Silver chalcogenide nanoparticles are biocompatible, low cost and straightforward to synthesize. They have quantum dot-like optical properties, which have been used in imaging and therapeutic applications. We have found these nanoparticles to have strong x-ray contrast production, especially for mammography. We have also innovated in their synthesis by developing an ambient conditions, room temperature method, using a microfluidic chip, which results in nanoparticles that have almost complete clearance within 24 hours. Last, encapsulation in a biodegradable polymer matrix results in remarkable enhancement of the optical properties of these nanoparticles, allowing markedly improved optical imaging and photothermal ablation of breast cancer.
Efficient removal of solid focal tumors is a major challenge in modern medicine. Percutaneous thermal ablation is a first-line treatment for patients not fit for surgical resection or when the disease burden is low, mainly due to expedited patient recovery times, lower rates of post-operative morbidity, and reduced healthcare costs. While continuously gaining popularity, ~100,000 yearly thermal hepatic ablation procedures are currently performed without actively monitoring temperature distributions, leading to high rates of incomplete ablations, local recurrences, and damage to surrounding structures. Recent advancements in computed tomography (CT), especially spectral CT, provide promising opportunities for lowering these rates. The additional information available with spectral CT can provide the necessary capabilities to achieve accurate, reliable, on-demand, and non-invasive thermometry during ablation procedures. By taking advantage of our newly developed spectral physical density maps and their direct relation with temperature changes, we performed experiments on phantoms and ex vivo tissue to develop, evaluate, optimize, and refine a method for generating thermometry maps from spectral CT scans. Our results validate the accuracy of the spectral physical density model, allowing “whole-organ” mass quantifications that are accurate within one percent, as well as demonstrate an ability to extract temperature changes (linear correlation coefficient of 0.9781) non-invasively and in real-time.
Synchrotron X-ray multi-spectral imaging is a novel imaging modality that may allow tracking cells at high resolution in small animal models. The data volume generated by such technique can be of hundreds of Gigabytes for one animal. Automatic, robust and rapid pipeline is therefore of paramount importance for large-scale studies. The goal of this article is to present a full image analysis pipeline ranging from the CT reconstruction up to the segmentation of nanoparticleslabeled- cells. Experimentally, rats that had received an intracerebral transplantation of gold nanoparticles-labeled cells were imaged in vivo in phase contrast mode (propagation-based imaging technique) at two different energies strategically chosen around the k-edge of gold. We apply a dedicated phase retrieval technique on each projection (out of 2000 for complete 2π rotation) before CT reconstruction. Then, a rigid registration is performed between the images below and above k-edge for accurate subtraction of the two data sets, leading to gold concentration maps. Due to the large number of specimens, the registration is based on the automatic segmentation of the cranial skull. Finally, an automatic segmentation of gold-labeled cells within the brain is performed based on high spots of gold concentrations. An example of an in-vivo data set for stroke cell therapy is presented.
The objective of our study was to evaluate the feasibility of using silver sulfide nanoparticles (Ag2S-NP) as a contrast agent for photon-counting mammography. The efficacies of Ag2S-NP and iodine contrast were evaluated using a contrastembedded gradient phantom. The phantom was constructed using tissue-equivalent materials and varied continuously in composition from 100% glandular tissue to 100% adipose tissue. Each contrast agent was prepared at eight different concentrations: 1, 2, 5, 10, 15, 20, 25, and 30 mg/mL. Tubes of contrast agent were inserted into holes bored through the phantom in the direction of varying glandularity. Various images of the phantom were acquired by altering the acquisition parameters (kV, mAs, and high bin fraction). A range of beam energies from 26 kV to 40 kV was tested in this study. Our results demonstrate that for a given contrast agent, the contrast-to-noise ratio (CNR) is linearly proportional to concentration, and its magnitude is dependent on the kV of the spectrum. At mammographic energies, the Ag2S-NP contrast increases with increasing kV and increasing solution concentration. Comparatively, the iodine signal becomes detectable only when the kV of the image is above iodine’s K-edge (33.2 keV). This indicates that the optimal energy for imaging iodine may exceed the clinical mammographic energy range. In summary, we have demonstrated the feasibility of using Ag2S-NP as a contrast agent for breast imaging. Preliminary results from spectral photon-counting mammography indicate that Ag2S-NP contrast has a significantly higher signal than iodine, especially when imaging at lower energies.
The objective of our study is to optimize the acquisition parameters for imaging Ag2S nanoparticles using contrast-enhanced digital mammography (CE-DM) by varying parameters such as kV, mAs, and filtration. The efficacies of three different contrast materials (Ag2S nanoparticles, silver nanoparticles, and iodine) were assessed using a contrast-embedded gradient phantom. The phantom was constructed using tissue-equivalent materials and varied continuously in composition from 100% glandular tissue to 100% adipose tissue. Each contrast agent was prepared at six different concentrations (1, 2, 5, 10, 15, and 25 mg/mL). Holes were bored through the phantom in the direction of varying glandularity, and tubes of contrast agents were inserted into the holes. Phantoms were imaged at four different energies (26 kV, 32 kV, 45 kV, and 49 kV); 5 energy pairs were considered in this study. Our results demonstrate that for a given contrast agent, the contrast-to-noise ratio is linearly proportional to concentration, and its magnitude is dependent on the energy of the low-energy (LE) image. In our study, it was shown that the LE images at 26 kV are better suited for imaging silver-based nanoparticles, and the LE images at 32 kV are better suited for imaging iodine contrast. Thus, the energy of the LE image should be chosen so that it is as close as possible to the k-edge of the contrast material. Preliminary results from CE-DM imaging indicate that silver contrast has a significantly higher signal than iodine contrast when imaging at lower energies, thus demonstrating the feasibility of using silver-based nanoparticles in breast imaging.
Purpose: Multi-energy CT (e.g., dual energy or photon counting) facilitates the identification of certain compounds via data decomposition. However, the standard approach to decomposition (i.e., solving a system of linear equations) fails if – due to noise - a pixel’s vector of HU values falls outside the boundary of values describing possible pure or mixed basis materials. Typically, this is addressed by either throwing away those pixels or projecting them onto the closest point on this boundary. However, when acquiring four (or more) energy volumes, the space bounded by three (or more) materials that may be found in the human body (either naturally or through injection) can be quite small. Noise may significantly limit the number of those pixels to be included within. Therefore, projection onto the boundary becomes an important option. But, projection in higher than 3 dimensional space is not possible with standard vector algebra: the cross-product is not defined. Methods: We describe a technique which employs Clifford Algebra to perform projection in an arbitrary number of dimensions. Clifford Algebra describes a manipulation of vectors that incorporates the concepts of addition, subtraction, multiplication, and division. Thereby, vectors may be operated on like scalars forming a true algebra. Results: We tested our approach on a phantom containing inserts of calcium, gadolinium, iodine, gold nanoparticles and mixtures of pairs thereof. Images were acquired on a prototype photon counting CT scanner under a range of threshold combinations. Comparison of the accuracy of different threshold combinations versus ground truth are presented. Conclusions: Material decomposition is possible with three or more materials and four or more energy thresholds using Clifford Algebra projection to mitigate noise.
Recent advances in Photon Counting CT (PCCT) have facilitated the simultaneous acquisition of multiple
image volumes with differing energy thresholds. This presents the user with several choices for energy
threshold combinations. As compared to standard clinical Dual kVp CT , where the user typically has only
three choices of kVp pairings (e.g., 80/150Sn, 90/150Sn, 100/150Sn), a “quad” PCCT system with 14
threshold settings has Choose(14,4)= 1001 possible threshold combinations (assuming no restrictions). In this
paper we describe a computationally tractable means to order, from best (most accurate) to worst (least
accurate), threshold combinations for the task of discriminating pure materials of assumed approximate
concentrations using the Bhattacharyya Coefficient. We observe that this ordering is not necessarily identical
to the ordering for the task of decomposing material mixtures into their components. We demonstrate our
approach on phantom data.
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