Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.
Li ZhouZizhen YangLi GuoQuan ZouHong ZhangShao-Kai SunZhaoxiang YeCai ZhangPublished in: ACS applied materials & interfaces (2024)
Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadolinium-based contrast agents. Herein, we propose a noninvasive method for the assessment of kidney injury by combining structure and function information based on manganese (Mn)-enhanced MRI for the first time. As a proof of concept, the Mn-melanin nanoprobe with good biocompatibility and excellent T1 relaxivity is applied in MRI of a unilateral ureteral obstruction mice model. The abundant renal structure and function information is obtained through qualitative and quantitative analysis of MR images, and a brand new comprehensive assessment framework is proposed to precisely identify the degree of kidney injury successfully. Our study demonstrates that Mn-enhanced MRI is a promising approach for the highly sensitive and biosafe assessment of kidney injury in vivo.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- diffusion weighted
- magnetic resonance
- computed tomography
- diffusion weighted imaging
- artificial intelligence
- deep learning
- machine learning
- big data
- type diabetes
- optical coherence tomography
- high resolution
- dual energy
- convolutional neural network
- room temperature
- insulin resistance
- social media
- metal organic framework
- quantum dots