Alireza Kamali-Asl | Neuroimaging | Best Researcher Award

Published on by Neuroscientists

Prof. Dr. Alireza Kamali-Asl | Neuroimaging | Best Researcher Award

Prof. Dr. Alireza Kamali-Asl | Freelance organization | United Kingdom

Professor Alireza Kamaliasl is a distinguished expert in medical radiation engineering and serves as the Director of the Medical Imaging Instruments Laboratory and Head of Molecular Imaging Modality. With over two decades of experience in healthcare technology and molecular imaging, he has made pioneering contributions to the design, simulation, and manufacture of advanced medical imaging instruments across modalities such as SPECT, PET, CT, and radiography. His interdisciplinary research integrates mathematical modeling, computational analysis, and clinical collaboration to enhance diagnostic and theranostic imaging systems. Professor Kamaliasl has authored more than 150 publications in top-tier international journals and conferences, achieving an h-index of 28, with over 3,800 citations and 160 research documents indexed in global databases. He has successfully supervised more than 45 postgraduate research projects, fostering innovation and leadership in radiological sciences. His expertise spans radio-isotopic imaging, system performance optimization, radiation shielding, device calibration, and preventive maintenance management. Recognized for his role as a visionary mentor and strategic planner, Professor Kamaliasl continues to advance multimodality molecular imaging and medical instrumentation, bridging the gap between engineering innovation and clinical application to improve diagnostic precision and therapeutic outcomes.

Profiles: Scopus | Orcid | Google Scholar | Research Gate | Linked In

Featured Publications

  1. Habibzadeh, M. A., Ay, M. R., Kamali-Asl, A. R., Ghadiri, H., & Zaidi, H. (2012). Impact of miscentering on patient dose and image noise in X-ray CT imaging: Phantom and clinical studies. Physica Medica, 28(3), 191–199.

  2. Aghakhan Olia, N., Kamali-Asl, A., Hariri Tabrizi, S., Geramifar, P., et al. (2022). Deep learning–based denoising of low-dose SPECT myocardial perfusion images: Quantitative assessment and clinical performance. European Journal of Nuclear Medicine and Molecular Imaging, 49(5), 1508–1522.

  3. Arefan, D., Talebpour, A., Ahmadinejhad, N., & Kamali-Asl, A. (2015). Automatic breast density classification using neural network. Journal of Instrumentation, 10(12), T12002.

  4. Poorbaygi, H., Aghamiri, S. M. R., Sheibani, S., Kamali-Asl, A., et al. (2011). Production of glass microspheres comprising 90Y and 177Lu for treating hepatic tumors with SPECT imaging capabilities. Applied Radiation and Isotopes, 69(10), 1407–1414.

  5. Khazaee Moghadam, M., Kamali-Asl, A., Geramifar, P., & Zaidi, H. (2016). Evaluating the application of tissue-specific dose kernels instead of water dose kernels in internal dosimetry: A Monte Carlo study. Cancer Biotherapy and Radiopharmaceuticals, 31(10), 367–379.*