Ms. Madhuri Nagare Profile

Madhuri Nagare

at Purdue Univ.

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Proceedings Article | 1 April 2024 Presentation + Paper

MACE CT reconstruction for modular material decomposition from energy resolving photon-counting data

Natalie Jadue, Madhuri Nagare, Jonathan Maltz, Gregery Buzzard, Charles Bouman

Proceedings Volume 12925, 1292505 (2024) https://doi.org/10.1117/12.3005870

KEYWORDS: Sensors, Reconstruction algorithms, Photon counting, Calibration, Image restoration, CT reconstruction

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X-ray computed tomography (CT) based on photon counting detectors (PCD) extends standard CT by counting detected photons in multiple energy bins. PCD data can be used to increase the contrast-to-noise ratio (CNR), increase spatial resolution, reduce radiation dose, reduce injected contrast dose, and compute a material decomposition using a specified set of basis materials.1 Current commercial and prototype clinical photon counting CT systems utilize PCD-CT reconstruction methods that either reconstruct from each spectral bin separately, or first create an estimate of a material sinogram using a specified set of basis materials and then reconstruct from these material sinograms. However, existing methods are not able to utilize simultaneously and in a modular fashion both the measured spectral information and advanced prior models in order to produce a material decomposition. We describe an efficient, modular framework for PCD-based CT reconstruction and material decomposition using on Multi-Agent Consensus Equilibrium (MACE). Our method employs a detector proximal map or agent that uses PCD measurements to update an estimate of the pathlength sinogram. We also create a prior agent in the form of a sinogram denoiser that enforces both physical and empirical knowledge about the material-decomposed sinogram. The sinogram reconstruction is computed using the MACE algorithm, which finds an equilibrium solution between the two agents, and the final image is reconstructed from the estimated sinogram. Importantly, the modularity of our method allows the two agents to be designed, implemented, and optimized independently. Our results on simulated data show a substantial (450%) CNR boost vs conventional maximum likelihood reconstruction when applied to a phantom used to evaluate low contrast detectability.

Proceedings Article | 18 October 2022 Open Access

Design of novel loss functions for deep learning in x-ray CT

Obaidullah Rahman, Ken Sauer, Madhuri Nagare, Charles Bouman, Roman Melnyk, Jie Tang, Brian Nett

Proceedings Volume 12304, 123042S (2022) https://doi.org/10.1117/12.2646473

KEYWORDS: Signal attenuation, X-ray computed tomography, Error analysis, X-rays, Linear filtering, Spatial frequencies, Image quality, Electronic filtering, Data modeling, X-ray imaging

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Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks’ being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both. X-ray computed tomography (CT), one of the most valuable tools in medical diagnostics, is already being improved by DL methods. Whether for removal of common quantum noise resulting from the Poisson-distributed photon counts, or for reduction of the ill effects of metal implants on image quality, researchers have begun employing DL widely in CT. The selection of training data is driven quite directly by the corruption on which the focus lies. However, the way in which differences between the target signal and measured data is penalized in training generally follows conventional, pointwise loss functions. This work introduces a creative technique for favoring reconstruction characteristics that are not well described by norms such as mean-squared or mean-absolute error. Particularly in a field such as X-ray CT, where radiologists’ subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively. This penalty may be applied in the data domain, here the CT sinogram, or in the reconstructed image. We design loss functions for both shaping and selectively preserving frequency content of the signal.

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