Evidence map›Paper›PMID 41233645›Full record

ArticlePhysical and engineering sciences in medicine2026

Design and validation of a technology for 3D printing training phantoms for ultrasound imaging.

Veronika Grebennikova, Denis Leonov, Zhuhuang Zhou, José Francisco Silva Costa-Júnior, Daria Shestakova, Manob Jyoti Saikia, Natalia Vetsheva, Nicholas Kulberg, Kristina Pashinceva, Olga Omelianskaya and 1 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Physical and engineering sciences in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Veronika GrebennikovaResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051, Moscow, Russia.ORCID http://orcid.org/0009-0003-6041-132X
Denis LeonovResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051, Moscow, Russia. strat89@mail.ru.ORCID http://orcid.org/0000-0003-0916-6552
Zhuhuang ZhouDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.ORCID https://orcid.org/0000-0003-0570-8473
José Francisco Silva Costa-JúniorBrazilian Air Force Academy, 13643-000, Pirassununga, São Paulo, Brazil.ORCID https://orcid.org/0000-0002-6526-2760
Daria ShestakovaSmolensk State Medical University, 214019, Moscow, Russia.ORCID https://orcid.org/0000-0001-5497-1476
Manob Jyoti SaikiaUniversity of Memphis, Memphis, TN, 38152, USA.ORCID https://orcid.org/0000-0001-6656-4333
Natalia VetshevaRussian Medical Academy of Continuous Professional Education, 125445, Moscow, Russia.ORCID https://orcid.org/0000-0002-9017-9432
Nicholas KulbergBauman Moscow State Technical University, 105005, Moscow, Russia.ORCID https://orcid.org/0000-0001-7046-7157
Kristina PashincevaMoscow State University of Medicine and Dentistry, 127473, Moscow, Russia.ORCID https://orcid.org/0000-0003-1850-1973
Olga OmelianskayaResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051, Moscow, Russia.ORCID https://orcid.org/0000-0002-0245-4431
Yuriy VasilevResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051, Moscow, Russia.ORCID https://orcid.org/0000-0002-5283-5961

Funding

Moscow Healthcare Department Moscow Healthcare Department
6 · The paper itself

Abstract

Due to high cost, training phantoms are often inaccessible and their manufacturing technologies are quite sophisticated. The purpose of this paper is to develop an inexpensive and reproducible technology for creating ultrasound training phantoms. These phantoms are a 3D printed porous medium composed of 156-µm-thick photopolymer resin fibers and include models of cysts ranging from 4 to 8 mm in diameter, effectively simulating a muscle tissue with anechoic lesions. A custom software generates a virtual phantom model, enabling precise control over its properties. We believe that the results of the acoustic characteristics' measurements for the designed phantoms provide an opportunity to mimic muscle (1547 m/s) and breast (1510 m/s) tissues. Following the creation of the phantom, a series of assessments were conducted to evaluate its efficacy for needle insertion (involving 3 observers) and to identify its mimicked tissue type (with 29 observers participating). The findings revealed that the phantom is capable of enduring up to 300 punctures in a single location without exhibiting significant decline in image quality. A subsequent survey of ultrasound specialists, who possessed a range of professional experiences, indicated that the ultrasound images produced by the phantom predominantly corresponded to those of muscle tissues upon visual examination. The 3D printing process for the phantom 60 mm × 60 mm × 30 mm in size was completed in 3 h and 23 min. The proposed technology allows creating low-cost, long-lasting phantoms for training in ultrasound diagnostics and ultrasound-guided procedures. The phantom designed using widely available photopolymer resin, while the custom software and high-resolution 3D printing ensures reproducibility of the shape and positions of the fibers and inclusions. The phantom mimics muscle tissues with multiple cysts and can be used to develop basic coordination and navigation skills required for ultrasound diagnostics.

Indexed as

Phantoms, ImagingPrinting, Three-DimensionalEquipment DesignHumansUltrasonographyCystic massesMuscle tissueThree-dimensional printingTissue-mimicking phantomTraining model

Identifiers

What OpenQuestion holds

Textmetadata
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.