CoChat
Sign Up
Cited by 40Advanced X-ray and CT ImagingMedical Imaging Techniques and ApplicationsRadiation Dose and Imaging

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time x-ray scatter prediction in cone-beam CT

Maier, Joscha, Sawall, Stefan, Kachelriess, Marc, Berker, Yannick·2018·Medical Imaging 2018: Physics of Medical Imaging
DOI 10.1117/12.2292919
Open this paper in CoChat — free account

Abstract

The contribution of scattered x-rays to the acquired projection data is a severe issue in cone-beam CT (CBCT). Due to the large cone angle, scatter-to-primary ratios may easily be in the order of 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair the diagnostic value of the CT examination. Therefore, appropriate scatter correction is essential. The gold standard is to use a Monte Carlo photon transport code to predict the distribution of scattered x-rays which can be subtracted from the measurement subsequently. However, long processing times of Monte Carlo simulations prohibit them to be used routinely. To enable fast and accurate scatter estimation we propose the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of Monte Carlo simulations using only the acquired projection data as input. Once the network is trained, DSE performs in real-time. In the present study we demonstrate the feasibility of DSE using simulations of CBCT head scans at different tube voltages. The performance is tested on data sets that significantly differ from the training data. Thereby, the scatter estimates deviate less than 2% from the Monte Carlo ground truth. A comparison to kernel-based scatter estimation techniques, as they are used today, clearly shows superior performance of DSE while being similar in terms of processing time.

Overview

Integrity signals

Registry

Crossref · 10.1117/12.2292919

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Cited by

Sorted by citations

See all 40

Related

See all

Metadata

Authors
Maier, J., Sawall, S., Kachelriess, M., & Berker, Y.
Affiliations
German Cancer Research Center (DE)
ORCID
Venue
Medical Imaging 2018: Physics of Medical Imaging
Publisher
SPIE
Published
2018-03-09
Pages
56
DOI
10.1117/12.2292919
OpenAlex
W2793826529
Cited by
40
References
24
Citations by year
2024: 2 · 2023: 8 · 2022: 3 · 2021: 6 · 2020: 7 · 2019: 8 · 2018: 6
FWCI
14.76
Type
conference paper
Language
en
Open access
closed
Locations
SPIE Medical Imaging: Physics of Medical Imaging (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Advanced X-ray and CT Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
Keywords
cone-beam computed tomography, scatter correction, deep convolutional neural networks, Monte Carlo simulation, cupping artifacts, tube voltage

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review