Evidence map›Paper›PMID 28741597›Full record

ArticlePhysics in medicine and biology2017

Motion compensation for cone-beam CT using Fourier consistency conditions.

M Berger, Y Xia, W Aichinger, K Mentl, M Unberath, A Aichert, C Riess, J Hornegger, R Fahrig, A Maier

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. [A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024
    Article
  7. Article
  8. Article
  9. Flat-panel conebeam CT in the clinic: history and current state.Journal of medical imaging (Bellingham, Wash.) · 2021
    Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

M BergerPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
Y XiaPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
W AichingerPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
K MentlPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
M UnberathPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
A AichertPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
C RiessPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
J HorneggerPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.
R FahrigRadiological Sciences Laboratory, Stanford University, Stanford, CA 94305, United States of America.
A MaierPattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany.

Funding

Weight-Bearing Imaging of the Knee Using C-Arm CTR01AR065248 · NIAMS · STANFORD UNIVERSITY · PI GOLD, GARRY E · 2015 to 2018
$2.0M
Advanced MR Imaging of Early OsteoarthritisK24AR062068 · NIAMS · STANFORD UNIVERSITY · PI GOLD, GARRY E · 2012 to 2022
$1.7M
Axiom zeego shared instrument grantS10RR026714 · NCRR · STANFORD UNIVERSITY · PI FAHRIG, REBECCA · 2010 to 2010
$500k
NCRR NIH HHS S10 RR026714NIAMS NIH HHS K24 AR062068NIAMS NIH HHS R01 AR065248
6 · The paper itself

Abstract

In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 μm and 1184 μm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.

Indexed as

MotionPhantoms, ImagingWeight-BearingAlgorithmsArtifactsCone-Beam Computed TomographyHumansImage Processing, Computer-AssistedKnee

Identifiers

PMID28741597
PMCPMC5748518

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.