Browsing by Author "Greenberg, Joel A"
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Item Open Access Analysis of X-Ray Diffraction Imaging for Thick Tissue Imaging Using a GPU-Accelerated Monte Carlo Code(2023) Ferguson, KyleOur group has shown X-ray diffraction imaging for thin samples, however, its applicability to thick samples for pathology diagnostics, small animal imaging, and potentially in-vivo applications has yet to be explored. Single scatter events dominate in tissues on the order of a few millimeters, and solving the inverse problem of scatter localization is straightforward. As the sample thickness increases, multiple scatter and geometric blurring effects become important. We look to quantify the role of optical and geometric object thickness, explore the effects of tumor size and location, and quantify the role of varying breast density in medical X-ray diffraction imaging. Using MC-GPU, a Monte Carlo GPU-accelerated photon simulation code, we simulate X-ray diffraction studies for various energies with phantoms combining a variety of object compositions, tumor sizes, tumor locations, and distances from the detector. Previous validation of MC-GPU conducted by our group has added additional form factors allowing for the implementation of the molecular interference function. A notional pencil beam diffraction system is modeled with a 2D grid of pixels approximating a 2D flat panel, noise-free, and 100% efficient detector. Results are analyzed using different metrics, including signal-to-background, signal-to-noise, and signal-to-multiple scatter. Analysis showed optimal energy ranges for thick tissue diffraction simulations, non-significant effects of tumor location in the object, and varying breast composition playing a pivotal role in tumor detectability. This work has indicated the potential for significant advances in medical X-ray imaging, specifically in the application to in vivo and thick tissue imaging. We provide evidence that X-ray diffraction imaging on thick tissue samples is feasible under proper conditions. Overall, we further our understanding of the role of thick tissues, tumor location, and tumor size in medical X-ray diffraction imaging and provide a framework for analyzing and implementing XRD imaging on thick samples.
Item Open Access Bunching-induced optical nonlinearity and instability in cold atoms [Invited].(Opt Express, 2011-11-07) Greenberg, Joel A; Schmittberger, Bonnie L; Gauthier, Daniel JWe report a new nonlinear optical process that occurs in a cloud of cold atoms at low-light-levels when the incident optical fields simultaneously polarize, cool, and spatially-organize the atoms. We observe an extremely large effective fifth-order nonlinear susceptibility of χ(⁵) = 7.6 × 10⁻¹⁵ (m/V)⁴, which results in efficient Bragg scattering via six-wave mixing, slow group velocities (∼ c/10⁵), and enhanced atomic coherence times (> 100 μs). In addition, this process is particularly sensitive to the atomic temperatures, and provides a new tool for in-situ monitoring of the atomic momentum distribution in an optical lattice. For sufficiently large light-matter couplings, we observe an optical instability for intensities as low as ∼ 1 mW/cm² in which new, intense beams of light are generated and result in the formation of controllable transverse optical patterns.Item Embargo Development of X-ray Fan Beam Coded Aperture Diffraction Imaging for Improving Breast Cancer Diagnostics(2021) Stryker, StefanX-ray imaging technology has been used for a multitude of medical applications over the years. The typically measured X-ray transmission data, which records shape and density information by measuring the differences in X-ray attenuation throughout a material, have been used in the imaging modalities of radiography and computed tomography (CT), but there are cases where this information alone is not enough for diagnosis. In contrast, X-ray diffraction (XRD) is another X-ray measurement modality, one that typically does not produce spatially resolved 2D/3D images, but instead investigates small spatial spots for assessing material properties/molecular structures based on scattered X-rays. While XRD measurements of human breast tissue have previously suggested differences between signatures of cancerous and benign tissues, the typical diffraction system architectures do not support fast, large field of view imaging that is necessary for medical applications.In this work, an XRD imaging system was developed that can scan a 15x15 cm2 field of view in minutes with an XRD spatial resolution of 1.4 mm2 and momentum transfer (q) resolution of 0.02 Å-1. An X-ray fan beam was used to collect a 15 cm line of XRD measurements in a single snapshot, while a coded aperture is placed between imaged objects and detector, enabling XRD spectra for individual pixels along the fan beam extent to be recovered from the multiplexed measurement. Simulations were used to identify a suitable geometry for the system, while newly designed phantoms and test objects were used to evaluate the resolution/measurement quality. Upon finishing the design, construction, and characterization of the imaging system, studies on cancerous and benign tissue simulant phantoms were conducted to develop and identify top performing machine learning classification algorithms in a well-controlled study. With a shallow neural network (SNN) developed that achieved ≈99% accuracy on XRD image data, studies progressed to real human tissues. With these developments achieved, the final study was conducted where 22 human breast lumpectomy specimens were scanned and the SNN algorithm was modified for identification of human breast cancer. For 15 primary lumpectomy cases used for training and testing, an accuracy of 99.7% was achieved, with an ROC curve AUC of 0.953 and precision-recall curve AUC of 0.771. On the remaining 7 corner/rare cases present that were held out from initial training/testing (as an external dataset), an accuracy of 99.3% was achieved by the SNN, suggesting high performance along with a need for further representation of rare tissue cases in the training process to improve classifier generalization to new lumpectomy cases. This work demonstrates that fast, large field of view XRD imaging of thin samples on a millimeter spatial scale can be achieved using coded apertures. Further, the work shows that machine learning algorithms can complement this imaging modality by making great use of the multitude of input features available when each image pixel contains a full spectrum of XRD intensity vs angle values, allowing for algorithms to differentiate between cancerous and healthy tissue with higher accuracy (99.7%) compared to simple classification approaches (97.3%). Due to this promising potential, future work should seek to further the technology, by improving the spatial/spectral resolution, scan speed, and adding depth resolution, while applying the technology to useful medical tasks including (but not limited to) intraoperative surgical margin assessment, in-vivo imaging for biopsy vetting, and improved radiation therapy tumor localization.
Item Open Access Implementation and Validation of a GPU-based X-ray Diffraction Monte Carlo Simulator for in-vivo Breast imaging applications(2021) Fasina, OluwadamilolaTBD
Item Open Access Quantifying the Sensitivity in X-Ray Diffraction Measurements in Thick Tissue Samples(2023) Miller, CaseyPrevious studies have shown different tissue types and manifestations of diseases result in different X-ray diffraction (XRD) signatures. Earlier efforts by our group have demonstrated the uses and benefits of XRD imaging of thin tissue samples; however, additional questions remain about the ability for XRD-based tissue analysis to provide useful and quantitative data for thicker samples. Thicker samples, up to several centimeters, are representative of real diagnostic scenarios and freshly excised biospecimens in need of further analysis. The goal of our new experimental set-up is to demonstrate that there is a useful signal in XRD imaging that can be extracted from a target composed of thicker materials. My testbed system uses a pencil beam and a Kromek D-matrix detector. This detector has a 2D array, which allows for energy independent and angle dispersive XRD measurements with a very short acquisition time. This study utilizes biologically-relevant phantoms to quantify detection limits in terms of signal-to-noise. Theoretical calculations using Beer’s law are compared to my experimental measurements. The results show the impact of self-attenuation and scatter on signal strength for narrow (10 mm x 10 mm) and wide (50 mm x 50 mm) phantom of varying thicknesses. Ultimately, this new system will provide the quantitative and experimental results to support future studies on in-vivo and ex-vivo XRD diagnostic techniques in the medical community.