Evidence map›Paper›PMID 34960354›Full record

ArticleSensors (Basel, Switzerland)2021

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis.

Ana Catarina Pelicano, Maria C T Gonçalves, Daniela M Godinho, Tiago Castela, M Lurdes Orvalho, Nuno A M Araújo, Emily Porter, Raquel C Conceição

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Ana Catarina PelicanoInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal.ORCID 0000-0003-1793-8233
Maria C T GonçalvesInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal.ORCID 0000-0002-9914-9184
Daniela M GodinhoInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal.ORCID 0000-0003-4053-8887
Tiago CastelaDepartamento de Radiologia, Hospital da Luz Lisboa, Luz Saúde, 1500-650 Lisbon, Portugal.
M Lurdes OrvalhoDepartamento de Radiologia, Hospital da Luz Lisboa, Luz Saúde, 1500-650 Lisbon, Portugal.
Nuno A M AraújoCentro de Física Teórica e Computacional, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal.ORCID 0000-0002-1677-6060
Emily PorterDepartment of Electrical and Computer Engineering, The University of Texas at Austin, 2501 Speedway, Austin, TX 78712, USA.ORCID 0000-0002-7787-3139
Raquel C ConceiçãoInstituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal.ORCID 0000-0002-0025-863X

Funding

Fundação para a Ciência e Tecnologia 2021.07228.BD (grant awarded on the 21/10/2021)Fundação para a Ciência e Tecnologia PTDC/FIS-MAC/28146/2017 (LISBOA-01-0145-FEDER-028146)Fundação para a Ciência e Tecnologia UI/BD/150762/2020Fundação para a Ciência e Tecnologia UIDB/00618/2020Fundação para a Ciência e Tecnologia UIDB/00645/2020Fundação para a Ciência e Tecnologia UIDP/00618/2020Fundação para a Ciência e Tecnologia UIDP/00645/2020
6 · The paper itself

Abstract

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

Indexed as

Breast NeoplasmsMicrowave ImagingAlgorithmsBreastFemaleHumansImaging, Three-DimensionalMagnetic Resonance Imagingbreast model repository for microwave diagnosisbreast tumour modelsdielectric propertiesimage segmentationrealistic numerical models

Identifiers

PMID34960354
PMCPMC8708261

What OpenQuestion holds

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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.