Complex relationship between artificial intelligence and CT radiation dose
RV Gupta, MK Kalra, S Ebrahimian, P Kaviani… - Academic …, 2022 - Elsevier
Concerns over need for CT radiation dose optimization and reduction led to improved
scanner efficiency and introduction of several reconstruction techniques and image …
scanner efficiency and introduction of several reconstruction techniques and image …
Addressing CT metal artifacts using photon‐counting detectors and one‐step spectral CT image reconstruction
TG Schmidt, BA Sammut, RF Barber, X Pan… - Medical …, 2022 - Wiley Online Library
Purpose The constrained one‐step spectral CT image reconstruction (cOSSCIR) algorithm
with a nonconvex alternating direction method of multipliers optimizer is proposed for …
with a nonconvex alternating direction method of multipliers optimizer is proposed for …
Ideal observer computation by use of Markov-chain Monte Carlo with generative adversarial networks
Medical imaging systems are often evaluated and optimized via objective, or task-specific,
measures of image quality (IQ) that quantify the performance of an observer on a specific …
measures of image quality (IQ) that quantify the performance of an observer on a specific …
Constrained one‐step material decomposition reconstruction of head CT data from a silicon photon‐counting prototype
Background Spectral CT material decomposition provides quantitative information but is
challenged by the instability of the inversion into basis materials. We have previously …
challenged by the instability of the inversion into basis materials. We have previously …
Optimizing model observer performance in learning-based CT reconstruction
Deep neural networks used for reconstructing sparse-view CT data are typically trained by
minimizing a pixel-wise mean-squared error or similar loss function over a set of training …
minimizing a pixel-wise mean-squared error or similar loss function over a set of training …
Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function
M Lantz, EY Sidky, IS Reiser, X Pan… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep neural networks used for reconstructing sparse-view CT data are typically trained by
minimizing a pixel-wise mean-squared error or similar loss function over a set of training …
minimizing a pixel-wise mean-squared error or similar loss function over a set of training …
Visualization of the distortion induced by nonlinear noise reduction in computed tomography
J Larsson, M Båth… - Journal of Medical …, 2023 - spiedigitallibrary.org
Purpose We developed a method to visualize the image distortion induced by nonlinear
noise reduction algorithms in computed tomography (CT) systems. Approach Nonlinear …
noise reduction algorithms in computed tomography (CT) systems. Approach Nonlinear …
A hybrid channelized Hotelling observer for estimating the ideal linear observer for total-variation-based image reconstruction
In this work, we focus on developing a channelized Hotelling observer (CHO) that estimates
ideal linear observer performance on signal detection in images resulting from non-linear …
ideal linear observer performance on signal detection in images resulting from non-linear …
Investigation of different model observers for including signal-detectability in the training of CNNs for CT image reconstruction
Recent studies have proposed methods to preserve and enhance signal-detection
performance in learning-based CT reconstruction with CNNs. Prior work has focused on …
performance in learning-based CT reconstruction with CNNs. Prior work has focused on …
[HTML][HTML] Ingrid Reiser
S Data - profiles.uchicago.edu
Dr. Reiser is a clinical Medical Physicist and Associate Professor of Radiology and the
Committee on Medical Physics. She is board-certified in diagnostic medical physics. Her …
Committee on Medical Physics. She is board-certified in diagnostic medical physics. Her …