Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

Wearable brain imaging with multimodal physiological monitoring

The brain is a central component of cognitive and physical human performance. Measures, including functional brain activation, cerebral perfusion, cerebral oxygenation, evoked electrical responses, and resting hemodynamic and electrical activity are all related to, or can predict, health status or performance decrements. However, measuring brain physiology typically requires large, stationary machines that are not suitable for mobile or self-monitoring. Moreover, when individuals are ambulatory, systemic physiological fluctuations—e.g., in heart rate, blood pressure, skin perfusion, and more—can interfere with noninvasive brain measurements. In efforts to address the physiological monitoring and performance assessment needs for astronauts during spaceflight, we have developed easy-to-use, wearable prototypes, such as NINscan, for near-infrared scanning, which can collect synchronized multimodal physiology data, including hemodynamic deep-tissue imaging (including brain and muscles), electroencephalography, electrocardiography, electromyography, electrooculography, accelerometry, gyroscopy, pressure, respiration, and temperature measurements. Given their self-contained and portable nature, these devices can be deployed in a much broader range of settings—including austere environments—thereby, enabling a wider range of novel medical and research physiology applications. We review these, including high-altitude assessments, self-deployable multimodal e.g., (polysomnographic) recordings in remote or low-resource environments, fluid shifts in variable-gravity, or spaceflight analog environments, intracranial brain motion during high-impact sports, and long-duration monitoring for clinical symptom-capture in various clinical conditions. In addition to further enhancing sensitivity and miniaturization, advanced computational algorithms could help support real-time feedback and alerts regarding performance and health.

Ambulatory diffuse optical tomography and multimodality physiological monitoring system for muscle and exercise applications

Ambulatory diffuse optical tomography (aDOT) is based on near-infrared spectroscopy (NIRS) and enables three-dimensional imaging of regional hemodynamics and oxygen consumption during a person’s normal activities. Although NIRS has been previously used for muscle assessment, it has been notably limited in terms of the number of channels measured, the extent to which subjects can be ambulatory, and/or the ability to simultaneously acquire synchronized auxiliary data such as electromyography (EMG) or electrocardiography (ECG). We describe the development of a prototype aDOT system, called NINscan-M, capable of ambulatory tomographic imaging as well as simultaneous auxiliary multimodal physiological monitoring. Powered by four AA size batteries and weighing 577 g, the NINscan-M prototype can synchronously record 64-channel NIRS imaging data, eight channels of EMG, ECG, or other analog signals, plus force, acceleration, rotation, and temperature for 24+ h at up to 250 Hz. We describe the system’s design, characterization, and performance characteristics. We also describe examples of isometric, cycle ergometer, and free-running ambulatory exercise to demonstrate tomographic imaging at 25 Hz. NINscan-M represents a multiuse tool for muscle physiology studies as well as clinical muscle assessment.

Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors.

Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography

Topographic non-invasive near infrared spectroscopy (NIRS) has become a well-established tool for functional brain imaging. Applying up to 100 optodes over the head of a subject, allows achieving a spatial resolution in the centimeter range. This resolution is poor compared to other functional imaging tools. However, recently it was shown that diffuse optical tomography (DOT) as an extension of NIRS based on high-density (HD) probe arrays and supplemented by an advanced image reconstruction procedure allows describing activation patterns with a spatial resolution in the millimeter range. Building on these findings, we hypothesize that HD-DOT may render very focal activations accessible which would be missed by the traditionally used sparse arrays. We examined activation patterns in the primary somatosensory cortex, since its somatotopic organization is very fine-grained. We performed a vibrotactile stimulation study of the first and fifth finger in eight human subjects, using a 900-channel continuous-wave DOT imaging system for achieving a higher resolution than conventional topographic NIRS. To compare the results to a well-established high-resolution imaging technique, the same paradigm was investigated in the same subjects by means of functional magnetic resonance imaging (fMRI). In this work, we tested the advantage of ultrahigh-density probe arrays and show that highly focal activations would be missed by classical next-nearest neighbor NIRS approach, but also by DOT, when using a sparse probe array. Distinct activation patterns for both fingers correlated well with the expected neuroanatomy in five of eight subjects. Additionally we show that activation for different fingers is projected to different tissue depths in the DOT image. Comparison to the fMRI data yielded similar activation foci in seven out of ten finger representations in these five subjects when comparing the lateral localization of DOT and fMRI results.

Dynamic optical imaging of vascular and metabolic reactivity in rheumatoid joints

Dynamic optical imaging is increasingly applied to clinically relevant areas such as brain and cancer imaging. In this approach, some external stimulus is applied and changes in relevant physiological parameters (e.g., oxy- or deoxyhemoglobin concentrations) are determined. The advantage of this approach is that the prestimulus state can be used as a reference or baseline against which the changes can be calibrated. Here we present the first application of this method to the problem of characterizing joint diseases, especially effects of rheumatoid arthritis (RA) in the proximal interphalangeal finger joints. Using a dual-wavelength tomographic imaging system together with previously implemented model-based iterative image reconstruction schemes, we have performed initial dynamic imaging case studies on a limited number of healthy volunteers and patients diagnosed with RA. Focusing on three cases studies, we illustrated our major finds. These studies support our hypothesis that differences in the vascular reactivity exist between affected and unaffected joints.