Identifying the presence of axillary node and internal mammary node metastases in patients with invasive breast cancer is critical for determining prognosis and for deciding on appropriate treatment. Sentinel lymph node biopsy (SLNB) is the definitive method to exclude axillary metastases. Patients with positive SLNB results generally undergo axillary lymph node dissection (ALND). The benefit of preoperative identification of axillary metastases is that it allows the surgeon to proceed directly to ALND and to avoid an unnecessary SLNB and the need for a second surgical procedure involving the axillary nodes. Knowledge of the important anatomic landmarks of the axilla is important in finding and accurately reporting suspicious lymph nodes. The pathologic features of nodal metastases illuminate the imaging appearances of these nodes, as depicted with all modalities. Ultrasonography (US) is the primary imaging modality for evaluating axillary nodes. Morphologic criteria, such as cortical thickening, hilar effacement, and nonhilar cortical blood flow, are more important than size criteria in the identification of metastases. US-guided lymph node sampling, especially with core biopsy, is invaluable in confirming the presence of a metastasis in a suspicious node. Core biopsy has been shown to be equal in safety to fine needle aspiration and has a significantly lower false-negative rate. Magnetic resonance imaging is also useful, with the added benefit of providing a global view of both axillae. Computed tomography and radionuclide imaging play a lesser role in imaging the axilla. Preoperative image-based identification and sampling of abnormal lymph nodes that have a high positive predictive value for metastases is an extremely important component in the management of patients with invasive breast cancer.

Purpose The purpose of this work is to address the unsolved problem of quantitative susceptibility mapping (QSM) of tissue with fat where both fat and susceptibility change the MR signal phase. Methods The chemical shift of fat was treated as an additional unknown and was estimated jointly with susceptibility to provide the best data fitting using an automated and iterative algorithm. A simplified susceptibility model was used to calculate an updated value of the chemical shift based on the local magnetic field in each iteration. Numerical simulation, phantom experiments and in vivo imaging were performed. Artifacts were assessed by measuring the susceptibility variance in uniform regions. Accuracy was assessed by comparison with ground truth in simulation, and using a susceptibility matching approach in phantom. Results Using the proposed method, artifacts on the QSM image were markedly suppressed in all tested datasets compared to results generated using fixed chemical shifts. Accuracy of the estimated susceptibility was also improved in numerical simulation and phantom experiments. Conclusion A joint estimation of fat content and magnetic susceptibility using an iterative chemical shift update was shown to improve image quality and accuracy on QSM images.

Purpose The purpose of this work is to address the unsolved problem of quantitative susceptibility mapping (QSM) of tissue with fat where both fat and susceptibility change the MR signal phase. Theory and Methods The chemical shift of fat was treated as an additional unknown and was estimated jointly with susceptibility to provide the best data fitting using an automated and iterative algorithm. A simplified susceptibility model was used to calculate an updated value of the chemical shift based on the local magnetic field in each iteration. Numerical simulation, phantom experiments and in vivo imaging were performed. Artifacts were assessed by measuring the susceptibility variance in uniform regions. Accuracy was assessed by comparison with ground truth in simulation, and using a susceptibility matching approach in phantom. Results Using the proposed method, artifacts on the QSM image were markedly suppressed in all tested datasets compared with results generated using fixed chemical shifts. Accuracy of the estimated susceptibility was also improved in numerical simulation and phantom experiments. Conclusion A joint estimation of fat content and magnetic susceptibility using an iterative chemical shift update was shown to improve image quality and accuracy on QSM images. Magn Reson Med 73:2100–2110, 2015. © 2014 Wiley Periodicals, Inc.