Ke Qi | Imaging Technology | Best Researcher Award

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Ms. Ke Qi | Imaging Technology | Best Researcher Award

Postgraduate student at First Affiliated Hospital of Zhengzhou University, China

Ke Qi is a graduate student at Zhengzhou University and a researcher at the Affiliated Hospital of Zhengzhou University, specializing in medical imaging, CT angiography, and deep learning applications in radiology. With multiple academic honors, including the Zhengzhou University Outstanding Student Scholarship, Ke Qi has published extensively on optimizing contrast enhancement, radiation dose reduction, and AI-driven image quality improvements. Their research focuses on personalized imaging techniques to enhance diagnostic accuracy while minimizing risks, contributing to advancements in radiology and medical AI applications.

Publication Profile 

Scopus

Educational Background 🎓

  • Undergraduate Studies:
    • Zhengzhou University
    • Received multiple scholarships and merit-based awards
  • Postgraduate Studies:
    • 2023-2024: Graduate Student at Zhengzhou University
    • Awarded the Zhengzhou University Outstanding Student Scholarship First Prize

Professional Experience 💼

  • Affiliation: Affiliated Hospital of Zhengzhou University
  • Specialization: Medical Imaging, Radiology, and Deep Learning in CT Angiography
  • Research Contributions: Published multiple articles in high-impact journals on CT imaging, radiation reduction, and AI-based image enhancement

Research Interests 🔬

  • Medical Imaging & Radiology
  • CT Angiography (CTA) and Contrast Optimization
  • Deep Learning in Medical Imaging
  • Radiation Dose Reduction Techniques
  • Personalized Post-Trigger Delay in Imaging

Awards and Honors🏆✨

  • Undergraduate Level:
    • Multiple scholarships from Zhengzhou University (2019-2023), including:
      • Outstanding Student Scholarship (First & Second Prizes)
      • Three Merit Student Recognition
      • New Oriental Education Scholarship
  • Postgraduate Level:
    • Outstanding Student Scholarship First Prize (2023-2024)

Key Research Contributions 

  1. Ultra-low Radiation & Contrast Medium Dosage in Aortic CTA
    • Journal: Academic Radiology (2024)
    • Focus: Deep Learning reconstruction to enhance image quality with minimal radiation
  2. Optimized Contrast Enhancement in Aortic CT Angiography
    • Journal: Quant Imaging Med Surg (2025)
    • Focus: Personalized bolus tracking for improved contrast homogeneity
  3. Patient-Specific Delay in Coronary CT Angiography
    • Journal: European Journal of Radiology (2023)
    • Focus: Comparison of individualized post-trigger delay with standard protocols
  4. Individualized Post-Trigger Delay in Head & Neck CT Angiography
    • Journal: European Journal of Radiology (2023)
    • Focus: Enhancing image quality through optimized scan timing
  5. Low Flow Rate Abdominal Contrast-Enhanced CT for Chemotherapy Patients
    • Journal: Journal of Computer-Assisted Tomography (2024)
    • Focus: Using dual-source CT for low-dose, high-quality imaging

Conclusion🌟

Ke Qi is an emerging researcher in medical imaging, specializing in CT angiography and AI-driven image enhancement. With a strong academic background and numerous awards, their research significantly contributes to improving imaging quality while reducing radiation exposure. Their work, published in leading radiology journals, highlights innovation in personalized imaging techniques, making them a promising name in the field of radiology and medical AI applications.

Publications 📚

📄 Article • Open access
Optimized contrast enhancement and homogeneity in aortic CT angiography: Bolus tracking with personalized post-trigger delay
🖊️ Qi, K., Li, L., Yuan, D., … Gao, J., Liu, J.
📚 Quantitative Imaging in Medicine and Surgery, 2025, 15(1), pp. 709–720
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🔢 0 Citations

📄 Article
Feasibility Analysis of Individualized Low Flow Rate Abdominal Contrast-Enhanced Computed Tomography in Chemotherapy Patients: Dual-Source Computed Tomography With Low Tube Voltage
🖊️ Zhang, Y., Yuan, D., Qi, K., … Gao, J., Liu, J.
📚 Journal of Computer Assisted Tomography, 2024, 48(6), pp. 844–852
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🔢 1 Citation

📄 Article • In Press
Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment
🖊️ Qi, K., Xu, C., Yuan, D., … Gao, J., Liu, J.
📚 Academic Radiology, 2024
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🔢 0 Citations

📄 Article
Image quality improvement in head and neck CT angiography: Individualized post-trigger delay versus fixed delay
🖊️ Yuan, D., Li, L., Zhang, Y., … Gao, J., Liu, J.
📚 European Journal of Radiology, 2023, 168, 111142
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🔢 4 Citations

 

 

 

Chitra P | Image Processing | Best Researcher Award

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Dr. Chitra P | Image Processing | Best Researcher Award

Professor at Sathyabama Institute of Science and Technology, India

Dr. P. Chitra is an accomplished academic and researcher with a Ph.D. in Applied Electronics from Sathyabama University (2014) and a Master’s in Applied Electronics (M.E.) from Coimbatore Institute of Technology (2004). She has been serving as an Assistant Professor in Sathyabama University, Chennai, since 2004. Her research interests span across image processing, signal processing, communication systems, and electronic circuit analysis. She has secured multiple sponsored projects, including collaborations with the Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. Dr. Chitra has contributed significantly to the field with numerous publications in both international journals and conferences, with notable works in deep learning for medical diagnostics, radiographic image processing, and PCOS detection using AI. Apart from her academic duties, she is a life member of the Indian Society for Technical Education (MISTE) and Institution of Engineers (IEI), and actively participates as a reviewer for various journals. Her dedication to advancing technology is reflected in her extensive training, certifications, and contributions to AI and machine learning. 🌐📚🔬

Publication Profile : 

Scopus

Google Scholar

Educational Background 🎓

  • Ph.D. in Electronics and Communication Engineering (2014), Sathyabama University, Chennai
  • M.E. in Applied Electronics (2004), Coimbatore Institute of Technology, Coimbatore (CGPA: 8.16)
  • B.E. in Electronics and Communication Engineering (2002), Nooral Islam College of Engineering, Kumaracoil, Nagercoil (73.55%)
  • H.S.C. (1998), Duthie Girls Higher Secondary School, Nagercoil (85.58%)
  • S.S.L.C. (1996), Duthie Girls Higher Secondary School, Nagercoil (87.00%)

Professional Experience 💼

Dr. P. Chitra has been an Assistant Professor at Sathyabama University since June 2004. With a career spanning over two decades, she has a rich academic experience in teaching and research, specializing in image processing, signal processing, and communication systems. She has been actively involved in various sponsored projects, including collaborations with Indira Gandhi Centre for Atomic Research and the Department of Biotechnology. She has led and contributed to multiple funded projects, including the development of digitization protocols for weld images and the application of AI for detecting PCOS (Polycystic Ovary Syndrome). Her expertise also extends to deep learning and AI-based algorithms in medical imaging and diagnostics.

Research Interests 🔬

Dr. Chitra’s research primarily focuses on image processing, signal processing, communication systems, and electronic circuit analysis. She is particularly passionate about applying AI and deep learning techniques for medical image analysis and diagnostic applications. Her ongoing research explores areas such as PCOS detection, brain tumor detection, and lung cancer classification, leveraging AI for better healthcare solutions.

Certifications & Achievements🎓

Dr. Chitra is a proud recipient of numerous NPTEL certifications, including courses in Deep Learning, Data Science for Engineers, and Introduction to AI. She is also a Life Member of Indian Society for Technical Education (ISTE) and the Institution of Engineers (IEI).

Publications 📚

  • Chitra P., Beryl Vedha Y. Johnson, Retnaraj Samuel S., et al. (2024), “Classification of Microglial Cells using Deep Learning Techniques”, Proceedings – 2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024
  • Sheeba I.R., Jegan G., Jayasudha F.V., Chitra P., et al. (2024), “Brain Tumor Detection- ISM Band SAR Reduction Analysis Using Microstrip Patch Antenna”, Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024
  • Chitra P., Srilatha K., Sumathi M., et al. (2023), “Automated Detection of Polycystic Ovaries using Pretrained Deep Learning Models”, AICERA/ICIS 2023
  • Chitra P., Srilatha K., Jayasudha F.V., et al. (2023), “Lung Cancer Detection Using Classification Algorithm”, RAEEUCCI 2023
  • Amudha S., Shobana J., Satheesh Kumar M., Chitra P. (2022), “Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application”, IconDeepCom 2022
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Comparison of Image Enhancement Techniques for Radiographic Weld Images”, Instrumentation Society Of India
  • Chitra P., Sheela Rani B., Venkataraman B., et al. (2011), “Evaluation of Signal To Noise in Different Radiographic Methods and Standard Digitizer”, Indian Journal of Computer Science and Engineering
  • Chitra P., Sheela Rani B., Manoharan N., et al. (2007), “A Comparative Study on the Digitization Parameters of Radiographic Weld Image Digitizers for Weld Defect Detection”, ECHDEM 2007, Chennai
  • Chitra P., Arulmozhi N., Sheela Rani B., et al. (2008), “Evaluation of Radiographic Image Quality through Standard Weld Image Digitizers”, ESSTA 2008
  • Chitra P., Sheela Rani B. (2012), “Study and Analysis on the Effect of Source to Film Distance on the Radiographic Image”, ICCCT 2012
  • Chitra P., Arulmozhi N., et al. (2009), “SNR Based Evaluation of Radiographic Weld Image Using Selenium 75, Ir-192, and X-rays”, National Seminar and Exhibition on NDE
  • Chitra P., Sheela Rani B., et al. (2011), “Extraction of Radiographic Weld Defects Using Pixel Based Segmentation”, NCICM 2011