Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice

Takahiro Ueda; Yoshiharu Ohno; Kaori Yamamoto; Akiyoshi Iwase; Takashi Fukuba; Satomu Hanamatsu; Yuki Obama; Hirotaka Ikeda; Masato Ikedo; Masao Yui; Kazuhiro Murayama; Hiroshi Toyama

doi:10.1016/j.ejrad.2020.109430

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice

Eur J Radiol. 2021 Jan:134:109430. doi: 10.1016/j.ejrad.2020.109430. Epub 2020 Nov 21.

Authors

Takahiro Ueda¹, Yoshiharu Ohno², Kaori Yamamoto³, Akiyoshi Iwase⁴, Takashi Fukuba⁵, Satomu Hanamatsu⁶, Yuki Obama⁷, Hirotaka Ikeda⁸, Masato Ikedo⁹, Masao Yui¹⁰, Kazuhiro Murayama¹¹, Hiroshi Toyama¹²

Affiliations

¹ Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
² Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan; Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
³ Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
⁴ Department of Radiology, Fujita Health University Hospital, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
⁵ Department of Radiology, Fujita Health University Hospital, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
⁶ Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
⁷ Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
⁸ Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
⁹ Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
¹⁰ Canon Medical Systems Corporation, 1385, Shimoishigami, Otawara, Tochigi, 324-0036, Japan. Electronic address: [email protected].
¹¹ Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].
¹² Department of Radiology, Fujita Health University, School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. Electronic address: [email protected].

PMID: 33276249
DOI: 10.1016/j.ejrad.2020.109430

Abstract

Purpose: To demonstrate the utility of compressed sensing with parallel imaging (Compressed SPEEDER) and AiCE compared with that of conventional parallel imaging (SPEEDER) for shortening examination time and improving image quality of women's pelvic MRI.

Method: Thirty consecutive patients with women's pelvic diseases (mean age 50 years) underwent T2-weighted imaging using Compressed SPEEDER as well as conventional SPEEDER reconstructed with and without AiCE. The examination times were recorded, and signal-to-noise ratio (SNR) was calculated for every patient. Moreover, overall image quality was assessed using a 5-point scoring system, and final scores for all patients were determined by consensus of two readers. Mean examination time, SNR and overall image quality were compared among the four data sets by Wilcoxon signed-rank test.

Results: Examination times for Compressed SPEEDER with and without AiCE were significantly shorter than those for conventional SPEEDER with and without AiCE (with AiCE: p < 0.0001, without AiCE: p < 0.0001). SNR of Compressed SPEEDER and of SPEEDER with AiCE was significantly superior to that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER, p = 0.01; vs. SPEEDER, p = 0.009). Overall image quality of Compressed SPEEDER with AiCE and of SPEEDER with and without AiCE was significantly higher than that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER with AiCE, p < 0.0001; vs. SPEEDER with AiCE, p < 0.0001; SPEEDER without AiCE, p = 0.0003).

Conclusion: Image quality and shorten examination time for T2-weighted imaging in women's pelvic MRI can be significantly improved by using Compressed SPEEDER with AiCE in comparison with conventional SPEEDER, although other sequences were not tested.

Keywords: Compressed sensing; Deep learning; MRI; Parallel imaging; Women’s imaging.

MeSH terms

Deep Learning*
Female
Humans
Magnetic Resonance Imaging
Middle Aged
Radiography
Signal-To-Noise Ratio