Towards registration of temporal mammograms by finite element simulation of MR breast volumes

Qiu, Yan; Sun, Xuejun; Manohar, Vasant; Goldgof, Dmitry B.

doi:10.1117/12.770547

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…The MR intensities were converted into linear attenuation coefficients using an approximate histogram-based mapping rule. Similar studies using breast MR phantoms were carried out by Qiu et al (2008) and Sarkar et al (2007). Unfortunately, MR scans image magnetic properties of the tissue, rather than X-ray attenuation values as obtained by CT scans.…”

Section: Previous Worksupporting

confidence: 56%

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

Sompel

Brady

Boone

2011

Medical Image Analysis

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Section: Previous Worksupporting

confidence: 56%

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

Sompel

Brady

Boone

2011

Medical Image Analysis

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“…However, few papers provide the 3D geometrical information of the suspicious lesion in the MR image when they are located in the mammograms. Qiu et al used a 3D FE model to register temporal mammograms. The process consisted of compressing the mechanical model and aligning it with the corresponding mammographic view (CC or MLO).…”

Section: Evaluation Methods and Clinical Applicationsmentioning

confidence: 99%

“…The mechanical toolbox includes support for linear, nonlinear, and dynamic studies. ANSYS TM has been used by Qiu et al, Solves‐Llorens et al and Hipwell et al, to simulate the breast model compression.…”

Section: Patient‐specific Biomechanical Modelingmentioning

confidence: 99%

“…Finite element models have been widely used in various medical applications, including brain, heart, liver, lungs, or prostate imaging, composing a wide bibliography . In breast modeling, they have been used in several challenging problems, such as the colocalization of information between different image modalities, temporal studies, identifying lesions or tumors, tracking of these lesions during biopsy, review of the progress of suspicious lesions or evaluation of the effectiveness of treatments and, even, aiding implant selection for breast augmentation procedures …”

Section: Introductionmentioning

confidence: 99%

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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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“…A subsequent direction we have attempted to explore is in correspondence identification between image features identified in two-view mammography taken at different times [42,43]. We use the 3D finite element model as a tool for simulating and analyzing breast deformation to improve the accuracy of matching in temporal mammograms [44].…”

mentioning

confidence: 99%

Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration

Goldgof¹

2008

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Towards registration of temporal mammograms by finite element simulation of MR breast volumes

Cited by 6 publications

References 13 publications

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

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

Accurate 3D Modeling of Breast Deformation for Temporal Mammogram Registration

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