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使用基于物理的生成式人工智能在3T条件下对人脑胶质瘤进行超分辨率钠磁共振成像。

Super-resolution sodium MRI of human gliomas at 3T using physics-based generative artificial intelligence.

作者信息

Raymond Catalina, Yao Jingwen, Kolkovsky Alfredo L Lopez, Feiweier Thorsten, Clifford Bryan, Meyer Heiko, Zhong Xiaodong, Han Fei, Cho Nicholas S, Sanvito Francesco, Oshima Sonoko, Salamon Noriko, Liau Linda M, Patel Kunal S, Everson Richard G, Cloughesy Timothy F, Ellingson Benjamin M

机构信息

UCLA Brain Tumor Imaging Laboratory (BTIL), Departments of Radiological Sciences, Psychiatry, and Neurosurgery, David Geffen School of Medicine, Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.

Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

出版信息

J Neurooncol. 2025 Jun 3. doi: 10.1007/s11060-025-05094-x.


DOI:10.1007/s11060-025-05094-x
PMID:40459830
Abstract

PURPOSE: Sodium neuroimaging provides unique insights into the cellular and metabolic properties of brain tumors. However, at 3T, sodium neuroimaging MRI's low signal-to-noise ratio (SNR) and resolution discourages routine clinical use. We evaluated the recently developed Anatomically constrained GAN using physics-based synthetic MRI artifacts" (ATHENA) for high-resolution sodium neuroimaging of brain tumors at 3T. We hypothesized the model would improve the image quality while preserving the inherent sodium information. METHODS: 4,573 proton MRI scans from 1,390 suspected brain tumor patients were used for training. Sodium and proton MRI datasets from Twenty glioma patients were collected for validation. Twenty-four image-guided biopsies from seven patients were available for sodium-proton exchanger (NHE1) expression evaluation on immunohistochemistry. High-resolution synthetic sodium images were generated using the ATHENA model, then compared to native sodium MRI and NHE1 protein expression from image-guided biopsy samples. RESULTS: The ATHENA produced synthetic-sodium MR with significantly improved SNR (native SNR 18.20 ± 7.04; synthetic SNR 23.83 ± 9.33, P = 0.0079). The synthetic-sodium values were consistent with the native measurements (P = 0.2058), with a strong linear correlation within contrast-enhancing areas of the tumor (R = 0.7565, P = 0.0005), T2-hyperintense (R = 0.7325, P < 0.0001), and necrotic areas (R = 0.7678, P < 0.0001). The synthetic-sodium MR and the relative NHE1 expression from image-guided biopsies were better correlated for the synthetic (ρ = 0.3269, P < 0.0001) than the native (ρ = 0.1732, P = 0.0276) with higher sodium signal in samples expressing elevated NHE1 (P < 0.0001). CONCLUSION: ATHENA generates high-resolution synthetic-sodium MRI at 3T, enabling clinically attainable multinuclear imaging for brain tumors that retain the inherent information from the native sodium. The resulting synthetic sodium significantly correlates with tissue expression, potentially supporting its utility as a non-invasive marker of underlying sodium homeostasis in brain tumors.

摘要

目的:钠神经成像为脑肿瘤的细胞和代谢特性提供了独特的见解。然而,在3T场强下,钠神经成像MRI的低信噪比(SNR)和分辨率阻碍了其在临床中的常规应用。我们评估了最近开发的“基于物理的合成MRI伪影的解剖学约束生成对抗网络”(ATHENA),用于3T场强下脑肿瘤的高分辨率钠神经成像。我们假设该模型将在保留固有钠信息的同时提高图像质量。 方法:使用来自1390例疑似脑肿瘤患者的4573次质子MRI扫描进行训练。收集了20例胶质瘤患者的钠和质子MRI数据集用于验证。来自7例患者的24次图像引导活检可用于免疫组织化学评估钠-质子交换器(NHE1)的表达。使用ATHENA模型生成高分辨率合成钠图像,然后与天然钠MRI以及图像引导活检样本中的NHE1蛋白表达进行比较。 结果:ATHENA生成的合成钠MR的SNR显著提高(天然SNR为18.20±7.04;合成SNR为23.83±9.33,P = 0.0079)。合成钠值与天然测量值一致(P = 0.2058),在肿瘤的对比增强区域(R = 0.7565,P = 0.0005)、T2高信号区域(R = 0.7325,P < 0.0001)和坏死区域(R = 0.7678,P < 0.0001)内具有很强的线性相关性。对于合成图像,合成钠MR与图像引导活检中的相对NHE1表达的相关性更好(ρ = 0.3269,P < 0.0001),高于天然图像(ρ = 0.1732,P = 0.0276),在表达升高的NHE1的样本中钠信号更高(P < 0.0001)。 结论:ATHENA在3T场强下生成高分辨率合成钠MRI,实现了临床上可达到的脑肿瘤多核成像,同时保留了来自天然钠的固有信息。生成的合成钠与组织表达显著相关,可能支持其作为脑肿瘤潜在钠稳态的非侵入性标志物的实用性。

相似文献

[1]
Super-resolution sodium MRI of human gliomas at 3T using physics-based generative artificial intelligence.

J Neurooncol. 2025-6-3

[2]
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.

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[3]
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Cochrane Database Syst Rev. 2018-1-22

[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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本文引用的文献

[1]
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.

AJNR Am J Neuroradiol. 2025-4-2

[2]
Rapid 2D Na MRI of the calf using a denoising convolutional neural network.

Magn Reson Imaging. 2024-7

[3]
Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas.

J Neurooncol. 2023-6

[4]
A Review Paper about Deep Learning for Medical Image Analysis.

Comput Math Methods Med. 2023

[5]
NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics.

Neurooncol Adv. 2022-12-20

[6]
Repeatability of simultaneous 3D H MRF/Na MRI in brain at 7 T.

Sci Rep. 2022-8-19

[7]
Score-based diffusion models for accelerated MRI.

Med Image Anal. 2022-8

[8]
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks.

Tomography. 2022-3-24

[9]
Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

J Digit Imaging. 2022-4

[10]
Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging.

Med Phys. 2022-1

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