Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis

Krüger, Julia; Ehrhardt, Jan; Bischof, Arpad; Handels, Heinz

doi:10.1007/s11548-014-0976-1

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…Computational modeling theories have been used to simulate soft‐tissue biomechanics. Briefly, these theories can be categorized into nonphysical models, mainly statistical deformation or geometrical deformable models, and physical models, which include heuristic (mass–spring or mass–tensor models) and continuum mechanics approaches. Heuristic models are less computationally expensive than continuum mechanical models and can be used to solve time‐dependent mechanical problems in real‐time simulations.…”

Section: Patient‐specific Biomechanical Modelingmentioning

confidence: 99%

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

et al. 2017

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Breast magnetic resonance imaging (MRI) and x-ray mammography are two image modalities widely used for the early detection and diagnosis of breast diseases in women. The combination of these modalities leads to a more accurate diagnosis and treatment of breast diseases. The aim of this paper is to review the registration between breast MRI and x-ray mammographic images using patient-specific finite element-based biomechanical models. Specifically, a biomechanical model is obtained from the patient's MRI volume and is subsequently used to mimic the mammographic acquisition. Due to the different patient positioning and movement restrictions applied in each image modality, the finite element analysis provides a realistic physics-based approach to perform the breast deformation. In contrast with other reviews, we do not only expose the overall process of compression and registration but we also include main ideas, describe challenges, and provide an overview of the used software in each step of the process. Extracting an accurate description from the MR images and preserving the stability during the finite element analysis require an accurate knowledge about the algorithms used, as well as the software and underlying physics. The wide perspective offered makes the paper suitable not only for expert researchers but also for graduate students and clinicians. We also include several medical applications in the paper, with the aim to fill the gap between the engineering and clinical performance.

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Section: Patient‐specific Biomechanical Modelingmentioning

confidence: 99%

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

et al. 2017

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“…The next three contributions [9][10][11] address different aspects of medical image analysis and image-based simulation in the field of computer-assisted diagnostics. In [9], medical image analysis networks are used to quantify changes in language-related brain areas in autism spectrum disorders.…”

Section: Resultsmentioning

confidence: 99%

“…Goch et al mainly focus on assessing the concept of network centrality as a tool to perform brain structure specific analysis. Krüger et al [10] present a method to support multimodal breast diagnostics analyzing 2D mammography and 3D MRI of the female breast. An iterative closest point (ICP)-based B-spline approach is proposed to compensate large differences in breast deformation between mammography and MRI.…”

Section: Resultsmentioning

confidence: 99%

Medical image computing and image-based simulation: recent developments and advances in Germany

et al. 2014

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“…The spiral scan is such that PA data acquired is at equidistant locations. 28,44,48 The advantage of hemispherical detection is that it allows for nearly spatially isotropic resolution in 3D. Figure 3(a) gives a schematic overview of the system geometry.…”

Section: Hemispherical Imaging Systemsmentioning

confidence: 99%

Photoacoustic imaging of breast cancer: a mini review of system design and image features

2019

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Breast cancer is one of the leading causes for cancer related deaths in women, and early detection is extremely important to improve survival rates. Currently, x-ray mammogram is the only modality for mass screening of asymptomatic women. However, it has decreased sensitivity in radiographically dense breasts, which is also associated with a higher risk for breast cancer. Photoacoustic (PA) imaging is an emerging modality that enables deep tissue imaging of optical contrast at ultrasonically defined spatial resolution, which is much higher than that can be achieved in purely optical imaging modalities. Because of high optical absorption from hemoglobin molecules, PA imaging can map out hemo distribution and dynamics in breast tissue and identify malignant lesions based on tumor associated angiogenesis and hypoxia. We review various PA breast imaging systems proposed over the past few years and summarize the PA features of breast cancer identified in these systems.

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Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis

Cited by 9 publications

References 14 publications

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

A step‐by‐step review on patient‐specific biomechanical finite element models for breast MRI to x‐ray mammography registration

Medical image computing and image-based simulation: recent developments and advances in Germany

Photoacoustic imaging of breast cancer: a mini review of system design and image features

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