Speaker: Berkin Bilgic, Ph.D. – Associate Professor in Radiology, MGH/Harvard

Abstract:
MRI has demonstrated ability to provide exquisite contrast for non-invasive imaging. What limits its efficiency and sensitivity
are the tradeoffs between scan time, resolution, and signal-to-noise ratio (SNR). Our research is devoted to breaking this
stalemate by developing new acquisition and reconstruction methods that simultaneously exploit MR physics, signal processing,
deep learning, and hardware capabilities. We develop these technologies and pursue their applications to enable,
i. high-resolution neuroimaging at the mesoscale to examine structural and functional architecture of the human cortex in vivo,
ii. efficient quantitative imaging to map biophysical tissue parameters and probe tissue composition,
iii. open-source imaging for harmonization of data acquisition, reconstruction and analysis, and easier dissemination,
iv. rapid and motion-robust abdominal and fetal imaging, and
v. synergistic combination of self-supervised deep learning with physics-based image reconstruction.
The overarching goal in creating these techniques is to push the limits of spatial/temporal resolution and SNR of MRI to make it
a better neuroscientific and clinical tool and improve efficiency of MRI exams to make them more cost effective and more widely
used in the clinic.

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