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15-02-2022 | Magnetic Resonance

Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method

Authors: Hiroyuki Uetani, Takeshi Nakaura, Mika Kitajima, Kosuke Morita, Kentaro Haraoka, Naoki Shinojima, Machiko Tateishi, Taihei Inoue, Akira Sasao, Akitake Mukasa, Minako Azuma, Osamu Ikeda, Yasuyuki Yamashita, Toshinori Hirai

Published in: European Radiology | Issue 7/2022

Abstract

Objectives

This study aimed to evaluate the efficacy of a combined wavelet and deep-learning reconstruction (DLR) method for under-sampled pituitary MRI.

Methods

This retrospective study included 28 consecutive patients who underwent under-sampled pituitary T2-weighted images (T2WI). Images were reconstructed using either the conventional wavelet denoising method (wavelet method) or the wavelet and DLR methods combined (hybrid DLR method) at five denoising levels. The signal-to-noise ratio (SNR) of the CSF, hypothalamic, and pituitary images and the contrast between structures were compared between the two image types. Noise quality, contrast, sharpness, artifacts, and overall image quality were evaluated by two board-certified radiologists. The quantitative and the qualitative analyses were performed with robust two-way repeated analyses of variance.

Results

Using the hybrid DLR method, the SNR of the CSF progressively increased as denoising levels increased. By contrast, with the wavelet method, the SNR of the CSF, hypothalamus, and pituitary did not increase at higher denoising levels. There was a significant main effect of denoising methods (p < 0.001) and denoising levels (p < 0.001), and an interaction between denoising methods and denoising levels (p < 0.001). For all five qualitative scores, there was a significant main effect of denoising methods (p < 0.001) and an interaction between denoising methods and denoising levels (p < 0.001).

Conclusions

The hybrid DLR method can provide higher image quality for T2WI of the pituitary with compressed sensing (CS) than the wavelet method alone, especially at higher denoising levels.

Key Points

• The signal-to-noise ratios of cerebrospinal fluid progressively increased with the hybrid DLR method, with an increase in the denoising level for cerebrospinal fluid in pituitary T2WI with CS.

• The signal-to-noise ratios of cerebrospinal fluid using the conventional wavelet method did not increase at higher denoising levels.

• All qualitative scores of hybrid deep-learning reconstructions at all denoising levels were higher than those for the wavelet denoising method.

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Title: Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method
Authors: Hiroyuki Uetani
Takeshi Nakaura
Mika Kitajima
Kosuke Morita
Kentaro Haraoka
Naoki Shinojima
Machiko Tateishi
Taihei Inoue
Akira Sasao
Akitake Mukasa
Minako Azuma
Osamu Ikeda
Yasuyuki Yamashita
Toshinori Hirai
Publication date: 15-02-2022
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 7/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08552-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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