Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging
| Autor: | Carl D. Herickhoff, Jeremy J. Dahl, Lotfi Abou-Elkacem, Leandra L. Brickson, Dongwoon Hyun, Rakesh Bam |
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| Rok vydání: | 2020 |
| Předmět: |
Channel (digital image)
Computer science Contrast Media Convolutional neural network Article 030218 nuclear medicine & medical imaging Mice 03 medical and health sciences Deep Learning 0302 clinical medicine Breast cancer medicine Animals Humans Electrical and Electronic Engineering Microvessel Ultrasonography Microbubbles Radiological and Ultrasound Technology Artificial neural network business.industry Deep learning Ultrasound Fundamental frequency medicine.disease Molecular Imaging Computer Science Applications Ultrasonic imaging Artificial intelligence Molecular imaging business Software Biomedical engineering |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2020.2986762 |
| Popis: | Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vivo . The preferred preclinical technique is differential targeted enhancement (DTE), wherein a strong acoustic pulse is used to destroy MBs to verify their locations. However, DTE intrinsically cannot be used for real-time imaging and may cause undesirable bioeffects. In this work, we propose a simple 4-layer convolutional neural network to nondestructively detect adherent MB signatures. We investigated several types of input data to the network: “anatomy-mode” (fundamental frequency), “contrast-mode” (pulse-inversion harmonic frequency), or both, i.e., “dual-mode”, using IQ channel signals, the channel sum, or the channel sum magnitude. Training and evaluation were performed on in vivo mouse tumor data and microvessel phantoms. The dual-mode channel signals yielded optimal performance, achieving a soft Dice coefficient of 0.45 and AUC of 0.91 in two test images. In a volumetric acquisition, the network best detected a breast cancer tumor, resulting in a generalized contrast-to-noise ratio (GCNR) of 0.93 and Kolmogorov-Smirnov statistic (KSS) of 0.86, outperforming both regular contrast mode imaging (GCNR = 0.76, KSS = 0.53) and DTE imaging (GCNR = 0.81, KSS = 0.62). Further development of the methodology is necessary to distinguish free from adherent MBs. These results demonstrate that neural networks can be trained to detect targeted MBs with DTE-like quality using nondestructive dual-mode data, and can be used to facilitate the safe and real-time translation of UMI to clinical applications. |
| Databáze: | OpenAIRE |
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