Hand-held spectroscopic device for in vivo and intraoperative tumor detection: contrast enhancement, detection sensitivity, and tissue penetration.

Surgery is one of the most effective and widely used procedures in treating human cancers, but a major problem is that the surgeon often fails to remove the entire tumor, leaving behind tumor-positive margins, metastatic lymph nodes, and/or satellite tumor nodules. Here we report the use of a hand-held spectroscopic pen device (termed SpectroPen) and near-infrared contrast agents for intraoperative detection of malignant tumors, based on wavelength-resolved measurements of fluorescence and surface-enhanced Raman scattering (SERS) signals. The SpectroPen utilizes a near-infrared diode laser (emitting at 785 nm) coupled to a compact head unit for light excitation and collection. This pen-shaped device effectively removes silica Raman peaks from the fiber optics and attenuates the reflected excitation light, allowing sensitive analysis of both fluorescence and Raman signals. Its overall performance has been evaluated by using a fluorescent contrast agent (indocyanine green, or ICG) as well as a surface-enhanced Raman scattering (SERS) contrast agent (pegylated colloidal gold). Under in vitro conditions, the detection limits are approximately 2-5 × 10(-11) M for the indocyanine dye and 0.5-1 × 10(-13) M for the SERS contrast agent. Ex vivo tissue penetration data show attenuated but resolvable fluorescence and Raman signals when the contrast agents are buried 5-10 mm deep in fresh animal tissues. In vivo studies using mice bearing bioluminescent 4T1 breast tumors further demonstrate that the tumor borders can be precisely detected preoperatively and intraoperatively, and that the contrast signals are strongly correlated with tumor bioluminescence. After surgery, the SpectroPen device permits further evaluation of both positive and negative tumor margins around the surgical cavity, raising new possibilities for real-time tumor detection and image-guided surgery.

Piezopotential gated nanowire--nanotube hybrid field-effect transistor.

We report the first piezoelectric potential gated hybrid field-effect transistors based on nanotubes and nanowires. The device consists of single-walled carbon nanotubes (SWNTs) on the bottom and crossed ZnO piezoelectric fine wire (PFW) on the top with an insulating layer between. Here, SWNTs serve as a carrier transport channel, and a single-crystal ZnO PFW acts as the power-free, contact-free gate or even an energy-harvesting component later on. The piezopotential created by an external force in the ZnO PFW is demonstrated to control the charge transport in the SWNT channel located underneath. The magnitude of the piezopotential in the PFW at a tensile strain of 0.05% is measured to be 0.4-0.6 V. The device is a unique coupling between the piezoelectric property of the ZnO PFW and the semiconductor performance of the SWNT with a full utilization of its mobility. The newly demonstrated device has potential applications as a strain sensor, force/pressure monitor, security trigger, and analog-signal touch screen.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Using Coding to Improve Localization and Backscatter Communication Performance in Low-Power Sensor Networks

Backscatter communication is an emerging wireless technology that recently has gained an increase in attention from both academic and industry circles. The key innovation of the technology is the ability of ultra-low power devices to utilize nearby existing radio signals to communicate. As there is no need to generate their own energetic radio signal, the devices can benefit from a simple design, are very inexpensive and are extremely energy efficient compared with traditional wireless communication. These benefits have made backscatter communication a desirable candidate for distributed wireless sensor network applications with energy constraints.

The backscatter channel presents a unique set of challenges. Unlike a conventional one-way communication (in which the information source is also the energy source), the backscatter channel experiences strong self-interference and spread Doppler clutter that mask the information-bearing (modulated) signal scattered from the device. Both of these sources of interference arise from the scattering of the transmitted signal off of objects, both stationary and moving, in the environment. Additionally, the measurement of the location of the backscatter device is negatively affected by both the clutter and the modulation of the signal return.

This work proposes a channel coding framework for the backscatter channel consisting of a bi-static transmitter/receiver pair and a quasi-cooperative transponder. It proposes to use run-length limited coding to mitigate the background self-interference and spread-Doppler clutter with only a small decrease in communication rate. The proposed method applies to both binary phase-shift keying (BPSK) and quadrature-amplitude modulation (QAM) scheme and provides an increase in rate by up to a factor of two compared with previous methods.

Additionally, this work analyzes the use of frequency modulation and bi-phase waveform coding for the transmitted (interrogating) waveform for high precision range estimation of the transponder location. Compared to previous methods, optimal lower range sidelobes are achieved. Moreover, since both the transmitted (interrogating) waveform coding and transponder communication coding result in instantaneous phase modulation of the signal, cross-interference between localization and communication tasks exists. Phase discriminating algorithm is proposed to make it possible to separate the waveform coding from the communication coding, upon reception, and achieve localization with increased signal energy by up to 3 dB compared with previous reported results.

The joint communication-localization framework also enables a low-complexity receiver design because the same radio is used both for localization and communication.

Simulations comparing the performance of different codes corroborate the theoretical results and offer possible trade-off between information rate and clutter mitigation as well as a trade-off between choice of waveform-channel coding pairs. Experimental results from a brass-board microwave system in an indoor environment are also presented and discussed.

Mechanochemistry for Active Materials and Devices

The coupling of mechanical stress fields in polymers to covalent chemistry (polymer mechanochemistry) has provided access to previously unattainable chemical reactions and polymer transformations. In the bulk, mechanochemical activation has been used as the basis for new classes of stress-responsive polymers that demonstrate stress/strain sensing, shear-induced intermolecular reactivity for molecular level remodeling and self-strengthening, and the release of acids and other small molecules that are potentially capable of triggering further chemical response. The potential utility of polymer mechanochemistry in functional materials is limited, however, by the fact that to date, all reported covalent activation in the bulk occurs in concert with plastic yield and deformation, so that the structure of the activated object is vastly different from its nascent form. Mechanochemically activated materials have thus been limited to “single use” demonstrations, rather than as multi-functional materials for structural and/or device applications. Here, we report that filled polydimethylsiloxane (PDMS) elastomers provide a robust elastic substrate into which mechanophores can be embedded and activated under conditions from which the sample regains its original shape and properties. Fabrication is straightforward and easily accessible, providing access for the first time to objects and devices that either release or reversibly activate chemical functionality over hundreds of loading cycles.

While the mechanically accelerated ring-opening reaction of spiropyran to merocyanine and associated color change provides a useful method by which to image the molecular scale stress/strain distribution within a polymer, the magnitude of the forces necessary for activation had yet to be quantified. Here, we report single molecule force spectroscopy studies of two spiropyran isomers. Ring opening on the timescale of tens of milliseconds is found to require forces of ~240 pN, well below that of previously characterized covalent mechanophores. The lower threshold force is a combination of a low force-free activation energy and the fact that the change in rate with force (activation length) of each isomer is greater than that inferred in other systems. Importantly, quantifying the magnitude of forces required to activate individual spiropyran-based force-probes enables the probe behave as a “scout” of molecular forces in materials; the observed behavior of which can be extrapolated to predict the reactivity of potential mechanophores within a given material and deformation.

We subsequently translated the design platform to existing dynamic soft technologies to fabricate the first mechanochemically responsive devices; first, by remotely inducing dielectric patterning of an elastic substrate to produce assorted fluorescent patterns in concert with topological changes; and second, by adopting a soft robotic platform to produce a color change from the strains inherent to pneumatically actuated robotic motion. Shown herein, covalent polymer mechanochemistry provides a viable mechanism to convert the same mechanical potential energy used for actuation into value-added, constructive covalent chemical responses. The color change associated with actuation suggests opportunities for not only new color changing or camouflaging strategies, but also the possibility for simultaneous activation of latent chemistry (e.g., release of small molecules, change in mechanical properties, activation of catalysts, etc.) in soft robots. In addition, mechanochromic stress mapping in a functional actuating device might provide a useful design and optimization tool, revealing spatial and temporal force evolution within the actuator in a way that might also be coupled to feedback loops that allow autonomous, self-regulation of activity.

In the future, both the specific material and the general approach should be useful in enriching the responsive functionality of soft elastomeric materials and devices. We anticipate the development of new mechanophores that, like the materials, are reversibly and repeatedly activated, expanding the capabilities of soft, active devices and further permitting dynamic control over chemical reactivity that is otherwise inaccessible, each in response to a single remote signal.

Experimental and Theoretical Models to Probe Mechanisms of Biological Charge Flow

Nature is challenged to move charge efficiently over many length scales. From sub-nm to μm distances, electron-transfer proteins orchestrate energy conversion, storage, and release both inside and outside the cell. Uncovering the detailed mechanisms of biological electron-transfer reactions, which are often coupled to bond-breaking and bond-making events, is essential to designing durable, artificial energy conversion systems that mimic the specificity and efficiency of their natural counterparts. Here, we use theoretical modeling of long-distance charge hopping (Chapter 3), synthetic donor-bridge-acceptor molecules (Chapters 4, 5, and 6), and de novo protein design (Chapters 5 and 6) to investigate general principles that govern light-driven and electrochemically driven electron-transfer reactions in biology. We show that fast, μm-distance charge hopping along bacterial nanowires requires closely packed charge carriers with low reorganization energies (Chapter 3); singlet excited-state electronic polarization of supermolecular electron donors can attenuate intersystem crossing yields to lower-energy, oppositely polarized, donor triplet states (Chapter 4); the effective static dielectric constant of a small (~100 residue) de novo designed 4-helical protein bundle can change upon phototriggering an electron transfer event in the protein interior, providing a means to slow the charge-recombination reaction (Chapter 5); and a tightly-packed de novo designed 4-helix protein bundle can drastically alter charge-transfer driving forces of photo-induced amino acid radical formation in the bundle interior, effectively turning off a light-driven oxidation reaction that occurs in organic solvent (Chapter 6). This work leverages unique insights gleaned from proteins designed from scratch that bind synthetic donor-bridge-acceptor molecules that can also be studied in organic solvents, opening new avenues of exploration into the factors critical for protein control of charge flow in biology.