The Napoli-Varna-Davis project for virtual clinical trials in X-ray breast imaging

Mettivier, Giovanni; Sarno, Antonio; Franco, F. di; Bliznakova, Kristina; Bliznakov, Zhivko; Hernandez, Andrew M.; Boone, John M.; Russo, Paolo

doi:10.1109/nss/mic42101.2019.9059828

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…The compressed thickness was extracted from the DICOM header information obtained from original DM examination of the recruited patients. The compression software has been largely described and used in papers about dosimetry and imaging investigations in 2D digital mammography 2,8,19,45,51 . To reduce the tissue loss due to the elaboration, 2 the distance between the chest wall and the compression paddle was taken as small as possible.…”

Section: Methodsmentioning

confidence: 99%

“…The uncompressed computational breast phantoms were derived from the clinical images via a semi-automatic tissue classification algorithm. 8,11,45,46,49 The algorithm was developed in Matlab R2019a, relying on routines derived from the Segmentation toolkit, and it classifies each image voxel into four categories representing the four main materials: adipose tissue, fibroglandular tissue, skin, and air. The algorithm operates first in each of the coronal image slice.…”

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

confidence: 99%

“…These computational phantoms will be adopted within the Naples‐Varna‐Davis project for VCTs in two‐dimensional (2D) and 3D x‐ray breast imaging, 45 to be implemented on the software and hardware platform under development by the AGATA project in Italy 46 . In these projects, a distinctive feature is the original adoption of a tool based on Monte Carlo simulation code using realistic digital breast models derived from real high‐resolution 3D breast images, hence reflecting the real breast anatomy and silhouette instead of “artificially” breast phantoms.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…In our current approach, breast MRI images are subjected to tissue segmentation in a breast volume, where the breast tissues are represented by HUs. The developed technique in this study is fully applicable to breast models obtained from breast CT images, and may be used with the dataset of 150 computational breast models reported by Sarno et al (Mettivier et al 2019, Sarno et al 2021, as a result of a classification algorithm applied on clinical breast CT images.…”

Section: Discussionmentioning

confidence: 99%

Thermoplastic 3D printing technology using a single filament for producing realistic patient-derived breast models

Dukov

Bliznakova

Okkalidis³

et al. 2022

Phys. Med. Biol.

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Objective. This work describes an approach for producing physical anthropomorphic breast phantoms from clinical patient data using three-dimensional (3D) fused-deposition modelling (FDM) printing. Approach. The source of the anthropomorphic model was a clinical Magnetic Resonance Imaging (MRI) patient image set, which was segmented slice by slice into adipose and glandular tissues, skin and tumour formations; thus obtaining a four component computational breast model. The segmented tissues were mapped to specific Hounsfield Units (HU) values, which were derived from clinical breast Computed Tomography (CT) data. The obtained computational model was used as a template for producing a physical anthropomorphic breast phantom using 3D printing. FDM technology with only one polylactic acid filament was used. The physical breast phantom was scanned at Siemens SOMATOM Definition CT. Quantitative and qualitative evaluation were carried out to assess the clinical realism of CT slices of the physical breast phantom. Main results. The comparison between selected slices from the computational breast phantom and CT slices of the physical breast phantom shows similar visual x-ray appearance of the four breast tissue structures: adipose, glandular, tumour and skin. The results from the task-based evaluation, which involved three radiologists, showed a high degree of realistic clinical radiological appearance of the modelled breast components. Measured HU values of the printed structures are within the range of HU values used in the computational phantom. Moreover, measured physical parameters of the breast phantom, such as weight and linear dimensions, agree very well with the corresponding ones of the computational breast models. Significance. The presented approach, based on a single FDM material, was found suitable for manufacturing of a physical breast phantom, which mimics well the 3D spatial distribution of the different breast tissues and their x-ray absorption properties. As such, it could be successfully exploited in advanced x-ray breast imaging research applications.

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“…An early simulation tool for radiographic imaging was described by Lazos et al (Lazos et al 2000, Lazos et al 2003 and this was later validated via simulations of a physical CIRS 011A phantom using synchrotron imaging (Bliznakova et al 2010). In later work, a software tool called BreastSimulator was developed (Bliznakova et al 2012) and validated by Mettivier et al (2017) for tomographic imaging, which led on to the Napoli-Varna-Davis project to perform VCTs in x-ray breast imaging (Mettivier et al 2019). The same group has since published a proof of concept for a simulation platform using the Geant4 Monte Carlo (MC) toolkit (di Franco et al 2020), referred to the Agata platform.…”

Section: Selection Of the Vct Platforms For Detailed Studymentioning

confidence: 99%

Performance evaluation of digital breast tomosynthesis systems: comparison of current virtual clinical trial methods

Marshall¹,

Bosmans²

2022

Phys. Med. Biol.

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Virtual clinical trials (VCT) have been developed by a number of groups to study breast imaging applications, with the focus on digital breast tomosynthesis (DBT) imaging. In this review, the main components of these simulation platforms are compared, along with the validation steps, a number of practical applications and some of the limitations associated with this method. VCT platforms simulate, up to a certain level of detail, the main components of the imaging chain: the x-ray beam, system geometry including the antiscatter grid and the x-ray detector. In building VCT platforms, groups use a number of techniques, including x-ray spectrum modelling, Monte Carlo (MC) simulation for x-ray imaging and scatter estimation, ray tracing, breast phantom models and modelling of the detector. The incorporation of different anthropomorphic breast models is described, together with the lesions needed to simulate clinical studies and to study detection performance. A step by step comparison highlights the need for transparency when describing the simulation frameworks. Current simulation bottlenecks include resolution and memory constraints when generating high resolution breast phantoms, difficulties in accessing/applying relevant, vendor specific image processing and reconstruction methods, while the imaging tasks considered are generally detection tasks without search, evaluated by computational observers. A number of applications are described along with some future avenues for research.

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The Napoli-Varna-Davis project for virtual clinical trials in X-ray breast imaging

Cited by 10 publications

References 24 publications

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

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

Thermoplastic 3D printing technology using a single filament for producing realistic patient-derived breast models

Performance evaluation of digital breast tomosynthesis systems: comparison of current virtual clinical trial methods

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