Rebecca Axelsson1,2, Hanna Tomic1, Sophia Zackrisson2, Anders Tingberg1, Hanna Isaksson3, Predrag R Bakic1,2,4, Magnus Dustler1,2. 1. Lund University, Skåne University Hospital, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden. 2. Lund University, Skåne University Hospital, Diagnostic Radiology, Department of Translational Medicine, Department in Imaging and Functional Medicine, Malmö, Sweden. 3. Lund University, Department of Biomedical Engineering, Lund, Sweden. 4. University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States.
Abstract
Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions. Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated. Results: The average stress varied 6.2-6.5 kPa over the breast surface and 7.8-11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface. Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.
Purpose: Malignant breast lesions can be distinguished from benign lesions by their mechanical properties. This has been utilized for mechanical imaging in which the stress distribution over the breast is measured. Mechanical imaging has shown the ability to identify benign or normal cases and to reduce the number of false positives from mammography screening. Our aim was to develop a model of mechanical imaging acquisition for simulation purposes. To that end, we simulated mammographic compression of a computer model of breast anatomy and lesions. Approach: The breast compression was modeled using the finite element method. Two finite element breast models of different sizes were used and solved using linear elastic material properties in open-source virtual clinical trial (VCT) software. A spherical lesion (15 mm in diameter) was inserted into the breasts, and both the location and stiffness of the lesion were varied extensively. The average stress over the breast and the average stress at the lesion location, as well as the relative mean pressure over lesion area (RMPA), were calculated. Results: The average stress varied 6.2-6.5 kPa over the breast surface and 7.8-11.4 kPa over the lesion, for different lesion locations and stiffnesses. These stresses correspond to an RMPA of 0.80 to 1.46. The average stress was 20% to 50% higher at the lesion location compared with the average stress over the entire breast surface. Conclusions: The average stress over the breast and the lesion location corresponded well to clinical measurements. The proposed model can be used in VCTs for evaluation and optimization of mechanical imaging screening strategies.
Authors: Per Skaane; Andriy I Bandos; Randi Gullien; Ellen B Eben; Ulrika Ekseth; Unni Haakenaasen; Mina Izadi; Ingvild N Jebsen; Gunnar Jahr; Mona Krager; Loren T Niklason; Solveig Hofvind; David Gur Journal: Radiology Date: 2013-01-07 Impact factor: 11.105
Authors: Vladimir Egorov; Thomas Kearney; Stanley B Pollak; Chand Rohatgi; Noune Sarvazyan; Suren Airapetian; Stephanie Browning; Armen Sarvazyan Journal: Breast Cancer Res Treat Date: 2009-03-21 Impact factor: 4.872