Proximal ADMM for multi-channel image reconstruction in spectral X-ray CT.

The development of spectral X-ray computed tomography (CT) using binned photon-counting detectors has received great attention in recent years and has enabled selective imaging of contrast agents loaded with K-edge materials. A practical issue in implementing this technique is the mitigation of the high-noise levels often present in material-decomposed sinogram data. In this work, the spectral X-ray CT reconstruction problem is formulated within a multi-channel (MC) framework in which statistical correlations between the decomposed material sinograms can be exploited to improve image quality. Specifically, a MC penalized weighted least squares (PWLS) estimator is formulated in which the data fidelity term is weighted by the MC covariance matrix and sparsity-promoting penalties are employed. This allows the use of any number of basis materials and is therefore applicable to photon-counting systems and K-edge imaging. To overcome numerical challenges associated with use of the full covariance matrix as a data fidelity weight, a proximal variant of the alternating direction method of multipliers is employed to minimize the MC PWLS objective function. Computer-simulation and experimental phantom studies are conducted to quantitatively evaluate the proposed reconstruction method.

Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT.

The development of spectral computed tomography (CT) using binned photon-counting detectors has garnered great interest in recent years and has enabled selective imaging of K-edge materials. A practical challenge in CT image reconstruction of K-edge materials is the mitigation of image artifacts that arise from reduced-view and/or noisy decomposed sinogram data. In this note, we describe and investigate sparsity-regularized penalized weighted least squares-based image reconstruction algorithms for reconstructing K-edge images from few-view decomposed K-edge sinogram data. To exploit the inherent sparseness of typical K-edge images, we investigate use of a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ1-norm with a wavelet sparsifying transform. Computer-simulation and experimental phantom studies are conducted to quantitatively demonstrate the effectiveness of the proposed reconstruction algorithms.

Multicolor computed tomographic molecular imaging with noncrystalline high-metal-density nanobeacons.

Computed tomography (CT) is one of the most frequently pursued radiology technologies applied in the clinics today and in the preclinical field of biomedical imaging. Myriad advances have been made to make this technique more powerful with improved signal sensitivity, rapid image acquisition and faster reconstruction. Synergistic development of novel nanoparticles has been adopted to produce the next-generation CT contrasts agents for imaging specific biological markers. Nanometer-sized agents are anticipated to play a critical part in the prospect of medical diagnostics owing to their capabilities of targeting specific biological markers, extended blood circulation time and defined biological clearance. This review paper introduces the readers to the fundamental design principles of nanoparticulate CT contrast agents with a special emphasis on molecular imaging with noncrystalline high-metal-density nanobeacons.

Spectral CT: a technology primer for contrast agent development.

Recent developments in spectral CT systems featuring binned photon-counting detector technology have enabled an imaging concept on a pre-clinical level that has been coined K-edge imaging. This exciting concept allows the selective and quantitative imaging of contrast media by exploiting the K-edge discontinuity in the photo-electric component of X-ray absorption. An ideal application for K-edge imaging is CT imaging of target-specific and conventional contrast agents that have been designed to be spectral-CT-visible. Current limitations in detector hardware, however, result in typically high noise levels that hamper the application of K-edge imaging. In order to battle noise and assure sufficient sensitivity, the development of dedicated K-edge contrast media in combination with advanced image processing techniques is imperative. This work attempts a comprehensive overview on how the concert of dedicated contrast media, optimized data acquisition and innovative data processing techniques improve sensitivity of K-edge imaging which will foster clinical translation of the technology.

Statistical reconstruction of material decomposed data in spectral CT.

Photon-counting detector technology has enabled the first experimental investigations of energy-resolved computed tomography (CT) imaging and the potential use for K-edge imaging. However, limitations in regards to detecter technology have been imposing a limit to effective count rates. As a consequence, this has resulted in high noise levels in the obtained images given scan time limitations in CT imaging applications. It has been well recognized in the area of low-dose imaging with conventional CT that iterative image reconstruction provides a superior signal to noise ratio compared to traditional filtered backprojection techniques. Furthermore, iterative reconstruction methods also allow for incorporation of a roughness penalty function in order to make a trade-off between noise and spatial resolution in the reconstructed images. In this work, we investigate statistically-principled iterative image reconstruction from material-decomposed sinograms in spectral CT. The proposed reconstruction algorithm seeks to minimize a penalized likelihood-based cost functional, where the parameters of the likelihood function are estimated by computing the Fisher information matrix associated with the material decomposition step. The performance of the proposed reconstruction method is quantitatively investigated by use of computer-simulated and experimental phantom data. The potential for improved K-edge imaging is also demonstrated in an animal experiment.

Second Generation Gold Nanobeacons for Robust K-Edge Imaging with Multi-Energy CT.

Spectral CT is the newest advancement in CT imaging technology, which enhances traditional CT images with the capability to image and quantify certain elements based on their distinctive K-edge energies. K-edge imaging feature recognizes high accumulations of targeted elements and presents them as colorized voxels against the normal grayscale X-ray background offering promise to overcome the relatively low inherent contrast within soft tissue and distinguish the high attenuation of calcium from contrast enhanced targets. Towards this aim, second generation gold nanobeacons (GNB(2)), which incorporate at least five times more metal than the previous generation was developed. The particles were synthesized as lipid-encapsulated, vascularly constrained (>120 nm) nanoparticle incorporating tiny gold nanoparticles (2-4 nm) within a polysorbate core. The choice of core material dictated to achieve a higher metal loading. The particles were thoroughly characterized by physicochemical techniques. This study reports one of the earlier examples of spectral CT imaging with gold nanoparticles demonstrating the potential for targeted in vitro and in vivo imaging and eliminates calcium interference with CT. The use of statistical image reconstruction shows high SNR may allow dose reduction and/or faster scan times.

Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data.

A challenge in photoacoustic tomography (PAT) brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. By use of information regarding the skull morphology and composition obtained from adjunct x-ray computed tomography image data, we developed a subject-specific imaging model that accounts for such aberrations. A time-reversal-based reconstruction algorithm was employed with this model for image reconstruction. The image reconstruction methodology was evaluated in experimental studies involving phantoms and monkey heads. The results establish that our reconstruction methodology can effectively compensate for skull-induced acoustic aberrations and improve image fidelity in transcranial PAT.

An early investigation of ytterbium nanocolloids for selective and quantitative "multicolor" spectral CT imaging.

We report a novel molecular imaging agent based on ytterbium designed for use with spectral "multicolor" computed tomography (CT). Spectral CT or multicolored CT provides all of the benefits of traditional CT, such as rapid tomographic X-ray imaging, but in addition, it simultaneously discriminates metal-rich contrast agents based on the element's unique X-ray K-edge energy signature. Our synthetic approach involved the use of organically soluble Yb(III) complex to produce nanocolloids of Yb of noncrystalline nature incorporating a high density of Yb (>500K/nanoparticle) into a stable metal particle. The resultant particles are constrained to vasculature (∼200 nm) and are highly selective for binding fibrin in the ruptured atherosclerotic plaque. Nanoparticles exhibited excellent signal sensitivity, and the spectral CT technique uniquely discriminates the K-edge signal (60 keV) of Yb from calcium (bones). Bioelimination and preliminary biodistribution reflected the overall safety and defined clearance of these particles in a rodent model.