6 research outputs found
Imaging dynamics beneath turbid media via parallelized single-photon detection
Noninvasive optical imaging through dynamic scattering media has numerous
important biomedical applications but still remains a challenging task. While
standard methods aim to form images based upon optical absorption or
fluorescent emission, it is also well-established that the temporal correlation
of scattered coherent light diffuses through tissue much like optical
intensity. Few works to date, however, have aimed to experimentally measure and
process such data to demonstrate deep-tissue imaging of decorrelation dynamics.
In this work, we take advantage of a single-photon avalanche diode (SPAD) array
camera, with over one thousand detectors, to simultaneously detect speckle
fluctuations at the single-photon level from 12 different phantom tissue
surface locations delivered via a customized fiber bundle array. We then apply
a deep neural network to convert the acquired single-photon measurements into
video of scattering dynamics beneath rapidly decorrelating liquid tissue
phantoms. We demonstrate the ability to record video of dynamic events
occurring 5-8 mm beneath a decorrelating tissue phantom with mm-scale
resolution and at a 2.5-10 Hz frame rate
Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding
Fast noninvasive probing of spatially varying decorrelating events, such as
cerebral blood flow beneath the human skull, is an essential task in various
scientific and clinical settings. One of the primary optical techniques used is
diffuse correlation spectroscopy (DCS), whose classical implementation uses a
single or few single-photon detectors, resulting in poor spatial localization
accuracy and relatively low temporal resolution. Here, we propose a technique
termed Classifying Rapid decorrelation Events via Parallelized single photon
dEtection (CREPE)}, a new form of DCS that can probe and classify different
decorrelating movements hidden underneath turbid volume with high sensitivity
using parallelized speckle detection from a pixel SPAD array. We
evaluate our setup by classifying different spatiotemporal-decorrelating
patterns hidden beneath a 5mm tissue-like phantom made with rapidly
decorrelating dynamic scattering media. Twelve multi-mode fibers are used to
collect scattered light from different positions on the surface of the tissue
phantom. To validate our setup, we generate perturbed decorrelation patterns by
both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as
well as a vessel phantom containing flowing fluid. Along with a deep
contrastive learning algorithm that outperforms classic unsupervised learning
methods, we demonstrate our approach can accurately detect and classify
different transient decorrelation events (happening in 0.1-0.4s) underneath
turbid scattering media, without any data labeling. This has the potential to
be applied to noninvasively monitor deep tissue motion patterns, for example
identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates
within a compact and static detection probe.Comment: Journal submissio
Myofibrillar Lattice Remodeling Is a Structural Cytoskeletal Predictor of Diaphragm Muscle Weakness in a Fibrotic mdx (mdx Cmah-/-) Model.
Duchenne muscular dystrophy (DMD) is a degenerative genetic myopathy characterized by complete absence of dystrophin. Although the mdx mouse lacks dystrophin, its phenotype is milder compared to DMD patients. The incorporation of a null mutation in the Cmah gene led to a more DMD-like phenotype (i.e., more fibrosis). Although fibrosis is thought to be the major determinant of 'structural weakness', intracellular remodeling of myofibrillar geometry was shown to be a major cellular determinant thereof. To dissect the respective contribution to muscle weakness, we assessed biomechanics and extra- and intracellular architecture of whole muscle and single fibers from extensor digitorum longus (EDL) and diaphragm. Despite increased collagen contents in both muscles, passive stiffness in mdx Cmah-/- diaphragm was similar to wt mice (EDL muscles were twice as stiff). Isometric twitch and tetanic stresses were 50% reduced in mdx Cmah-/- diaphragm (15% in EDL). Myofibrillar architecture was severely compromised in mdx Cmah-/- single fibers of both muscle types, but more pronounced in diaphragm. Our results show that the mdx Cmah-/- genotype reproduces DMD-like fibrosis but is not associated with changes in passive visco-elastic muscle stiffness. Furthermore, detriments in active isometric force are compatible with the pronounced myofibrillar disarray of the dystrophic background
Imaging Dynamics Beneath Turbid Media via Parallelized Single-Photon Detection
Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1–0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels
Transient Motion Classification Through Turbid Volumes via Parallelized Single-Photon Detection and Deep Contrastive Embedding
Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE), a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a 32 × 32 pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5 mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1–0.4 s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to non-invasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe