Undergraduate Research & Scholarships

Pranav Kolluri

Deep Learning to Accelerate Motion Grading of HR-pQCTs

High Resolution Peripheral Quantitative Computed Tomography, or HR-pQCT, is a medical imaging technique used to assess the architecture of the of the cortical and trabecular bone. Our lab, the Bone Quality Research Laboratory, uses this imaging technique in addition to MRIs to better understand how bone structure changes with disease. Typically, at the time of image capture, the operators have to manually (and subjectively) “score” if the scan has been impacted by motion artifacts. My work focuses on automating the scoring process of HR-pQCT capture via deep learning on a dataset of our own creation to remove this capture bottleneck for the lab and perhaps even more broadly.

Message To Sponsor

I wanted to express my profound gratitude for the opportunity to extend my involvement with the Bone Quality Research lab alongside Professor Kazakia. Your support has made it possible for me to continue this important work beyond graduation, and I cannot overstate how much it means to me. This opportunity not only allows me to contribute to meaningful research but also provides invaluable learning experiences that will shape my future endeavors. Thank you for believing in my project and believing in my success.
Major: Electrical Engineering and Computer Science
Mentor: Galateia Kazakia, UCSF Radiology
Sponsor: Cheunkarndee Fund
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