Portable, field-based neuroimaging using high-density diffuse optical tomography

Fishell, Andrew K.; Arbeláez, Ana María; Valdés, Claudia P.; Burns‐Yocum, Tracy M.; Sherafati, Arefeh; Richter, Edward J.; Torres, Margarita; Eggebrecht, Adam T.; Smyser, Christopher D.; Culver, Joseph P.

doi:10.1016/j.neuroimage.2020.116541

Cited by 32 publications

(34 citation statements)

References 68 publications

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“…While fMRI has become a gold standard for cognitive neuroimaging, it is contraindicated in subjects with metal implants and cannot be used in many clinical settings, and studies seeking more naturalistic imaging environments. In contrast, functional near‐infrared spectroscopy (fNIRS)‐based methods are portable, suitable for naturalistic imaging, and not contraindicated in subjects with electronic or metal implants (Dashtestani et al, 2018; Dashtestani et al, 2019; Farzam et al, 2017; Fishell et al, 2016; Fishell et al, 2020; Fishell, Burns‐Yocum, Bergonzi, Eggebrecht, & Culver, 2019; Franceschini & Boas, 2004; Khaksari et al, 2019; Liao et al, 2012; Lloyd‐Fox, Blasi, & Elwell, 2010; Morishita et al, 2016; Saliba, Bortfeld, Levitin, & Oghalai, 2016; Salsabilian et al, 2019; Salsabilian et al, 2020). Sparse fNIRS imaging arrays yield poor resolution and low image quality.…”

Section: Introductionmentioning

confidence: 99%

Global motion detection and censoring in high‐density diffuse optical tomography

Sherafati

Snyder

Eggebrecht

et al. 2020

Human Brain Mapping

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Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography (HD‐DOT) with hundreds to thousands of source‐detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near‐infrared spectroscopy (fNIRS). This limitation restricts the application of HD‐DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation‐based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.

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Section: Introductionmentioning

confidence: 99%

Global motion detection and censoring in high‐density diffuse optical tomography

Sherafati

Snyder

Eggebrecht

et al. 2020

Human Brain Mapping

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“…The main advantage of using sparse sequences is the net reduction in processing time and computational capacity demands. 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 .…”

Section: Discussionmentioning

confidence: 99%

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

Ianni

Airan

2020

Preprint

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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.

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“…Importantly, the recently reported approach showed that recovery of parameters in a multi-wavelength time-resolved data can be achieved without prior instrument calibration [47]. Other notable NIR innovations in instrumental methods for spectral analysis during the review period includes a dual detection channel instrument for NIR time-resolved diffuse optical spectroscopy [48], functional NIR spectroscopy (fNIRS) instruments for high-density diffuse optical tomography [49,50], and other NIR-based sensors [51][52][53][54][55].…”

Section: Portable Nir Sensorsmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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Portable, field-based neuroimaging using high-density diffuse optical tomography

Cited by 32 publications

References 68 publications

Global motion detection and censoring in high‐density diffuse optical tomography

Global motion detection and censoring in high‐density diffuse optical tomography

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

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

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