Dual-Energy Computed Tomography for Evaluation of Breast Cancer Follow-Ups: Comparison of Virtual Monoenergetic Images and Iodine-Map.
Jun-Xian LiFeng-Ji XieChia-Hui ChenKuan-Ming ChenChia-Jung TsaiPublished in: Diagnostics (Basel, Switzerland) (2022)
Differentiating tumor tissue from dense breast tissue can be difficult. Dual-energy CT (DECT) could be suitable for making diagnoses at breast cancer follow-ups. This study investigated the contrast in DECT images and iodine maps for patients with breast cancer being followed-up. Chest CT images captured in 2019 were collected. Five cases of metastatic breast cancer in the lungs were analyzed; the contrast-to-noise ratio (for breast tissue and muscle: CNR b and CNR m , respectively), tumor-to-breast mammary gland ratio (T/B), and tumor-to-muscle ratio (T/M) were calculated. For 84 cases of no metastasis, monochromatic spectral and iodine maps were obtained to compare differences under various breast densities using the K-means algorithm. The optimal T/B, T/M, and CNR b (related to mammary glands) were achieved for the 40-keV image. Conversely, CNR m (related to lungs) was better for higher energy. The optimal balance was achieved at 80 keV. T/B, T/M, and CNR were excellent for iodine maps, particularly for density > 25%. In conclusion, energy of 80 keV is the parameter most suitable for observing the breast and lungs simultaneously by using monochromatic spectral images. Adding iodine mapping can be appropriate when a patient's breast density is greater than 25%.
Keyphrases
- dual energy
- computed tomography
- deep learning
- contrast enhanced
- image quality
- positron emission tomography
- convolutional neural network
- optical coherence tomography
- magnetic resonance imaging
- magnetic resonance
- skeletal muscle
- metastatic breast cancer
- high resolution
- young adults
- atomic force microscopy
- single molecule