X-Ray Scatter Estimation Using Deep Splines

Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben, Christopher; Felsner, Lina; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas

doi:10.1109/tmi.2021.3074712

“…In a more ad-hoc manner, also other inductive biases can be implemented, e.g. Schirrmacher et al showed how to integrate median and quantile image filters [79], Maier et al integrated heuristic convolutional scatter models [80] while Roser et al preferred splines for this purpose [81]. Even metal artifact reduction can be made trainable using hybrids as demonstrated by Gottschalk et al [82].…”

Section: Image Reconstructionmentioning

confidence: 99%

Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

Maier

¹

,

Köstler

²

,

Heisig

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.

show abstract

“…Maier et al integrated heuristic convolutional scatter models [78] while Roser et al preferred splines for this purpose [79]. Even metal artifact reduction can be made trainable using hybrids as demonstrated by Gottschalk et al [80].…”

Section: Image Reconstructionmentioning

confidence: 99%

Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

Maier,

Köstler,

Heisig

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.

show abstract

“…proposed the deep scatter estimation, 40,41 a method that uses a deep convolutional neural network to estimate scatter intensities from input projections. Since then, the application of deep learning for scatter correction is a field of growing interest 42–51 . A limitation of learning‐based algorithms is that they require large datasets for training the neural network.…”

Section: Introductionmentioning

confidence: 99%

Empirical scatter correction: CBCT scatter artifact reduction without prior information

Trapp

¹

,

Maier

²

,

Susenburger

³

et al. 2022

View full text Add to dashboard Cite

Background The image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software‐based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of the aforementioned. Purpose This study proposes a simple and effective postprocessing software‐based correction method of scatter artifacts in CBCT scans without specific prior knowledge. Methods We propose the empirical scatter correction (ESC), which generates scatter‐like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table‐top CBCT, and a pelvis scan from a Varian Edge CBCT scanner. Results ESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter‐free reference image was as low as −6 HU after using ESC, whereas the uncorrected data deviated by more than −200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35 HU corrected and −289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. Conclusions ESC allows to reduce artifacts caused by patient scatter solely based on the projection data.

show abstract

X-Ray Scatter Estimation Using Deep Splines

Cited by 12 publications

References 49 publications

Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

Empirical scatter correction: CBCT scatter artifact reduction without prior information

Contact Info

Product

Resources

About