The dramatic improvement in magnetic resonance imaging (MRI) scan time over the past fifteen years through gradient-based methods that sample k-space more efficiently and quickly cannot be sustained, as thresholds regarding hardware and safety limitations are already being approached. Parallel imaging methods (using multiple receiver coils to partially encode k-space) have offered some relief in the efforts and are rapidly becoming the focus of current endeavors to decrease scan time. Ideally, for some applications, phase encoding would be eliminated completely, replaced with array coil encoding instead, and the entire image formed in a single echo. The primary objective of this work was to explore that acceleration limit – to implement and investigate the methodology of single echo acquisition magnetic resonance imaging (SEA MRI). The initial evaluation of promising array coil designs is described, based on parameters determined by the ability to enable the imaging method. The analyses of field patterns, decoupling, and signal-to-noise ratio (SNR) that led to the final 64-channel array coil design are presented, and the fabrication and testing of coils designed for 4.7T and 1.5T are described. A detailed description of the obtainment of the first SEA images – 64xNreadout images, acquired in a single echo – is provided with an evaluation of those images and highly accelerated images (through parallel imaging techniques) based on SNR and artifact power. Finally, the development of methodologies for various MR applications is described: applications that would particularly benefit from the speed of the imaging method, or those to which the method or the tool (array coil) lends itself. These applications include, but are not limited to, 3D imaging (phase encode in the slice select direction), resolution-enhanced imaging, large-scale (field-of-view) microscopy, and conformal surface imaging. Finally, using the primary enablement of the method – the ability to obtain complete MR images at speeds limited only by the time it takes to acquire a single echo – is presented with a discussion of extremely high frame rate imaging. The contribution to the field of medical imaging is the first implementation, characterization, and demonstration of applications for the acquisition of MR images in a single echo.

Nanoparticles have garnered much attention in pharmaceutical and biomedical fields because their small size and high surface area facilitate drug absorption, improve access to cells and organs, and enhance optical imaging. However, delivery of nanoparticles to the body is not always feasible or effective. Here, nanoparticle assemblies (flocs or clusters) for pulmonary drug delivery and biomedical imaging in cells are shown to facilitate delivery, interactions with cells, and manipulation of optical properties of inorganic/organic nanocomposites. The formation of aggregates by physical techniques and their mechanisms are described in detail. For pulmonary delivery, particles with aerodynamic diameters between 1-5 [mu]m deposit efficiently in the deep lungs. However, crystalline, non-porous, poorly water soluble drugs of this size require long dissolution times, limiting absorption by the body. Therefore, drug dissolution must be “decoupled” from deposition to improve absorption. To address this challenge, drug nanoparticles were dispersed within 4-[mu]m water droplets when administered via nebulization or as micron-sized flocs using a pressurized metered dose inhaler (pMDI). Upon deposition in aqueous media, the aerosolized nanoparticle assemblies dissociated into constituent nanoparticles, raising the available surface area for dissolution and increasing dissolution rates, relative to solid particles. Poorly water soluble drug nanoparticles were prepared using a controlled precipitation (CP) or thin film freezing (TFF) process, in which stable nanoparticles (30-300 nm in diameter) with high potencies (>90 wt% drug) were produced by rapidly nucleating drug solutions in the presence of strongly adsorbing polymers or by freezing, respectively. Amorphous, nanoparticles prepared by CP produced stable aqueous dispersions with high fine particle fractions (FPF) of 77% and total emitted doses (TED) of 1.5 mg/min upon nebulization. CP and TFF also produced anisotropic particles (aspect ratios >5), which formed stable suspensions in a hydrofluoroalkane propellant. Inefficient packing of anisotropic particles formed loose, open flocs that stacked upon each other to prevent settling. Upon pMDI actuation, atomized propellant droplets shear apart and template portions of the floc to yield porous particles with high FPFs (49-64%) and TEDs (2.4 mg/actuation). The controlled assembly of gold nanoparticles into clusters is also of great interest for biomedical imaging and therapy because clusters exhibit improved near infrared absorbance (where blood and tissue are most transparent), relative to single spherical particles, and can biodegrade into clearable particles. Gold nanoparticles (5 nm) were assembled into clusters between 30 to 100 nm in diameter with high gold loadings, resulting in strong NIR absorbance. The assembly was kinetically controlled with weakly adsorbing polymers by manipulating electrostatic, van der Waals, steric, and depletion forces. Furthermore, clusters assembled with a biodegradable polymer deaggregated back into primary particles in physiological media and within cells. This kinetic assembly platform is applicable to a wide variety of fields that require high metal loadings and small particle sizes. / text

Optical Coherence Tomography (OCT) is a subsurface imaging technique with many biomedical and industrial applications. In this thesis, we describe our design and implementation of a time domain OCT system. We used this system to obtain OCT images of objects that are important in different applications. We also used an existing quadrature OCT system to obtain both real and imaginary parts of an OCT image. We introduced a new interpretation of OCT images as the 2nd derivative of the scattering potential of an object. To obtain this scattering potential from its 2nd derivative, we implemented a method of definite integration in the spectral-domain. The obtained scattering potential was used to separate the scattering profile from the absorption profile of an object. We applied this new spectroscopic imaging method to quadrature OCT images of different objects.

Stimulated Raman scattering (SRS) imaging is a chemical imaging scheme that can visualize cellular content based on intrinsic chemical bond vibrations. To resolve chemicals with overlapping Raman bands, spectroscopic SRS platforms have been developed. To date, endeavors on high-speed instrumentation have achieved spectral acquisition at the microsecond level, enabling in vivo imaging of cells and tissues. Nevertheless, due to the extremely small Raman cross-sections, the current performance of SRS is bounded by a design space that trades off speed, signal fidelity, and spectral bandwidth. The lack of tailored data mining algorithms further limits the chemical information one can extract from the spectroscopic images. My thesis work focuses on developing computational SRS imaging approaches to break the physical tradeoffs and novel data analytical tools to decipher essential chemical information from stimulated Raman spectroscopic images. Utilizing data redundancy of spectroscopic images, we developed two compressive sensing schemes to improve the imaging speed by one order of magnitude without information loss. To break the sensitivity limit, we proposed an ultrafast spectroscopic SRS system and further integrated it with a deep neural network to synergistically achieve microsecond level imaging in the fingerprint region. To improve the chemical specificity and content levels, we implemented a sparsity-regularized spectral unmixing algorithm, realizing multiplexed imaging of up to six major metabolites in a cell. Finally, enabled by advances in low-exposure imaging and spectral unmixing, longitudinal imaging of biofuel synthesis in live cells with sophisticated chemical information is demonstrated. / 2022-09-24T00:00:00Z

Acousto-optic (AO) sensing and imaging (AOI) is a dual-wave modality that combines ultrasound with diffusive light to measure and/or image the optical properties of optically diffusive media, including biological tissues such as breast and brain. The light passing through a focused ultrasound beam undergoes a phase modulation at the ultrasound frequency that is detected using an adaptive interferometer scheme employing a GaAs photorefractive crystal (PRC). The PRC-based AO system operating at 1064 nm is described, along with the underlying theory, validating experiments, characterization, and optimization of this sensing and imaging apparatus. The spatial resolution of AO sensing, which is determined by spatial dimensions of the ultrasound beam or pulse, can be sub-millimeter for megahertz-frequency sound waves.A modified approach for quantifying the optical properties of diffuse media with AO sensing employs the ratio of AO signals generated at two different ultrasound focal pressures. The resulting “pressure contrast signal” (PCS), once calibrated for a particular set of pressure pulses, yields a direct measure of the spatially averaged optical transport attenuation coefficient within the interaction volume between light and sound. This is a significant improvement over current AO sensing methods since it produces a quantitative measure of the optical properties of optically diffuse media without a priori knowledge of the background illumination. It can also be used to generate images based on spatial variations in both optical scattering and absorption. Finally, the AO sensing system is modified to monitor the irreversible optical changes associated with the tissue heating from high intensity focused ultrasound (HIFU) therapy, providing a powerful method for noninvasively sensing the onset and growth of thermal lesions in soft tissues. A single HIFU transducer is used to simultaneously generate tissue damage and pump the AO interaction. Experimental results performed in excised chicken breast demonstrate that AO sensing can identify the onset and growth of lesion formation in real time and, when used as feedback to guide exposure parameters, results in more predictable lesion formation. / Bernard M. Gordon Center for Subsurface and Imaging Systems (CenSSIS) via the NSF ERC award number EEC-9986821.

<p>  </p> <p>        Analyzing single molecule emission patterns plays a critical role in retrieving the structural and physiological information of their tagged targets, and further, understanding their interactions and cellular context. These emission patterns of tiny light sources (i.e. point spread functions, PSFs) simultaneously encode information such as the molecule’s location, orientation, the environment within the specimen, and the paths the emitted photons took before being captured by the camera. However, retrieving multiple classes of information beyond the 3D position from complex or high-dimensional single molecule data remains challenging, due to the difficulties in perceiving and summarizing a comprehensive yet succinct model. We developed smNet, a deep neural network that can extract multiplexed information near the theoretical limit from both complex and high-dimensional point spread functions. Through simulated and experimental data, we demonstrated that smNet can be trained to efficiently extract both molecular and specimen information, such as molecule location, dipole orientation, and wavefront distortions from complex and subtle features of the PSFs, which otherwise are considered too complex for established algorithms. </p> <p>        Single molecule localization microscopy (SMLM) forms super-resolution images with a resolution of several to tens of nanometers, relying on accurate localization of molecules’ 3D positions from isolated single molecule emission patterns. However, the inhomogeneous refractive indices distort and blur single molecule emission patterns, reduce the information content carried by each detected photon, increase localization uncertainty, and thus cause significant resolution loss, which is irreversible by post-processing. To compensate tissue induced aberrations, conventional sensorless adaptive optics methods rely on iterative mirror-changes and image-quality metrics to compensate aberrations. But these metrics result in inconsistent, and sometimes opposite, metric responses which fundamentally limited the efficacy of these approaches for aberration correction in tissues. Bypassing the previous iterative trial-then-evaluate processes, we developed deep learning driven adaptive optics (DL-AO), for single molecule localization microscopy (SMLM) to directly infer wavefront distortion and compensate distortion near real-time during data acquisition. our trained deep neural network monitors the individual emission patterns from single molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter (Kalman), and drives a deformable mirror to compensate sample induced aberrations. We demonstrated that DL-AO restores single molecule emission patterns approaching the conditions untouched by specimen and improves the resolution and fidelity of 3D SMLM through brain tissues over 130 µm, with as few as 3-20 mirror changes.</p>

No description available.

<p dir="ltr">Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) via MRI are powerful, noninvasive methods for imaging of the human brain. Here, two studies are presented which explore algorithm development for the processing and analysis of fMRI and DTI-MRI data.</p><p dir="ltr">In the first study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34–80 days after the contact activities play season ended (post-season). For each imaging session, functional connectomes (FCs) were computed for each athlete and compared across sessions using a metric reflecting the (self) similarity between two FCs. HAEs were monitored during all practices and games throughout the season using head-mounted sensors. Relative to the pre-season scan session, football athletes exhibited decreased FC self-similarity at the later in-season session, with apparent recovery of self-similarity by the time of the post-season session. In addition, both within and post-season self-similarity was correlated with cumulative exposure to head acceleration events. These results suggest that repetitive exposure to HAEs produces alterations in functional brain connectivity and highlight the necessity of collision-free recovery periods for football athletes.</p><p dir="ltr">In the second study, a method for harmonization of DTI-MRI data across sites was assessed. Pooling of data from multiple sites is limited by noise characteristics of individual scanners and their receive chain elements (e.g., coils, filters, algorithms), requiring careful consideration of methods to harmonize multisite data. Here, the ComBat data harmonization method was assessed on DTI-MRI data to determine if the harmonizing transformation produced by the algorithm could be transferred to harmonize new subject data from previously-observed sites without necessitating reharmonization of pre-existing data. Results indicated that this transferable ComBat methodology (T-ComBat) yielded reduced differences in fractional anisotropy and mean diffusivity across sites when compared with unharmonized data but did not fully reach the performance of ComBat applied to the entire dataset. Results of this study provide guidelines for circumstances (namely, the proportion of subjects one may wish to add to an existing dataset) under which T-ComBat may be effectively applied to harmonize new subject DTI-MRI data.</p>

Computed tomography (CT) combined with deep learning (DL) has recently shown great potential in biomedical imaging. Complex DL models with varying architectures inspired by the human brain are improving imaging software and aiding diagnosis. However, the accuracy of these DL models heavily relies on the datasets used for training, which often contain low-quality CT images from low-dose CT (LDCT) scans. Moreover, in contrast to the neural architecture of the human brain, DL models today are dense and complex, resulting in a significant computational footprint. Therefore, in this work, we propose sparse optimizations to minimize the complexity of the DL models and leverage architecture-aware optimization to reduce the total training time of these DL models. To that end, we leverage a DL model called DenseNet and Deconvolution Network (DDNet). The model enhances LDCT chest images into high-quality (HQ) ones but requires many hours to train. To further improve the quality of final HQ images, we first modified DDNet's architecture with a more robust multi-level VGG (ML-VGG) loss function to achieve state-of-the-art CT image enhancement. However, improving the loss function results in increased computational cost. Hence, we introduce sparse optimizations to reduce the complexity of the improved DL model and then propose architecture-aware optimizations to efficiently utilize the underlying computing hardware to reduce the overall training time. Finally, we evaluate our techniques for performance and accuracy using state-of-the-art hardware resources. / Master of Science / Deep learning-based (DL) techniques that leverage computed tomography (CT) are becoming omnipresent in diagnosing diseases and abnormalities associated with different parts of the human body. However, their diagnostic accuracy is directly proportional to the quality of the CT images used in training the DL models, which is majorly governed by the radiation dose of the X-ray in the CT scanner. To improve the quality of low-dose CT (LDCT) images, DL-based techniques show promising improvements. However, these techniques require substantial computational resources and time to train the DL models. Therefore, in this work, we incorporate algorithmic techniques inspired by sparse neural architecture of the human brain to reduce the complexity of such DL models. To that end, we leverage a DL model called DenseNet and Deconvolution Network (DDNet) that enhances the quality of CT images generated by low X-ray dosage into high-quality CT images. However, due to its architecture, it takes hours to train DDNet on state-of-the-art hardware resources. Hence, in this work, we propose techniques that efficiently utilize the hardware resources and reduce the time required to train DDNet. We evaluate the efficacy of our techniques on modern supercomputers in terms of speed and accuracy.

<p dir="ltr">Cerebrospinal fluid (CSF) plays a diverse role within the skull including cushioning the brain, regulating intracranial pressure, and clearing metabolic waste via the glymphatic system. Disruptions in CSF flow have long been investigated for hydrocephalus-related diseases such as idiopathic normal pressure hydrocephalus (iNPH). Recently, changes in CSF flow have been implicated in neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease. It remains difficult to obtain <i>in vivo </i>measurements of CSF flow which contribute to disease initiation, progression, and treatment. Three-directional phase-contrast MR imaging (4D flow MRI) has been used to measure CSF velocities within the cerebral ventricles. However, there remain challenges in balancing acquisition time, spatiotemporal resolution, and velocity-to-noise ratio. This is complicated by the low velocities and long relaxation times associated with CSF flow. Additionally, flow-derived metrics associated with cellular adaptations and transport rely on near-wall velocities which are poorly resolved and noisy. To address these challenges, we have applied physics-guided neural networks (PGNN) to super-resolve and denoise synthetic 4D flow MRI of CSF flow within the 3rd and 4th ventricles using novel physics-based loss functions. These loss functions are specifically designed to ensure that high-resolution estimations of flow fields are physically consistent and temporarily coherent. We apply these PGNN to various test cases including synthetically generated 4D flow MRI in the cerebral ventricles and vasculature, <i>in vitro</i> 4D flow MRI acquired at two resolutions in 3D printed phantoms of the 3rd and 4th ventricles, and in vivo 4D flow MRI in a healthy subject. Lastly, we apply these physics-guided networks to investigate blood flow through cerebral aneurysms. These techniques can empower larger studies investigating the coupling between arterial blood flow and CSF flow in conditions such as iNPH and AD.</p>