Focused Ultrasound for Noninvasive, Focal Pharmacologic Neurointervention

Wang, Jeffrey B.; Ianni, Tommaso Di; Vyas, Daivik B.; Huang, Ziming; Park, Sunmee; Hosseini-Nassab, Niloufar; Aryal, Muna; Airan, Raag D.

doi:10.3389/fnins.2020.00675

Cited by 29 publications

(18 citation statements)

References 139 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested approach may facilitate the development of fUS neuroimaging in any setting where dedicated hardware is not available or in clinical scanners, making this technology more affordable and opening the way to new potential applications based on this imaging modality. Additionally, sparse sequences may prove beneficial in experimental situations where fUS acquisitions need to be interleaved with long therapeutic ultrasound pulses, such as in the monitoring of focused ultrasound neurointerventions [45], [46]. Although in this study we retrospectively under-sampled the compound data, we clearly demonstrate that the network may considerably reduce the beamforming complexity and eliminate the need for computationally demanding filters [15], [16].…”

Section: Discussionmentioning

confidence: 85%

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Ianni

Airan

2022

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an image reconstruction method based on deep learning that significantly reduces the amount of data necessary while retaining imaging performance. We trained convolutional neural networks to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with compression factors of up to 95%. High-quality images from in vivo acquisitions in rats were used for training and performance evaluation. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.

show abstract

Section: Discussionmentioning

confidence: 85%

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Ianni

Airan

2022

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, sparse sequences can prove beneficial in experimental situations where fUS acquisitions need to be interleaved with therapeutic ultrasound pulses, such as in the monitoring of focused ultrasound neurointerventions 40 . Importantly, this method significantly reduces the exposure time and lowers the risk of harmful bioeffects, making brain ultrasound imaging safer 3,17 .…”

Section: Discussionmentioning

confidence: 99%

“…The suggested approach can facilitate the development of fUS neuroimaging in any setting where dedicated hardware is not available or even in clinical scanners, making this technology more affordable and opening the way to new potential applications based on this imaging modality 3,39 . Additionally, sparse sequences can prove beneficial in experimental situations where fUS acquisitions need to be interleaved with therapeutic ultrasound pulses, such as in the monitoring of focused ultrasound neurointerventions 40 . Importantly, this method significantly reduces the exposure time and lowers the risk of harmful bioeffects, making brain ultrasound imaging safer 3,17 .…”

Section: Discussionmentioning

confidence: 99%

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Ianni

Airan

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Functional ultrasound imaging is rapidly establishing itself as a state-of-the-art neuroimaging modality owing to its ability to image neural activation in awake and mobile small animals, its relatively low cost, and its unequaled portability. To achieve high blood flow sensitivity in the brain microvasculature, functional ultrasound relies on long sequences of ultrasound data acquired at high frame rates, which pose high demands on the hardware architecture and effectively limit the practical utility and clinical translation of this imaging modality. Here we propose Deep-fUS, a deep learning approach that aims to significantly reduce the amount of ultrasound data necessary, while retaining the imaging performance. We trained a neural network to learn the power Doppler reconstruction function from sparse sequences of compound ultrasound data, using ground truth images from high-quality in vivo acquisitions in rats, and with a custom loss function. The network produces highly accurate images with restored sensitivity in the smaller blood vessels even when using heavily under-sampled data. We tested the network performance in a task-evoked functional neuroimaging application, demonstrating that time series of power Doppler images can be reconstructed with adequate accuracy to compute functional activity maps. Notably, the network reduces the occurrence of motion artifacts in awake functional ultrasound imaging experiments. The proposed platform can facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or even in clinical scanners, opening the way to new potential applications based on this technology. To our knowledge, this is the first attempt to implement a convolutional neural network approach for power Doppler reconstruction from sparse ultrasonic datasets.

show abstract

“…Through the combined use of FUS to generate oscillation openings and microbubbles—most commonly Optison, Definity, and Sonovue—to transport drugs, medications are more likely to reach their target sites in the brain and have increased efficacy [ 136 ]. Despite these potential benefits, however, it is important to note that FUS-mediated drug delivery presents certain risks that must continue to be investigated, including acute complications such as microhemorrhages and vacuolation of pericytes and other cells at the BBB following sonication, likely due to the temporary BBB disruption [ 129 , 137 ].…”

Section: Mechanisms Of Deliverymentioning

confidence: 99%

Polymeric Nanoparticles in Brain Cancer Therapy: A Review of Current Approaches

et al. 2022

View full text Add to dashboard Cite

Translation of novel therapies for brain cancer into clinical practice is of the utmost importance as primary brain tumors are responsible for more than 200,000 deaths worldwide each year. While many research efforts have been aimed at improving survival rates over the years, prognosis for patients with glioblastoma and other primary brain tumors remains poor. Safely delivering chemotherapeutic drugs and other anti-cancer compounds across the blood–brain barrier and directly to tumor cells is perhaps the greatest challenge in treating brain cancer. Polymeric nanoparticles (NPs) are powerful, highly tunable carrier systems that may be able to overcome those obstacles. Several studies have shown appropriately-constructed polymeric NPs cross the blood–brain barrier, increase drug bioavailability, reduce systemic toxicity, and selectively target central nervous system cancer cells. While no studies relating to their use in treating brain cancer are in clinical trials, there is mounting preclinical evidence that polymeric NPs could be beneficial for brain tumor therapy. This review includes a variety of polymeric NPs and how their associated composition, surface modifications, and method of delivery impact their capacity to improve brain tumor therapy.

show abstract

Focused Ultrasound for Noninvasive, Focal Pharmacologic Neurointervention

Cited by 29 publications

References 139 publications

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Polymeric Nanoparticles in Brain Cancer Therapy: A Review of Current Approaches

Contact Info

Product

Resources

About