High Conductivity Saline Nanodroplets-Enhanced Thermoacoustic Imaging for Brain Tumor Detection

Maximally safe neurosurgical resection remains the gold standard for glioblastoma management. Nevertheless, the precise intraoperative localization of deep-seated glioblastoma continues to present substantial clinical challenges. To overcome these limitations, a microwave-induced thermoacoustic imaging (MTAI)-guided detection system was innovatively developed for real-time tumor tracking. This integrated platform synergizes nanosecond-pulsed microwave excitation with ultrasound detection, achieving a high spatiotemporal resolution (∼280 μm) at depths exceeding 5 cm in brain-mimicking phantoms. By employing engineered physiological saline nanodroplets exhibiting dual-enhanced glioblastoma targeting and microwave absorption, the MTAI system enables real-time imaging and localization of deep-seated glioblastoma. This overcomes the current limitations of a low imaging depth in fluorescence imaging and low contrast in ultrasound imaging. The pragmatic viability of this approach was evaluated in living mice with glioblastoma. The results indicate that the approach has the ability to real-time localize glioblastoma in deep tissues and has the potential for improving extent-of-resection metrics in neuro-oncological surgery.

Authors

Institutions

Publication Details

Journal
Langmuir
Published
2025-06-27
DOI
https://doi.org/10.1021/acs.langmuir.5c02011
Primary Topic
Photoacoustic and Ultrasonic Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High Conductivity Saline Nanodroplets-Enhanced Thermoacoustic Imaging for Brain Tumor Detection

Huan Qin, Jia Fu, Shiu Keung Tang, Caixia Wang et al.
Langmuir
Photoacoustic and Ultrasonic Imaging
article

High Conductivity Saline Nanodroplets-Enhanced Thermoacoustic Imaging for Brain Tumor Detection

Huan Qin, Jia Fu, Shiu Keung Tang, Caixia Wang, Xinhua Xu, Xinghua Wang, Xiaoyu Tang, Yu Wang, Lizhang Zeng, Yichao Fu
article en

Abstract

Maximally safe neurosurgical resection remains the gold standard for glioblastoma management. Nevertheless, the precise intraoperative localization of deep-seated glioblastoma continues to present substantial clinical challenges. To overcome these limitations, a microwave-induced thermoacoustic imaging (MTAI)-guided detection system was innovatively developed for real-time tumor tracking. This integrated platform synergizes nanosecond-pulsed microwave excitation with ultrasound detection, achieving a high spatiotemporal resolution (∼280 μm) at depths exceeding 5 cm in brain-mimicking phantoms. By employing engineered physiological saline nanodroplets exhibiting dual-enhanced glioblastoma targeting and microwave absorption, the MTAI system enables real-time imaging and localization of deep-seated glioblastoma. This overcomes the current limitations of a low imaging depth in fluorescence imaging and low contrast in ultrasound imaging. The pragmatic viability of this approach was evaluated in living mice with glioblastoma. The results indicate that the approach has the ability to real-time localize glioblastoma in deep tissues and has the potential for improving extent-of-resection metrics in neuro-oncological surgery.

LangmuirVol. 41(27)
Sun Yat-sen University (CN), South China Normal University (CN), Key Laboratory of Guangdong Province (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), Laser Research Institute (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 86%
Photoacoustic and Ultrasonic Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.