S, Benoit D, Schachoff S, Fürst S, Chonde DB, Catana C. "New SPM8-based MRAC method combining segmentation and atlas approaches for simultaneous PET/MRI brain images." EJNMMI Physics 2014 1(Suppl 1):A29.
40. Chen KT, Izquierdo-Garcia D, Poynton C, Chonde DB, Catana C. “Probabilistic Atlas-Based Generation of Continuous-Valued Attenuation Correction Maps for Hybrid MR-PET Imaging.” Proc. Joint Annual Meeting ISMRM-ESMRMB 2014, Milan, Italy.
41. Chen KT, Chonde DB, Catana C. “On the Reproducibility of MR-based PET Attenuation Correction using a Probabilistic Atlas-based Method.” Proc. Annual Meeting ISMRM 2013, Salt Lake City, UT, USA. (Oral presentation)

Books and Chapter in Book