ACMD Seminar: Accurate, provable, and fast nonlinear tomographic reconstruction

Sara Fridovich-Keil
Assistant Professor, Electrical and Computer Engineering, Georgia Tech

Tuesday, April 22, 2025, 3:00-4:00 PM ET (1:00-2:00 PM MT)

Virtual viewing*: Boulder 1-4072 & Gaithersburg Bldg. 101 LR-D**
Online at: Zoom Gov (email seminar chairs for link to talk)

Abstract: X-ray computed tomography (CT) is widely used in medical imaging, and often analyzed as a linear inverse problem (i.e., convex optimization) where the forward model consists of projections from known angles (Radon transform). We first study a more accurate, nonlinear forward model that accounts for the exponential attenuation of photon intensity according to the Beer-Lambert law. Under a Gaussian approximation of this nonlinear forward model, we show the first theoretical guarantees of image recovery for nonconvex CT reconstruction. We next consider an even more accurate forward model that drops the Gaussian measurement approximation, accounts for polychromatic X-rays, and allows for an arbitrary noise model. We introduce a simple iterative algorithm for reconstruction, which we call EXACT (EXtragradient Algorithm for CT), based on formulating our estimate as the fixed point of a monotone variational inequality. Despite nonconvexity, EXACT enjoys theoretical guarantees on statistical and computational performance under practical assumptions on the measurement process. We apply our algorithm to a CT phantom image recovery task and show that it achieves lower reconstruction error than the state-of-the-art while also being faster to run. In particular, EXACT often requires fewer X-ray projection exposures, lower source intensity, and less computation time to achieve similar reconstruction quality to existing methods.

The talk is based on https://arxiv.org/abs/2310.03956 and https://arxiv.org/abs/2503.19925, which include joint work with Mahdi Soltanolkotabi, Ashwin Pananjady, Mengqi Lou, Kabir Verchand, Fabrizio Valdivia, Ben Recht, and Gordon Wetzstein. 

Bio: Sara Fridovich-Keil is an incoming assistant professor at Georgia Tech ECE and a current postdoctoral fellow at Stanford EE, where she works with Mert Pilanci and Gordon Wetzstein on foundations and applications of machine learning and signal processing in computational imaging. She is currently supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship. Sara received her PhD in electrical engineering and computer sciences in 2023 from UC Berkeley, where she was advised by Ben Recht and supported by an NSF GRFP fellowship. Sara received her BSE in electrical engineering from Princeton University in 2018, where she was advised by Peter Ramadge.

Host: Zach Grey

Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)

**Safety Precaution: The hallway leading from the Courtyard to the exit closest to B-111 and B-113 will be used by contractors to move debris, machinery, and other supplies, as well as will be heavily trafficked by the contractors throughout the process. Be aware of the safety precautions posted during this time.

Note: Visitors from outside NIST must contact Meliza Lane at least 24 hours in advance.