Jingmin Luan | Medical Imaging Process | Best Researcher Award

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Mr. Jingmin Luan | Medical Imaging Process | Best Researcher Award

Lecturer at Northeastern University, China

Dr. Jingmin Luan is a dedicated academic and researcher currently serving as a Lecturer in the Department of Electronic Information Engineering at Northeastern University at Qinhuangdao. With a solid foundation in engineering and a specialized focus on biomedical signal processing and deep learning, he has been contributing meaningfully to interdisciplinary research. Dr. Luan’s work bridges the gap between traditional Chinese medical theories and modern computational techniques, offering innovative perspectives and methodologies in biomedical analysis and signal interpretation. His academic career reflects a deep engagement with both theoretical frameworks and practical applications, evidenced by numerous scholarly contributions to international journals and conferences.

Profile

Scopus

ORCID

Education

Dr. Luan earned his Doctor of Engineering degree with a specialization in biomedical signal processing and information systems. His academic journey was grounded in a strong interdisciplinary curriculum that integrated engineering principles with medical applications, which later served as a robust foundation for his research into decision-making models and syndrome identification in traditional Chinese medicine. His doctoral training equipped him with a refined understanding of mathematical modeling, machine learning algorithms, and data analysis, tools that he has consistently applied in his research and teaching roles.

Experience

As a faculty member at Northeastern University at Qinhuangdao, Dr. Luan has developed a portfolio that encompasses teaching, research, and academic leadership. He has successfully led funded projects from both national and provincial foundations. Notably, he served as the principal investigator on a National Natural Science Foundation of China project focusing on three-branch decision problems in traditional Chinese medicine from 2017 to 2019. He also directed a project under the Natural Science Foundation of Hebei Province from 2018 to 2020, which examined compatibility identification using mathematical theories. These experiences have allowed him to supervise research teams, publish extensively, and contribute to the academic development of his students and peers.

Research Interest

Dr. Luan’s research interests lie primarily in biomedical signal processing and deep learning, with a distinctive focus on integrating traditional Chinese medicine (TCM) diagnostic models with modern computational approaches. His work emphasizes the use of three-way decision theories, partial-ordered attribute frameworks, and image processing techniques to interpret complex health data. He is particularly interested in how advanced imaging technologies like Optical Coherence Tomography (OCT) can be enhanced using signal processing methods to provide better diagnostic and therapeutic outcomes. His interdisciplinary research serves as a bridge between ancient diagnostic wisdom and 21st-century computational science.

Award

Dr. Luan has been recognized for his academic leadership through various competitive research grants. He was awarded the National Natural Science Foundation of China grant for his study on decision-making models in TCM, a testament to the innovation and scientific merit of his work. Additionally, his leadership in the Hebei Province Natural Science Foundation project reflects regional recognition of his contributions to computational methods in medicine. These prestigious grants underscore his impact and relevance in the research community, particularly in developing new approaches to medical diagnostics using artificial intelligence and mathematical theory.

Publication

Dr. Luan’s research findings have been disseminated through high-quality peer-reviewed journals.

  1. Optical Attenuation Coefficient Based Optical Coherence Tomography Angiography, Optics Communications, 2025.

  2. Compact Photoacoustic Endoscopy by Measuring Initial Photoacoustic Pressure Using Phase-Shift Interferometry, Photoacoustics, 2025.

  3. Non-contact All-optic OCT–PAM Imaging with Shared Detection Light, Applied Optics, 2025.

  4. The Stress Phase Angle Measurement Using Spectral Domain Optical Coherence Tomography, Sensors, 2023.

  5. Spectral Interference Contrast Based Non-contact Photoacoustic Microscopy Realized by SDOCT, Optics Letters, 2022.

  6. Evaluation of Mannitol Intervention Effects on Ischemic Cerebral Edema in Mice Using Swept Source Optical Coherence Tomography, Photonics, 2022.

  7. Optimized Depth-resolved Estimation to Measure Optical Attenuation Coefficients from Optical Coherence Tomography and Its Application in Cerebral Damage Determination, Journal of Biomedical Optics, 2019.

Conclusion

Dr. Jingmin Luan exemplifies a modern researcher whose work transcends disciplinary boundaries, merging engineering, medicine, and artificial intelligence. His contributions to biomedical signal processing and deep learning, particularly within the context of traditional Chinese medicine, demonstrate both academic rigor and practical relevance. With a robust track record of funded research, high-impact publications, and academic mentorship, Dr. Luan continues to shape the future of interdisciplinary health sciences. His career reflects a unique blend of traditional insight and cutting-edge technology, making him a distinguished candidate for any academic recognition or award.

Jiyo Athertya | Medical Imaging using MRI – Animal Model | Best Academic Researcher Award

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Dr. Jiyo Athertya | Medical Imaging using MRI – Animal Model | Best Academic Researcher Award

Post Doctoral – Fellow at University of California, San Diego, United States

Jiyo S. Athertya is a passionate biomedical engineer and researcher with a strong foundation in medical image processing and ultrashort echo time (UTE) MRI technologies. With an emphasis on advancing early diagnostics and monitoring of musculoskeletal and neurological diseases, Jiyo has made significant contributions to the fields of MRI reconstruction, neuroimaging, and radiomics. He specializes in the design and development of innovative MRI techniques that enhance diagnostic sensitivity, particularly in degenerative spine disorders and neurodegenerative conditions like Alzheimer’s disease. His interdisciplinary approach combines engineering design, image analysis, and machine learning, aiming to bridge the gap between clinical imaging and precision diagnostics.

Profile

Scopus

Education

Jiyo earned his Ph.D. in Engineering Design from the Indian Institute of Technology Madras in 2018, where he focused on advanced imaging studies of the human spine. His doctoral work included creating algorithms for the segmentation and classification of vertebral deformities using both CT and MR images. Prior to this, he completed a Master of Engineering in Biomedical Engineering from the College of Engineering, Anna University in 2012, where his thesis centered on 3D CT image reconstruction of the vertebral column. His foundational education in Electrical and Electronics Engineering, completed in 2010 at Anna University, provided him with a robust technical base in signal analysis and instrumentation.

Experience

Jiyo is currently a postdoctoral researcher at UC San Diego, where he leads investigations into UTE-MRI techniques for improved neuroimaging and myelin quantification. He plays a pivotal role in developing quantitative MRI analysis pipelines, collaborating across disciplines, and mentoring junior researchers. Since 2022, he has also served as a Health Science Research Specialist at the VA Hospital San Diego, where he conducts MRI scanning of musculoskeletal tissues and participates in histological analyses in neurological studies. His earlier experience as a graduate research assistant at IIT-Madras further honed his skills in algorithm design, vertebral segmentation, and the analysis of spinal degenerative markers.

Research Interest

Jiyo’s research spans medical image processing, machine learning, radiomics, and deep learning applications in MRI. His current focus lies in advancing UTE MRI methodologies for detecting microstructural tissue properties such as myelin content in the brain, especially relevant to Alzheimer’s and traumatic brain injury models. He is particularly interested in automating diagnostic processes using AI, improving classification performance through data augmentation and feature optimization, and integrating fuzzy logic and soft computing in medical diagnostics. His investigations extend to spine imaging, Modic changes, and structural recovery in intervertebral discs.

Award

Jiyo’s research excellence has been acknowledged with several prestigious awards. He received the ISMRM Trainee Stipend for 2022–2024 and was honored with the Outstanding Author Award in 2024 for his publication on myelin water quantification in multiple sclerosis. He has been a finalist in the Postdoc Power Pitch competition and delivered an invited talk at UCSD’s Radiology Research+Education Seminar Series. Additionally, his presence at major conferences like ISMRM and EYH reflects his active engagement with the scientific community and dedication to public education in medical imaging.

Publication

Jiyo has an impressive publication record in peer-reviewed journals. Some selected works include:

  1. Athertya, Jiyo S., & Kumar, G. S. (2016). “Automatic segmentation of vertebral contours from CT images using fuzzy corners.” Computers in Biology and Medicine, 72, 75–89. [Cited by 45 articles]

  2. Athertya, Jiyo S., & Kumar, G. S. (2021). “Classification of certain vertebral degenerations using MRI image features.” Biomedical Physics & Engineering Express, 7(4), 045013. [Cited by 32 articles]

  3. Afsahi, A. M., Athertya, J., et al. (2022). “High-contrast lumbar spinal bone imaging using a 3D slab-selective UTE sequence.” Frontiers in Endocrinology, 12, 800398. [Cited by 29 articles]

  4. Jang, H., Athertya, J. S., et al. (2022). “UTE-QSM with 3D cones trajectory in human brain.” Frontiers in Neuroscience, 16, 1033801. [Cited by 18 articles]

  5. Athertya, Jiyo S., et al. (2023). “Detection of iron oxide nanoparticle-labeled stem cells using UTE.” Quantitative Imaging in Medicine and Surgery, 13(2), 585. [Cited by 22 articles]

  6. Moazamian, D., Athertya, J. S., et al. (2024). “Assessment of Achilles tendon using UTE-MRI T1 and MT modeling in psoriatic arthritis.” NMR in Biomedicine, 37(1), e5040. [Cited by 17 articles]

  7. Athertya, Jiyo S., et al. (2024). “High contrast cartilaginous endplate imaging in spine using 3D DIR-UTE.” Skeletal Radiology, 53(5), 881–890. [Cited by 10 articles]

Conclusion

Jiyo S. Athertya stands as a leading figure in biomedical imaging research, bringing innovative MRI techniques from theory to practice. His integrated approach in engineering and clinical imaging not only advances diagnostic capabilities but also fosters future innovation through mentorship and collaboration. With a clear vision for translational research, he continues to shape the field of neuroimaging and musculoskeletal diagnostics, making significant strides in both academic and clinical domains.