Material-selective Images Research Articles

A feasibility study on deep-neural-network-based dose-neutral dual-energy digital breast tomosynthesis.

Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.

X-ray Stain Localization with Near-Field Ptychographic Computed Tomography.

Although X‐ray contrast agents offer specific characteristics in terms of targeting and attenuation, their accumulation in the tissue on a cellular level is usually not known and difficult to access, as it requires high resolution and sensitivity. Here, quantitative near‐field ptychographic X‐ray computed tomography is demonstrated to assess the location of X‐ray stains at a resolution sufficient to identify intracellular structures by means of a basis material decomposition. On the example of two different X‐ray stains, the nonspecific iodine potassium iodide, and eosin Y, which mostly interacts with proteins and peptides in the cell cytoplasm, the distribution of the stains within the cells in murine kidney samples is assessed and compared to unstained samples with similar structural features. Quantitative nanoscopic stain concentrations are in good agreement with dual‐energy micro computed tomography measurements, the state‐of‐the‐art modality for material‐selective imaging. The presented approach can be applied to a variety of X‐ray stains advancing the development of X‐ray contrast agents.

New advances in CT imaging of pancreas diseases: a narrative review.

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Virtual Monoenergetic CT Imaging via Deep Learning

SummaryConventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.

An Efficient One-Step Method for Spectral CT Based on an Approximate Linear Model

Spectral computed tomography (spectral CT) is a medical and biomedical imaging technique which uses the spectral information of the attenuated X-ray beam. Energy-resolved photon-counting detector is a promising technique for improved spectral CT imaging and allows to obtain material selective images. Two different kind of approaches can be used to solve the problem of spectral reconstruction consisting of material decomposition and tomographic reconstruction: the “two steps” methods which are most often projection-based methods, and the “one step” methods. While the projection-based methods are interesting for the fast computational time, the one-step methods link directly the material image domain and the multienergy sinograms domain, which is an advantage to introduce spatial prior information in the material image domain. In this work we propose a proximal operator associated to a data fidelity term (based on an empirical linear forward model) for multimaterial decomposition. This proximal operator, which has a closed-form solution, makes it possible to propose fast regularized reconstruction methods for spectral CT, in particular for data of photon-counting detectors with many energy bins. We propose a one-step method based on the alternating direction method of multipliers (ADMM) and this proximal operator. We present promising results on dual material image reconstructions obtained from simulated and experimental data. We show qualitative and quantitative comparisons with two previously published multimaterial decomposition methods.

Spectral Differential Phase Contrast X-Ray Radiography.

We investigate the combination of two emerging X-ray imaging technologies, namely spectral imaging and differential phase contrast imaging. By acquiring spatially and temporally registered images with several different X-ray spectra, spectral imaging can exploit differences in the energy-dependent attenuation to generate material selective images. Differential phase contrast imaging uses an entirely different contrast generation mechanism: The phase shift that an X-ray wave exhibits when traversing an object. As both methods can determine the (projected) electron density, we propose a novel material decomposition algorithm that uses the spectral and the phase contrast information simultaneously. Numerical experiments show that the combination of these two imaging techniques benefits from the strengths of the individual methods while the weaknesses are mitigated: Quantitatively accurate basis material images are obtained and the noise level is strongly reduced, compared to conventional spectral X-ray imaging.

A unified material decomposition framework for quantitative dual‐ and triple‐energy CT imaging

Many clinical applications depend critically on the accurate differentiation and classification of different types of materials in patient anatomy. This work introduces a unified framework for accurate nonlinear material decomposition and applies it, for the first time, in the concept of triple-energy CT (TECT) for enhanced material differentiation and classification as well as dual-energy CT (DECT). We express polychromatic projection into a linear combination of line integrals of material-selective images. The material decomposition is then turned into a problem of minimizing the least-squares difference between measured and estimated CT projections. The optimization problem is solved iteratively by updating the line integrals. The proposed technique is evaluated by using several numerical phantom measurements under different scanning protocols. The triple-energy data acquisition is implemented at the scales of micro-CT and clinical CT imaging with commercial "TwinBeam" dual-source DECT configuration and a fast kV switching DECT configuration. Material decomposition and quantitative comparison with a photon counting detector and with the presence of a bow-tie filter are also performed. The proposed method provides quantitative material- and energy-selective images examining realistic configurations for both DECT and TECT measurements. Compared to the polychromatic kV CT images, virtual monochromatic images show superior image quality. For the mouse phantom, quantitative measurements show that the differences between gadodiamide and iodine concentrations obtained using TECT and idealized photon counting CT (PCCT) are smaller than 8 and 1mg/mL, respectively. TECT outperforms DECT for multicontrast CT imaging and is robust with respect to spectrum estimation. For the thorax phantom, the differences between the concentrations of the contrast map and the corresponding true reference values are smaller than 7mg/mL for all of the realistic configurations. A unified framework for both DECT and TECT imaging has been established for the accurate extraction of material compositions using currently available commercial DECT configurations. The novel technique is promising to provide an urgently needed solution for several CT-based diagnostic and therapy applications, especially for the diagnosis of cardiovascular and abdominal diseases where multicontrast imaging is involved.

Joint Statistical Iterative Material Image Reconstruction for Spectral Computed Tomography Using a Semi-Empirical Forward Model.

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.

A post-processing algorithm for spectral CT material selective images using learned dictionaries

In spectral computed tomography (spectral CT), the additional information about the energy dependence of the linear attenuation coefficients can be exploited to produce material selective images. These images have proven to be useful for various applications such as quantitative imaging or clinical diagnosis. However, noise amplification on material decomposed images remains a fundamental problem which limits the utility of basis material images. In this work, we present a new post-processing algorithm for material selective images which is based on dictionary denoising and specifically tailored to take the properties of the basis material images into account. Dictionary denoising is a powerful noise reduction technique which separates image features from noise by modeling small image patches as a sparse linear combination of dictionary atoms. These dictionary atoms are learned from training images prior to the denoising process. We have adapted the dictionary denoising algorithm to make use of the structural correlation as well as the anti-correlated noise which is typically present in material selective images. Dictionary denoising is first applied to the virtual monochromatic image for which the anti-correlated noise maximally cancels out (minimum noise image) in order to identify the structures and edges of the material selective images. In a second step, the basis material images are compiled by finding local linear transformations between the minimum noise image and the basis material images. Numerical simulations as well as an experimental measurement show that our algorithm achieves improved image quality compared to two other post-processing methods, namely conventional dictionary denoising and bilateral filtering. As a post-processing method, it can be combined with image-based as well as projection-based material decomposition techniques. Our algorithm therefore has the potential to improve the usability of basis material images for various tasks such as artifact reduction, quantitative imaging and clinical diagnosis.

Sporadically‐driven clock‐less in‐pixel multi‐bit energy discriminator for X‐ray spectrometry CMOS PCD imagers

A compact size and low-power multi-bit energy discriminator circuit is presented for X-ray spectrometry CMOS photon-counting detector (PCD) pixels. Because the circuit design is based on an asynchronous linear search, the discriminator with only a single-comparator classifies a sporadically incoming photon into multiple energy-bins, resulting in high spatial resolution and functionality of material-selective imaging. The proposed pixel power required to perform a minimal linear search is significantly less than the power of a conventional parallel search implemented in a flash-type discriminator. A prototype is fabricated in a 130-nm CMOS, and a 3-bit discriminator occupies only 23 × 60 μm2. The circuit dissipates a static power of 1 μW with a power supply of 3.3 V (analogue) and 1.2 V (digital). The inaccuracy of energy-binning is measured to be 2.6–3.3 mV rms.

A practical material decomposition method for x-ray dual spectral computed tomography.

X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today's clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.

A feasibility study for anatomical noise reduction in dual-energy chest digital tomosynthesis

Lung cancer is the leading cause of cancer death worldwide. Thus, early diagnosis is of considerable importance. For early screening of lung cancer, computed tomography (CT) has been used as the gold standard. Chest digital tomosynthesis (CDT) is a recently introduced modality for lung cancer screening with a relatively low radiation dose compared to CT. The dual energy material decomposition method has been proposed for better detection of pulmonary nodules by means of reducing anatomical noise. In this study, the possibility of material decomposition in CDT was tested by both a simulation study and an experimental study using a CDT prototype. The Geant4 application for tomographic emission (GATE) v6 and tungsten anode spectral model using interpolating polynomials (TASMIP) codes were used for the simulation study to create simulated phantom shapes consisting of five inner cylinders filled with different densities of bone and airequivalent materials. Furthermore, the CDT prototype system and human phantom chest were used for the experimental study. CDT scan in both the simulation and experimental studies was performed with linear movement and 21 projection images were obtained over a 30 degree angular range with a 1.5 degree angular interval. To obtain materialselective images, a projectionbased energy subtraction technique was applied to high and low energy images. The resultant simulation images showed that dual-energy reconstruction could achieve an approximately 32% higher contrast to noise ratio (CNR) in images and the difference in CNR value according to bone density was significant compared to single energy CDT. Additionally, image artifacts were effectively corrected in dual energy CDT simulation studies. Likewise the experimental study with dual energy produced clear images of lung fields and bone structure by removing unnecessary anatomical structures. Dual energy tomosynthesis is a new technique; therefore, there is little guidance regarding its integration into clinical practice and this study can be used to improve the diagnostic efficiency of lung field and spinal bone screening using CDT.

Image-based dual energy CT using optimized precorrection functions: A practical new approach of material decomposition in image domain

Dual energy CT (DECT) measures the object of interest using two different x-ray spectra in order to provide energy-selective CT images or in order to get the material decomposition of the object. Today, two decomposition techniques are known. Image-based DECT uses linear combinations of reconstructed images to get an image that contains material-selective DECT information. Rawdata-based DECT correctly treats the available information by passing the rawdata through a decomposition function that uses information from both rawdata sets to create DECT specific (e.g., material-selective) rawdata. Then the image reconstruction yields material-selective images. Rawdata-based image decomposition generally obtains better image quality; however, it needs matched rawdata sets. This means that physically the same lines need to be measured for each spectrum. In today's CT scanners, this is not the case. The authors propose a new image-based method to combine mismatched rawdata sets for DECT information. The method allows for implementation in a scanner's rawdata precorrection pipeline or may be used in image domain. They compare the ability of the three methods (image-based standard method, proposed method, and rawdata-based standard method) to perform material decomposition and to provide monochromatic images. Thereby they use typical clinical and preclinical scanner arrangements including circular cone-beam CT and spiral CT. The proposed method is found to perform better than the image-based standard method and is inferior to the rawdata-based method. However, the proposed method can be used with the frequent case of mismatched data sets that exclude rawdata-based methods.

Reduction of motion artefacts in non-gated dual-energy radiography

The objective of this work was to reduce motion artefacts in non-gated dual-energy subtraction radiography whilst preserving the contrast-to-noise ratio (CNR) in regions with low motion. Dual-energy radiography provides material-selective information (soft-tissue and bone images) that may be used for improved detection of calcifications in lung nodules. The weighted logarithmic dual-energy subtraction of thoracic images performed without electrocardiogram gating results in motion-induced artefacts. The low-energy image was acquired at the usual dose setting at 60 kV. To obtain the high-energy (120 kV) information, a series of consecutive images at a time interval of 30 ms were made. The series integral dose was equivalent to the dose of a single conventional high-energy image. A motion-free merging technique was introduced that combines standard images yielding low image noise with phase-selective images yielding motion artefact-free image regions which are used for dual-energy subtraction. Evaluations of the method were performed with simulations and measurements using a C-arm system (Axiom Artis; Siemens AG, Germany) equipped with a flat detector of 40 x 30 cm(2). The merging approach conserved standard image noise levels and the CNR in areas without cardiac motion, whereas image noise in pericardial lung regions and in the heart was increased compared with standard images. Motion artefacts in the heart and in the lung areas close to the heart are significantly reduced in the material-selective images when compared with a standard non-gated subtraction.

Empirical dual energy calibration (EDEC) for cone‐beam computed tomography

Material-selective imaging using dual energy CT (DECT) relies heavily on well-calibrated material decomposition functions. These require the precise knowledge of the detected x-ray spectra, and even if they are exactly known the reliability of DECT will suffer from scattered radiation. We propose an empirical method to determine the proper decomposition function. In contrast to other decomposition algorithms our empirical dual energy calibration (EDEC) technique requires neither knowledge of the spectra nor of the attenuation coefficients. The desired material-selective raw data p1 and p2 are obtained as functions of the measured attenuation data q1 and q2 (one DECT scan = two raw data sets) by passing them through a polynomial function. The polynomial's coefficients are determined using a general least squares fit based on thresholded images of a calibration phantom. The calibration phantom's dimension should be of the same order of magnitude as the test object, but other than that no assumptions on its exact size or positioning are made. Once the decomposition coefficients are determined DECT raw data can be decomposed by simply passing them through the polynomial. To demonstrate EDEC simulations of an oval CTDI phantom, a lung phantom, a thorax phantom and a mouse phantom were carried out. The method was further verified by measuring a physical mouse phantom, a half-and-half-cylinder phantom and a Yin-Yang phantom with a dedicated in vivo dual source micro-CT scanner. The raw data were decomposed into their components, reconstructed, and the pixel values obtained were compared to the theoretical values. The determination of the calibration coefficients with EDEC is very robust and depends only slightly on the type of calibration phantom used. The images of the test phantoms (simulations and measurements) show a nearly perfect agreement with the theoretical micro values and density values. Since EDEC is an empirical technique it inherently compensates for scatter components. The empirical dual energy calibration technique is a pragmatic, simple, and reliable calibration approach that produces highly quantitative DECT images.

Three-dimensional monochromatic x-ray computed tomography using synchrotron radiation

We describe a technique of three-dimensional computed to- mography (3-D CT) using monochromatic x rays generated by synchro- tron radiation, which performs a direct reconstruction of a 3-D volume image of an object from its cone-beam projections. For the development, we propose a practical scanning orbit of the x-ray source to obtain com- plete 3-D information on an object, and its corresponding 3-D image reconstruction algorithm. The validity and usefulness of the proposed scanning orbit and reconstruction algorithm were confirmed by computer simulation studies. Based on these investigations, we have developed a prototype 3-D monochromatic x-ray CT using synchrotron radiation, which provides exact 3-D reconstruction and material-selective imaging by using the K-edge energy subtraction technique. © 1998 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(98)03508-9)

Single-exposure dual-energy computed radiography: improved detection and processing.

Recent reports have emphasized the potential for dual-energy computed radiographic applications. An improved method for single-exposure material-selective imaging with a photostimulable phosphor computed radiography system was investigated. The essential elements of the technique are (a) prefiltration with gadolinium, which divides the incident broad-beam x-ray spectrum into low-energy and high-energy peaks; (b) a cassette consisting of four photostimulable phosphor plates that record images of increasing mean energies, with a computed energy separation of 23 keV from the front to the rear plate; (c) spatially dependent scatter and beam-hardening corrections; and (d) a noise-reduction algorithm based on noise correlations between bone-selective and soft-tissue-selective dual-energy images. These elements result in improved material cancellation and signal-to-noise ratio throughout the image.

Computing material-selective projection images in MR.

We detail a robust, general method for computing projection images of individual materials in a volume as linear combinations of MR projection images with different material-dependent weightings. Signal per unit volume for each material in each raw image is acquired directly for accurate cancellation of undesired, overlapping materials. The weighted sum of the input images is determined to maximize the signal-to-noise ratio (SNR) and minimize inhomogeneity effects in the material-selective images. We tested the implementation experimentally in both phantom and human studies, producing selective images with reasonable SNRs and material isolation. With further development of sequences to rapidly acquire input images having greater material differentiability, we envision the application of the selective projection imaging format to screening studies searching over large volumes for diseased tissues.