Improving Image Quality by Deconvolution Recovery Filter in Ultrasound Imaging. (January 2023)

• Record Type: Journal Article
• Authors: Pan, Jingwen; Peng, Hu; Han, Zhihui; Hu, Dan; Wang, Yadan; Wang, Yuanguo
• Is Part Of: Ultrasonic imaging. Volume 45:Number 1(2023)
• Page Start: 3
• Page End: 16
• Publication Date: 2023-01
• Subjects: ultrasound imaging -- image quality -- axial resolution -- deconvolution -- frequency filter; Diagnostic ultrasonic imaging -- Methodology -- Periodicals; Ultrasonic testing -- Periodicals; Ultrasonic imaging -- Periodicals; Ultrasonography -- Periodicals; Échographie -- Méthodologie -- Périodiques; Essais par ultrasons -- Périodiques; Imagerie ultrasonore -- Périodiques; 616.07543
• DOI: 10.1177/01617346221141634
• Languages: English
• ISSNs: 0161-7346

Abstract

Due to the advantages of non-radiation and real-time performance, ultrasound imaging is essential in medical imaging. Image quality is affected by the performance of the transducer in an ultrasound imaging system. For example, the bandwidth controls the pulse length, resulting in different axial resolutions. Therefore, a transducer with a large bandwidth helps to improve imaging quality. However, large bandwidths lead to increased system cost and sometimes a loss of sensitivity and lateral resolution in attenuating media. In this paper, a deconvolution recovery method combined with a frequency-domain filtering technique (DRF) is proposed to improve the imaging quality, especially for the axial resolution. In this method, the received low-bandwidth echo signals are converted into high-bandwidth signals, which is similar to the echo signals produced by a high-bandwidth transducer, and the imaging quality is improved. Simulation and experiment results show that, compared with Delay-and-sum (DAS) method, the DRF method improved axial resolution from 0.60 to 0.41 mm in simulation and from 0.62 to 0.47 mm in the tissue-mimicking phantom experiment. The contrast ratio performance is improved to some extent compared with the DAS in experimental and in-vivo images. Besides, the proposed method has the potential to further improve image quality by combining it with adaptive weightings, such as the minimum variance method.