Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.

Autor: Fujima, Noriyuki, Nakagawa, Junichi, Kameda, Hiroyuki, Ikebe, Yohei, Harada, Taisuke, Shimizu, Yukie, Tsushima, Nayuta, Kano, Satoshi, Homma, Akihiro, Kwon, Jihun, Yoneyama, Masami, Kudo, Kohsuke
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Zdroj: MAGMA: Magnetic Resonance Materials in Physics, Biology & Medicine; Jul2024, Vol. 37 Issue 3, p439-447, 9p
Abstrakt: Objectives: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). Materials and methods: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. Results: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). Discussion: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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