A Modelling Framework for Evaluation of 2D-Mammography and Breast Tomosynthesis Systems

Elangovan, Premkumar; Mackenzie, Alistair; Díaz, Oliver; Rashidnasab, Alaleh; Dance, David R.; Young, Kenneth C.; Warren, Lucy M.; Shaheen, Eman; Bosmans, Hilde; Bakić, Predrag R.; Wells, Kevin

doi:10.1007/978-3-642-31271-7_44

Cited by 4 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The corresponding insertion location and local glandularity estimation were then calculated for the DBT 0° projection. Ray tracing was then undertaken on fifteen DBT projections for calculation of primary transmission using the method developed by Elangovan et al 8 , followed by the addition of detector blur using the system modulation transmission function (MTF). The mass was then inserted into the clinical projections and reconstructed into planes using the manufacturer-specific tomosynthesis reconstruction package.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of 3D DLA masses in digital breast tomosynthesis

Rashidnasab

Elangovan

Díaz

et al. 2013

Medical Imaging 2013: Physics of Medical Imaging

Self Cite

View full text Add to dashboard Cite

Digital breast tomosynthesis (DBT) is suggested to have superior performance compared to 2D mammography in terms of cancer visibility, especially in the case of dense breasts. However, the overall performance of tomosynthesis for screening applications, and the manner in which tomosynthesis should be optimally used for screening remains unclear. This motivates the development of software tools that can insert user-defined synthetic pathology of realistic appearance into clinical tomosynthesis images for subsequent use in virtual clinical trials. We present a method for inserting lesions grown using Diffusion Limited Aggregation, previously validated in 2D mammograms, into clinical DBT images. A preliminary pilot study was used to validate the realism of the masses, wherein three readers each viewed 19 cases and rated the realism of the inserted masses. Each case included a simulated mass inserted in the tomosynthesis projections and the counterpart digital 2D mammogram. These results show that masses can be successfully embedded in the tomosynthesis projections and can produce visually authentic DBT images containing synthetic pathology. These results will be used to further optimize the appearance of these masses in DBT for an upcoming validation.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Figure 1(right) shows the steps in insertion of masses into DBT projections using the method developed by Elangovan et al 8 . A voxelised insertion volume of known length, width and thickness (estimated from DICOM header of the raw clinical projection) was created (block 10).…”

Section: Ray Tracing and Mtf Filteringmentioning

confidence: 99%

Simulation of 3D DLA masses in digital breast tomosynthesis

Rashidnasab

Elangovan

Díaz

et al. 2013

Medical Imaging 2013: Physics of Medical Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several groups are dedicated to developing realistic digital breast phantoms (Bakic et al 2002, Bliznakova et al 2003, Li et al 2009, Bakic et al 2011, Chen et al 2011, Pokrajac et al 2012, O'Connor et al 2013. In this work, we used the breast phantoms developed by Bakic et al (Bakic et al 2011), which has been utilized in previous studies involving breast imaging system optimization or evaluation (Kontos et al 2008, Young et al 2009, Bakic et al 2010a, 2010b, Elangovan et al 2012, Young et al 2013. The Bakic phantom creation software simulates the basic breast anatomy components including the skin, adipose tissue, glandular tissue, and Cooper's ligaments.…”

Section: System Simulationmentioning

confidence: 99%

Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study

et al. 2015

View full text Add to dashboard Cite

Due to the limited number of views and limited angular span in digital breast tomosynthesis (DBT), the acquisition geometry design is an important factor that affects the image quality. Therefore, intensive studies have been conducted regarding the optimization of the acquisition geometry. However, different reconstruction algorithms were used in most of the reported studies. Because each type of reconstruction algorithm can provide images with its own image resolution, noise properties and artifact appearance, it is unclear whether the optimal geometries concluded for the DBT system in one study can be generalized to the DBT systems with a reconstruction algorithm different to the one applied in that study. Hence, we investigated the effect of the reconstruction algorithm on the optimization of acquisition geometry parameters through carefully designed simulation studies. Our results show that using various reconstruction algorithms, including the filtered back-projection, the simultaneous algebraic reconstruction technique, the maximum-likelihood method and the total-variation regularized least-square method, gave similar performance trends for the acquisition parameters for detecting lesions. The consistency of system ranking indicates that the choice of the reconstruction algorithm may not be critical for DBT system geometry optimization.

show abstract

“…For studies involving screening populations, an even greater challenge is presented because of ethical issues of repeated radiation exposure of asymptomatic subjects. One possible solution is to conduct virtual trials to evaluate and compare modalities using validated modelling tools (Elangovan et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images

et al. 2014

View full text Add to dashboard Cite

Planar 2D x-ray mammography is generally accepted as the preferred screening technique used for breast cancer detection. Recently, digital breast tomosynthesis (DBT) has been introduced to overcome some of the inherent limitations of conventional planar imaging, and future technological enhancements are expected to result in the introduction of further innovative modalities. However, it is crucial to understand the impact of any new imaging technology or methodology on cancer detection rates and patient recall. Any such assessment conventionally requires large scale clinical trials demanding significant investment in time and resources. The concept of virtual clinical trials and virtual performance assessment may offer a viable alternative to this approach. However, virtual approaches require a collection of specialized modelling tools which can be used to emulate the image acquisition process and simulate images of a quality indistinguishable from their real clinical counterparts. In this paper, we present two image simulation chains constructed using modelling tools that can be used for the evaluation of 2D-mammography and DBT systems. We validate both approaches by comparing simulated images with real images acquired using the system being simulated. A comparison of the contrast-to-noise ratios and image blurring for real and simulated images of test objects shows good agreement ( < 9% error). This suggests that our simulation approach is a promising alternative to conventional physical performance assessment followed by large scale clinical trials.

show abstract

A Modelling Framework for Evaluation of 2D-Mammography and Breast Tomosynthesis Systems

Cited by 4 publications

References 12 publications

Simulation of 3D DLA masses in digital breast tomosynthesis

Simulation of 3D DLA masses in digital breast tomosynthesis

Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study

Development and validation of a modelling framework for simulating 2D-mammography and breast tomosynthesis images

Contact Info

Product

Resources

About