Search

In this work, we applied the singular value decomposition (SVD) method to a set of monochromatic images to extract the dominant physical contributions to image formation. We showed that the first two principal components can be related to an arbitrary pair of basis material in mathematically enclosed expression. The later principal components are assumed to carry mostly sub-leading image formation effects, noise, and reconstruction artifact contribution. The proof of concept is shown on numerical (linear) images and later confirmed on physical spectral CT phantom images obtained with monochromatic x-ray radiation at Elettra synchrotron in Trieste, Italy. Following material decomposition, we also performed a quantitative description of tissue-equivalent phantom materials in terms of material density and effective atomic number.

Breast density (BD) has been shown to be an independent risk factor for breast cancer, and it can be quantified by measuring the volumetric breast density (VBD). The longitudinal change in VBD has been used as a biomarker to assess the effect of chemoprevention drug for breast cancer risk reduction. However, the reproducibility of VBD measurement is hindered by breast compression, which causes temporal changes in the distribution of breast tissues, making it difficult to assess small longitudinal changes in VBD. Dual energy digital breast tomosynthesis (DE DBT) may provide more reproducible VBD measurements over different breast compressions. In this study, we aim to evaluate the reproducibility of VBD measurement using DE DBT. Digital breast phantoms were generated using a virtual clinical trial software (VICTRE) and were compressed multiple times into different clinical-relevant compressed breast thicknesses to emulate the change in repeated imaging of the same breast. The compressed thickness difference of 10 mm exaggerates the variation seen in clinical practice. DE DBT projection images of the phantoms were generated by Monte-Carlo simulations and included quantum noise and scatter radiation. A previously developed scatter correction method and a DE material decomposition technique were applied to obtain the BD map and calculate VBD. Our results show that DE DBT can provide reproducible VBD measurements for breasts under different compressions with the average absolute discrepancy of 2.3% ± 1.1% between two repeated measurements for all phantoms. The largest discrepancy is 3.9% for the extreme case with 20 mm compressed thickness difference.

The feasibility of acquiring multi-energy CT data through slow modulation of the kVp as an alternative to photon-counting detectors (PCDs) is currently under exploration. A low kVp-switching rate can be enabled with a conventional CT system but raises challenges due to missing sinogram views. Our previous work used a CNN-based method for sinogram completion by generating full-sampled images from undersampled sinograms, providing an acceptable image quality at a 22°/kVp switching rate. The purpose of this study was to investigate a GAN-based spectral sinogram completion method for enabling a lower kVp switching rate. A Pix2Pix GAN model with paired undersampled sinogram of 45° or 120° projections/kVp and its corresponding full-sampled sinogram was implemented and trained. The completed data was subsequently used to perform sinogram domain material decomposition. Our results on a simulated FORBILD abdomen phantom dataset showed that the GAN-based method can further lower the kVp switching rate to 45° projections/kVp. The proposed GAN-based sinogram completion method facilitates slow-kVp switching acquisitions and thus further relaxes hardware requirements.

Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DE-CBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.

Accurate quantitation of bone density variation would allow monitoring changes due to aging, disease progression, therapy outcomes or drug response. We present results from our material decomposition method using spectral computed tomography (CT) with a photon counting detector (PCD) for estimating quantitative bone density variations in healthy and arthritic mice. Limited samples of healthy mice and collagen antibody-induced arthritis (CAIA) mice were imaged on our home-built benchtop micro CT system with a wide area photon counting detector with a high resolution 55 micrometer pixel size. A material decomposition algorithm was applied in the image domain to separate and quantify bone and soft tissue variations. The resulting bone densities were quantified globally and locally for both mice. Higher bone densities were seen in the control mice over their arthritis counterparts by about 6-12%. Based on our preliminary results, material decomposition of multi-energy CT images collected with a PCD appears promising for bone density quantification. Our results show that these methods could be successfully applied to arthritis diagnosis and treatment monitoring in a mouse model. We will further investigate the utility of our methods to simultaneously quantify bone density as well as tagged contrast agents. These investigations are expected to help with longitudinal imaging studies to understand disease progression in animal models.

This manuscript explores a new approach for spectral X-ray tomography that uses K-edge filtering structures to attain spectral and spatially coded illumination which enables the acquisition of compressive measurements for the reconstruction of energy-binned images. The system is coined compressive spectral X-ray imaging (CSXI). A multi-stage algorithm is proposed to solve the non-linear ill-posed problem using sparse and low-rank regularization constraints to exploit the structure of the spectral data cube. The proposed algorithm can reconstruct both the energy binned images as well as the material decomposition of the object given a set of basis materials.

Mice are used for models of almost all human diseases and are routinely scanned by micro-CT scanners. Mouse phantoms are often used for image-quality assessment. With recent developments in deep-learning-based preclinical imaging, there is a major need for large micro-CT datasets in which ground truth is known. In this study, we investigate the feasibility of making cost-effective deformable and reconfigurable mouse phantoms to generate real micro-CT datasets that reflect realistic underlying physical characteristics. Such datasets are highly desirable; for example, complicated photon-counting micro-CT datasets are needed for deep-learning-based material decomposition. In our scheme, mouse body parts are 3Dprinted with high precision using rigid or flexible materials. Liquid tissue surrogates (LTSs) or bioinks/cell lines could be used to emulate mouse organs and physiological fluid in the animals. LTSs provide realistic x-ray properties of their biological counterparts. The LTS organs could be contained in not only 3D-printed chambers, but also dialysis tubing, which emulates the cell membrane. Furthermore, through bioprinting and tissue engineering, organs and tissues can be made even more realistic for micro-CT and other types of tomographic scanning.

Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which is able to distinguish different material compositions. Nowadays, deep learning has generated widespread attention in CT imaging applications. In this paper, a method of material decomposition for spectral CT based on improved Fully Convolutional DenseNets (FC-DenseNets) was proposed. Spectral data were acquired by a photon-counting detector and reconstructed spectral CT images were used to construct a training dataset. Experimental results showed that the proposed method could effectively identify bone and different tissues in high noise levels. This work could establish guidelines for multi-material decomposition approaches with spectral CT.

While energy-integration spectral CT with the capability of material decomposition has been providing added value to diagnostic CT imaging in the clinic, photon-counting spectral CT is gaining momentum in research and development, with the potential of overcoming more clinically relevant challenges. In practice, the photon-counting spectral CT provides the opportunity for principal component analysis to effectively extract information from the raw data. However, the principal component analysis in spectral CT may suffer from high noise induced by photon starvation, especially in energy bins at the high energy end. Via phantom and small animal studies, we investigate the feasibility of principal component analysis in photon-counting spectral CT and the benefit that can be offered by de-noising with the Content-Oriented Sparse Representation method.

We present a proof-of-principle demonstration of material decomposition using a single X-ray tube potential (38 kVp + 0.2 mm Sn, for an effective energy around 27 keV) with a Talbot-Lau grating-based phase-contrast computed tomography (CT) system. We show good material separation of water and fat and an accurate quantitative measurement of isopropyl alcohol. This method utilizes the distinctiveness of both components of the refractive index, δ and β, and is promising for separating soft tissue materials that have similar attenuation values such as water and fat.

Accurate physics modeling of a photon counting detector is essential for detector design and performance evaluation, Computer Tomography (CT) system-level performance investigation, material decomposition, image reconstruction. The detector response is complicated because various effects involve, including fluorescence X-rays, primary electron path, charge diffusion, charge repulsion, and charge trapping. In this paper, we will present a comprehensive detector modeling approach, which incorporates all these effect into account.

Raw-data–based material decomposition in spectral CT using photon–counting energy–selective detectors relies on a precise forward model that predicts a count–rate given intersection lengths for each material. This re- quires extensive system–specific measurements or calibration techniques. Existing calibrations either estimate a detected spectrum and are able to account for spectrally distorted assumptions or correct the predicted count rate using a correction function and can accommodate for count rate–dependent effects such as pulse pileup. We propose a calibration method that uses transmission measurements to optimize a correction function that, unlike existing methods, depends both on the photon energy and the count rate. It is thus able to correct for both kinds of distortions. In a simulated material decomposition into water and iodine, the error was reduced by 96 % compared to the best performing reference method if only pulse pileup was present and reduced by 23 % if additionally spectral distortions were taken into account. In phantom measurements using a Dectris SANTIS prototype detector, the proposed method allowed to reduce the error by 29 % compared to the best performing reference method. Artifacts were below the noise level for the proposed method, while the reference methods either showed an offset in the water region or ring artifacts.

Dual-layer spectral computed tomography (CT) provides a novel clinically available concept for material decomposition (calcium hydroxyapatite, HA) and thus to estimate the bone mineral density (BMD) based on non-dedicated clinical examinations. In this study, we assessed whether HA specific BMD measurements with dual-layer spectral CT are accurate in phantoms and vertebral specimens. Dual-layer spectral CT was performed at different tube current settings (500, 250, 125 and 50 mAs) with a tube voltage of 120 kVp. Ex-vivo human vertebrae (n e 13) and a phantom containing different known HA concentrations were placed in a semi-anthropomorphic abdomen phantom. BMD was derived with an in-house developed algorithm from spectral-based virtual monoenergetic images at 50 keV and 200 keV. Values were compared to the HA concentrations of the phantoms and conventional quantitative CT (QCT) measurements using a reference phantom, respectively. Above 125 mAs, which is the radiation exposure level of clinical examinations, errors for phantom measurements based on spectral information were less than 5p, compared to known concentrations. In vertebral specimens, high correlations were found between BMD values assessed with spectral CT and conventional QCT (correlation coefficients > 0.96; p These results suggest a high accuracy of quantitate HA-specific BMD measurements based on dual-layer spectral CT examinations without the need for a reference phantom, thus demonstrating their feasibility in clinical routine.

This project assessed the effectiveness of using two different detectors to obtain dual-energy (DE) micro-CT data for the carrying out of material decomposition. A micro-CT coupled to either a complementary metal-oxide semiconductor (CMOS) or an electron multiplying CCD (EMCCD) detector was used to acquire image data of a 3D-printed phantom with channels filled with different materials. At any instance, materials such as iohexol contrast agent, water, and platinum were selected to make up the scanned object. DE micro-CT data was acquired, and slices of the scanned object were differentiated by material makeup. The success of the decomposition was assessed quantitatively through the computation of percentage normalized root-mean-square error (%NRMSE). Our results indicate a successful decomposition of iohexol for both detectors (%NRMSE values of 1.8 for EMCCD, 2.4 for CMOS), as well as platinum (%NRMSE value of 4.7). The CMOS detector performed material decomposition on air and water on average with 7 times more %NRMSE, possibly due to the decreased sensitivity of the CMOS system. Material decomposition showed the potential to differentiate between materials such as the iohexol and platinum, perhaps opening the door for its use in the neurovascular anatomical region. Work supported by Toshiba America Medical Systems, and partially supported by NIH grant 2R01EB002873.

The energy resolving capabilities of Photon Counting Detectors (PCD) in Computed Tomography (CT) facilitate energy-sensitive measurements. The provided image-information can be processed with Dual Energy and Multi Energy algorithms. A research PCD-CT firstly allows acquiring images with a close to clinical configuration of both the X-ray tube and the CT-detector. In this study, two algorithms (Material Decomposition and Virtual Non-Contrast-imaging (VNC)) are applied on a data set acquired from an anesthetized rabbit scanned using the PCD-CT system. Two contrast agents (CA) are applied: A gadolinium (Gd) based CA used to enhance contrasts for vascular imaging, and xenon (Xe) and air as a CA used to evaluate local ventilation of the animal's lung. Four different images are generated: a) A VNC image, suppressing any traces of the injected Gd imitating a native scan, b) a VNC image with a Gd-image as an overlay, where contrast enhancements in the vascular system are highlighted using colored labels, c) another VNC image with a Xe-image as an overlay, and d) a 3D rendered image of the animal's lung, filled with Xe, indicating local ventilation characteristics. All images are generated from two images based on energy bin information. It is shown that a modified version of a commercially available dual energy software framework is capable of providing images with diagnostic value obtained from the research PCD-CT system.

We discriminated various concentrations’ contrast mediums by spectral photon-counting CT imaging. Contrast media increase the contrast of tissues in x-ray computed tomography (CT) imaging. Conventional x-ray CT uses contrast agent liquids including high x-ray absorption materials like iodine. Spectral photon-counting computed tomography (SPCCT) imaging has the capability of discriminate each contrast mediums with low concentration in one scan by their K-edge if its energy is located in the detectable energy range. The purpose of our research is to reveal the discriminability of contrast mediums by K-edge imaging under the various conditions of concentration included in a water included acrylic phantom with low x-ray exposure.

CT colonography (CTC) uses orally administered fecal-tagging agents to enhance retained fluid and feces that would otherwise obscure or imitate polyps on CTC images. To visualize the complete region of colon without residual materials, electronic cleansing (EC) can be used to perform virtual subtraction of the tagged materials from CTC images. However, current EC methods produce subtraction artifacts and they can fail to subtract unclearly tagged feces. We developed a novel multi-material EC (MUMA-EC) method that uses dual-energy CTC (DE-CTC) and machine-learning methods to improve the performance of EC. In our method, material decomposition is performed to calculate water-iodine decomposition images and virtual monochromatic (VIM) images. Using the images, a random forest classifier is used to label the regions of lumen air, soft tissue, fecal tagging, and their partial-volume boundaries. The electronically cleansed images are synthesized from the multi-material and VIM image volumes. For pilot evaluation, we acquired the clinical DE-CTC data of 7 patients. Preliminary results suggest that the proposed MUMA-EC method is effective and that it minimizes the three types of image artifacts that were present in previous EC methods.

Material decomposition in absorption-based X-ray CT imaging suffers certain inefficiencies when differentiating among soft tissue materials. To address this problem, decomposition techniques turn to spectral CT, which has gained popularity over the last few years. Although proven to be more effective, such techniques are primarily limited to the identification of contrast agents and soft and bone-like materials. In this work, we introduce a novel conditional likelihood, material-decomposition method capable of identifying any type of material objects scanned by spectral CT. The method takes advantage of the statistical independence of spectral data to assign likelihood values to each of the materials on a pixel-by-pixel basis. It results in likelihood images for each material, which can be further processed by setting certain conditions or thresholds, to yield a final material-diagnostic image. The method can also utilize phase-contrast CT (PCI) data, where measured absorption and phase-shift information can be treated as statistically independent datasets. In this method, the following cases were simulated: (i) single-scan PCI CT, (ii) spectral PCI CT, (iii) absorption-based spectral CT, and (iv) single-scan PCI CT with an added tumor mass. All cases were analyzed using a digital breast phantom; although, any other objects or materials could be used instead. As a result, all materials were identified, as expected, according to their assignment in the digital phantom. Materials with similar attenuation or phase-shift values (e.g., glandular tissue, skin, and tumor masses) were especially successfully when differentiated by the likelihood approach.

Material basis decomposition in the sinogram domain requires accurate knowledge of the forward model in spectral CT. Misspecifications over a certain limit will result in biased estimates and make quantum limited quantitative CT difficult. We present a method whereby users can determine the degree of allowed misspecification error in a spectral CT forward model, and still have quantification errors that are quantum limited.

In this study, the feasibility of differentiating uric acid from non-uric acid kidney stones in the presence of iodinated contrast material was evaluated using dual-energy CT (DECT). Iodine subtraction was accomplished with a commercial three material decomposition algorithm to create a virtual non-contrast (VNC) image set. VNC images were then used to segment stone regions from tissue background. The DE ratio of each stone was calculated using the CT images acquired at two different energies with DECT using the stone map generated from the VNC images. The performance of DE ratio-based stone differentiation was evaluated at five different iodine concentrations (21, 42, 63, 84 and 105 mg/ml). The DE ratio of stones in iodine solution was found larger than those obtained in non-iodine cases. This is mainly caused by the partial volume effect around the boundary between the stone and iodine solution. The overestimation of the DE ratio leads to substantial overlap between different stone types. To address the partial volume effect, an expectation-maximization (EM) approach was implemented to estimate the contribution of iodine and stone within each image pixel in their mixture area. The DE ratio of each stone was corrected to maximally remove the influence of iodine solutions. The separation of uric-acid and non-uric-acid stone was improved in the presence of iodine solution.

It is of clinical interest to quantify the concentration of materials in a three-component mixture with known chemical compositions, such as bone-mineral density (BMD) in a trabecular bone composed of calcium hydroxyappitite (CaHA), yellow- and red-marrow, and iron content in the liver composed of soft tissue, fat, and iron. Both pre- and postreconstruction dual-energy CT methods have been used to achieve this goal. The pre-reconstruction method is more accurate due to the elimination of beam-hardening artifacts. After obtaining the equivalent densities of the two basis materials, however, it is unclear how to accurately estimate the concentration of each material in the presence of the third material in the mixture. In this work, we present a pre-reconstruction three-material decomposition method in dualenergy CT to quantify the concentration of each material in a three-component mixture with known chemical compositions. This method employs a specific physical constraint on the equivalent densities of the two basis materials obtained from the conventional basis-material decomposition. We evaluated this method using simulation studies on two types of three-component mixtures: bone-water-fat and Iron-water-CaHA. The results demonstrated that an accurate estimation of the concentration for each material can be achieved with the proposed method.

In addition to a conventional Computed Tomography (CT) image, dual energy (dual kVp) imaging can be used to generate an image of the same anatomy that represents the equivalent density of a particular material, for example, calcium, iodine, water, etc. This image can be used to improve the differentiation of materials as well as improve the accuracy of absolute density measurements in a cross-sectional image. It is important to understand the certainty of the estimation of the density of the material. Both simulations and measurements are used to quantify these errors. Data are acquired using a flat-panel based volumetric CT system, by taking two scans and adjusting the maximum energy of the source spectrum (kVp). Physics based simulations are used to compare with the measurements. After validating the simulation algorithms, the accuracy of the dual kVp method is determined using the simulations in a perturbation study.