Evidence map›Paper›PMID 42850146›Full record

ArticleBrachytherapy2026

Deep learning-assisted needle artifact suppression for enhanced anatomical visualization in prostate high-dose-rate brachytherapy ultrasound imaging.

Yu Gao, Thomas Niedermayr, Xianjin Dai, Serdar Charyyev, Lei Xing, Hilary Bagshaw, Mark K Buyyounouski

Abstract read
In one paragraph

Article in Brachytherapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

7 authors.

Yu GaoDepartment of Radiation Oncology, Stanford University, Palo Alto, CA. Electronic address: Yugao97@stanford.edu.
Thomas NiedermayrDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.
Xianjin DaiDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.
Serdar CharyyevDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.
Lei XingDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.
Hilary BagshawDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.
Mark K BuyyounouskiDepartment of Radiation Oncology, Stanford University, Palo Alto, CA.

Funding

Leveraging deep learning for markerless motion management in radiation therapyR01CA256890 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2021 to 2025
$2.1M
Development of AI-Augmented quality assurance tools for radiation therapyR01CA275772 · NCI · STANFORD UNIVERSITY · PI Lei Xing · 2023 to 2026
$2.1M
NCI NIH HHS R01 CA256890NCI NIH HHS R01 CA275772
6 · The paper itself

Abstract

purposeImplanted needles introduce acoustic artifacts that degrade transrectal ultrasound (TRUS) images during high-dose-rate (HDR) prostate brachytherapy, complicating ultrasound-only contouring. We developed an artificial intelligence (AI) needle eraser to remove needles and associated artifacts and facilitate structure delineation.

methodsTRUS images from 120 patients undergoing HDR prostate brachytherapy were retrospectively collected. Three-dimensional volumes were acquired immediately before and after needle insertion. A Cycle Generative Adversarial Network (CycleGAN) was trained to transform postneedle images into needle-free images. Two physicians independently rated clinical utility for prostate and urethra delineation using a four-point scale (1 = poor; 4 = excellent). Prostate and urethra contours from preneedle and needle-erased images were compared with clinical reference contours using Dice similarity coefficient (DSC).

resultsThe AI tool suppressed needles and associated artifacts, improving visualization of the prostate and urethral lumen. Mean reader scores were 3.80 ± 0.43 for prostate and 3.75 ± 0.37 for urethra delineation. Compared with preneedle contours, needle-erased contours showed higher agreement with clinical references: prostate DSC increased from 0.91 to 0.93, and urethra DSC increased from 0.70 to 0.89.

conclusionsA CycleGAN-based needle eraser can generate needle-free ultrasound images from postinsertion scans and improve visualization and contour agreement for physician delineation.

Indexed as

Deep learningProstate HDRUltrasound

Identifiers

PMID42850146
PMCPMC13674576

What OpenQuestion holds

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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.