Dual-energy material decomposition for cone-beam computed tomography in image-guided radiotherapy.

Background: Dual-energy (DE) diagnostic computed tomography (CT) combines two scans of different photon energy spectra which can provide additional image information as compared to standard CT. We developed a DE material decomposition scan protocol for daily cone-beam CT (CBCT) of head-and-neck patients receiving radiotherapy and tested it in a clinical trial. Material and methods: Our DE CBCT protocol consisted of an 80 and 140 kVp scan. The material decomposition algorithm split the low and high energy scan into components of two basis materials, aluminum and acrylic. Scans of different thicknesses and overlap of the basis materials were acquired to calibrate the model which decomposed the CBCT projections into thicknesses of aluminum and acrylic on a per-pixel basis. Pseudo monochromatic projections were created from these thicknesses and the known energy dependence of the attenuation coefficient of the basis materials. A frequency selective de-noising method was further applied to the basis material projections. The DE CBCT protocol was tested on seven patients. Two DE images were chosen, one at low (50-60) keV to evaluate soft tissue image quality and one at 150 keV to assess metal artifact reduction as compared to standard CBCT. Results: The de-noising algorithm reduced noise by 41% and 69% in the 60 and 150 keV images, respectively, compared to images without the de-noising. The low keV image showed an increase in soft tissue contrast-to-noise ratio of 7-43% compared to the standard clinical CBCT for six of the seven patients. The 150 keV DE CBCT image reduced metal artifacts. Enhanced streaking from metal artifacts were observed in some of the DE CBCT images. Conclusion: Monochromatic DE images from material decomposition can improve soft tissue contrast-to-noise ratio and metal artifact reduction. Improvements are limited, however, and new artifacts were also introduced by the DE algorithm.

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single-energy CT (SECT). Recent research shows that automatic multi-organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. METHODS: In the scope of this work, we proposed four three-dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. RESULTS: Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual-source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross-validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual-energy spectra and outperform deep learning for SECT. CONCLUSIONS: The results of the cross-validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

Effects of Detector Sampling on Noise Reduction in Clinical Photon-Counting Whole-Body Computed Tomography.

OBJECTIVES: Reconstructing images from measurements with small pixels below the system's resolution limit theoretically results in image noise reduction compared with measurements with larger pixels. We evaluate and quantify this effect using data acquired with the small pixels of a photon-counting (PC) computed tomography scanner that can be operated in different detector pixel binning modes and with a conventional energy-integrating (EI) detector. MATERIALS AND METHODS: An anthropomorphic abdominal phantom that can be extended to 3 sizes by adding fat extension rings, equipped with iodine inserts as well as human cadavers, was measured at tube voltages ranging from 80 to 140 kV. The images were acquired with the EI detector (0.6 mm pixel size at isocenter) and the PC detector operating in Macro mode (0.5 mm pixel size at iso) and ultrahigh-resolution (UHR) mode (0.25 mm pixel size at iso). Both detectors are components of the same dual-source prototype computed tomography system. During reconstruction, the modulation transfer functions were matched to the one of the EI detector. The dose-normalized contrast-to-noise ratio (CNRD) values are evaluated as a figure of merit. RESULTS: Images acquired in UHR mode achieve on average approximately 6% higher CNRD compared with Macro mode at the same spatial resolution for a quantitative D40f kernel. Using a sharper B70f kernel, the improvement increases to 21% on average. In addition, the better performance of PC detectors compared with EI detectors with regard to iodine imaging has been evaluated by comparing CNRD values for Macro and EI. Combining both of these effects, a CNRD improvement of up to 34%, corresponding to a potential dose reduction of up to 43%, can be achieved for D40f. CONCLUSIONS: Reconstruction of UHR data with a modulation transfer function below the system's resolution limit reduces image noise for all patient sizes and tube voltages compared with standard acquisitions. Thus, a relevant dose reduction may be clinically possible while maintaining image quality.

Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study.

PURPOSE: The purpose of this study was to assess, using an anthropomorphic digital phantom, the accuracy of algorithms in registering precontrast and contrast-enhanced computed tomography (CT) chest images for generation of iodine maps of the pulmonary parenchyma via temporal subtraction. MATERIALS AND METHODS: The XCAT phantom, with enhanced airway and pulmonary vessel structures, was used to simulate precontrast and contrast-enhanced chest images at various inspiration levels and added CT simulation for realistic system noise. Differences in diaphragm position were varied between 0 and 20 mm, with the maximum chosen to exceed the 95th percentile found in a dataset of 100 clinical subtraction CTs. In addition, the influence of whole body movement, degree of iodine enhancement, beam hardening artifacts, presence of nodules and perfusion defects in the pulmonary parenchyma, and variation in noise on the registration were also investigated. Registration was performed using three lung registration algorithms - a commercial (algorithm A) and a prototype (algorithm B) version from Canon Medical Systems and an algorithm from the MEVIS Fraunhofer institute (algorithm C). For each algorithm, we calculated the voxel-by-voxel difference between the true deformation and the algorithm-estimated deformation in the lungs. RESULTS: The median absolute residual error for all three algorithms was smaller than the voxel size (1.0 × 1.0 × 1.0 mm3 ) for up to an 8 mm diaphragm difference, which is the average difference in diaphragm levels found clinically, and increased with increasing difference in diaphragm position. At 20 mm diaphragm displacement, the median absolute residual error after registration was 0.85 mm (interquartile range, 0.51-1.47 mm) for algorithm A, 0.82 mm (0.50-1.40 mm) for algorithm B, and 0.91 mm (0.54-1.52 mm) for algorithm C. The largest errors were seen in the paracardiac regions and close to the diaphragm. The impact of all other evaluated conditions on the residual error varied, resulting in an increase in the median residual error lower than 0.1 mm for all algorithms, except in the case of whole body displacements for algorithm B, and with increased noise for algorithm C. CONCLUSION: Motion correction software can compensate for respiratory and cardiac motion with a median residual error below 1 mm, which was smaller than the voxel size, with small differences among the tested registration algorithms for different conditions. Perfusion defects above 50 mm will be visible with the commercially available subtraction CT software, even in poorly registered areas, where the median residual error in that area was 7.7 mm.

Towards context-sensitive CT imaging - organ-specific image formation for single (SECT) and dual energy computed tomography (DECT).

PURPOSE: The purpose of this study was to establish a novel paradigm to facilitate radiologists' workflow - combining mutually exclusive CT image properties that emerge from different reconstructions, display settings and organ-dependent spectral evaluation methods into a single context-sensitive imaging by exploiting prior anatomical information. METHODS: The CT dataset is segmented and classified into different organs, for example, the liver, left and right kidney, spleen, aorta, and left and right lung as well as into the tissue types bone, fat, soft tissue, and vessels using a cascaded three-dimensional fully convolutional neural network (CNN) consisting of two successive 3D U-nets. The binary organ and tissue masks are transformed to tissue-related weighting coefficients that are used to allow individual organ-specific parameter settings in each anatomical region. Exploiting the prior knowledge, we develop a novel paradigm of a context-sensitive (CS) CT imaging consisting of a prior-based spatial resolution (CSR), display (CSD), and dual energy evaluation (CSDE). The CSR locally emphasizes desired image properties. On a per-voxel basis, the reconstruction most suitable for the organ, tissue type, and clinical indication is chosen automatically. Furthermore, an organ-specific windowing and display method is introduced that aims at providing superior image visualization. The CSDE analysis allows to simultaneously evaluate multiple organs and to show organ-specific DE overlays wherever appropriate. The ROIs that are required for a patient-specific calibration of the algorithms are automatically placed into the corresponding anatomical structures. The DE applications are selected and only applied to the specific organs based on the prior knowledge. The approach is evaluated using patient data acquired with a dual source CT system. The final CS images simultaneously link the indication-specific advantages of different parameter settings and result in images combining tissue-related desired image properties. RESULTS: A comparison with conventionally reconstructed images reveals an improved spatial resolution in highly attenuating objects and in air while the compound image maintains a low noise level in soft tissue. Furthermore, the tissue-related weighting coefficients allow for the combination of varying settings into one novel image display. We are, in principle, able to automate and standardize the spectral analysis of the DE data using prior anatomical information. Each tissue type is evaluated with its corresponding DE application simultaneously. CONCLUSION: This work provides a proof of concept of CS imaging. Since radiologists are not aware of the presented method and the tool is not yet implemented in everyday clinical practice, a comprehensive clinical evaluation in a large cohort might be topic of future research. Nonetheless, the presented method has potential to facilitate workflow in clinical routine and could potentially improve diagnostic accuracy by improving sensitivity for incidental findings. It is a potential step toward the presentation of evermore increasingly complex information in CT and toward improving the radiologists workflow significantly since dealing with multiple CT reconstructions may no longer be necessary. The method can be readily generalized to multienergy data and also to other modalities.