Evidence map›Paper›PMID 36973106›Full record

ArticleZeitschrift fur medizinische Physik2024

Source-detector trajectory optimization for CBCT metal artifact reduction based on PICCS reconstruction.

Sepideh Hatamikia, Ander Biguri, Gernot Kronreif, Tom Russ, Joachim Kettenbach, Wolfgang Birkfellner

Open access · goldAbstract read
In one paragraph

Article in Zeitschrift fur medizinische Physik, 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
0.5field-weighted citation impact, top 40% of its field
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, 5 citations in OpenAlex.

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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

6 authors at 6 institutions in 3 countries.

Sepideh HatamikiaAustrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria; Research center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria. Electronic address: sepideh.hatamikia@acmit.at.
Ander BiguriDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
Gernot KronreifAustrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria.
Tom RussComputer Assisted Clinical Medicine, Heidelberg University, Heidelberg, Germany.
Joachim KettenbachInstitute of Diagnostic, Interventional Radiology and Nuclear Medicine, Landesklinikum Wiener Neustadt, Wiener Neustadt, Austria.
Wolfgang BirkfellnerCenter for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Austrian Center for Medical Innovation and Technology · ATDanube Private University · ATFachhochschule Wiener Neustadt · ATHeidelberg University · DEMedical University of Vienna · ATUniversity of Cambridge · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise instrument placement plays a critical role in all interventional procedures, especially percutaneous procedures such as needle biopsies, to achieve successful tumor targeting and increased diagnostic accuracy. C-arm cone beam computed tomography (CBCT) has the potential to precisely visualize the anatomy in direct vicinity of the needle and evaluate the adequacy of needle placement during the intervention, allowing for instantaneous adjustment in case of misplacement. However, even with the most advanced C-arm CBCT devices, it can be difficult to identify the exact needle position on CBCT images due to the strong metal artifacts around the needle. In this study, we proposed a framework for customized trajectory design in CBCT imaging based on Prior Image Constrained Compressed Sensing (PICCS) reconstruction with the goal of reducing metal artifacts in needle-based procedures. We proposed to optimize out-of-plane rotations in three-dimensional (3D) space and minimize projection views while reducing metal artifacts at specific volume of interests (VOIs). An anthropomorphic thorax phantom with a needle inserted inside and two tumor models as the imaging targets were used to validate the proposed approach. The performance of the proposed approach was also evaluated for CBCT imaging under kinematic constraints by simulating some collision areas on the geometry of the C-arm. We compared the result of optimized 3D trajectories using the PICCS algorithm and 20 projections with the result of a circular trajectory with sparse view using PICCS and Feldkamp, Davis, and Kress (FDK), both using 20 projections, and the circular FDK method with 313 projections. For imaging targets 1 and 2, the highest values of structural similarity index measure (SSIM) and universal quality index (UQI) between the reconstructed image from the optimized trajectories and the initial CBCT image at the VOI was calculated 0.7521, 0.7308 and 0.7308, 0.7248 respectively. These results significantly outperformed the FDK method (with 20 and 313 projections) and the PICCS method (20 projections) both using the circular trajectory. Our results showed that the proposed optimized trajectories not only significantly reduce metal artifacts but also suggest a dose reduction for needle-based CBCT interventions, considering the small number of projections used. Furthermore, our results showed that the optimized trajectories are compatible with spatially constrained situations and enable CBCT imaging under kinematic constraints when the standard circular trajectory is not feasible.

Indexed as

ArtifactsCone-Beam Computed TomographyMetalsPhantoms, ImagingAlgorithmsHumansImage Processing, Computer-AssistedMetalsCBCT reconstructionMetal artifact reductionNeedle-based interventionsTrajectory optimization

Identifiers

PMID36973106
PMCPMC11624347
OpenAlexW4360938193

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.