Increased separability of K-edge nanoparticles by photon-counting detectors for spectral micro-CT.

BACKGROUND: X-ray CT/micro-CT methods with photon-counting detectors (PCDs) and high Z materials are a hot research topic. One method using PCDs allows for spectral imaging in 5 energy windows while conventional X-ray detectors only collect energy-integrating data. OBJECTIVE: To demonstrate the enhanced separation of contrast materials by using PCDs, multivariate analysis, and linear discriminant methods. METHODS: Phantoms containing iodine and aqueous nanomaterials were scanned on a MARS spectral micro-CT. Image volumes were segmented into separate material-specific populations. Contrast comparisons were made by calculating T2 test statistics in the univariate, pseudo-conventional and multivariate, spectral CT data sets. Separability after Fisher discriminant analysis (FDA) was also assessed. RESULTS: The T2 values calculated for material comparisons increased as a result of the spectral expansion. The majority of the tested contrast agents showed increased T2 values by a factor of ∼2 -3. The total significant T2 statistics in the pure and mixed lanthanide image sets increased in the spectral data set. CONCLUSION: This work consolidates the groundwork for photon-counting-based material decomposition with micro-CT, facilitating future development of novel nanomaterials and their preclinical applications.

Growth of tumor emboli within a vessel model reveals dependence on the magnitude of mechanical constraint.

Tumor emboli-aggregates of tumor cells within vessels-pose a clinical challenge as they are associated with increased metastasis and tumor recurrence. When growing within a vessel, tumor emboli are subject to a unique mechanical constraint provided by the tubular geometry of the vessel. Current models of tumor emboli use unconstrained multicellular tumor spheroids, which neglect this mechanical interplay. Here, we modeled a lymphatic vessel as a 200 µm-diameter channel in either a stiff or soft, bioinert agarose matrix to create a vessel-like constraint model (VLCM), and we modeled colon or breast cancer tumor emboli with aggregates of HCT116 or SUM149PT cells, respectively. The stiff matrix VLCM constrained the tumor emboli to the cylindrical channel, which led to continuous growth of the emboli, in contrast to the growth rate reduction that unconstrained spheroids exhibit. Emboli morphology in the soft matrix VLCM, however, was dependent on the magnitude of mechanical mismatch between the matrix and the cell aggregates. In general, when the elastic modulus of the matrix of the VLCM was greater than the emboli (EVLCM/Eemb > 1), the emboli were constrained to grow within the channel, and when the elastic modulus of the matrix was less than the emboli (0 < EVLCM/Eemb < 1), the emboli bulged into the matrix. Due to a large difference in myosin II expression between the cell lines, we hypothesized that tumor cell aggregate stiffness is an indicator of cellular force-generating capability. Inhibitors of myosin-related force generation decreased the elastic modulus and/or increased the stress relaxation of the tumor cell aggregates, effectively increasing the mechanical mismatch. The increased mechanical mismatch after drug treatment was correlated with increased confinement of tumor emboli growth along the channel, which may translate to increased tumor burden due to the increased tumor volume within the diffusion distance of nutrients and oxygen.

Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising.

Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in popular network architectures, simulating human-like learning in the form of "quadratic-neuron-based deep learning". Our prior theoretical studies have shown important merits of quadratic neurons and networks in representation, efficiency, and interpretability. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred as the quadratic autoencoder, and apply it to low-dose CT denoising. The experimental results on the Mayo low-dose CT dataset demonstrate the utility and robustness of quadratic autoencoder in terms of image denoising and model efficiency. To our best knowledge, this is the first time that the deep learning approach is implemented with a new type of neurons and demonstrates a significant potential in the medical imaging field.

A novel calibration method incorporating nonlinear optimization and ball-bearing markers for cone-beam CT with a parameterized trajectory.

PURPOSE: Cone-beam (CB) CT is a powerful noninvasive imaging modality, and is widely used in many applications. Accurate geometric parameters are essential for high-quality image reconstruction. Usually, a CBCT system with higher spatial resolution, particularly on the order of microns or nanometers, will be more sensitive to the parametric accuracy. Here, we propose a novel calibration method combining a simple phantom containing ball bearing markers and an advanced optimization procedure. This method can be applied to CBCT with reproducible geometry and frame-to-frame invariant geometric parameters. METHODS: Our proposed simplex-simulated annealing procedure minimizes the cost function that associates the geometrical parameters with the degree to which the back projections of the ball bearings in projections from various viewing angles converge, and the global minimum of the cost function corresponds to the actual geometric parameters. Specifically, six geometric parameters can be directly obtained by minimizing the cost function, and the last parameter, the distance from source to rotation axis (SRD), can be obtained using prior knowledge of the phantom - the spacing between the two ball bearings. RESULTS: Numerical simulation was performed to validate that the proposed method with various noise levels. With the proposed method, the mean errors and standard deviations can be reduced to ∼10% and less than 1/3 of a competing benchmark method in the case of strong Gaussian noise (sigma = 200% of the pixel size) and large tilt angle (tilt angle =  - 4 ∘ ). The calibration experiments with micro-CT and high-resolution CT scanners demonstrate that the proposed method recovers imaging parameters accurately, leading to superior image quality. CONCLUSION: The proposed method can obtain accurate geometric parameters of a CBCT system with a circular trajectory. While in the case of micro-CT the proposed method has a performance comparable to the competing method, for high-resolution CT, which is more sensitive to geometric calibration, the proposed method demonstrates higher calibration accuracy and more robustness than the benchmark algorithm.

Nanophosphor-Based Contrast Agents for Spectral X-ray Imaging.

Lanthanide-based nanophosphors (NPhs) are herein developed as contrast agents for spectral X-ray imaging, highlighting the chemical, macromolecular and structural differences derived from ligand exchange on computed tomography (CT) and solvent dispersibility. Taking advantage of the ability of spectral X-ray imaging with photon-counting detectors to perform image acquisition, analysis, and processing at different energy windows (bins), enhanced signal of our K-edge materials was derived, improving sensitivity of CT imaging, and differentiation between water, tumor-mimic phantoms, and contrast materials. Our results indicate that the most effective of our oleic acid-stabilized K-edge nanoparticles can achieve 2-4x higher contrast than the examined iodinated molecules, making them suitable for deep tissue imaging of tissues or tumors. On the other hand, ligand exchange yielding poly(acrylic acid)-stabilized K-edge nanoparticles allows for high dispersibility and homogeneity in water, but with a lower contrast due to the high density of the polymer grafted, unless further engineering is probed. This is the first well-defined study that manages to correlate NPh grafting density with CT numbers and water dispersibility, laying the groundwork for the development of the next generation CT-guided diagnostic and/or theranostic materials.

Vision 20/20: Simultaneous CT-MRI--Next chapter of multimodality imaging.

Multimodality imaging systems such as positron emission tomography-computed tomography (PET-CT) and MRI-PET are widely available, but a simultaneous CT-MRI instrument has not been developed. Synergies between independent modalities, e.g., CT, MRI, and PET/SPECT can be realized with image registration, but such postprocessing suffers from registration errors that can be avoided with synchronized data acquisition. The clinical potential of simultaneous CT-MRI is significant, especially in cardiovascular and oncologic applications where studies of the vulnerable plaque, response to cancer therapy, and kinetic and dynamic mechanisms of targeted agents are limited by current imaging technologies. The rationale, feasibility, and realization of simultaneous CT-MRI are described in this perspective paper. The enabling technologies include interior tomography, unique gantry designs, open magnet and RF sequences, and source and detector adaptation. Based on the experience with PET-CT, PET-MRI, and MRI-LINAC instrumentation where hardware innovation and performance optimization were instrumental to construct commercial systems, the authors provide top-level concepts for simultaneous CT-MRI to meet clinical requirements and new challenges. Simultaneous CT-MRI fills a major gap of modality coupling and represents a key step toward the so-called "omnitomography" defined as the integration of all relevant imaging modalities for systems biology and precision medicine.