Mr. Yan Qiu Profile

Yan Qiu

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Proceedings Article | 17 March 2008 Paper

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

Yan Qiu, Xuejun Sun, Vasant Manohar, Dmitry Goldgof

Proceedings Volume 6918, 69182F (2008) https://doi.org/10.1117/12.770547

KEYWORDS: Breast, 3D modeling, Mammography, Finite element methods, Data modeling, Magnetic resonance imaging, Chemical elements, Computer simulations, 3D acquisition, Tissues

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Performing regular mammographic screening and comparing corresponding mammograms taken from multiple views or at different times are necessary for early detection and treatment evaluation of breast cancer, which is key to successful treatment. However, mammograms taken at different times are often obtained under different compression, orientation, or body position. A temporal pair of mammograms may vary significantly due to the spatial disparities caused by the variety in acquisition environments, including 3D position of the breast, the amount of pressure applied, etc. Such disparities can be corrected through the process of temporal registration. We propose to use a 3D finite element model for temporal registration of digital mammography. In this paper, we apply patient specific 3D breast model constructed from MRI data of the patient, for cases where lesions are detectable in multiple mammographic views across time. The 3D location of the lesion in the breast model is computed through a breast deformation simulation step presented in our earlier work. Lesion correspondence is established by using a nearest neighbor approach in the uncompressed breast volume. Our experiments show that the use of a 3D finite element model for simulating and analyzing breast deformation contributes to good accuracy when matching suspicious regions in temporal mammograms.

Proceedings Article | 12 May 2004 Paper

Three-dimensional finite element model for lesion correspondence in breast imaging

Yan Qiu, Lihua Li, Dmitry Goldgof, Sudeep Sarkar, Sorin Anton, Robert Clark

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535719

KEYWORDS: 3D modeling, Breast, Finite element methods, Mammography, Magnetic resonance imaging, 3D image processing, X-rays, Mathematical modeling, Tissues, X-ray imaging

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Predicting breast tissue deformation is of great significance in several medical applications such as biopsy, diagnosis, and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately beforehand. Also clinically it is important for combining the information provided by images from several modalities or at different times, for the detection/diagnosis, treatment planning and guidance of interventions. Multi-modality imaging of the breast obtained by X-ray mammography, MRI is thought to be best achieved through some form of data fusion technique. However, images taken by these various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. In these cases some form of spatial transformation of image data from one geometry to another is required such that the tissues are represented in an equivalent configuration. We propose to use a 3D finite element model for lesion correspondence in breast imaging. The novelty of the approach lies in the following facts: (1) Finite element is the most accurate technique for modeling deformable objects such as breast. The physical soundness and mathematical rigor of finite element method ensure the accuracy and reliability of breast modeling that is essential for lesion correspondence. (2) When both MR and mammographic images are available, a subject-specific 3D breast model will be built from MRIs. If only mammography is available, a generic breast model will be used for two-view mammography reading. (3) Incremental contact simulation of breast compression allows accurate capture of breast deformation and ensures the quality of lesion correspondence. (4) Balance between efficiency and accuracy is achieved through adaptive meshing. We have done intensive research based on phantom and patient data.

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