In radiation treatment planning (RTP), CT reconstruction that combats projection truncation artifacts induced by the patient being positioned partially outside the scan field-of-view (FOV) needs to maintain high geometric and Hounsfield Unit (HU) accuracy outside the scan FOV. A new image reconstruction method has been proposed for clinical helical CT simulation scans. This method generates support images using the Discrete Algebraic Reconstruction Technique to accurately estimate patient contours outside the scan FOV and then uses support images to guide the projection extension. The proposed method improved geometric accuracy in objects outside the scan FOV compared to a more conventional method and kept the geometric distortion within 5 mm under very severe truncation. It also demonstrated HU accuracy in objects outside the scan FOV within 2.5% for a variety of soft tissues and 15% for bone tissues on a typical electron density phantom. Images of three radiotherapy patient cases reconstructed with the proposed method exhibited clearly defined, naturally looking patient contours, including the recovery of skinfold outside the scan FOV. The proposed method shows the potential for providing clinically desirable extended FOV images for a variety of patient setups in RTP.
Purpose: About one third of all deaths worldwide can be traced to some form of cardiovascular disease. The gold standard for the diagnosis and interventional treatment of blood vessels is digital subtraction angiography (DSA). An alternative to DSA is K-edge subtraction (KES) imaging, which has been shown to be advantageous for moving organs and for eliminating image artifacts caused by patient movement. As highly brilliant, monochromatic x-rays are required for this method, it has been limited to synchrotron facilities so far, restraining the applicability in the clinical routine. Over the past decades, compact synchrotron x-ray sources based on inverse Compton scattering have been evolving; these provide x-rays with sufficient brilliance and meet spatial and financial requirements for laboratory settings or university hospitals.
Approach: We demonstrate a proof-of-principle KES imaging experiment using the Munich Compact Light Source (MuCLS), the first user-dedicated installation of a compact synchrotron x-ray source worldwide. A series of experiments were performed both on a phantom and an excised human carotid to demonstrate the ability of the proposed KES technique to separate the iodine contrast agent and calcifications.
Results: It is shown that the proposed filter-based KES method allows for the iodine-contrast agent and calcium to be clearly separated, thereby providing x-ray images only showing one of the two materials.
Conclusions: The results show that the quasimonochromatic spectrum of the MuCLS enables filter-based KES imaging and can become an important tool in preclinical research and possible future clinical diagnostics.
Dual Energy CT is a modern imaging technique that is utilized in clinical practice to acquire spectral information for various diagnostic purposes including the identification, classification, and characterization of different liver lesions. It provides additional information that, when compared to the information available from conventional CT datasets, has the potential to benefit existing computer vision techniques by improving their accuracy and reliability. In order to evaluate the additional value of spectral versus conventional datasets when being used as input for machine learning algorithms, we implemented a weakly-supervised Convolutional Neural Network (CNN) that learns liver lesion localization and classification without pixel-level ground truth annotations. We evaluated the lesion classification (healthy, cyst, hypodense metastasis) and localization performance of the network for various conventional and spectral input datasets obtained from the same CT scan. The best results for lesion localization were found for the spectral datasets with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV virtual mono-energetic images, respectively, while lesion localization distances of 10.58 ± 17.65 mm were measured for the conventional dataset. In addition, the 40 keV virtual mono-energetic datasets achieved the highest overall lesion classification accuracy of 0.899 compared to 0.854 measured for the conventional datasets. The enhanced localization and classification results that we observed for spectral CT data demonstrates that combining machine-learning technology with spectral CT information may improve the clinical workflow as well as the diagnostic accuracy.
The application of 3D X-ray imaging for biological samples (e.g. biopsies) to gain a deeper understanding of microscopic structures on a (sub)cellular level is restricted by the weak attenuation contrast of soft tissue. The development of novel staining tools for X-ray soft-tissue imaging will overcome these challenges. Here, we present the application of a recently developed method combining a laboratory-based nanoscopic X-ray CT setup enabling resolutions down to 100 nm with a target-specific X-ray staining protocol. The results clearly show that the X-ray attenuation contrast in the samples is remarkably improved by our staining method and detailed tissue (sub)structures are apparent, which cannot be visualized without the staining. The nanoscopic CT data reproduce the tissue morphology with a similar level of detail as the corresponding histological light microscopy images in 2D and enable pathological characterization of the crucial structures. Beyond that, the applied method allows for visualization of the 3D tissue architecture, offering deeper insights into the 3D microscopic structure of soft-tissue. Moreover, we demonstrate the compatibility of the X-ray stain with standard histological staining methods. Beside medical research, the methodology has the potential to contribute to advances in zoology and developmental biology.
In clinical X-ray imaging, the quantitative information in a CT scan has recently been extended by the possibility of using dual-energy information. Dual-energy CT has found its way into clinical imaging during the last few years and has been proven to add additional diagnostic information in different pathologies. It is based on a dual measurement at different photon energies, such that the energy dependence of the linear attenuation coefficient can be used for improved material discrimination. Here, we demonstrate how the dual information accessed with grating-based phase-contrast CT can be used to provide the same quantitative information. Different from dual energy, the phase-contrast measurement directly yields the electron-density and the total attenuation coefficient in a single measurement. With algebraic basis transformation this can be used for quantitative material decomposition, allowing the visualization of quantitative material maps. Further, a simple interaction parametrization has been used for the generation of effective atomic number maps and virtual monochromatic images. The approach has been demonstrated with an experimental angiography simulation with a chicken heart. The results have been compared with iodine staining, which is a current approach for ex-vivo soft-tissue contrast enhancement. The measurements have been performed at a compact laser-undulator synchrotron X-ray source with a tunable quasi-monochromatic X-ray energy. The simultaneous image acquisition guarantees an inherent registration of the two original data-sets. In total, the method provides a range of novel quantitative image representations which can be helpful for specific material discrimination tasks in medical imaging in the future.
About one third of all deaths worldwide can be traced back to some form of cardiovascular disease. The gold standard for the diagnosis and interventional treatment of blood vessels is digital subtraction angiography (DSA). An alternative to DSA is K-edge subtraction (KES) imaging, which has been shown to be advantageous for moving organs and to eliminate image artifacts caused by patient movement. As highly brilliant, monochromatic X-rays are required for this method, it has been limited to synchrotron facilities so far, restraining the applicability in clinical routine. Over the past decades, compact synchrotron X-ray sources based on inverse Compton scattering have been evolving, which provide X-rays with sufficient brilliance and that meet spatial and financial requirements affordable in laboratory settings or for university hospitals. In this study, we demonstrate a proofof-principle KES imaging experiment using the Munich Compact Light Source (MuCLS), the first user-dedicated installation of a compact synchrotron X-ray source worldwide. It is shown that the proposed filter-based KES method allows for iodine-contrast agent and calcium to be clearly separated, thereby providing X-ray images only showing one of the two materials. The results show that the quasi-monochromatic spectrum of the MuCLS enables filter-based K-edge subtraction imaging and can become an important tool in pre-clinical research and possible future clinical diagnostics.
In the medical imaging domain, image quality assessment is usually carried out by human observers (HuO) performing a clinical task in reader studies. To overcome time-consuming reader studies numerical model observers (MO) were introduced and are now widely used in the CT research community to predict the performance of HuOs. In the recent years, machine learning based MOs showed promising results for SPECT. Therefore, we built a neural network, a socalled softmax regression model based on machine learning, as MO for x-ray CT. Performance was evaluated by comparing to one of the most prevalent MOs, the channelized Hotelling observer (CHO). CT image data labeled with confidence ratings assessed in a reader study for a detection-task of signals of different sizes, different noise levels and different reconstruction algorithms were used to train and test the MOs. Data was acquired with a clinical CT scanner. For each of four different x-ray radiation exposures, there were 208 repeated scans of a Catphan phantom. The neural network based MO (NN-MO) as well as the CHO showed good agreement with the performance in the reader study.
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