Development of breast lesions models database

Bliznakova, Kristina; Dukov, Nikolay; Feradov, Firgan; Gospodinova, Galja; Bliznakov, Zhivko; Russo, Paolo; Mettivier, Giovanni; Bosmans, Hilde; Cockmartin, Lesley; Sarno, Antonio; Kostova-Lefterova, D.; Encheva, Elitsa; Tsapaki, Virginia; Bulyashki, Daniel; Buliev, Ivan

doi:10.1016/j.ejmp.2019.07.017

Cited by 34 publications

(23 citation statements)

References 38 publications

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“…Two of the most recent refinements to be employed are the use of modified printers to improve resolution and the use of dithering to produce smooth transitions between materials (Rossman et al 2019). (Bliznakova et al 2019). However there has been less success to date in realizing these in a physical form which can be inserted or included in a physical breast phantom.…”

Section: Latest Developments 2331 Texturementioning

confidence: 99%

Tissue mimicking materials for imaging and therapy phantoms: a review

et al. 2020

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Tissue mimicking materials (TMMs), typically contained within phantoms, have been used for many decades in both imaging and therapeutic applications. This review investigates the specifications that are typically being used in development of the latest TMMs. The imaging modalities that have been investigated focus around CT, mammography, SPECT, PET, MRI and ultrasound. Therapeutic applications discussed within the review include radiotherapy, thermal therapy and surgical applications. A number of modalities were not reviewed including optical spectroscopy, optical imaging and planar x-rays. The emergence of image guided interventions and multimodality imaging have placed an increasing demand on the number of specifications on the latest TMMs. Material specification standards are available in some imaging areas such as ultrasound. It is recommended that this should be replicated for other imaging and therapeutic modalities. Materials used within phantoms have been reviewed for a series of imaging and therapeutic applications with the potential to become a testbed for cross-fertilization of materials across modalities. Deformation, texture, multimodality imaging and perfusion are common themes that are currently under development.

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Section: Latest Developments 2331 Texturementioning

confidence: 99%

Tissue mimicking materials for imaging and therapy phantoms: a review

et al. 2020

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“…We used breast tumoral lesions from a previously generated database 23,24 for inserting lesions inside the digital breast phantoms. That database was produced within the framework of the European Union MaXIMA project (https:// maxima-tuv.eu/) and is comprised of 138 3D digital models of breast tumors.…”

Section: B Generation Of a Dataset Of Breast Ct Computational Phantomsmentioning

confidence: 99%

“…A possible approach is to devise computational models of the lesion-free breast, then allowing for insertion of malignant details like microcalcifications and tumor masses. 23,24 Hardware versions of these software phantoms have been produced in the form of complex physical phantoms manufactured by 3D printing. [25][26][27][28][29][30] Digital phantoms have been developed using either an entirely computational approach, or as patient-like phantoms derived from the analysis of patients' breasts images obtained from clinical DM, DBT or BCT exams.…”

Section: Introductionmentioning

confidence: 99%

Dataset of patient‐derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography

et al. 2021

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Purpose: To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. Acquisition and validation methods: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. Data format and access: The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://

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“…Therefore, the segmented lesions have been enhanced to include those high spatial frequency contents either using higher resolution images (e.g., digital pathology) or with assumptions informed by morphometry studies. 66,67 Figure 7 shows examples of simulated oncological lesions using these methodologies. Lesion models have been further enhanced by creating a model by incorporating cell-level biological parameters and physiologically realistic growth mechanisms.…”

Section: Modeling Diseasementioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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Development of breast lesions models database

Cited by 34 publications

References 38 publications

Tissue mimicking materials for imaging and therapy phantoms: a review

Tissue mimicking materials for imaging and therapy phantoms: a review

Dataset of patient‐derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography

Virtual clinical trials in medical imaging: a review

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