A 3D surface coil with deep learning-based noise reduction for parotid gland imaging at 7T

Abstract

Background

Background: Parotid gland neoplasms occur near the facial nerve. Hence, it is crucial to determine whether the malignant neoplasms involve the facial nerve and whether sacrifice of the nerve in surgery is necessary. Furthermore, while 20% of all neoplasms are malignant, the most common benign neoplasm, pleomorphic adenoma, has a risk for malignant transformation, making early detection and treatment essential. 7T magnetic resonance imaging offers increased signal-to-noise ratio (SNR) and sensitivity.

Aim

In this work, we address imaging the parotid gland since it remains challenging at 7T because of its spatial location.

Materials and Methods

Here, we present a novel three-dimensional surface coil (3D Coil) architecture that offers increased depth penetration and SNR compared to the single channel surface coil. We further developed a deep learning (DL)-based noise reduction method that receives inputs from three elements of the 3D Coil.

Results

The 3D coil with DL-based denoising method offers twice the SNR compared to the single channel surface coil for parotid gland imaging at 7T.

Discussion and Conclusion

The proposed 3D Coil and DL-based noise reduction method offers a promising way of achieving higher SNR for parotid salivary gland imaging at 7T, paving the road for clinical applications.