Currently, High Field (1.5T) Superconducting MR image-guided needle breast procedures allow the physician only to calculate approximately the location and extent of a cancerous tumor in the compressed patient breast before inserting the needle. It can then become relatively uncertain that the tissue specimen removed during the biopsy actually belongs to the lesion of interest. A new method for guiding clinical breast biopsy is presented, based on a deformable finite element model of the breast. The geometry of the model is constructed from MR data, and its mechanical properties are modeled using a non-linear material model. This method allows imaging the breast without compression before the procedure, then compressing the breast and using the finite element model to predict the tumor's position during the procedure. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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In the near future, investigation and refinement of emerging anatomic and functional breast imaging techniques will enable clinical trials that will evaluate their utility and potential for improving the survival and quality of life for patients with breast cancer. In the longer term, strategic research collaborations among investigators in the fields of functional imaging, molecular biology, and pathology are needed to merge existing science and advance the development of biomarker and genetic techniques focused on detecting and characterizing disease at the cellular and molecular levels. This research could create clinical tools for (a) detecting breast cancer earlier, (b) more accurately quantifying the extent of disease, (c) noninvasively evaluating lymph node involvement, (d) identifying micrometastases and residual microscopic disease, and (e) enhancing therapy by means of imaging-guided biomarker or tumor-specific delivery of pharmacologic, chemosensitizing, or radiosensitizing agents to tumors.

Key Words: high risk, screening S ignificant advances have been made in identifying women at high risk for developing breast cancer. The most notable is the discovery of the BRCA-1 and BRCA-2 genes. For women carrying the BRCA mutations, approximately 80% have developed the disease by age 70 years (1,2). However, to date, it appears that a minority of breast cancers occur in women with known mutations and that, in fact, women at increased risk for breast cancer comprise a diverse population. Because genetic testing is only available for the known mutations ( BRCA-1 and 2 ), the majority of the actual hereditary breast cancers seemingly occur in "mutation-negative" patients. For the same reason, a negative mutational analysis cannot be used to rule out familial breast cancer. Instead, personal and family history are used for risk assessment.In addition to the actual "hereditary" breast cancers, where a single malfunctioning tumor suppressor gene facilitates breast cancer genesis secondary to a mutation of a single gene locus, it is well known that a strong family history of breast cancer increases the risk for developing nonhereditary, sporadic breast cancer. In fact, family history has long been identified as the major risk factor for developing sporadic breast cancer. It is hypothesized that cancer genesis in sporadic breast cancer is mediated by a multifactorial process that includes several different genes. Thus women with no known mutations but with strong family histories are known to be at increased risk for breast cancer. For example, women with two firstdegree relatives with breast cancer have a more than fourfold increased risk compared to women with no family history (3).Women with a personal history of breast cancer are at increased risk. These women have a two-to sixfold increased risk of developing breast cancer in the contralateral breast than women in the general population have of developing a first breast cancer (4 -6). In addition, women with a history of therapeutic chest radiation have a significantly increased risk of developing breast cancer. Breast cancers have been reported at relatively high rates in women treated with radiation for Hodgkin's disease as early as 10 years after treatment (7).We know how to identify many of these women at high risk, but we have not known how best to care for them. In 1996 a task force was convened by the Cancer Genetics Studies Consortium, sponsored by the National Human Genome Research Institute, in order to identify appropriate recommendations for individuals found to have an inherited predisposition to cancer. After careful review of the relevant published literature, the task force published the following provisional guidelines for BRCA-1 and BRCA-2 mutation carriers and for those with a family history compatible with hereditary breast cancer: monthly breast self-examinations beginning at age 18 to 21 years, annual or semiannual clinical breast examinations, and annual mammography beginning between age 25 and 35 years (8). The task force cautioned that ...

Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T1 map estimation (E-Step) and the step of T1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T1 map can be noisy due to the complexity of T1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement. Keywords ABSTRACTBreast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.

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