Particle-Mediated Histotripsy for the Targeted Treatment of Intraluminal Biofilms in Catheter-Based Medical DevicesRead the full article
The open access journal BME Frontiers (BMEF), published in association with SIBET CAS, is a platform for the multidisciplinary community of biomedical engineering, publishing wide-ranging research in the field.
BMEF's editorial board is led by Xingde Li (Johns Hopkins University), Yuguo Tang (Suzhou Institute of Biomedical Engineering and Technology), and Guoqi Zhang (Delft University of Technology) and is comprised of leading experts in the field of biomedical engineering.
Special Issues now open for submissions:
Latest ArticlesMore articles
Noninvasive Low-Intensity Focused Ultrasound Mediates Tissue Protection following Ischemic Stroke
Objective and Impact Statement. This study examined the efficacy and safety of pulsed, low-intensity focused ultrasound (LIFU) and determined its ability to provide neuroprotection in a murine permanent middle cerebral artery occlusion (pMCAO) model. Introduction. Focused ultrasound (FUS) has emerged as a new therapeutic strategy for the treatment of ischemic stroke; however, its nonthrombolytic properties remain ill-defined. Therefore, we examined how LIFU influenced neuroprotection and vascular changes following stroke. Due to the critical role of leptomeningeal anastomoses or pial collateral vessels, in cerebral blood flow restoration and tissue protection following ischemic stroke, we also investigated their growth and remodeling. Methods. Mice were exposed to transcranial LIFU (fundamental frequency: 1.1 MHz, sonication duration: 300 ms, interstimulus interval: 3 s, pulse repetition frequency: 1 kHz, duty cycle per pulse: 50%, and peak negative pressure: -2.0 MPa) for 30 minutes following induction of pMCAO and then evaluated for infarct volume, blood-brain barrier (BBB) disruption, and pial collateral remodeling at 24 hrs post-pMCAO. Results. We found significant neuroprotection in mice exposed to LIFU compared to mock treatment. These findings correlated with a reduced area of IgG deposition in the cerebral cortex, suggesting attenuation of BBB breakdown under LIFU conditions. We also observed increased diameter of CD31-postive microvessels in the ischemic cortex. We observed no significant difference in pial collateral vessel size between FUS and mock treatment at 24 hrs post-pMCAO. Conclusion. Our data suggests that therapeutic use of LIFU may induce protection through microvascular remodeling that is not related to its thrombolytic activity.
High-Frequency 3D Photoacoustic Computed Tomography Using an Optical Microring Resonator
3D photoacoustic computed tomography (3D-PACT) has made great advances in volumetric imaging of biological tissues, with high spatial-temporal resolutions and large penetration depth. The development of 3D-PACT requires high-performance acoustic sensors with a small size, large detection bandwidth, and high sensitivity. In this work, we present a new high-frequency 3D-PACT system that uses a microring resonator (MRR) as the acoustic sensor. The MRR sensor has a size of 80 μm in diameter and was fabricated using the nanoimprint lithography technology. Using the MRR sensor, we have developed a transmission-mode 3D-PACT system that has achieved a detection bandwidth of ~23 MHz, an imaging depth of ~8 mm, a lateral resolution of 114 μm, and an axial resolution of 57 μm. We have demonstrated the 3D PACT’s performance on in vitro phantoms, ex vivo mouse brain, and in vivo mouse ear and tadpole. The MRR-based 3D-PACT system can be a promising tool for structural, functional, and molecular imaging of biological tissues at depths.
Three-Dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array
Objective. To develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source. Impact Statement. The proposed method paves the way for clinical translation of 3D SWE based on the 2D RCA, providing a low-cost and high volume rate solution that is compatible with existing clinical systems. Introduction. SWE is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness, which is significant for a wide range of clinical applications including cancer and liver fibrosis. SWE requires high frame rate imaging for robust shear wave tracking. Due to the technical challenges associated with high volume rate imaging in 3D, current SWE techniques are typically confined to 2D. Advancing SWE from 2D to 3D is significant because of the heterogeneous nature of tissue, which demands 3D imaging for accurate and comprehensive evaluation. Methods. A 3D SWE method using a RCA array was developed with a volume rate up to 2000 Hz. The performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms and in an in vivo case study. Results. 3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed method. Robust 3D shear wave speed maps were reconstructed for phantoms and in vivo. Conclusion. The high volume rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.
Endoscopic Coregistered Ultrasound Imaging and Precision Histotripsy: Initial In Vivo Evaluation
Objective. Initial performance evaluation of a system for simultaneous high-resolution ultrasound imaging and focused mechanical submillimeter histotripsy ablation in rat brains. Impact Statement. This study used a novel combination of high-resolution imaging and histotripsy in an endoscopic form. This would provide neurosurgeons with unprecedented accuracy in targeting and executing nonthermal ablations in minimally invasive surgeries. Introduction. Histotripsy is a safe and effective nonthermal focused ablation technique. However, neurosurgical applications, such as brain tumor ablation, are difficult due to the presence of the skull. Current devices are too large to use in the minimally invasive approaches surgeons prefer. We have developed a combined imaging and histotripsy endoscope to provide neurosurgeons with a new tool for this application. Methods. The histotripsy component had a 10 mm diameter, operating at 6.3 MHz. Affixed within a cutout hole in its center was a 30 MHz ultrasound imaging array. This coregistered pair was used to ablate brain tissue of anesthetized rats while imaging. Histological sections were examined, and qualitative descriptions of ablations and basic shape descriptive statistics were generated. Results. Complete ablations with submillimeter area were produced in seconds, including with a moving device. Ablation progress could be monitored in real time using power Doppler imaging, and B-mode was effective for monitoring post-ablation bleeding. Collateral damage was minimal, with a 100 μm maximum distance of cellular damage from the ablation margin. Conclusion. The results demonstrate a promising hardware suite to enable precision ablations in endoscopic procedures or fundamental preclinical research in histotripsy, neuroscience, and cancer.
Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis
Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.
Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model
Objective and Impact Statement. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location. Introduction. The accurate and automatic localization of anatomical landmarks plays an essential role in medical image analysis. However, recent deep learning-based methods only utilize limited data from a single dataset. It is promising and desirable to build a model learned from different regions which harnesses the power of big data. Methods. Our model consists of a local network and a global network, which capture local features and global features, respectively. The local network is a fully convolutional network built up with depth-wise separable convolutions, and the global network uses dilated convolution to enlarge the receptive field to model global dependencies. Results. We evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical regions. Extensive experimental results show that our model improves the detection accuracy compared to the state-of-the-art methods. Conclusion. Our model makes the first attempt to train a single network on multiple datasets for landmark detection. Experimental results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.