Journal of Diagnostics Concepts & Practice >

2024 , Vol. 23 >Issue 06: 594 - 601

DOI: https://doi.org/10.16150/j.1671-2870.2024.06.006

Study on deep learning reconstruction technology in improving image quality of pituitary neuroendocrine tumors in coronal T1WI magnetic resonance image

  • ZHANG Huihui ,
  • FANG Shu ,
  • WU Mengxiong ,
  • LIU Fangtao ,
  • HE Naying ,
  • DONG Haipeng ,
  • YAN Fuhua
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  • Department of Radiology, Ruijin Hospital, Shanghai Jiao tong University School of Medicine, Shanghai 200025, China

Received date: 2023-02-22

  Online published: 2024-12-25

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Abstract

Objective To investigate the role of thin-layer pituitary T1WI fat suppression (FS) coronal enhanced sequences based on deep learning (DL) reconstruction technology in improving image quality for pituitary neuroendocrine tumors. Methods From June 2023 to June 2024, 46 patients diagnosed or suspected of having pituitary lesions were prospectively and consecutively enrolled, with a total of 40 pituitary neuroendocrine tumor lesions identified. All patients underwent thin-layer pituitary DL T1WI FS coronal enhanced scanning. Original reconstruction (OR) images without DL application were retained, and the images were divided into DL and OR groups according to the reconstruction method. Two neuroradiologists, using a double-blind method, subjectively evaluated (using the five-point Likert scale) the image quality of the two groups in six aspects: uniformity, sharpness, artifact, pituitary structure identification, lesion identification, and overall quality. Objective evaluation included measuring and calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pituitary neuroendocrine tumor and tumor-free area. The differences in image quality scores between the two groups were compared using the Wilcoxon rank-sum test. The intra-class correlation coefficient (ICC) was used to assess the consistency of subjective and objective image measurement results of both doctors. Results The ICC values of subjective and objective image quality scores between the DL and OR groups were all greater than 0.81, indicating extremely high consistency. In terms of subjective image quality evaluation, the image uniformity of the DL and OR groups was 4.33 (3, 5) and 3.73 (3, 4), respectively. Sharpness was 4.25 (3, 5) and 3.50 (3, 4), artifact was 4.35 (4, 5) and 2.95 (2, 4), pituitary structure identification was 4.38 (3, 5) and 3.35 (2, 5), lesion identification was 4.6 (3, 5) and 3.15 (2, 4), and overall quality was 4.30 (4, 5) and 2.63 (2, 3), respectively. The DL group showed higher scores than the OR group, and the differences were statistically significant (all P<0.001). For objective image quality evaluation, the SNRs of pituitary tumors in the DL and OR groups were 26.96 (18.10, 34.15) and 16.51 (11.24, 20.65), respectively, while the CNRs were 11.30 (6.74, 19.72) and 4.34 (3.07, 6.00), respectively. The SNRs of the pituitary in the DL and OR groups were 38.36 (31.93, 47.03) and 17.02 (15.49, 20.51), while the CNRs were 29.89 (23.28, 39.75) and 18.44 (16.61, 24.56), respectively. The DL group had higher scores than the OR group, and the differences were statistically significant (all P<0.001). The subjective and objective indicators evaluated by the two doctors showed good consistency. Conclusions The T1WI FS coronal enhanced sequences based on DL can significantly improve the image quality of pituitary neuroendocrine tumors and enhance the SNR and CNR of the images while ensuring the spatial resolution of the images, providing accurate imaging support for clinical diagnosis and treatment.

Key words： Pituitary; Neuroendocrine tumor; Deep learning; Image reconstruction; Magnetic resonance imaging

Cite this article

ZHANG Huihui , FANG Shu , WU Mengxiong , LIU Fangtao , HE Naying , DONG Haipeng , YAN Fuhua . Study on deep learning reconstruction technology in improving image quality of pituitary neuroendocrine tumors in coronal T1WI magnetic resonance image[J]. Journal of Diagnostics Concepts & Practice, 2024 , 23(06) : 594 -601 . DOI: 10.16150/j.1671-2870.2024.06.006

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