Integrating prior information into microwave tomography part 2: Impact of errors in prior information on microwave tomography image quality. Issue 12 (23rd October 2017)

• Record Type: Journal Article
• Authors: Kurrant, Douglas; Fear, Elise; Baran, Anastasia; LoVetri, Joe
• Is Part Of: Medical physics. Volume 44:Issue 12(2017)
• Page Start: 6482
• Page End: 6503
• Publication Date: 2017-10-23
• Subjects: breast cancer -- microwave tomography -- performance metrics -- prior information; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12584
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The authors have developed a method to combine a patient‐specific map of tissue structure and average dielectric properties with microwave tomography. The patient‐specific map is acquired with radar‐based techniques and serves as prior information for microwave tomography. The impact that the degree of structural detail included in this prior information has on image quality was reported in a previous investigation. The aim of the present study is to extend this previous work by identifying and quantifying the impact that errors in the prior information have on image quality, including the reconstruction of internal structures and lesions embedded in fibroglandular tissue. This study also extends the work of others reported in literature by emulating a clinical setting with a set of experiments that incorporate heterogeneity into both the breast interior and glandular region, as well as prior information related to both fat and glandular structures. Methods: Patient‐specific structural information is acquired using radar‐based methods that form a regional map of the breast. Errors are introduced to create a discrepancy in the geometry and electrical properties between the regional map and the model used to generate the data. This permits the impact that errors in the prior information have on image quality to be evaluated. Image quality is quantitatively assessed by measuring the ability of the algorithm to reconstruct both internal structures and lesions embedded in fibroglandular tissue. The study is conducted using both 2D and 3D numerical breast models constructed from MRI scans. Results: The reconstruction results demonstrate robustness of the method relative to errors in the dielectric properties of the background regional map, and to misalignment errors. These errors do not significantly influence the reconstruction accuracy of the underlying structures, or the ability of the algorithm to reconstruct malignant tissue. Although misalignment errors do not significantly impact the quality of the reconstructed fat and glandular structures for the 3D scenarios, the dielectric properties are reconstructed less accurately within the glandular structure for these cases relative to the 2D cases. However, general agreement between the 2D and 3D results was found. Conclusion: A key contribution of this paper is the detailed analysis of the impact of prior information errors on the reconstruction accuracy and ability to detect tumors. The results support the utility of acquiring patient‐specific information with radar‐based techniques and incorporating this information into MWT. The method is robust to errors in the dielectric properties of the background regional map, and to misalignment errors. Completion of this analysis is an important step toward developing the method into a practical diagnostic tool.