Advances in personalized medicine are symbiotic with the development of novel technologies for biomedical devices. We present an approach that combines enhanced imaging of malignancies, therapeutics, and feedback about therapeutics in a single implantable, biocompatible, and resorbable device. This confluence of form and function is accomplished by capitalizing on the unique properties of silk proteins as a mechanically robust, biocompatible, optically clear biomaterial matrix that can house, stabilize, and retain the function of therapeutic components. By developing a form of high-quality microstructured optical elements, improved imaging of malignancies and of treatment monitoring can be achieved. The results demonstrate a unique family of devices for in vitro and in vivo use that provide functional biomaterials with built-in optical signal and contrast enhancement, demonstrated here with simultaneous drug delivery and feedback about drug delivery with no adverse biological effects, all while slowly degrading to regenerate native tissue.

Cerebral autoregulation (CA) is the mechanism that allows the brain to maintain a stable blood flow despite changes in blood pressure. Dynamic CA can be quantified based on continuous measurements of systemic mean arterial pressure (MAP) and global cerebral blood flow. Here, we show that dynamic CA can be quantified also from local measurements that are sensitive to the microvasculature. We used near-infrared spectroscopy (NIRS) to measure temporal changes in oxy- and deoxy-hemoglobin concentrations in the prefrontal cortex of 11 human subjects. A novel hemodynamic model translates those changes into changes of cerebral blood volume and blood flow. The interplay between them is described by transfer function analysis, specifically by a high-pass filter whose cutoff frequency describes the autoregulation efficiency. We have used pneumatic thigh cuffs to induce MAP perturbation by a fast release during rest and during hyperventilation, which is known to enhance autoregulation. Based on our model, we found that the autoregulation cutoff frequency increased during hyperventilation in comparison to normal breathing in 10 out of 11 subjects, indicating a greater autoregulation efficiency. We have shown that autoregulation can reliably be measured noninvasively in the microvasculature, opening up the possibility of localized CA monitoring with NIRS.

Noninvasive recording of fast optical signals presumably reflecting neuronal activity is a challenging task because of a relatively low signal-to-noise ratio. To improve detection of those signals in rapid object recognition tasks, we used the Independent Component Analysis (ICA) to reduce “global interference” (heartbeat and contribution of superficial layers). We recorded optical signals from the left prefrontal cortex in 10 right-handed participants with a continuous-wave instrument (DYNOT, NIRx, Brooklyn, NY). Visual stimuli were pictures of urban, landscape and seashore scenes with various vehicles as targets (target-to-non-target ratio 1:6) presented at ISI = 166 ms or 250 ms. Subjects mentally counted targets. Data were filtered at 2–30 Hz and artifactual components were identified visually (for heartbeat) and using the ICA weight matrix (for superficial layers). Optical signals were restored from the ICA components with artifactual components removed and then averaged over target and non-target epochs. After ICA processing, the event-related response was detected in 70–100% of subjects. The refined signal showed a significant decrease from baseline within 200–300 ms after targets and a slight increase after non-targets. The temporal profile of the optical signal corresponded well to the profile of a “differential ERP response”, the difference between targets and non-targets which peaks at 200 ms in similar object detection tasks. These results demonstrate that the detection of fast optical responses with continuous-wave instruments can be improved through the ICA method capable to remove noise, global interference and the activity of superficial layers. Fast optical signals may provide further information on brain processing during higher-order cognitive tasks such as rapid categorization of objects.

This study reports the optical characterization and quantitative oximetry of human breast cancer using spectrally-resolved images collected with a broadband, continuous-wave optical mammography instrument. On twenty-six cancer patients, we collected two-dimensional optical mammograms and created maps of the concentrations of hemoglobin, water, and lipids, as well as the oxygen saturation of hemoglobin. For each cancerous breast, we analyzed the difference between the tumor region (as identified by x-ray and optical mammography) and the remainder of breast tissue. With respect to the surrounding tissue, we found that cancer regions have significantly higher concentrations of total hemoglobin (+2.4±0.4 μM) and water (+7±1% v/v), and significantly lower lipid concentration (8±2% v/v) and oxygen saturation of hemoglobin (5±1%). We also found a significant correlation between the tumor optical contrast and the grade of breast cancer as quantified by the Nottingham histologic score; this demonstrates how optical signatures may be representative of metabolic and morphological features, as well as the aggressive potential of the tumor.

Measuring intracranial pressure (ICP) is necessary for the treatment of severe head injury but measurement systems are highly invasive and introduce risk of infection and complications. We developed a non-invasive alternative for quantifying ICP using measurements of cerebral blood flow (CBF) by diffuse correlation spectroscopy. The recorded cardiac pulsation waveform in CBF undergoes morphological changes in response to ICP changes. We used the pulse shape to train a randomized regression forest to estimate the underlying ICP and demonstrate in five non-human primates that DCS-based estimation can explain over 90% of the variance in invasively measured ICP.

Rationale and Objectives Perturbations in cerebral blood volume (CBV), blood flow (CBF), and metabolic rate of oxygen (CMRO2) lead to associated changes in tissue concentrations of oxy- and deoxy-hemoglobin (ΔO and ΔD), which can be measured by near-infrared spectroscopy (NIRS). A novel hemodynamic model has been introduced to relate physiological perturbations and measured quantities. We seek to use this model to determine functional traces of cbv(t) and cbf(t) − cmro2(t) from time-varying NIRS data, and cerebrovascular physiological parameters from oscillatory NIRS data (lowercase letters denote the relative changes in CBV, CBF, and CMRO2 with respect to baseline). Such a practical implementation of a quantitative hemodynamic model is an important step toward the clinical translation of NIRS. Materials and Methods In the time domain, we have simulated O(t) and D(t) traces induced by cerebral activation. In the frequency domain, we have performed a new analysis of frequency-resolved measurements of cerebral hemodynamic oscillations during a paced breathing paradigm. Results We have demonstrated that cbv(t) and cbf(t) − cmro2(t) can be reliably obtained from O(t) and D(t) using the model, and that the functional NIRS signals are delayed with respect to cbf(t) − cmro2(t) as a result of the blood transit time in the microvasculature. In the frequency domain, we have identified physiological parameters (e.g., blood transit time, cutoff frequency of autoregulation) that can be measured by frequency-resolved measurements of hemodynamic oscillations. Conclusions The ability to perform noninvasive measurements of cerebrovascular parameters has far-reaching clinical implications. Functional brain studies rely on measurements of CBV, CBF, and CMRO2, whereas the diagnosis and assessment of neurovascular disorders, traumatic brain injury, and stroke would benefit from measurements of local cerebral hemodynamics and autoregulation.

We report an experimental validation and applications of the new hemodynamic model presented in the companion article (Fantini, 2013, this issue) both in the frequency domain and in the time domain. In the frequency domain, we have performed diffuse optical measurements for coherent hemodynamics spectroscopy (CHS) on the brain and calf muscle of human subjects, showing that the hemodynamic model predictions (both in terms of spectral shapes and absolute spectral values) are confirmed experimentally. We show how the quantitative analysis based on the new model allows for autoregulation measurements from brain data, and provides an analytical description of near-infrared spiroximetry from muscle data. In the time domain, we have used data from the literature to perform a comparison between brain activation signals measured with functional near-infrared spectroscopy (fNIRS) or with blood oxygenation level dependent (BOLD) fMRI, and the corresponding signals predicted by the new model. This comparison shows an excellent agreement between the model predictions and the reported fNIRS and BOLD fMRI signals. This new hemodynamic model provides a valuable tool for brain studies with hemodynamic-based techniques.

Noncontact optical imaging of curved objects can result in strong artifacts due to the object's shape, leading to curvature biased intensity distributions. This artifact can mask variations due to the object's optical properties, and makes reconstruction of optical/physiological properties difficult. In this work we demonstrate a curvature correction method that removes this artifact and recovers the underlying data, without the necessity of measuring the object's shape. This method is applicable to many optical imaging modalities that suffer from shape-based intensity biases. By separating the spatially varying data (e.g., physiological changes) from the background signal (dc component), we show that the curvature can be extracted by either averaging or fitting the rows and columns of the images. Numerical simulations show that our method is equivalent to directly removing the curvature, when the object's shape is known, and accurately recovers the underlying data. Experiments on phantoms validate the numerical results and show that for a given image with 16.5% error due to curvature, the method reduces that error to 1.2%. Finally, diffuse multispectral images are acquired on forearms in vivo. We demonstrate the enhancement in image quality on intensity images, and consequently on reconstruction results of blood volume and oxygenation distributions.