Diffuse correlation spectroscopy (DCS) has emerged as a versatile, non-invasive method for deep tissue perfusion assessment using near-infrared light. However, in its standard form DCS suffers from significant signal-to-noise limitations. This has spurred a number of hardware developments seeking to address these SNR limitations and increase the quality and fidelity of measurements to support translation to use in both neuroscience and clinical applications. This talk will review a number of approaches being developed by our group including pathlength selective methods, long wavelength operation and the use of novel detector types.
KEYWORDS: Tumors, Breast, Digital breast tomosynthesis, Biomedical optics, Mammography, Tissues, Hemodynamics, Chemotherapy, Breast cancer, Magnetic resonance imaging
SignificanceAchieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes.AimWe aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT.ApproachTwenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers.ResultsNormalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (p=0.042). In addition, the change in normalized tumor StO2 upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (p=0.038). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI.ConclusionsThese findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.
Recently we developed the open-source FlexNIRS: a battery-operated, wireless, wearable oximeter whose self-calibrating geometry allows measurements of oxygen saturation in tissue. The first implementation of the device operating at 100 Hz has been validated and is enrolled in several measurement campaigns across different research laboratories. A recent firmware upgrade provides 266 Hz sampling rate, and hardware modifications provide improved form factor, wearability, and multi-modal acquisition. The new version is currently adopted in multiple clinical measurement campaigns focusing on pulsatile component analysis. We will present the instrument performance, its recent and future upgrades, and the applications where the device is currently in use.
Diffuse correlation spectroscopy (DCS) is an optical technique which is used to estimate blood flow in tissue through the analysis of the temporal fluctuations in light intensity. Recently, the development of interferometric techniques (iDCS/iDWS), have allowed for drastic improvement in measurement SNR. In this work, we build upon the iDCS technique by combining it with another advanced DCS modality, time-domain DCS (TD-DCS). This combination allows for the application of pathlength specific coherent gain, which has the potential to further improve the performance of DCS in the non-invasive measurement of deep tissue blood flow.
Interferometric diffuse correlation spectroscopy (iDCS) is an emerging technique that enables high quality measurements of cerebral blood flow. By including a reference arm in the optical setup, the SNR of the measured signals are improved relative to traditional DCS. We report here an expansion of our previously demonstrated 1064 nm single channel line scan camera based iDCS system to a multi-source, multiple detection channel approach to enable imaging of the brain perfusion response to functional activation. We confirm the ability to image multiple detectors on a single camera with minimal cross-talk using phantom experiments and show initial functional imaging results.
Oncologic surgery can greatly benefit from imaging techniques for the accurate identification of tumor-positive margins both intraoperatively and in resection specimens immediately following surgery. We have demonstrated clinically that fluorescence lifetime can significantly improve the accuracy for tumor vs. normal classification compared to fluorescence intensity in multiple cancer types using tumor targeted agents. Ongoing efforts by our group towards the translation of fluorescence lifetime imaging for intraoperative image guidance using exogenous agents will also be discussed.
Peripheral edema, also known as leg swelling, is observed frequently because of various causes such as sitting or standing for a long time, inflammation, injury or diseases in venous circulation system, lymphatic system, kidney and heart. It is also a side effect of chemotherapy or hormonal therapy. But to our knowledge, there is no wearable optical monitor that can quantify changes in the tissue water content related to edema. We have conducted simulations on the minimal number of light source-detector pairs and the wavelengths of the sources that can measure changes in the water fraction in superficial tissues within a compact form factor wearable optical sensor using continuous wave near-infrared spectroscopy (CW NIRS). The wavelength range we have investigated is from 800 to 1100 nm. We will present the results of simulations under various device configurations.
Functional near-infrared spectroscopy (fNIRS) and diffuse correlation spectroscopy (DCS) have shown promise as non-invasive optical methods for cerebral functional imaging. Both approaches currently have limits to sensitivity in adults. Sensitivity can be improved using temporal discrimination, where the laser excitation is of short (~400ps) duration and the detector rejects early photons that have not penetrated into the brain while maintain high sensitivity to those that have. We report here further demonstration of a high-speed Read-Out Integrated Circuit (ROIC) that integrates with a 32x32 Single-Photon Avalanche photo-Detector (SPAD) array that can be either silicon (Si, for visible to infra-red) in indium-phosphide (InP, to allow operation at 1064nm). Data is exfiltrated serially directly to an FPGA where it can be processed in real time. This presentation will include results of recent detector performance tests and phantom demonstrations using this powerful new tool.
Speckle contrast optical spectroscopy (SCOS) is an emerging camera-based technique that can measure human cerebral blood flow (CBF) noninvasively with high signal-to-noise ratio (SNR). A noise correction procedure has previously been developed to improve SCOS measurement accuracy, which requires precise characterization of camera properties. Here, we provide guidance on choosing and characterizing a camera for SCOS, considering factors such as linearity, read noise, and gain. We then validate a noise-corrected SCOS measurement of flow changes in a liquid phantom against diffuse correlation spectroscopy (DCS).
KEYWORDS: Speckle, Monte Carlo methods, Sensors, Cameras, Pulsed laser operation, Light sources and illumination, Neurophotonics, Tissues, Signal to noise ratio, Cerebral blood flow
SignificanceThe non-invasive measurement of cerebral blood flow based on diffuse optical techniques has seen increased interest as a research tool for cerebral perfusion monitoring in critical care and functional brain imaging. Diffuse correlation spectroscopy (DCS) and speckle contrast optical spectroscopy (SCOS) are two such techniques that measure complementary aspects of the fluctuating intensity signal, with DCS quantifying the temporal fluctuations of the signal and SCOS quantifying the spatial blurring of a speckle pattern. With the increasing interest in the use of these techniques, a thorough comparison would inform new adopters of the benefits of each technique.AimWe systematically evaluate the performance of DCS and SCOS for the measurement of cerebral blood flow.ApproachMonte Carlo simulations of dynamic light scattering in an MRI-derived head model were performed. For both DCS and SCOS, estimates of sensitivity to cerebral blood flow changes, coefficient of variation of the measured blood flow, and the contrast-to-noise ratio of the measurement to the cerebral perfusion signal were calculated. By varying complementary aspects of data collection between the two methods, we investigated the performance benefits of different measurement strategies, including altering the number of modes per optical detector, the integration time/fitting time of the speckle measurement, and the laser source delivery strategy.ResultsThrough comparison across these metrics with simulated detectors having realistic noise properties, we determine several guiding principles for the optimization of these techniques and report the performance comparison between the two over a range of measurement properties and tissue geometries. We find that SCOS outperforms DCS in terms of contrast-to-noise ratio for the cerebral blood flow signal in the ideal case simulated here but note that SCOS requires careful experimental calibrations to ensure accurate measurements of cerebral blood flow.ConclusionWe provide design principles by which to evaluate the development of DCS and SCOS systems for their use in the measurement of cerebral blood flow.
KEYWORDS: Head, Simulations, Data modeling, Databases, Tissues, Monte Carlo methods, Sensors, Magnetic resonance imaging, Near infrared spectroscopy, Spectroscopy
SignificanceMonte Carlo (MC) simulations are currently the gold standard in the near-infrared and diffuse correlation spectroscopy (NIRS/DCS) communities for generating light transport paths through tissue. However, realistic and diverse models that capture complex tissue layers are not easily available to all; moreover, manually placing optodes on such models can be tedious and time consuming. Such limitations may hinder the adoption of representative models for basic simulations and the use of these models for large-scale simulations, e.g., for training machine learning algorithms.AimWe aim to provide the NIRS/DCS communities with an open-source, user-friendly database of morphologically and optically realistic head models, as well as a succinct software pipeline to prepare these models for mesh-based Monte Carlo simulations of light transport.ApproachSixteen anatomical models were created from segmented T1-weighted magnetic resonance imaging (MRI) head scans and converted to tetrahedral mesh volumes. Approximately 800 companion scalp surface locations were distributed on each model, comprising full head coverage. A pipeline was created to place custom source and optical detectors at each location, and guidance is provided on how to use these parameters to set up MC simulations.ResultsThe models, head surface locations, and all associated code are freely available under the scatterBrains project on Github.ConclusionsThe NIRS/DCS community benefits from having shared resources for conducting MC simulations on realistic head geometries. We hope this will make MRI-based head models and virtual optode placement easily accessible to all. Contributions to the database are welcome and encouraged.
SignificanceCombining near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) allows for quantifying cerebral blood volume, flow, and oxygenation changes continuously and non-invasively. As recently shown, the DCS pulsatile cerebral blood flow index (pCBFi) can be used to quantify critical closing pressure (CrCP) and cerebrovascular resistance (CVRi).AimAlthough current DCS technology allows for reliable monitoring of the slow hemodynamic changes, resolving pulsatile blood flow at large source–detector separations, which is needed to ensure cerebral sensitivity, is challenging because of its low signal-to-noise ratio (SNR). Cardiac-gated averaging of several arterial pulse cycles is required to obtain a meaningful waveform.ApproachTaking advantage of the high SNR of NIRS, we demonstrate a method that uses the NIRS photoplethysmography (NIRS-PPG) pulsatile signal to model DCS pCBFi, reducing the coefficient of variation of the recovered pulsatile waveform (pCBFi-fit) and allowing for an unprecedented temporal resolution (266 Hz) at a large source-detector separation (>3 cm).ResultsIn 10 healthy subjects, we verified the quality of the NIRS-PPG pCBFi-fit during common tasks, showing high fidelity against pCBFi (R2 0.98 ± 0.01). We recovered CrCP and CVRi at 0.25 Hz, >10 times faster than previously achieved with DCS.ConclusionsNIRS-PPG improves DCS pCBFi SNR, reducing the number of gate-averaged heartbeats required to recover CrCP and CVRi.
Functional near-infrared spectroscopy (fNIRS) and diffuse correlation spectroscopy (DCS) have shown promise as non-invasive optical methods for cerebral functional imaging. DCS approaches currently have limited sensitivity in adults. fNIRS sensitivity is also limited, particularly in high-detector-density applications. Sensitivity can be improved using temporal discrimination (TD), where the laser excitation is of short (~400ps) duration and the detector rejects early photons that have not penetrated into the brain while maintain high sensitivity to those that have. We report here on the development of a novel 32x32 Single-Photon Avalanche photo-Detector (SPAD) array and Read-Out Integrated Circuit (ROIC) that can operate in either the visible or NIR enabling high-channel-count TD-fNIRS or TD-DCS systems.
We piloted long-term diffuse correlation spectroscopy (DCS) monitoring of cerebral blood flow in a patient with an aneurysmal subarachnoid hemorrhage. Measurements were conducted for 18 days. We also recorded blood pressure, ECG, and other clinical monitors as available. The blood flow index from 5, 25, and 30 mm separation channels showed a variety of responses depending on the patient condition and treatment. As an example, repeated doses of nimodipine were given for treatment purposes, resulting in level or increased cerebral blood flow despite a decrease in mean arterial blood pressure. There was correlation between the short-distance channel and heart rate.
KEYWORDS: Speckle, Brain, Spectroscopy, Optical spectroscopy, Signal to noise ratio, Tissue optics, Skull, Monte Carlo methods, Laser speckle contrast imaging, Human subjects
Diffuse correlation spectroscopy (DCS) offers non-invasive measurements of tissue perfusion and is increasingly broadly applied in human subject research, in particular in the neuromonitoring arena. However, signal to noise (SNR) limitations have prompted great interest in alternative instrumentation approaches to address this issue, such as the speckle contrast optical spectroscopy (SCOS) technique which uses spatial multi-speckle contrast to estimate blood flow. Here we present a simulation study of the brain perfusion sensitivity achievable by each method on adults, to guide the use of SCOS vs DCS approaches in future studies. We find that SCOS brain sensitivity is comparable to DCS.
Cerebral autoregulation (CA), the brain’s ability to regulate perfusion independently of blood pressure, can be assessed by evaluating the degree of correlation between cerebral blood flow (CBF) and mean arterial pressure. Non-invasive, optical measurements of brain hemodynamics using DCS/NIRS can be used to assess CA and show agreement with invasive metrics (laser doppler perfusion and intracranial pressure) in a pediatric swine model of cardiac arrest. Wavelet based coherence methods of assessing autoregulation are a useful alternative to correlation based methods.
Transcatheter aortic valve replacement (TAVR) surgery has a risk of cognitive impairment and neurological injury. Currently, there are few options for non-invasively monitoring brain activity and perfusion, with electroencephalography, transcranial Doppler, and near-infrared spectroscopy (NIRS) all having significant drawbacks. By combining NIRS with diffuse correlation spectroscopy (DCS) we can obtain a more complete picture of cerebral hemodynamics during TAVR procedures and examine the link to neurological outcomes. We show examples of post-valve replacement hemodynamic changes that correspond with worse/better patient outcomes
Multimodal x-ray mammography and optical imaging data were acquired on six breast cancer patients who underwent neoadjuvant chemotherapy (NACT) but reponded differently to their treatment. Changes in tumor contrast quantified by total hemoglobin concentration (HbT) between baseline and pre-cycle 3 are distinctive across various levels of pathological outcomes. While decreases in lesion size have been observed in all cases regardless of pathological outcomes, optical contrast shows more distinctive response characteristics that could potentially be used to differentiate complete responders from partial responders.
We present a novel system based on a four-stage fiber delay network designed for multistate time-domain diffuse correlation spectroscopy, providing three output fibers per each delay state. The fiber delay network is coupled to a custom pulsed laser at 1064 nm and four SNSPDs, allowing to measure up to 12 independent source-detector pairs simultaneously. The system delivers 300ps optical pulses, 100 mW average optical power per fiber output, operates at 62.5 MHz and each cycle provides 4 laser pulses displaced of 4 ns. The instrument has been validated on healthy human subject during functional tasks, proving state-of-the-art performance.
Infants born at an extremely low gestational age are at an increased risk of intraventricular hemorrhaging during the first three postnatal days. We have built a standalone easy-to-use multi-wavelength multi-distance diffuse correlation spectroscopy system, which utilizes three time-multiplexed long coherence lasers at 785, 808, and 853 nm, single photon detectors, and photon time-tagging electronics to simultaneously quantify cerebral blood flow, tissue optical properties, and blood oxygen saturation. The system has been designed specifically for use on preterm infants. The device shows good agreement with a commercially available NIRS-DCS system. We are currently monitoring preterm infants and will show results.
KEYWORDS: Signal to noise ratio, Spectroscopy, Brain, Tissues, Near infrared spectroscopy, Source detector separation, Autocorrelation, Signal detection, Monte Carlo methods, Magnetic resonance imaging
Diffuse correlation spectroscopy (DCS) has emerged as a versatile, noninvasive method for deep tissue perfusion assessment using near-infrared light. A broad class of applications is being pursued in neuromonitoring and beyond. However, technical limitations of the technology as originally implemented remain as barriers to wider adoption. A wide variety of approaches to improve measurement performance and reduce cost are being explored; these include interferometric methods, camera-based multispeckle detection, and long path photon selection for improved depth sensitivity. We review here the current status of DCS technology and summarize future development directions and the challenges that remain on the path to widespread adoption.
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
Diffuse correlation spectroscopy (DCS) is an emerging near infrared spectroscopy modality able to measure cerebral blood flow (CBF) non-invasively and continuously in humans. We have reported a limited applicability in adults due to the significant extracerebral tissue thickness and the low signal-to-noise ratio (SNR) of the measurements. Improvements to DCS brain sensitivity and SNR can be achieved by operating DCS at 1064 and using superconducting nanowire single-photon detectors (SNSPDs). Initial human results show a 16-fold improvement in SNR and 20% improvement in depth sensitivity. This allows us to resolve changes in CBF in adult subjects more robustly and accurately than was previously achievable.
We present the design of an innovative time-gated 32×32 InP/InGaAs-based Single Photon Avalanche Diode (SPAD) array with sub-nanosecond gating capabilities operating up to 10MHz repetition rate specifically designed for time-domain diffuse correlation spectroscopy at 1064nm. We present the detector design, experimental characterization and preliminary validations on a liquid phantom. This testing is informing us for a revision of the photodetector which will allow to reach up to 192 optical channels towards functional blood flow changes measurements with full head coverage with improved brain sensitivity thanks to early-photons rejection.
We present the design of an innovative instrument for time-gated diffuse correlation spectroscopy. It features a 1064nm pulsed sub-ns long coherence-length laser operating up to 75MHz, a 100-channel in-FPGA correlator and a novel time-gated 32×32 InP/InGaAs-based Single Photon Avalanche Diode (SPAD) array with sub-nanosecond gating capabilities operating up to 10MHz repetition rate. We present components experimental characterization and preliminary validations on a liquid phantom. This testing is informing us for a revision of the photodetector which will allow to reach up to 192 optical channels towards functional blood flow changes measurements with full head coverage.
Significance: The ability of diffuse correlation spectroscopy (DCS) to measure cerebral blood flow (CBF) in humans is hindered by the low signal-to-noise ratio (SNR) of the method. This limits the high acquisition rates needed to resolve dynamic flow changes and to optimally filter out large pulsatile oscillations and prevents the use of large source-detector separations (≥3 cm), which are needed to achieve adequate brain sensitivity in most adult subjects.
Aim: To substantially improve SNR, we have built a DCS device that operates at 1064 nm and uses superconducting nanowire single-photon detectors (SNSPD).
Approach: We compared the performances of the SNSPD-DCS in humans with respect to a typical DCS system operating at 850 nm and using silicon single-photon avalanche diode detectors.
Results: At a 25-mm separation, we detected 13 ± 6 times more photons and achieved an SNR gain of 16 ± 8 on the forehead of 11 subjects using the SNSPD-DCS as compared to typical DCS. At this separation, the SNSPD-DCS is able to detect a clean pulsatile flow signal at 20 Hz in all subjects. With the SNSPD-DCS, we also performed measurements at 35 mm, showing a lower scalp sensitivity of 31 ± 6 % with respect to the 48 ± 8 % scalp sensitivity at 25 mm for both the 850 and 1064 nm systems. Furthermore, we demonstrated blood flow responses to breath holding and hyperventilation tasks.
Conclusions: While current commercial SNSPDs are expensive, bulky, and loud, they may allow for more robust measures of non-invasive cerebral perfusion in an intensive care setting.
Significance: Time domain diffuse correlation spectroscopy (TD-DCS) can offer increased sensitivity to cerebral hemodynamics and reduced contamination from extracerebral layers by differentiating photons based on their travel time in tissue. We have developed rigorous simulation and evaluation procedures to determine the optimal time gate parameters for monitoring cerebral perfusion considering instrumentation characteristics and realistic measurement noise.
Aim: We simulate TD-DCS cerebral perfusion monitoring performance for different instrument response functions (IRFs) in the presence of realistic experimental noise and evaluate metrics of sensitivity to brain blood flow, signal-to-noise ratio (SNR), and ability to reject the influence of extracerebral blood flow across a variety of time gates to determine optimal operating parameters.
Approach: Light propagation was modeled on an MRI-derived human head geometry using Monte Carlo simulations for 765- and 1064-nm excitation wavelengths. We use a virtual probe with a source–detector separation of 1 cm placed in the pre-frontal region. Performance metrics described above were evaluated to determine optimal time gate(s) for different IRFs. Validation of simulation noise estimates was done with experiments conducted on an intralipid-based liquid phantom.
Results: We find that TD-DCS performance strongly depends on the system IRF. Among Gaussian pulse shapes, ∼300 ps pulse length appears to offer the best performance, at wide gates (500 ps and larger) with start times 400 and 600 ps after the peak of the TPSF at 765 and 1064 nm, respectively, for a 1-s integration time at photon detection rates seen experimentally (600 kcps at 765 nm and 4 Mcps at 1064 nm).
Conclusions: Our work shows that optimal time gates satisfy competing requirements for sufficient sensitivity and sufficient SNR. The achievable performance is further impacted by system IRF with ∼300 ps quasi-Gaussian pulse obtained using electro-optic laser shaping providing the best results.
Real-time noninvasive cerebral blood flow monitoring during cardiac surgery could decrease rates of neurologic injury associated with hypothermic circulatory arrests (HCA). We used combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy (FDNIRS-DCS) to measure cerebral oxygen saturation and an index of blood flow (CBFi) in 12 adults undergoing HCA. Our measurements revealed negligible CBFi during retrograde cerebral perfusion (RCP: CBFi 91.2%±3.3% drop; HCA-only: 95.5%±1.8% drop). There was a significant difference during antegrade cerebral perfusion (p = 0.003). We conclude that FDNIRS-DCS can be a powerful tool to optimize cerebral perfusion and that RCP’s efficacy needs to be further examined.
Breast cancer is a highly heterogeneous disease comprising a variety of genotypes and phenotypes of varying levels of aggressiveness. This presents significant challenges to clinical management of early-stage cancers. In this paper, we describe the use of multimodal optical technologies including near-infrared (NIR) spectroscopy, diffuse correlation spectroscopy (DCS) and indocyanine green (ICG) fluorescence imaging to evaluate the aggressiveness and progression of two patient-derived xenograft models of human breast cancer. Optical markers reveal distinctive features between low- and high-aggressiveness tumors that could potentially be translated for clinical use.
In this paper, we describe a deep convolutional neural network (DNN) model trained with simulated breast diffuse optical tomography data with realistic noise characteristics to solve the inverse problem in a fast single-pass feed forward reconstruction. In addition to an AUTOMAP-inspired network structure, our DNN model, a.k.a. FDU-Net, is also comprised of a U-Net to further improve the image quality. We demonstrate that our FDU-Net model can successfully recover nearly the full contrast of inclusions with accurate localization at millisecond-scale speed, outperforming the conventional finite element-based (FEM) methods. Trained with cases with a single spherical inclusion, the FDU-Net model can also recover multi-inclusions and irregular-shaped cases, demonstrating advantages of generalization.
Diffuse correlation spectroscopy (DCS) is an established diffuse optical technique that uses the analysis of temporal speckle intensity fluctuations to measure blood flow in tissue. DCS cerebral blood flow measurements in clinical applications have shown promise, but measurements contain contamination of the signal from changes in superficial blood flow. Recent studies have shown that moving to wavelengths beyond the water absorption peak at 970 nm when making DCS measurements improves SNR and reduced influence of superficial flow. Here, we present a DCS system operating at 1064 nm utilizing two InGaAs SPADs to calculate the cross correlation to address detector non-idealities.
Recently, we developed a time-domain diffuse correlation spectroscopy (TD-DCS) method for neurovascular sensing with higher brain sensitivity. In this paper, laser pulse shaping was designed and demonstrated for TD-DCS at 1064 nm. A quantum superconducting nanowire single-photon detector (SNSPD) with high photon detection efficiency (PDE) and low jitter collects the back-scattered light from the brain. The presented approach is the first step towards scaling up a full fiber optic cap with 96 source channels and 192 custom-made single-photon detectors which will cover most of an adult head.
Non-invasive monitoring of cerebral blood flow at the bedside using diffuse correlation spectroscopy is being investigated as a potential tool to improve brain health outcomes after surgery. In this work we characterize the performance of diffuse correlation spectroscopy measurements in assessing cerebral blood flow in the presence of systemic physiology interference through measurements on several healthy volunteers during CO2 inhalation. We report group averaged responses and the role of multi-layer models in increasing the accuracy of CBF estimates. We compare optical blood flow recordings with transcranial Doppler ultrasound and MRI ASL data.
The ability of diffuse correlation spectroscopy (DCS) to measure tissue perfusion paves the way for monitoring cerebral blood flow (CBF) non-invasively. However, during measurements on human forehead, the measured blood flow index (BFi) is susceptible to contamination due to the blood flow in extracerebral tissue. Time domain DCS addresses this problem by selecting photons based on their travel time to obtain BFi at various depths. We have determined the gate start time(s) and width(s) that can lead to optimal sensitivity of BFi to CBF during actual measurements on human subjects through simulations. The simulated parameters were compared with measurement data.
Diffuse correlation spectroscopy (DCS) is an established diffuse optical technique that uses the analysis of temporal speckle intensity fluctuations to measure blood flow in tissue. Recent advances in the field have seen the introduction of iDWS/iDCS, which have allowed for the use of conventional photodetectors to replace the single photon counting detectors required to measure the traditional, homodyne DCS signal. Here we detail a high framerate, highly parallel iDCS system at 1064 nm which allows for improved signal to noise ratio at extended source detector separations.
Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index (CBFi) estimates.
Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi changes in the presence of strong systemic physiology variations during a hypercapnia maneuver.
Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi values estimated using a semi-infinite analytical model, as commonly used in the field.
Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBFi changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses.
Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.
Significance: The use of diffuse correlation spectroscopy (DCS) has shown efficacy in research studies as a technique capable of noninvasively monitoring blood flow in tissue with applications in neuromonitoring, exercise science, and breast cancer management. The ability of DCS to resolve blood flow in these tissues is related to the optical sensitivity and signal-to-noise ratio (SNR) of the measurements, which in some cases, particularly adult cerebral blood flow measurements, is inadequate in a significant portion of the population. Improvements to DCS sensitivity and SNR could allow for greater clinical translation of this technique.
Aim: Interferometric diffuse correlation spectroscopy (iDCS) was characterized and compared to traditional homodyne DCS to determine possible benefits of utilizing heterodyne detection.
Approach: An iDCS system was constructed by modifying a homodyne DCS system with fused fiber couplers to create a Mach–Zehnder interferometer. Comparisons between homodyne and heterodyne detection were performed using an intralipid phantom characterized at two extended source–detector separations (2.4, 3.6 cm), different photon count rates, and a range of reference arm power levels. Characterization of the iDCS signal mixing was compared to theory. Precision of the estimation of the diffusion coefficient and SNR of the autocorrelation curve were compared between different measurement conditions that mimicked what would be seen in vivo.
Results: The mixture of signals present in the heterodyne autocorrelation function was found to agree with the derived theory and resulted in accurate measurement of the diffusion coefficient of the phantom. Improvement of the SNR of the autocorrelation curve up to ∼2 × and up to 80% reduction in the variability of the diffusion coefficient fit were observed for all measurement cases as a function of increased reference arm power.
Conclusions: iDCS has the potential to improve characterization of blood flow in tissue at extended source–detector separations, enhancing depth sensitivity and SNR.
KEYWORDS: Blood circulation, Absorption, Scattering, Signal to noise ratio, Tissue optics, Near infrared spectroscopy, Spectroscopy, Tissues, Signal attenuation, Sensors
Significance: Diffuse correlation spectroscopy (DCS) is an established optical modality that enables noninvasive measurements of blood flow in deep tissue by quantifying the temporal light intensity fluctuations generated by dynamic scattering of moving red blood cells. Compared with near-infrared spectroscopy, DCS is hampered by a limited signal-to-noise ratio (SNR) due to the need to use small detection apertures to preserve speckle contrast. However, DCS is a dynamic light scattering technique and does not rely on hemoglobin contrast; thus, there are significant SNR advantages to using longer wavelengths (>1000 nm) for the DCS measurement due to a variety of biophysical and regulatory factors.
Aim: We offer a quantitative assessment of the benefits and challenges of operating DCS at 1064 nm versus the typical 765 to 850 nm wavelength through simulations and experimental demonstrations.
Approach: We evaluate the photon budget, depth sensitivity, and SNR for detecting blood flow changes using numerical simulations. We discuss continuous wave (CW) and time-domain (TD) DCS hardware considerations for 1064 nm operation. We report proof-of-concept measurements in tissue-like phantoms and healthy adult volunteers.
Results: DCS at 1064 nm offers higher intrinsic sensitivity to deep tissue compared with DCS measurements at the typically used wavelength range (765 to 850 nm) due to increased photon counts and a slower autocorrelation decay. These advantages are explored using simulations and are demonstrated using phantom and in vivo measurements. We show the first high-speed (cardiac pulsation-resolved), high-SNR measurements at large source–detector separation (3 cm) for CW-DCS and late temporal gates (1 ns) for TD-DCS.
Conclusions: DCS at 1064 nm offers a leap forward in the ability to monitor deep tissue blood flow and could be especially useful in increasing the reliability of cerebral blood flow monitoring in adults.
Diffuse correlation spectroscopy (DCS) is an established diffuse optical technique that uses the analysis of temporal speckle intensity fluctuations to measure blood flow in tissue. As a non-invasive technique, DCS has been used to monitor patient cerebral blood flow at the bedside. Though an effective measurement tool, extra-cerebral contamination of the DCS signal limits the sensitivity to changes in brain blood flow. In order to overcome this depth sensitivity challenge, we present a method, overlapping volumes, acousto-optic modulated DCS (AOM-DCS), to improve sensitivity to deeper tissue structures.
KEYWORDS: Photons, Blood, Liver, Tissue optics, Signal to noise ratio, Tissues, Monte Carlo methods, Near infrared spectroscopy, Computer simulations, Picosecond phenomena
Time-domain near-infrared spectroscopy (TD-NIRs) and Time-Domain Diffuse Correlation Spectroscopy (TD-DCS) are emerging imaging techniques that use a near-infrared, long coherence, pulsed laser to characterize oxygenation levels and blood flow. TD-DCS is a promising tool for bedside monitoring of brain activity due to its high time-resolution and portability. One potential new application area for TD-DCS is for detecting non-compressible torso hemorrhages (NCTH). NCTH is a serious traumatic injury that requires surgical intervention and is a leading cause of death in the military due to the lack of a rapid and portable imaging system sensitive enough to detect injury. Applying long wavelengths (1064 nm and 1120 nm) and time gating, TD-DCS can penetrate the superficial tissue layers and potentially detect bleeding deep within an organ. One limitation of current time-gating system is its reliance on full knowledge of the target tissue layers and properties in order to apply gating effectively. An automatic gating scheme that can adjust the time gate to quickly recalibrate itself to different imaging conditions, such as a different body area, can eliminate this limitation. Here, we use modeling and Monte Carlo simulations to search for characteristics in return signal profiles, specifically the time-of-flight and intensity profiles, as first step toward an automatic time-gating algorithm. We detail the simulation setups, parameter sweeps, and preliminary results in this report. These results show promise for TD-DCS as a tool for rapid and continuous monitoring of injuries in the field.
Diffuse correlation spectroscopy (DCS) is an established diffuse optical technique that uses the analysis of temporal speckle intensity fluctuations to measure blood flow in tissue. DCS has been shown to be an effective monitor of cerebral blood flow in many neuro-monitoring applications, though still suffers from depth sensitivity issues. Recent studies have shown that moving to 1064 nm when making DCS measurements improves SNR and sensitivity to depth, but detector challenges have slowed the change to that wavelength. Here, we present on a multipixel, interferometric DCS (iDCS) system that improves measurement capabilities at this wavelength.
The ability of diffuse correlation spectroscopy (DCS) to measure tissue perfusion paves the way for monitoring cerebral blood flow non-invasively. However, during measurements on human forehead, the measured blood flow index (BFi) is susceptible to contamination due to the blood flow in extracerebral tissue. Time domain DCS addresses this problem by selecting photons based on their travel time to obtain BFi at various depths. We have determined the gate start time(s) and width(s) that can lead to optimal sensitivity of BFi to brain blood flow during actual measurements on human subjects using commercially available hardware with accurate noise modelling.
Diffuse correlation spectroscopy (DCS) is an increasingly widespread non-invasive technology to measure tissue perfusion. Extending this technique into adult brain monitoring to assess real-time cerebral blood flow (CBF) requires addressing the influence of extracerebral contributions on DCS measurements. We compare several Monte Carlo based forward simulation models on the efficacy of CBF isolation, including ones generated directly from individual subject MRI scans. We conclude that a multi-layer curved surface representation is beneficial, and that the traditional single-layer homogenous model is insufficient; however, detailed structural information such as cortical folding represented in an individualized tissue-specific model may not be needed.
Our team has recently shown the SNR and depth-sensitivity advantages of using 1064 nm light for diffuse correlation spectroscopy as well as the challenges of commercially available single-photon detectors at this wavelength. We will review two strategies for custom readout integrated circuit designs that simultaneously target lower pixel dead times and lower afterpulsing probabilities. Both designs use macropixels comprising many detectors, each having a programmable hold-off time. We will compare simulated autocorrelations for our detector models and compare predicted performance against commercial InGaAs/InP detectors.
Diffuse correlation spectroscopy (DCS) is an emerging technology that allows for the quantitative estimation of blood flow in tissue. By monitoring the autocorrelation of the time course of light speckle intensity, information about the motion of scattering particles, mostly red blood cells in the microvasculature of biological tissues, can be determined. The speckle fluctuations are due to motion of scatters along the entire path length of the photon from the source to the detector, which makes the determination of the location of the motion a difficult task. Multi-distance and tomographic methods have been employed to measure decorrelation times at different source detector separations, which helps to separate superficial blood flow from blood flow deeper in the tissue. DCS in the time-domain (TD-DCS) is being evaluated as a method to increase depth sensitivity by considering only the late arriving photons. Depth resolved quantification of blood flow is especially important when blood flow measurements of the brain are desired, as the superficial blood flow of the scalp is a known contaminant to the cortical signal. Recent demonstrations by other groups have shown the utility of ultrasound tagging of light to be an effective method to discriminate flow at different depths.1 Here we utilize ultrasound pulses to modulate the motion of particles at specific depths, which is dependent upon the time-of-flight of the ultrasound pulse. By analyzing the autocorrelation of the speckle intensity at different delay periods after the pulse, quantitative, depth specific information about the flow can be determined.
References:
1. Tsalach, A. et al. Depth selective acousto-optic flow measurement. Biomed. Opt. Express 6, 4871–86 (2015).
Diffuse correlation spectroscopy (DCS) is an emerging technique that allows for estimation of the motion of particles. By monitoring the time course of the speckle intensity fluctuations, the motion of the scattering particles, usually red blood cells in the microvasculature of biological tissues, can be quantified. Though these measurements are traditionally taken at near infrared wavelengths, where the attenuation of light by tissue chromophores, primarily hemoglobin, is reduced, the multiply scattered field is still heavily attenuated and expensive photon counting detectors are required to measure the signal intensity. By decreasing the cost of these systems, they may be more applicable in measuring patient hemodynamics at the bedside. Other groups have explored the use of heterodyne techniques [1,2] to amplify the intensity of the scattered field for detection with less expensive detectors, showing the potential for lowering the cost of DCS systems. Here we detail the performance characteristics of a single mode fiber (SMF) interferometer as well as follow through to investigate the theoretical relationship between the measured correlation function and the underlying dynamics. DCS measurements in the traditional homodyne configuration made with photon counting detectors are compared with those made with the interferometer with the photon counting detectors to explore experimental parameters that optimize the SNR of the blood flow index. The feasibility of utilizing fast photodiodes in the detection of the amplified field is also explored. Through the use of amplified optical signals, the detection of the DCS signal using less expensive detectors is shown to be possible.
References:
1. Nakaji, H. US Application. No. 15/424581 (2017).
2. Zhou, W., Kholiqov, O., Chong, S. P. & Srinivasan, V. J. Highly parallel, interferometric diffusing wave spectroscopy for monitoring cerebral blood flow dynamics. Optica 5, 518 (2018).
Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to healthy tissue, produced models with robust predictions. Notably, logistic regression based on z-score normalization using only tissue oxygen saturation (StO2) measured within 10 days of the initial therapy dose was found to be a significant predictor of pCR (AUC = 0.92; 95% CI: 0.82 to 1). This observation suggests that patients who show rapid convergence of tumor tissue StO2 to surrounding tissue StO2 are more likely to achieve pCR. This early predictor of pCR occurs prior to reductions in tumor size and could enable dynamic feedback for optimization of chemotherapy strategies in breast cancer.
A great unmet need in oncologic surgery is the ability to accurately identify tumor-positive margins during surgical resections and to rapidly assess the margin status of resection specimens immediately following surgery. While the development of tumor-targeted fluorescent probes is a major area of investigation, it will be several years before these probes are realized for clinical use. We report the use of Indocyanine-green (ICG), a clinically approved, non-targeted dye, in conjunction with fluorescence lifetime detection to provide high accuracy for tumor detection in living mice. The improved performance relies on the distinct fluorescence lifetimes of ICG within tumors compared to tissue autofluorescence, and is further aided by the well-known enhanced permeability and retention of ICG in tumors and the clearance of ICG from normal tissue several hours after intravenous injection. Using in vivo models of human breast and brain tumors, we show that fluorescence lifetime contrast can provide a more than 98% sensitivity and specificity, and a 10-fold reduction in error rates compared to fluorescence intensity. Our studies suggest the significant potential of lifetime-contrast for accurate tumor detection using ICG and other targeted probes under development, both for intra-operative imaging and for ex-vivo margin assessment of surgical specimens
Dynamic Light Scattering-Optical Coherence Tomography (DLS-OCT) takes the advantages of using DLS to measure particle flow and diffusion within an OCT resolution-constrained 3D volume, enabling the simultaneous measurements of absolute RBC velocity and diffusion coefficient with high spatial resolution. In this work, we applied DLS-OCT to measure both RBC velocity and the shear-induced diffusion coefficient within penetrating venules of the somatosensory cortex of anesthetized mice. Blood flow laminar profile measurements indicate a blunted laminar flow profile, and the degree of blunting decreases with increasing vessel diameter. The measured shear-induced diffusion coefficient was proportional to the flow shear rate with a magnitude of ~ 0.1 to 0.5 × 10-6 mm2 . These results provide important experimental support for the recent theoretical explanation for why DCS is dominantly sensitive to RBC diffusive motion.
We present a framework for characterizing the performance of an experimental imaging technology, diffuse optical spectroscopic imaging (DOSI), in a 2-year multicenter American College of Radiology Imaging Network (ACRIN) breast cancer study (ACRIN-6691). DOSI instruments combine broadband frequency-domain photon migration with time-independent near-infrared (650 to 1000 nm) spectroscopy to measure tissue absorption and reduced scattering spectra and tissue hemoglobin, water, and lipid composition. The goal of ACRIN-6691 was to test the effectiveness of optically derived imaging endpoints in predicting the final pathologic response of neoadjuvant chemotherapy (NAC). Sixty patients were enrolled over a 2-year period at participating sites and received multiple DOSI scans prior to and during 3- to 6-month NAC. The impact of three sources of error on accuracy and precision, including different operators, instruments, and calibration standards, was evaluated using a broadband reflectance standard and two different solid tissue-simulating optical phantoms. Instruments showed <0.0010 mm−1 (10.3%) and 0.06 mm−1 (4.7%) deviation in broadband absorption and reduced scattering, respectively, over the 2-year duration of ACRIN-6691. These variations establish a useful performance criterion for assessing instrument stability. The proposed procedures and tests are not limited to DOSI; rather, they are intended to provide methods to characterize performance of any instrument used in translational optical imaging.
Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.
KEYWORDS: Near infrared spectroscopy, Monte Carlo methods, Brain, Oxygen, Spectroscopy, Mode conditioning cables, Tissues, Head, Magnetic resonance imaging, Thermal modeling
Diffuse correlation spectroscopy (DCS) is being employed alongside near-infrared spectroscopy (NIRS) measurements to track the cerebral oxygen metabolic rate (CMRO2). However, both techniques employ diffusely reflected light that has traveled mostly through extracerebral tissues. Recent studies indicate that depth sensitivity profiles are different for NIRS vs DCS measurements, with DCS appearing to be more sensitive to the brain than NIRS methods for a given source-detector separation. This mismatch can lead to erroneous conclusions with respect to the amount and perhaps even the direction of change in CMRO2. Recently, our group and others have demonstrated the use of Monte Carlo (MC) based multi-layer, multi-distance fitting, which offers increased accuracy for complex tissue structures such as the adult brain.
In this paper we employ a Monte Carlo light transport model based on a realistic head geometry that can be derived from MRI scans (if available) or approximated from head shape measurements. We consider DCS and CW-NIRS measurements taken at two or more distances and analyze simulated data generated using a fully segmented adult brain MRI scan. Through simulations, we explore the improvements offered by our method vs. processing the same measurements with a semi-infinite diffusion model and estimate the impact of errors in geometry and optical properties on relative blood flow and CMRO2 changes.
Monitoring phase transition in adipose tissue and formation of lipid crystals is important in Cryo-procedures such as cryosurgery or Selective Cryolipolysis (SC). In this work, we exploited a Near-Infrared Spectroscopy (NIRS) method to monitor the onset of fat freezing/melting. Concurrent measurements using frequency domain NIRS and MR Spectroscopy during cooling/heating were performed on an in vitro porcine skin sample with a thick subcutaneous fat layer in a human MR scanner. The NIRS probe was placed on the skin measuring the average optical scattering of the fatty layer. Two fiber optic temperature probes were inserted in the area of the MRS and NIRS measurements. To further investigate the microscopic features of the phase-transition, an identical cooling/heating procedure was replicated on the same fat tissue while being imaged by Optical Coherence Tomography. The temperature relationships of optical scattering, MRS peak characteristics and OCT reflection intensity were analyzed to find signatures related to the onset of phase transition.
The optical scattering in the fatty tissues decreases during the heating and increases by cooling. However, there is an inflexion in the rate of change of the scattering while the phase transition happens in the fatty layer. The methylene fat peaks on the MR Spectrum are also shown to be broadened during the cooling. OCT intensity displays a sharp increase at the transition temperature. The results from multiple samples show two transition points around 5-10 ˚C (cooling) and 15-20 ˚C (heating) through all three methods, demonstrating that adipose tissue phase change can be monitored non-invasively.
Although the development of tumor-targeted fluorescent probes is a major area of investigation, it will be several years before these probes are realized for clinical use. Here, we report an approach that employs indocyanine-green (ICG), a clinically approved, nontargeted dye, in conjunction with fluorescence lifetime (FLT) detection to provide high accuracy for tumor-tissue identification in mouse models of subcutaneous human breast and brain tmors. The improved performance relies on the distinct FLTs of ICG within tumors versus tissue autofluorescence and is further aided by the well-known enhanced permeability and retention of ICG in tumors and the clearance of ICG from normal tissue several hours after intravenous injection. We demonstrate that FLT detection can provide more than 98% sensitivity and specificity, and a 10-fold reduction in error rates compared to intensity-based detection. Our studies suggest the significant potential of FLT-contrast for accurate tumor-tissue identification using ICG and other targeted probes under development, both for intraoperative imaging and for ex-vivo margin assessment of surgical specimens.
Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity vRBC, the autocorrelation is expected to decay exponentially with (vRBCτ)2, where τ is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient Dshear and an MSD of 6Dshearτ. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index (BFi) to quantify tissue perfusion. We find BFi to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter.
Frequency domain near-infrared spectroscopy (FD-NIRS) has proven to be a reliable method for quantification of tissue absolute optical properties. We present a full-sampling direct analog-to-digital conversion FD-NIR imager. While we developed this instrument with a focus on high-speed optical breast tomographic imaging, the proposed design is suitable for a wide-range of biophotonic applications where fast, accurate quantification of absolute optical properties is needed. Simultaneous dual wavelength operation at 685 and 830 nm is achieved by concurrent 67.5 and 75 MHz frequency modulation of each laser source, respectively, followed by digitization using a high-speed (180 MS/s) 16-bit A/D converter and hybrid FPGA-assisted demodulation. The instrument supports 25 source locations and features 20 concurrently operating detectors. The noise floor of the instrument was measured at <1.4 pW/√Hz, and a dynamic range of 115+ dB, corresponding to nearly six orders of magnitude, has been demonstrated. Titration experiments consisting of 200 different absorption and scattering values were conducted to demonstrate accurate optical property quantification over the entire range of physiologically expected values.
Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) are two diffuse optical technologies for brain imaging that are sensitive to changes in hemoglobin concentrations and blood flow, respectively. Measurements for both modalities are acquired on the scalp, and therefore hemodynamic processes in the extracerebral vasculature confound the interpretation of cortical hemodynamic signals. The sensitivity of NIRS to the brain versus the extracerebral tissue and the contrast-to-noise ratio (CNR) of NIRS to cerebral hemodynamic responses have been well characterized, but the same has not been evaluated for DCS. This is important to assess in order to understand their relative capabilities in measuring cerebral physiological changes. We present Monte Carlo simulations on a head model that demonstrate that the relative brain-to-scalp sensitivity is about three times higher for DCS (0.3 at 3 cm) than for NIRS (0.1 at 3 cm). However, because DCS has higher levels of noise due to photon-counting detection, the CNR is similar for both modalities in response to a physiologically realistic simulation of brain activation. Even so, we also observed higher CNR of the hemodynamic response during graded hypercapnia in adult subjects with DCS than with NIRS.
Near-infrared optical measurements have been shown to offer a promising non-invasive way for monitoring breast neoadjuvant chemotherapy (NAC) and predicting outcome. In this study, we extend optical measurements to capture additional hemodynamic and metabolic biomarkers revealed by dynamically imaging breast tissue during fractional mammographic compression. We are obtaining pre-treatment, day 7 and optional monthly scans in breast cancer patients undergoing NAC. The difference in hemodynamic response to compression between healthy and tumor-bearing breast decreases over the course of neoadjuvant therapy in responders compared to nearly no change in patients not responding to the chemotherapy.
We present images of tissue phantoms and chicken chorio-allantoic membrane vasculature using a novel optoacoustic tomography technique based on the time-resolved interferometric measurement of laser-induced thermoelastic expansion. Our imaging system is based on a modified Mach-Zehnder interferometer that provides surface displacement measurements with a temporal resolution of 4 ns and a displacement sensitivity of 0.3 nm. The images are reconstructed from surface displacement measurements made at several locations following irradiation of the sample with Q-switched Nd:YAG (=532, 1064 nm) laser pulses using a delay and sum beam-forming algorithm. The images shown demonstrate the ability of our method to provide better than 200-µm lateral and 30-µm axial resolution at depths exceeding ten transport mean free paths in highly scattering in-vitro and in-vivo model systems.
In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis,
we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our
imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration
as well as in oxygen saturation, as was the case for our standalone breast compression study, which made
use of reflectance measurements. Simulation experiments show the importance of taking into account the
temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal
dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction,
we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic
spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously,
making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.
Combining 2D X-ray mammography or 3D tomosynthesis with diffuse optical tomography for breast imaging
is advantageous in facilitating clinical diagnosis by fusing the structural X-ray images with functional optical
images. In this study, we imaged 65 patients with a combined tomosynthesis/diffuse optical breast imaging
system developed at Massachusetts General Hospital. The bulk optical properties and patient demographics were
summarized in this paper. The averaged total-hemoglobin for 60 healthy breasts is 21 &mgr;M which is comparable
with literature values given the applied mammographic compression in our experiments. The averaged oxygen
saturation is 76%. The comparison of contra-lateral breast measurements also demonstrated correlations in
total hemoglobin and oxygen saturation. Image reconstructions of the healthy breasts with moderate-sized fibroglandular
regions correctly recovered the chest-wall muscle, fibro-glandular tissue as well as the surrounding fatty
tissue. For dense breasts, the contrast between the chest-wall and the fibro-glandular region is small and the
most pronounced feature of the image is a low-absorption region in the center of the breast. We hypothesized
that this is caused by pressure induced blood-redistribution. Supportive evidence for this hypothesis had been
shown with mechanical simulations of breast compression.
We use optical spectroscopy to characterize the influence of mammographic-like compression on the physiology of the breast. We note a reduction in total hemoglobin content, tissue oxygen saturation, and optical scattering under compression. We also note a hyperemic effect during repeated compression cycles. By modeling the time course of the tissue oxygen saturation, we are able to obtain estimates for the volumetric blood flow (1.64±0.6 mL/100 mL/min) and the oxygen consumption (1.97±0.6 µmol/100 mL/min) of compressed breast tissue. These values are comparable to estimates obtained from previously published positron emission tomography (PET) measurements. We conclude that compression-induced changes in breast physiological properties are significant and should be accounted for when performing optical breast imaging. Additionally, the dynamic characteristics of the changes in breast physiological parameters, together with the ability to probe the tissue metabolic state, may prove useful for breast cancer detection.
We report on the further development of our previously described spectroscopic imaging technique based on time-resolved interferometric measurements of laser-induced thermoelastic expansion (POISe: Pulsed Optoelastic Interferometric Spectroscopy and Imaging). We show the capability of POISe to form tomographic images of tissue phantoms and live animal tissues. By performing image reconstruction on data sets acquired from several tissue-like phantoms we demonstrate the ability of POISe to provide better than 200 microns spatial resolution in a strongly scattering medium (μs'=1.5/mm). Additionally we demonstrate the ability of POISe to image chicken chorio-allantoic membrane (CAM) blood vessels through a 6-10 mm layer of Intralipid with μs'=0.75/mm.
POISe is a spectroscopic imaging technique based on the measurement of surface motion resulting from thermoelastic stress waves produced by short pulse laser irradiation of optically heterogeneous turbid samples. Here we show the capability of POISe to form tomographic images of tissue phantoms using surface displacement measurements
taken at several locations following irradiation of a sample with a Q-switched Nd:YAG laser λ=1064 nm. The principal component of POISe is a modified Mach-Zehnder interferometer that provides surface displacement measurements with a temporal resolution of 4 ns and a displacement sensitivity of 0.2 nm. By performing simple image reconstructions on data sets acquired from several tissue-like phantoms, we demonstrate the ability of POISe to provide better than 250 μm spatial resolution at depths of 6 to 8 mm in a strongly scattering medium (μ's=1/mm). This technique shows great promise for high-resolution non-invasive imaging of superficial (< 1 cm) tissue structures.
Using the -P1 approximation to the Boltzmann transport equation we develop analytic solutions for the fluence rate produced by planar (1-D) and Gaussian beam (2-D) irradiation of a homogeneous, turbid, semi-infinite medium. To assess the performance of these solutions we compare the predictions for the fluence rate and two metrics of the optical penetration depth with Monte Carlo simulations. We provide results under both refractive-index matched and mismatched conditions for optical properties where the ratio of reduced scattering to absorption lies in the range 0(µ/µa)104. For planar irradiation, the -P1 approximation provides fluence rate profiles accurate to ±16% for depths up to six transport mean free paths (l*) over the full range of optical properties. Metrics for optical penetration depth are predicted with an accuracy of ±4%. For Gaussian irradiation using beam radii r03l*, the accuracy of the fluence rate predictions is no worse than in the planar irradiation case. For smaller beam radii, the predictions degrade significantly. Specifically for media with (µ/µa) = 1 irradiated with a beam radius of r0 = l*, the error in the fluence rate approaches 100%. Nevertheless, the accuracy of the optical penetration depth predictions remains excellent for Gaussian beam irradiation, and degrades to only ±20% for r0 = l*. These results show that for a given set of optical properties (µ/µa), the optical penetration depth decreases with a reduction in the beam diameter. Graphs are provided to indicate the optical and geometrical conditions under which one must replace the -P1 results for planar irradiation with those for Gaussian beam irradiation to maintain accurate dosimetry predictions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.