基于深度学习重建方法和去噪方法的胶质瘤多模态磁共振成像评估。

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Acta radiologica Pub Date : 2024-10-01 Epub Date: 2024-09-02 DOI:10.1177/02841851241273114
Jun Sun, Siyao Xu, Yiding Guo, Jinli Ding, Zhizheng Zhuo, Dabiao Zhou, Yaou Liu
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引用次数: 0

摘要

背景:据报道,深度学习重建(DLR)与去噪有可能改善磁共振成像(MRI)的图像质量。多模态磁共振成像是肿瘤检测、手术规划和预后评估的重要无创方法;然而,DLR对多模态胶质瘤成像的影响尚未得到评估:我们评估了 107 名胶质瘤患者(49 名术前患者和 58 名术后患者)的多模态图像。所有图像均采用 DLR 和传统重建方法重建,包括 T1 加权(T1W)、对比度增强 T1W(CE-T1)、T2 加权(T2W)和 T2 液体增强反转恢复(T2-FLAIR)。图像质量通过信噪比(SNR)、对比度与噪声比(CNR)和边缘锐利度进行评估。视觉评估和诊断评估由神经放射科医生盲法进行:与传统的重建图像相比,在T1W、T2W和T2-FLAIR序列中,所有模式的(残留)肿瘤信噪比和DLR图像的肿瘤与白质/灰质的CNR都更高。DLR 图像的视觉评估显示,T2W 对肿瘤的可视性更强,T2-FLAIR 对水肿的可视性更强,CE-T1 对肿瘤和坏死部分的可视性更强,所有模式的伪影更少。使用 DLR 图像可提高术前病例的诊断效率和可信度:结论:胶质瘤多模态磁共振成像重建原型的 DLR 显著提高了图像质量。此外,它还提高了胶质瘤的诊断效率和可信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Background: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed.

Purpose: To assess multi-modal MRI for glioma based on the DLR method.

Material and methods: We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists.

Results: In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images.

Conclusion: DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

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来源期刊
Acta radiologica
Acta radiologica 医学-核医学
CiteScore
2.70
自引率
0.00%
发文量
170
审稿时长
3-8 weeks
期刊介绍: Acta Radiologica publishes articles on all aspects of radiology, from clinical radiology to experimental work. It is known for articles based on experimental work and contrast media research, giving priority to scientific original papers. The distinguished international editorial board also invite review articles, short communications and technical and instrumental notes.
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