By acquiring tomographic measurements with several distinct photon energy spectra, spectral computed tomography (spectral CT) is able to provide additional material-specific information compared with conventional CT. This information enables the generation of material selective images, which have found various applications in medical imaging. However, material decomposition typically leads to noise amplification and a degradation of the signal-to-noise ratio. This is still a fundamental problem of spectral CT, especially for low-dose medical applications. Inspired by the success for low-dose conventional CT, several statistical iterative reconstruction algorithms for spectral CT have been developed. These algorithms typically rely on detailed knowledge about the spectrum and the detector response. Obtaining this knowledge is often difficult in practice, especially if photon counting detectors are used to acquire the energy specific information. In this paper, a new algorithm for joint statistical iterative material image reconstruction is presented. It relies on a semi-empirical forward model which is tuned by calibration measurements. This strategy allows to model spatially varying properties of the imaging system without requiring detailed prior knowledge of the system parameters. We employ an efficient optimization algorithm based on separable surrogate functions to accelerate convergence and reduce the reconstruction time. Numerical as well as real experiments show that our new algorithm leads to reduced statistical bias and improved image quality compared with projection-based material decomposition followed by analytical or iterative image reconstruction.

• Current dual-energy CT platforms provide accurate, reliable quantitative information. • Dual-energy CT cross-platform evaluation revealed noticeable performance differences between different systems. • Dual-layer CT offers constant noise levels over the complete energy range.

ObjectivesAfter endovascular aortic repair (EVAR), discrimination of endoleaks and intra-aneurysmatic calcifications within the aneurysm often requires multiphase computed tomography (CT). Spectral photon-counting CT (SPCCT) in combination with a two-contrast agent injection protocol may provide reliable detection of endoleaks with a single CT acquisition.MethodsTo evaluate the feasibility of SPCCT, the stent-lined compartment of an abdominal aortic aneurysm phantom was filled with a mixture of iodine and gadolinium mimicking enhanced blood. To represent endoleaks of different flow rates, the adjacent compartments contained either one of the contrast agents or calcium chloride to mimic intra-aneurysmatic calcifications. After data acquisition with a SPCCT prototype scanner with multi-energy bins, material decomposition was performed to generate iodine, gadolinium and calcium maps.ResultsIn a conventional CT slice, Hounsfield units (HU) of the compartments were similar ranging from 147 to 168 HU. Material-specific maps differentiate the distributions within the compartments filled with iodine, gadolinium or calcium.ConclusionSPCCT may replace multiphase CT to detect endoleaks without sacrificing diagnostic accuracy. It is a unique feature of our method to capture endoleak dynamics and allow reliable distinction from intra-aneurysmatic calcifications in a single scan, thereby enabling a significant reduction of radiation exposure.Key Points• SPCCT might enable advanced endoleak detection.• Material maps derived from SPCCT can differentiate iodine, gadolinium and calcium.• SPCCT may potentially reduce radiation burden for EVAR patients under post-interventional surveillance.

The performance of a recently introduced spectral computed tomography system based on a dual‐layer detector has been investigated. A semi‐anthropomorphic abdomen phantom for CT performance evaluation was imaged on the dual‐layer spectral CT at different radiation exposure levels (CTDI vol of 10 mGy, 20 mGy and 30 mGy). The phantom was equipped with specific low‐contrast and tissue‐equivalent inserts including water‐, adipose‐, muscle‐, liver‐, bone‐like materials and a variation in iodine concentrations. Additionally, the phantom size was varied using different extension rings to simulate different patient sizes. Contrast‐to‐noise (CNR) ratio over the range of available virtual mono‐energetic images (VMI) and the quantitative accuracy of VMI Hounsfield Units (HU), effective‐Z maps and iodine concentrations have been evaluated. Central and peripheral locations in the field‐of‐view have been examined. For all evaluated imaging tasks the results are within the calculated theoretical range of the tissue‐equivalent inserts. Especially at low energies, the CNR in VMIs could be boosted by up to 330% with respect to conventional images using iDose/spectral reconstructions at level 0. The mean bias found in effective‐Z maps and iodine concentrations averaged over all exposure levels and phantom sizes was 1.9% (eff. Z) and 3.4% (iodine). Only small variations were observed with increasing phantom size (+3%) while the bias was nearly independent of the exposure level (±0.2%). Therefore, dual‐layer detector based CT offers high quantitative accuracy of spectral images over the complete field‐of‐view without any compromise in radiation dose or diagnostic image quality.

Breast microcalcifications play an essential role in the detection and evaluation of early breast cancer in clinical diagnostics. However, in digital mammography, microcalcifications are merely graded with respect to their global appearance within the mammogram, while their interior microstructure remains spatially unresolved and therefore not considered in cancer risk stratification. In this article, we exploit the sub-pixel resolution sensitivity of X-ray dark-field contrast for clinical microcalcification assessment. We demonstrate that the micromorphology, rather than chemical composition of microcalcification clusters (as hypothesised by recent literature), determines their absorption and small-angle scattering characteristics. We show that a quantitative classification of the inherent microstructure as ultra-fine, fine, pleomorphic and coarse textured is possible. Insights underlying the micromorphological nature of breast calcifications are verified by comprehensive high-resolution micro-CT measurements. We test the determined microtexture of microcalcifications as an indicator for malignancy and demonstrate its potential to improve breast cancer diagnosis, by providing a non-invasive tool for sub-resolution microcalcification assessment. Our results indicate that dark-field imaging of microcalcifications may enhance the diagnostic validity of current microcalcification analysis and reduce the number of invasive procedures.