Preliminary experimental results from a MARS Micro-CT system

Improving dose calculation accuracy in preclinical radiation experiments using multi-energy element resolved cone beam CT

Cone-beam CT (CBCT) in modern pre-clinical small-animal radiation research platforms provides volumetric images for image guidance and experiment planning purposes. In this work, we implemented multi-energy element-resolved (MEER) CBCT using three scans with different kVps on a SmART platform (Precision X-ray Inc.) We performed comprehensive calibration tasks achieve sufficient accuracy for this quantitative imaging purpose. For geometry calibration, we scanned a ball bearing phantom and used an analytical method together with an optimization approach to derive gantry-angle specific geometry parameters. Intensity calibration and correction included the corrections for detector lag, glare, and beam hardening. The corrected CBCT projection images acquired at 30, 40 and 60 kVp in multiple scans were used to reconstruct CBCT images using the Feldkamp-Davis-Kress reconstruction algorithm. After that, an optimization problem was solved to determine images of relative electron density (rED) and elemental composition (EC) that are needed for Monte Carlo-based radiation dose calculation. We demonstrated effectiveness of our CBCT calibration steps by showing improvements in image quality and successful material decomposition in cases with a small animal CT calibration phantom and a plastinated mouse phantom. It was found that artifacts induced by geometry inaccuracy, detector lag, glare and beam hardening were visually reduced. CT number mean errors were reduced from 19\% to 5\%. In the CT calibration phantom case, median errors in H, O, and Ca fractions for all the inserts were below 1\%, 2\%, and 4\% respectively, and median error in rED was less than 5\%. Compared to standard approach deriving material type and rED via CT number conversion, our approach improved Monte Carlo simulation-based dose calculation accuracy in bone regions. Mean dose error was reduced from 47.5\% to 10.9\%.

Principal Component Analysis in Projection and Image Domains-Another Form of Spectral Imaging in Photon-Counting CT

OBJECTIVE

We explore the feasibility of principal component analysis (PCA) as a form of spectral imaging in photon-counting CT.

METHODS

Using the data acquired by a prototype system and simulated by computer, we investigate the feasibility of spectral imaging in photon-counting CT via PCA for feature extraction and study the impacts made by data standardization and de-noising on its performance.

RESULTS

The PCA in the projection domain maintains the data consistence that is essential for tomographic image reconstruction and performs virtually the same as that in the image domain. The first three primary components account for more than 99.99% covariance of the data. Within anticipation, the contrast-to-noise ratio (CNR) between the target and background in the first principal component image can be larger than that in the image generated from the data acquired in each energy bin. More importantly, the CNR in the first principal component image may be larger than that in the image formed by the summed data acquired in all energy bins (i.e., the conventional polychromatic CT image). In addition, de-noising can not only reduce the noise in images but also improve the effectiveness/efficiency of PCA in feature extraction.

CONCLUSION

The PCA in either projection or image domain provides another form of spectral imaging in photon-counting CT that fits the essential requirements on spectral imaging in true color.

SIGNIFICANCE

The verification of PCA's feasibility in CT as a form spectral imaging and observation of its potential superiority in CNR over conventional polychromatic CT are meaningful in theory and practice.

K-edge-based interior tomography

Interior tomography reconstructs a region of interest using truncated projection data, but it is subject to the ill-posedness of interior tomography. With the photon-counting detector, K-edge imaging uses data in the low and high energy bins around the K-edge of a contrast agent, and can faithfully recover true image contrast for improved diagnosis. The purpose of this paper is to reconstruct a region of interest inside a patient assuming the existence of a known K-edge material. In this case, there is a significant difference in x-ray attenuation around the K-edge, but these attenuation coefficients are inter-related to guide updating an intermediate reconstruction until a stopping criterion is satisfied. In our study, new interior tomography algorithms were developed without any major computational overhead, and several phantoms were used to validate the algorithms. The proposed methods are advantageous relative to the existing interior tomography algorithms, because of the available spectral information in the form of a known K-edge material.

Cardiac CT Imaging of Plaque Vulnerability: Hype or Hope?

Advances in cardiovascular computed tomography (CT) have resulted in an excellent ability to exclude coronary heart disease (CHD). Anatomical information, functional information, and spectral information can already be obtained with current CT technologies. Moreover, novel developments such as targeted nanoparticle contrast agents, photon-counting CT, and phase contrast CT will further enhance the diagnostic value of cardiovascular CT. This review provides an overview of current state of the art and future cardiovascular CT imaging.

New Applications of Cardiac Computed Tomography: Dual-Energy, Spectral, and Molecular CT Imaging

Computed tomography (CT) has evolved into a powerful diagnostic tool, and it is impossible to imagine current clinical practice without CT imaging. Because of its widespread availability, ease of clinical application, superb sensitivity for the detection of coronary artery disease, and noninvasive nature, CT has become a valuable tool within the armamentarium of cardiologists. In the past few years, numerous technological advances in CT have occurred, including dual-energy CT, spectral CT, and CT-based molecular imaging. By harnessing the advances in technology, cardiac CT has advanced beyond the mere evaluation of coronary stenosis to an imaging tool that permits accurate plaque characterization, assessment of myocardial perfusion, and even probing of molecular processes that are involved in coronary atherosclerosis. Novel innovations in CT contrast agents and pre-clinical spectral CT devices have paved the way for CT-based molecular imaging.

Material discrimination based on K-edge characteristics

Spectral/multienergy CT employing the state-of-the-art energy-discriminative photon-counting detector can identify absorption features in the multiple ranges of photon energies and has the potential to distinguish different materials based on K-edge characteristics. K-edge characteristics involve the sudden attenuation increase in the attenuation profile of a relatively high atomic number material. Hence, spectral CT can utilize material K-edge characteristics (sudden attenuation increase) to capture images in available energy bins (levels/windows) to distinguish different material components. In this paper, we propose an imaging model based on K-edge characteristics for maximum material discrimination with spectral CT. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed image contrast is. Here, we introduce the contrast-to-noise ratio (CNR) criterion to optimize the energy bin width after the K-edge jump for the maximum CNR. In the simulation, we analyze the reconstructed image quality in different energy bins and demonstrate that our proposed optimization approach can maximize CNR between target region and background region in reconstructed image.

Optimization of K-edge imaging with spectral CT

PURPOSE

Spectral∕multienergy CT has the potential to distinguish different materials by K-edge characteristics. K-edge imaging involves the two energy bins on both sides of a K-edge. The authors propose a K-edge imaging optimization model to determine these two energy bins.

METHODS

K-edge image contrast with spectral CT depends on the specifications of the two energy bins on both sides of a K-edge in the attenuation profile of a relatively high atomic number material. The wider the energy bin width is, the lower the noise level is, and the poorer the reconstructed image contrast is. Here the authors introduce the signal difference to noise ratio (SDNR) criterion to optimize the energy bin widths on both sides of the K-edge for the maximum SDNR.

RESULTS

The authors study K-edge imaging with spectral CT, analyze the effect of K-edge energy bins on the resultant image quality, and establish guidelines for the optimization of energy thresholds. In simulation, the authors demonstrate that our K-edge imaging optimization approach maximizes SDNR in reconstructed images.

CONCLUSIONS

This proposed approach can be readily generalized to deal with more general settings and determine the best energy bins for K-edge imaging.

Energy-discriminative performance of a spectral micro-CT system

Experiments were performed to evaluate the energy-discriminative performance of a spectral (multi-energy) micro-CT system. The system, designed by MARS (Medipix All Resolution System) Bio-Imaging Ltd. (Christchurch, New Zealand), employs a photon-counting energy-discriminative detector technology developed by CERN (European Organization for Nuclear Research). We used the K-edge attenuation characteristics of some known materials to calibrate the detector's photon energy discrimination. For tomographic analysis, we used the compressed sensing (CS) based ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) to reconstruct sample images, which is effective to reduce noise and suppress artifacts. Unlike conventional CT, the principal component analysis (PCA) method can be applied to extract and quantify additional attenuation information from a spectral CT dataset. Our results show that the spectral CT has a good energy-discriminative performance and provides more attenuation information than the conventional CT.

Medipix-based Spectral Micro-CT

Since Hounsfield's Nobel Prize winning breakthrough decades ago, X-ray CT has been widely applied in the clinical and preclinical applications - producing a huge number of tomographic gray-scale images. However, these images are often insufficient to distinguish crucial differences needed for diagnosis. They have poor soft tissue contrast due to inherent photon-count issues, involving high radiation dose. By physics, the X-ray spectrum is polychromatic, and it is now feasible to obtain multi-energy, spectral, or true-color, CT images. Such spectral images promise powerful new diagnostic information. The emerging Medipix technology promises energy-sensitive, high-resolution, accurate and rapid X-ray detection. In this paper, we will review the recent progress of Medipix-based spectral micro-CT with the emphasis on the results obtained by our team. It includes the state- of-the-art Medipix detector, the system and method of a commercial MARS (Medipix All Resolution System) spectral micro-CT, and the design and color diffusion of a hybrid spectral micro-CT.