Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

Modelling level I Axillary Lymph Nodes depth for Microwave Imaging.

PURPOSE: Patient-specific information on the depth of Axillary Lymph Nodes (ALNs) is important for the development of new diagnostic imaging technologies, e.g. Microwave Imaging (MWI), aiming to assess the diagnosis of ALNs during breast cancer staging. Studies about ALNs depth have been presented for treatment planning, but they lack information on sample size and usability of the data to infer the depth of ALNs. The aim of this study was to create a mathematical model that can be used to predict a depth interval where level I ALNs are likely to be located. METHODS: We extracted biometric features of 98 patients who underwent breast Magnetic Resonance Imaging (MRI) to train two types of regression models. We then tested different combination of features to predict ALNs depth and found the best predictor. The final prediction models were then implemented in an algorithm used for MWI and tested with anthropomorphic phantoms of the axillary region. RESULTS: Body Mass Index (BMI) was the feature with best performance to predict ALNs depth with coefficient of determination (R2) ranging from 0.49 to 0.55 and Root Mean Squared Error (RMSE) ranging from 0.68 to 0.91 cm. The proposed model showed satisfactory results in microwave images of patients with different BMIs. CONCLUSIONS: The presented results contribute to the development of reconstruction algorithms for new imaging technologies and to the assessment of ALNs in other medical applications.

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis.

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.