Evidence map›Paper›PMID 38733602›Full record

ArticleMedical physics2024

Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric.

Heyuan Huang, Yixuan Liu, Jeffrey H Siewerdsen, Alexander Lu, Yicheng Hu, Wojciech Zbijewski, Mathias Unberath, Clifford R Weiss, Alejandro Sisniega

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

9 authors.

Heyuan HuangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Yixuan LiuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Jeffrey H SiewerdsenDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Alexander LuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Yicheng HuDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Wojciech ZbijewskiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Mathias UnberathDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Clifford R WeissDepartment of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
Alejandro SisniegaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.

Funding

Deformable motion compensation for 3D image-guided interventional radiologyR01EB030547 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI ZBIJEWSKI, WOJCIECH BARTOSZ · 2021 to 2024
$1.5M
NIBIB NIH HHS R01 EB030547NIBIB NIH HHS R01EB030547
6 · The paper itself

Abstract

purposeInterventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric,

methodsThe proposed

resultsThe magnitude and spatial map of

conclusionThe proposed

Indexed as

Cone-Beam Computed TomographyImage Processing, Computer-AssistedMovementHumansdeep autofocusdeformable motioninterventional CBCT

Identifiers

PMID38733602
PMCPMC11155121

What OpenQuestion holds

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Registered trials

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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.