Evidence map›Paper›PMID 41409390›Full record

ArticleJournal of clinical imaging science2025

Improving cone-beam computed tomography image quality for transarterial therapy of liver malignancies: Evaluation of a motion correction algorithm with and without automated bone removal.

Abdul Rehman Mustafa, Adham Khalil, Arun Kamireddy, Dana Angelina Schaar, Fereshteh Khorshidi, Izzet Altun, Heyuan Huang, Christopher R Bailey, Robert P Liddell, Nariman Nezami and 4 more

Abstract read
In one paragraph

Article in Journal of clinical imaging science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Abdul Rehman MustafaRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.
Adham KhalilRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.ORCID https://orcid.org/0000-0002-0582-8160
Arun KamireddyRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.
Dana Angelina SchaarDepartment of Interventional Radiology, University of Pittsburgh School of Medicine, Pittsburgh, United States.ORCID https://orcid.org/0009-0009-9255-4732
Fereshteh KhorshidiDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States.ORCID https://orcid.org/0000-0002-5566-3608
Izzet AltunDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States.
Heyuan HuangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, United States.ORCID https://orcid.org/0000-0002-6010-0105
Christopher R BaileyRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.
Robert P LiddellRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.
Nariman NezamiDepartment of Radiology, Section of Vascular and Interventional Radiology, The Georgetown University Medical Center, Washington, DC, United States.
Peter FischerDepartment of AT Innovations, Siemens Healthineers AG, Forchheim, Germany.ORCID https://orcid.org/0000-0003-3242-9867
Alexander PreuhsDepartment of AT Innovations, Siemens Healthineers AG, Forchheim, Germany.
Tina EhtiatiDepartment of Research Collaborations, Siemens Medical Solutions Inc., Malvern, PA, United States.
Clifford R WeissRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The objectives of the study are to compare the effect on image quality of a motion artifact correction algorithm (CAVAREC) alone versus in combination with an automated bone removal algorithm (ZIBOS) for motion-degraded intraprocedural liver cone-beam computed tomography (CBCT) images. Material and Methods: In this retrospective, two-center, Institutional Review Board (IRB)-approved study, 48 CBCTs from 41 patients were included. Inclusion criteria were (a) age ≥18; (b) liver CBCT with contrast injected at the main, right, or left hepatic artery; and (c) CBCT motion-degraded. Exclusion criteria were (a) no visible tumor and (b) CBCT not capturing the entire liver. The mean (standard deviation [SD]) age was 64 (7) years, 81% male. 65% had hepatocellular carcinoma and 35% metastatic cancer. 69% CBCTs were from transarterial radioembolization (TARE) mapping, 25% transarterial chemoembolization, and 6% TARE treatment. Mean (SD) maximum tumor diameter was 4.6 (2.7) cm. CBCT images were processed with CAVAREC (prototype, Siemens Healthineers AG, Forchheim, Germany) and CAVAREC + ZIBOS (work in progress, Comprehenso, Hannover, Germany). Using CoroEval, sharpness for two segmental arteries per case was obtained quantitatively. Three blinded interventional radiologists independently evaluated overall image quality on a 0-100 scale and large vessels, small vessels, vessel sharpness, tumor feeders, tumor blush, and streak artifacts on a -50 to +50 scale relative to uncorrected images. Results were analyzed with paired Results: On quantitative assessment, mean (SD) sharpness for uncorrected, CAVAREC, and CAVAREC + ZIBOS (C+Z) images was 0.281 (0.04), 0.287 (0.04), and 0.284 (0.04), respectively, Conclusion: Motion artifact correction of liver CBCT images using CAVAREC improves image quality significantly. According to observer studies, integrating the automated bone segmentation algorithm ZIBOS with CAVAREC does not degrade image quality.

Indexed as

Bone segmentationCone-beam computed tomographyLiver malignanciesMotion artifact correctionTransarterial therapy

Identifiers

PMID41409390
PMCPMC12707598

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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.