Carsten Oliver Schirra

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.

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.

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.

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.

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.

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.

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.

Combined effects of pulse pile-up and energy response in energy-resolved, photon-counting computed tomography

The very high x-ray flux rates employed in todays human computed tomography (CT) scanners in order to keep scanning times at a conveniently low level constitute the most challenging obstacle to the advent of clinical, photon-counting (spectral) CT. Even with most sophisticated, application-specific, energy-discriminating, photon-counting readout electronics, designed for room-temperature semi-conductor sensors like CdTe or CZT, the effects of spectral degradation due to pulse pile-up, i.e., count rate losses and gains will have to be taken into account in a clinical setting. The energy registered in a first-order pile-up event (superposition of two pulses) depends strongly on the energies of the two primaries involved, the difference in their arrival times and the spectral detector response behavior. We present an analytic model for the number of expected counts in binned photon-counting detectors, which is based on work by Wielopolski and Gardner and takes into account the combined effects of a spectral detector response function and 1st order pulse pile-up. The analytic model is validated by means of Monte-Carlo simulations and is applied to a simulation of a clinical spectral CT scenario in the context of K-edge imaging of a high-atomic number element as a contrast material. The artifacts in the reconstructed single-bin images and their manifestation in material-decomposed images are discussed and interpreted in terms of gains and losses of counts due to pile-up. Finally, we discuss the shortcomings of the model like the limitation to 1st order pile-up and the inherent restriction of the Wielopolski-Gardner model to peak pile-up.

Maximum Likelihood CT reconstruction from material-decomposed sinograms using fisher information

Recently, a K-edge imaging technique for energy-resolving photon-counting detectors based on Maximum-Likelihood estimation for material separation in the projection domain with subsequent image formation using filtered back projection (FBP) was presented. Despite its computational load, use of statistical reconstruction techniques for image reconstruction from material-specific sinograms is favourable as they feature superior signal to noise ratio (SNR). This work presents an estimation of the noise in material-decomposed sinograms using Fisher information in order to enable statistical image reconstruction. Simulations demonstrate that a gain of two in SNR compared to FBP reconstruction can be obtained. This improvement may be used for x-ray dose reduction but is in particular desirable to limit count rates and, thus, which is a significant challenge with current photon-counting detectors.

Towards in-vivo K-edge imaging using a new semi-analytical calibration method

Flat field calibration methods are commonly used in computed tomography (CT) to correct for system imperfections. Unfortunately, they cannot be applied in energy-resolving CT when using bow-tie filters owing to spectral distortions imprinted by the filter. This work presents a novel semi-analytical calibration method for photon-counting spectral CT systems, which is applicable with a bow-tie filter in place and efficiently compensates pile-up effects at fourfold increased photon flux compared to a previously published method without degradation of image quality. The achieved reduction of the scan time enabled the first K-edge imaging in-vivo. The method employs a calibration measurement with a set of flat sheets of only a single absorber material and utilizes an analytical model to predict the expected photon counts, taking into account factors such as x-ray spectrum and detector response. From the ratios of the measured x-ray intensities and the corresponding simulated photon counts, a look-up table is generated. By use of this look-up table, measured photon-counts can be corrected yielding data in line with the analytical model. The corrected data show low pixel-to-pixel variations and pile-up effects are mitigated. Consequently, operations like material decomposition based on the same analytical model yield accurate results. The method was validated on a experimental spectral CT system equipped with a bow-tie filter in a phantom experiment and an in-vivo animal study. The level of artifacts in the resulting images is considerably lower than in images generated with a previously published method. First in-vivo K-edge images of a rabbit selectively depict vessel occlusion by an ytterbium-based thermoresponsive polymer.