Evidence map›Paper›PMID 38968908›Full record

ArticleMedical image analysis2024

Vessel-targeted compensation of deformable motion in interventional cone-beam CT.

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

8 authors.

Alexander LuDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA.
Heyuan HuangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA.
Yicheng HuDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Wojciech ZbijewskiDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA.
Mathias UnberathDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Jeffrey H SiewerdsenDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA; Departments of Imaging Physics, Radiation Physics, and Neurosurgery, The University of Texas M.D. Anderson Cancer Center, TX, USA.
Clifford R WeissDepartment of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Alejandro SisniegaDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA. Electronic address: asisnie1@jhu.edu.

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 EB030547
6 · The paper itself

Abstract

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm

Indexed as

Cone-Beam Computed TomographyImaging, Three-DimensionalLiver NeoplasmsAlgorithmsChemoembolization, TherapeuticHumansMotionMovementNeural Networks, ComputerRadiography, InterventionalCone-beam CTDeep learningImage-guided proceduresMotion compensationTACE

Identifiers

PMID38968908
PMCPMC11365791

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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.