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Background: Motion induced image artifacts have been the focus of many investigations for x‐ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post‐processing algorithms to minimize motion artifacts. Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image‐space spatial‐ and temporal‐consistency metric (CM) to detect and characterize the local motion. Method: In a non‐rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region‐of‐interests for consistency‐metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in‐plane motion, cross‐plane motion, cone‐beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view‐by‐view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact. [ABSTRACT FROM AUTHOR]

Background: Radiation dose reduction has been the focus of many research activities in x‐ray CT. Various approaches were taken to minimize the dose to patients, ranging from the optimization of clinical protocols, refinement of the scanner hardware design, and development of advanced reconstruction algorithms. Although significant progress has been made, more advancements in this area are needed to minimize the radiation risks to patients. Purpose: Reconstruction algorithm‐based dose reduction approaches focus mainly on the suppression of noise in the reconstructed images while preserving detailed anatomical structures. Such an approach effectively produces synthesized high‐dose images (SHD) from the data acquired with low‐dose scans. A representative example is the model‐based iterative reconstruction (MBIR). Despite its widespread deployment, its full adoption in a clinical environment is often limited by an undesirable image texture. Recent studies have shown that deep learning image reconstruction (DLIR) can overcome this shortcoming. However, the limited availability of high‐quality clinical images for training and validation is often the bottleneck for its development. In this paper, we propose a novel approach to generate SHD with existing low‐dose clinical datasets that overcomes both the noise texture issue and the data availability issue. Methods: Our approach is based on the observation that noise in the image can be effectively reduced by performing image processing orthogonal to the imaging plane. This process essentially creates an equivalent thick‐slice image (TSI), and the characteristics of TSI depend on the nature of the image processing. An advantage of this approach is its potential to reduce impact on the noise texture. The resulting image, however, is likely corrupted by the anatomical structural degradation due to partial volume effects. Careful examination has shown that the differential signal between the original and the processed image contains sufficient information to identify regions where anatomical structures are modified. The differential signal, unfortunately, contains significant noise and has to be removed. The noise removal can be accomplished by performing iterative noise reduction to preserve structural information. The processed differential signal is subsequently subtracted from TSI to arrive at SHD. Results: The algorithm was evaluated extensively with phantom and clinical datasets. For better visual inspection, difference images between the original and SHD were generated and carefully examined. Negligible residual structure could be observed. In addition to the qualitative inspection, quantitative analyses were performed on clinical images in terms of the CT number consistency and the noise reduction characteristics. Results indicate that no CT number bias is introduced by the proposed algorithm. In addition, noise reduction capability is consistent across different patient anatomical regions. Further, simulated water phantom scans were utilized in the generation of the noise power spectrum (NPS) to demonstrate the preservation of the noise‐texture. Conclusions: We present a method to generate SHD datasets from regularly acquired low‐dose CT scans. Images produced with the proposed approach exhibit excellent noise‐reduction with the desired noise‐texture. Extensive clinical and phantom studies have demonstrated the efficacy and robustness of our approach. Potential limitations of the current implementation are discussed and further research topics are outlined. [ABSTRACT FROM AUTHOR]

Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions. A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions. Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect. For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging. [ABSTRACT FROM AUTHOR]

A basic assumption in the classic computed tomography (CT) theory is that an object remains stationary in an entire scan. In biomedical CT/micro-CT, this assumption is often violated. To produce high-resolution images, such as for our recently proposed clinical micro-CT (CMCT) prototype, it is desirable to develop a precise motion estimation and image reconstruction scheme. In this paper, we first extend the Helgason-Ludwig consistency condition (HLCC) from parallel-beam to fan-beam geometry when an object is subject to a translation. Then, we propose a novel method to estimate the motion parameters only from sinograms based on the HLCC. To reconstruct the moving object, we formulate two generalized fan-beam reconstruction methods, which are in filtered backprojection and backprojection filtering formats, respectively. Furthermore, we present numerical simulation results to show that our approach is accurate and robust. Index Terms--Backprojection filtration, fan-beam, filtered backprojection, Helgason-Ludwig consistency condition, motion artifacts.

When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution. Index Terms--Iterated conditional mode, low dose, penalized weighted least square, sinogram smoothing, X-ray computed tomography (CT).

With the recent introduction of multislice computed tomography (MCT) scanners, image reconstruction techniques for multislice helical CT has become an active research topic. The majority of the previously proposed algorithms limits the plane-of-reconstruction (POR) to a flat plane (either tilted or nontilted). Additionally, linear interpolation is typically employed to estimate the projections at the POR from the measured conjugate samples. In this paper, we propose a new helical reconstruction algorithm for multislice helical CT. To avoid discontinuity in the helical weighting function, a double cone-shaped region is selected as the POR. All available projection samples that are located within a predefined distance from the POR are utilized to estimate a set of projections at the POR. In addition, a weighted interpolation-extrapolation approach is proposed to replace linear interpolation. The proposed scheme has the advantage of an improved slice sensitivity profile due to a better preservation of high-frequency contents present in the projection data set. It also exhibits better performance in terms of helical artifact suppression. Phantom experiments have been conducted to demonstrate the efficacy of our approach.

With the recent introduction of multislice computed tomography (MCT) by many CT manufacturers, research on the subject of multislice helical reconstruction has gained significant interest. These algorithms take advantage of the available conjugate samples in the multislice configuration and estimate a set of projections at the predefined plane of reconstruction (POR). In many clinical applications, submillimeter slice thickness is desirable. Unlike the case of a single-slice scanner, the slice thickness of a MCT is determined by the detector cell pitch. For a detector design that has a minimum cell pitch of 1 mm, submillimeter slice thickness is very difficult to achieve. In this paper, we propose a thin-twin scheme in which the X-ray flux is collimated to illuminate only a portion of the center two detector rows. Because the slice thickness in this scheme is determined by the prepatient collimator, submillimeter slice thickness is obtained. Observing the fact that the X-ray flux profile on the detectors is unsymmetrical, we propose a helical reconstruction algorithm that is adaptive to the detector response. Phantom experiments have been conducted to evaluate the performance of our reconstruction algorithm. Index Terms--Detector response, helical computed tomography, image reconstruction.

In recent years, the use of computer graphic techniques to produce three-dimensional (3-D) and reformatted images from a set of axial computed tomography (CT) images has gained significant interest. In most cases, the CT images are generated with the projection data set weighted prior to reconstruction, to combat motion artifacts, data inconsistency, or redundant data samples. In this paper, we investigate the potential bias introduced to the reconstruction as a result of the interaction of the projection weights and the isocenter misalignment (ISM). We demonstrate that when the weights applied to the conjugate rays are significantly different, bias will result which favors the sample with a higher weight. Although the error is not easily detected in axial CT images, it can be quite visible in 3-D or multiplanar reformatted (MPR) images. In this paper, we first present a theoretical framework to analyze and predict the bias. The theoretical prediction is validated by both computer simulations and phantom experiments. Several schemes to combat this artifact are subsequently presented: and their effectiveness is demonstrated. Index Terms - Computed tomography, isocenter misalignment, projection weights, three-dimensional and multiplanar reformatted images.

To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch ${p}$ (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When ${p}>{1.5}$ , it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to ${p}={4}$ without significant artifacts for single source MDCT scans. [ABSTRACT FROM AUTHOR]

In x‐ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x‐ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual‐energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low‐ and high‐tube potential scans, fast tube‐potential switching, beam filtration combined with spiral scanning, dual‐source, or dual‐layer detector approaches. The use of energy‐resolving, photon‐counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual‐energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast‐enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non‐contrast‐enhanced images from contrast‐enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non‐uric acid urinary stones or uric acid (gout) or non‐uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized. [ABSTRACT FROM AUTHOR]

Utilization of solid-state detectors for computed tomography (CT) has been the focus of many studies. Previous phantom and clinical experiments have shown that one of the important performance parameters for the solid-state detector is the primary speed and afterglow. In this paper, we present a detailed investigation on the signal decay characteristics of the HiLight (GE Medical Systems, Milwaukee, WI) scintillating detector. The detector primary speed and afterglow are modeled by a multiexponential function and fully characterized by a set of time constants and relative strengths. The sensitivity of these parameters to X-ray photon energy, detector aging, and radiation exposure is then established and analyzed. No statistically significant variation is observed in these parameters due to changes in the above external variables. The impact of various decay time constants on CT image quality, such as spatial resolution, noise, and artifacts, is subsequently illustrated with compute,' simulations and phantom experiments. Finally, an algorithmic correction scheme is derived to compensate for detector afterglow. The correction scheme employs a recursive filter to remove adverse effects of the detector decay on image quality. Experimental results have shown that the correction scheme successfully restores system spatial resolution, produces a more homogeneous noise pattern, and eliminates ring-band image artifacts due to detector afterglow. The effectiveness and robustness of the correction scheme are demonstrated by extensive phantom and clinical experiments. Index Terms--Computed tomography, primary speed and afterglow, recursive correction, solid-state detector.

One of the most recent technical advancements in computed tomography (CT) is the introduction of multislice CT (MCT). Because multiple detector rows are used for data acquisition, MCT offers higher volume coverage, faster scan speed, and reduced: X-ray tube loading. Recognizing its unique data-sampling pattern, several image reconstruction algorithms were developed. These algorithms have been shown to be adequate in producing clinically acceptable images. Recent studies, however, have revealed that the image quality of MCT can be significantly degraded when helical data are acquired with a tilted gantry. The degraded image quality has rendered this feature unacceptable for clinical usage. In this paper, we first: present a detailed investigation on the cause of the image quality degradation. An analytical model is derived to provide a mathematical basis for correction. Several compensation schemes are subsequently presented, and a detailed performance comparison is provided in terms of spatial resolution, noise, computation efficiency, and image artifacts. Index Terms--Computed tomography, helical CT, multislice CT, tilted helical CT.

Purpose: Noise characteristics of clinical multidetector CT (MDCT) systems can be quantified by the noise power spectrum (NPS). Although the NPS of CT has been extensively studied in the past few decades, the joint impact of the bowtie filter and object position on the NPS has not been systematically investigated. This work studies the interplay of these two factors on the two dimensional (2D) local NPS of a clinical CT system that uses the filtered backprojection algorithm for image reconstruction. Methods: A generalized NPS model was developed to account for the impact of the bowtie filter and image object location in the scan field-of-view (SFOV). For a given bowtie filter, image object, and its location in the SFOV, the shape and rotational symmetries of the 2D local NPS were directly computed from the NPS model without going through the image reconstruction process. The obtained NPS was then compared with the measured NPSs from the reconstructed noise-only CT images in both numerical phantom simulation studies and experimental phantom studies using a clinical MDCT scanner. The shape and the associated symmetry of the 2D NPS were classified by borrowing the well-known atomic spectral symbols s, p, and d, which correspond to circular, dumbbell, and cloverleaf symmetries, respectively, of the wave function of electrons in an atom. Finally, simulated bar patterns were embedded into experimentally acquired noise backgrounds to demonstrate the impact of different NPS symmetries on the visual perception of the object. Results: (1) For a central region in a centered cylindrical object, an s-wave symmetry was always present in the NPS, no matter whether the bowtie filter was present or not. In contrast, for a peripheral region in a centered object, the symmetry of its NPS was highly dependent on the bowtie filter, and both p-wave symmetry and d-wave symmetry were observed in the NPS. (2) For a centered region-ofinterest (ROI) in an off-centered object, the symmetry of its NPS was found to be different from that of a peripheral ROI in the centered object, even when the physical positions of the two ROIs relative to the isocenter were the same. (3) The potential clinical impact of the highly anisotropic NPS, caused by the interplay of the bowtie filter and position of the image object, was highlighted in images of specific bar patterns oriented at different angles. The visual perception of the bar patterns was found to be strongly dependent on their orientation. Conclusions: The NPS of CT depends strongly on the bowtie filter and object position. Even if the location of the ROI with respect to the isocenter is fixed, there can be different symmetries in the NPS, which depend on the object position and the size of the bowtie filter. For an isolated off-centered object, the NPS of its CT images cannot be represented by the NPS measured from a centered object. [ABSTRACT FROM AUTHOR]

Purpose: The authors investigated the performance of a recently introduced 160-mm/256-row CT system for low dose quantitative myocardial perfusion (MP) imaging of the whole heart. This platform is equipped with a gantry capable of rotating at 280 ms per full cycle, a second generation of adaptive statistical iterative reconstruction (ASiR-V) to correct for image noise arising from low tube voltage potential/tube current dynamic scanning, and image reconstruction algorithms to tackle beam-hardening, cone-beam, and partial-scan effects. Methods: Phantom studies were performed to investigate the effectiveness of image noise and artifact reduction with a GE Healthcare Revolution CT system for three acquisition protocols used in quantitative CT MP imaging: 100, 120, and 140 kVp/25 mAs. The heart chambers of an anthropomorphic chest phantom were filled with iodinated contrast solution at different concentrations (contrast levels) to simulate the circulation of contrast through the heart in quantitative CT MP imaging. To evaluate beam-hardening correction, the phantom was scanned at each contrast level to measure the changes in CT number (in Hounsfield unit or HU) in the water-filled region surrounding the heart chambers with respect to baseline. To evaluate cone-beam artifact correction, differences in mean water HU between the central and peripheral slices were compared. Partial-scan artifact correction was evaluated from the fluctuation of mean water HU in successive partial scans. To evaluate image noise reduction, a small hollow region adjacent to the heart chambers was filled with diluted contrast, and contrast-to-noise ratio in the region before and after noise correction with ASiR-V was compared. The quality of MP maps acquired with the CT system was also evaluated in porcine CT MP studies. Myocardial infarct was induced in a farm pig from a transient occlusion of the distal left anterior descending (LAD) artery with a catheter-based interventional procedure. MP maps were generated from the dynamic contrast-enhanced (DCE) heart images taken at baseline and three weeks after the ischemic insult. Results: Their results showed that the phantom and animal images acquired with the CT platform were minimally affected by image noise and artifacts. For the beam-hardening phantom study, changes in water HU in the wall surrounding the heart chambers greatly reduced from >±30 to ≤±5 HU at all kVp settings except one region at 100 kVp (7 HU). For the cone-beam phantom study, differences in mean water HU from the central slice were less than 5 HU at two peripheral slices with each 4 cm away from the central slice. These findings were reproducible in the pig DCE images at two peripheral slices that were 6 cm away from the central slice. For the partial-scan phantom study, standard deviations of the mean water HU in 10 successive partial scans were less than 5 HU at the central slice. Similar observations were made in the pig DCE images at two peripheral slices with each 6 cm away from the central slice. For the image noise phantom study, CNRs in the ASiR-V images were statistically higher (p < 0.05) than the non-ASiR-V images at all kVp settings. MP maps generated from the porcine DCE images were in excellent quality, with the ischemia in the LAD territory clearly seen in the three orthogonal views. Conclusions: The study demonstrates that this CT system can provide accurate and reproducible CT numbers during cardiac gated acquisitions across a wide axial field of view. This CT number fidelity will enable this imaging tool to assess contrast enhancement, potentially providing valuable added information beyond anatomic evaluation of coronary stenoses. Furthermore, their results collectively suggested that the 100 kVp/25 mAs protocol run on this CT system provides sufficient image accuracy at a low radiation dose (<3 mSv) for whole-heart quantitative CT MP imaging. [ABSTRACT FROM AUTHOR]

Purpose: The introduction of a High-Resolution (Hi-Res) scan mode and another associated option that combines Hi-Res mode with the so-called High Definition (HD) reconstruction kernels (referred to as a Hi-Res/HD mode in this paper) in some multi-detector CT (MDCT) systems offers new opportunities to increase spatial resolution for some clinical applications that demand high spatial resolution. The purpose of this work was to quantify the in-plane spatial resolution along both the radial direction and tangential direction for the Hi-Res and Hi-Res/HD scan modes at different off-center positions. Methods: A technique was introduced and validated to address the signal saturation problem encountered in the attempt to quantify spatial resolution for the Hi-Res and Hi-Res/HD scan modes. Using the proposed method, the modulation transfer functions (MTFs) of a 64-slice MDCT system (Discovery CT750 HD, GE Healthcare) equipped with both Hi-Res and Hi-Res/HD modes were measured using a metal bead at nine different off-centered positions (0-16 cm with a step size of 2 cm); at each position, both conventional scans and Hi-Res scans were performed. For each type of scan and position, 80 repeated acquisitions were performed to reduce noise induced uncertainties in the MTF measurements. A total of 15 reconstruction kernels, including eight conventional kernels and seven HD kernels, were used to reconstruct CT images of the bead. An ex vivo animal study consisting of a bone fracture model was performed to corroborate the MTF results, as the detection of this high-contrast and high frequency task is predominantly determined by spatial resolution. Images of this animal model generated by different scan modes and reconstruction kernels were qualitatively compared with the MTF results. Results: At the centered position, the use of Hi-Res mode resulted in a slight improvement in the MTF; each HD kernel generated higher spatial resolution than its counterpart conventional kernel. However, the MTF along the tangential direction of the scan field of view (SFOV) was significantly degraded at off-centered positions, yet the combined Hi-Res/HD mode reduced this azimuthal MTF degradation. Images of the animal bone fracture model confirmed the improved spatial resolution at the off-centered positions through the use of the Hi-Res mode and HD kernels. Conclusions: The Hi-Res/HD scan improve spatial resolution of MDCT systems at both centered and off-centered positions. [ABSTRACT FROM AUTHOR]

Purpose: Phantom-based objective image quality assessment methods are widely used in the medical physics community. For a filtered backprojection (FBP) reconstruction-based linear or quasilinear imaging system, the use of this methodology is well justified. Many key image quality metrics acquired with phantom studies can be directly applied to in vivo human subject studies. Recently, a variety of image quality metrics have been investigated for model-based iterative image reconstruction (MBIR) methods and several novel characteristics have been discovered in phantom studies. However, the following question remains unanswered: can certain results obtained from phantom studies be generalized to in vivo animal studies and human subject studies? The purpose of this paper is to address this question. Methods: One of the most striking results obtained from phantom studies is a novel power-law relationship between noise variance of MBIR (σ²) and tube current-rotation time product (mAs): σ² ∝ (mAs)-0.4 [K. Li et al., "Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance," Med. Phys. 41, 041906 (15pp.) (2014)]. To examine whether the same power-law works for in vivo cases, experimental data from two types of in vivo studies were analyzed in this paper. All scans were performed with a 64-slice diagnostic CT scanner (Discovery CT750 HD, GE Healthcare) and reconstructed with both FBP and a MBIR method (Veo, GE Healthcare). An Institutional Animal Care and Use Committee-approved in vivo animal study was performed with an adult swine at six mAs levels (10-290). Additionally, human subject data (a total of 110 subjects) acquired from an IRB-approved clinical trial were analyzed. In this clinical trial, a reduced-mAs scan was performed immediately following the standard mAs scan; the specific mAs used for the two scans varied across human subjects and were determined based on patient size and clinical indications. The measurements of σ² were performed at different mAs by drawing regions-of-interest (ROIs) in the liver and the subcutaneous fat. By applying a linear least-squares regression, the β values in the power-law relationship σ² ∝ (mAs)-β were measured for the in vivo data and compared with the value found in phantom experiments. Results: For the in vivo swine study, an exponent of &#946 = 0.43 was found for MBIR, and the coefficient of determination (R²) for the corresponding least-squares power-law regression was 0.971. As a reference, the β and R² values for FBP were found to be 0.98 and 0.997, respectively, from the same study, which are consistent with the well-known σ² ?∝ (mAs)-1.0 relationship for linear CT systems. For the human subject study, the measured β values for the MBIR images were 0.41?±0.12 in the liver and 0.37?±0.12 in subcutaneous fat. In comparison, the β values for the FBP images were 1.04±0.10 in the liver and 0.97±0.12 in subcutaneous fat. The β values of MBIR and FBP obtained from the in vivo studies were found to be statistically equivalent to the corresponding β values from the phantom study within an equivalency interval of [-0.1, 0.1] (p < 0.05); across MBIR and FBP, the difference in β was statistically significant (p < 0.05). Conclusions: Despite the nonlinear nature of the MBIR method, the power-law relationship, σ² ẙ (mAs)-0.4, found from phantom studies can be applied to in vivo animal and human subject studies. [ABSTRACT FROM AUTHOR]

Purpose: For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. Methods: The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d'(kV,mAs) is not lower than the prescribed value of d' for a given imaging task. Since it is difficult to analytically model the dependence of d' on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d'(kV,mAs) at a variety of kV-mAs combinations, after which the overlay of the dose contours and d' contours is used to graphically determine the optimal kV-mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, R' for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50-700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d'R = 16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. Results: For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d' map and the iso-CNR map were the same for FBP; thus, the use of d'- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d' and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA= 350, dose = 37.5 mGy; for MBIR, kV = 120, mA= 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. Conclusions: A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance. [ABSTRACT FROM AUTHOR]

Purpose: Organ dose estimation for a patient undergoing computed tomography (CT) scanning is very important. Although Monte Carlo methods are considered gold-standard in patient dose estimation, the computation time required is formidable for routine clinical calculations. Here, the authors instigate a deterministic method for estimating an absorbed dose more efficiently. Methods: Compared with current Monte Carlo methods, a more efficient approach to estimating the absorbed dose is to solve the linear Boltzmann equation numerically. In this study, an axial CT scan was modeled with a software package, Denovo, which solved the linear Boltzmann equation using the discrete ordinates method. The CT scanning configuration included 16 x-ray source positions, beam collimators, flat filters, and bowtie filters. The phantom was the standard 32 cm CT dose index (CTDI) phantom. Four different Denovo simulations were performed with different simulation parameters, including the number of quadrature sets and the order of Legendre polynomial expansions. A Monte Carlo simulation was also performed for benchmarking the Denovo simulations. A quantitative comparison was made of the simulation results obtained by the Denovo and the Monte Carlo methods. Results: The difference in the simulation results of the discrete ordinates method and those of the Monte Carlo methods was found to be small, with a root-mean-square difference of around 2.4%. It was found that the discrete ordinates method, with a higher order of Legendre polynomial expansions, underestimated the absorbed dose near the center of the phantom (i.e., low dose region). Simulations of the quadrature set 8 and the first order of the Legendre polynomial expansions proved to be the most efficient computation method in the authors' study. The single-thread computation time of the deterministic simulation of the quadrature set 8 and the first order of the Legendre polynomial expansions was 21 min on a personal computer. Conclusions: The simulation results showed that the deterministic method can be effectively used to estimate the absorbed dose in a CTDI phantom. The accuracy of the discrete ordinates method was close to that of a Monte Carlo simulation, and the primary benefit of the discrete ordinates method lies in its rapid computation speed. It is expected that further optimization of this method in routine clinical CT dose estimation will improve its accuracy and speed. [ABSTRACT FROM AUTHOR]

Purpose: A number of different techniques have been developed to reduce radiation dose in x-ray computed tomography (CT) imaging. In this paper, the authors will compare task-based measures of image quality of CT images reconstructed by two algorithms: conventional filtered back projection (FBP), and a new iterative reconstruction algorithm (IR). Methods: To assess image quality, the authors used the performance of a channelized Hotelling observer acting on reconstructed image slices. The selected channels are dense difference Gaussian channels (DDOG).A body phantom and a head phantom were imaged 50 times at different dose levels to obtain the data needed to assess image quality. The phantoms consisted of uniform backgrounds with low contrast signals embedded at various locations. The tasks the observer model performed included (1) detection of a signal of known location and shape, and (2) detection and localization of a signal of known shape. The employed DDOG channels are based on the response of the human visual system. Performance was assessed using the areas under ROC curves and areas under localization ROC curves. Results: For signal known exactly (SKE) and location unknown/signal shape known tasks with circular signals of different sizes and contrasts, the authors' task-based measures showed that a FBP equivalent image quality can be achieved at lower dose levels using the IR algorithm. For the SKE case, the range of dose reduction is 50%-67% (head phantom) and 68%-82% (body phantom). For the study of location unknown/signal shape known, the dose reduction range can be reached at 67%-75% for head phantom and 67%-77% for body phantom case. These results suggest that the IR images at lower dose settings can reach the same image quality when compared to full dose conventional FBP images. Conclusions: The work presented provides an objective way to quantitatively assess the image quality of a newly introduced CT IR algorithm. The performance of the model observers using the IR images was always higher than that seen using the FBP images in the authors' SKE and SKE location unknown detection tasks. To achieve a FBP-equivalent image quality in CT systems, the authors can lower the radiation dose by using this IR image reconstruction algorithm. Further studies are warranted using clinical data and human observer to validate these results for more complicated and realistic tasks. [ABSTRACT FROM AUTHOR]

Dual-energy X-ray CT (DECT) has the potential to improve contrast and reduce artifacts as compared to traditional CT. Moreover, by applying model-based iterative reconstruction (MBIR) to dual-energy data, one might also expect to reduce noise and improve resolution. However, the direct implementation of dual-energy MBIR requires the use of a nonlinear forward model, which increases both complexity and computation. Alternatively, simplified forward models have been used which treat the material-decomposed channels separately, but these approaches do not fully account for the statistical dependencies in the channels. In this paper, we present a method for joint dual-energy MBIR (JDE-MBIR), which simplifies the forward model while still accounting for the complete statistical dependency in the material-decomposed sinogram components. The JDE-MBIR approach works by using a quadratic approximation to the polychromatic log-likelihood and a simple but exact nonnegativity constraint in the image domain. We demonstrate that our method is particularly effective when the DECT system uses fast kVp switching, since in this case the model accounts for the inaccuracy of interpolated sinogram entries. Both phantom and clinical results show that the proposed model produces images that compare favorably in quality to previous decomposition-based methods, including FBP and other statistical iterative approaches. [ABSTRACT FROM AUTHOR]

Future generations of CT systems would need a mean to cover an entire organ in a single rotation. A way to accomplish this is to physically increase detector size to provide, e.g., 120∼160 mm z (head-foot) coverage at iso-center. The x-ray cone angle of such a system is usually 3∼4 times of that of a 64-slice (40 mm) system, which leads to more severe cone beam artifacts in cardiac scans. In addition, the extreme x-ray take-off angles for such a system cause severe heel effect, which would require an increase in anode target angle to compensate for it. One shortcoming of larger target angle is that tube output likely decreases because of shorter thermal length. This would result in an increase of image noise. Our goal is to understand from a physics and math point of view, what is the theoretical entitlement of artifacts, resolution, and noise impact of such a system. The image artifacts are assessed through computer simulation of a helical body phantom and visual comparison of reconstructed images between a 140 mm system and a 64-slice system. The IQ impact from target angle increase is studied analytically and experimentally by first finding the proper range of target angles that give the acceptable heel effect, then estimating the impact on peak power (flux) and z resolution using an empirical model of heel effect for given target angle and analytical models of z resolution and tube current loading factor for given target thermal length. The results show that, for a 140 mm system, 24.5% of imaging volume exhibits more severe cone beam artifacts than a 64-slice system, which also poses a patient dose concern. In addition, this system may suffer from a 36% peak power (flux) loss, which is equivalent to about 20% image noise increase. Therefore, a wide coverage CT system using a single x-ray source is likely to face some severe challenges in IQ and clinical accuracy. [ABSTRACT FROM AUTHOR]

Purpose: To further improve the image quality, in particularly, to suppress the boundary artifacts, in the extended scan field-of-view (SFOV) reconstruction. Methods: To combat projection truncation artifacts and to restore truncated objects outside the SFOV, an algorithm has previously been proposed based on fitting a partial water cylinder at the site of the truncation. Previous studies have shown this algorithm can simultaneously eliminate the truncation artifacts inside the SFOV and preserve the total amount of attenuation, owing to its emphasis on consistency conditions of the total attenuation in the parallel sampling geometry. Unfortunately, the water cylinder fitting parameters of this 2D algorithm are inclined to high noise fluctuation in the projection samples from image to image, causing anatomy boundaries artifacts, especially during helical scans with higher pitch (≥1.0). To suppress the boundary artifacts and further improve the image quality, the authors propose to use a roughness penalty function, based on the Huber regularization function, to reinforce the z-dimensional boundary consistency. Extensive phantom and clinical tests have been conducted to test the accuracy and robustness of the enhanced algorithm. Results: Significant reduction in the boundary artifacts is observed in both phantom and clinical cases with the enhanced algorithm. The proposed algorithm also reduces the percent difference error between the horizontal and vertical diameters to well below 1%. It is also noticeable that the algorithm has improved CT number uniformity outside the SFOV compared to the original algorithm. Conclusions: The proposed algorithm is capable of suppressing boundary artifacts and improving the CT number uniformity outside the SFOV. [ABSTRACT FROM AUTHOR]

Abstract: Application of quantitative myocardial CT perfusion (CTP) for the assessment of coronary artery disease may have a significant effect on patient care as the functional significance of a coronary stenosis can be evaluated through absolute measurement of the downstream myocardial perfusion (MP) both at rest and under exercise or pharmacologic stress. A main challenge of myocardial CTP is beam hardening (BH), arising from the polychromatic nature of x-rays used in CT scanning and the presence of highly attenuating contrast agent in the heart chambers during the CT acquisition. The BH effect induces significant nonuniform shifts in CT numbers which, if uncorrected, can lead to inaccurate assessment of MP. With the recent developments of dual-energy CT (DECT) scanning on clinical scanners, the BH effect on MP measurement could be reduced with the generation of monochromatic images relatively free of BH artifacts from the acquired dual-energy data. Here, we review the different techniques of acquiring dual-energy scans and generating monochromatic images, followed by discussion on the progress of developing a DECT technique with reduced radiation dose for quantitative myocardial CTP. [Copyright &y& Elsevier]

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. [ABSTRACT FROM AUTHOR]

Purpose: Recently, a fast-kVp switching (FKS) dual-energy method has been presented with clinical and phantom results to demonstrate its efficacy. Patient dose concern has been raised on FKS dual-energy since it involves higher energy acquisition at 140 kVp and slower gantry rotation time (e.g., 0.9-1 s) as opposed to 0.5 s as used in routine single-energy exams. The purpose of our study was to quantitatively compare the CTDIVOL of FKS and routine CT exams under the body and head conditions. Methods: For a fair comparison, we have to overcome the difficulty of unmatched protocols between FKS and routine CT exams. In this paper, we propose to match the low contrast detectability (LCD), a critical image quality metric impacting diagnostic quality, before measuring CTDIVOL. The kVp pair, flux ratio, and optimal monochromatic energy have been carefully optimized for FKS protocols prior to the comparison. Our baseline single-energy protocols were per IEC-61223-3-5 under head and body conditions except for mA, which was iteratively adjusted to match the LCD of FKS. CTDIVOL was measured using either a 16 cm (for head scanning) or a 32 cm (for body scanning) PMMA phantom of at least 14 cm in length. The LCD was measured using the uniform section of Catphan 600. To make the study repeatable, the automated statistical LCD measurement tool available on GE Discovery CT750 scanner was used in this work. A visual LCD phantom and a Gammex tissue characterization phantom were also employed to verify the statistical LCD measurements and to introduce various patient sizes and contrast levels. Results: The mean CTDIVOL for the head and body single-energy acquisitions was 57.5 and 29.2 mGy, respectively. The LCD was measured at 0.45% and 0.42%, respectively. The average CTDIVOL for FKS head and body scans was 70.4 and 33.4 mGy, respectively. The corresponding LCD was measured at 0.45% and 0.43%, respectively. The results from the visual LCD phantom and Gammex phantom supported the statistical LCD measurements. Conclusions: For equal image quality as measured by low contrast detectability, the CTDIVOL of a FKS head and body exam is roughly 22% and 14% higher than that of a routine single-energy head and body exam, respectively, for the phantom measured. [ABSTRACT FROM AUTHOR]

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries. [ABSTRACT FROM AUTHOR]

The spatial resolution of diagnostic Computed Tomography (CT) has increased substantially, and 3D isotropic sub-millimeter spatial resolution in both axial and helical scan modes is routinely available in the clinic. However, driven by advanced clinical applications, the pursuit for higher spatial resolution and free of aliasing artifacts in diagnostic CT has never stopped. A method to accommodate focal spot wobbling at an arbitrary number of projection views per gantry rotation in CT is presented and evaluated here. The method employs a beta-correction scheme in the row-wise fan-to-parallel rebinning to transform the native cone beam geometry into the cone-parallel geometry under which existing 3D weighted cone beam filtered backprojection algorithms can be utilized for image reconstruction. The experimental evaluation shows that the row-wise fan-to-parallel rebinning with the beta-correction can increase the quantitative in-plane spatial resolution (Modulation Transfer Function) substantially, while the visual spatial resolution can be enhanced significantly. Consequently, the architectural designers of CT scanners are no longer constrained to choosing the number of projection views per rotation determined by gantry geometry. Instead, they can choose the number of projection views per rotation to optimize the trade-offs between in-plane spatial resolution and noise characteristics. Therefore, the presented method is of practical relevance in the architectural design of state-of-the-art diagnostic CT. [ABSTRACT FROM AUTHOR]

Purpose: The recently proposed prior image constrained compressed sensing (PICCS) method has been applied in cardiac MDCT to improve the temporal resolution by approximately a factor of 2, by using projection data acquired from half of the standard short-scan angular range to reconstruct images with improved temporal resolution. The method was referred to as temporal resolution improvement using PICCS (TRI-PICCS). The primary purpose of this article is to study (1) the relationship between the performance of the TRI-PICCS algorithm and the angular range of projection data used in image reconstruction; (2) the relationship between the performance of the TRI-PICCS algorithm and the motion orientations and motion patterns of moving objects; and (3) the relationship between the performance of the TRI-PICCS algorithm and various heart rates. Methods: A hybrid phantom consisting of realistic cardiac anatomy and eight moving objects with known motion profiles to simulate coronary arteries was constructed by superimposing the analytical projection data of eight simulated moving vessels to the in vivo projection data from a cardiac MDCT scan. The motion profiles of the moving objects may independently change orientations, period, and amplitude. A prior image was reconstructed using a short-scan filtered backprojection method from a gated short-scan data set for each given motion profile. The TRI-PICCS method was applied to improve temporal resolution for each configuration of given motion profiles of moving objects and given active angular range specified by the target temporal resolution. To quantitatively study the performance, figures of merit were introduced to quantify signal intensity deficit, image distortion, and residual motion artifacts, respectively. Results: The performance of the TRI-PICCS method is the same when the projection data are taken from 100° to 120°. The performance of the TRI-PICCS method is independent of location and motion orientations. The performance of the TRI-PICCS method does not significantly degrade for heart rates up to 100 bpm with a gantry rotation speed of 350 ms per rotation. Conclusions: The TRI-PICCS method can be used to systematically improve temporal resolution for MDCT cardiac imaging by a factor of 2–2.3 and the performance of the TRI-PICCS method is insensitive to motion locations and motion orientations. The TRI-PICCS method enables a single-source MDCT scanner with 350 ms or faster gantry speed to scan patients with heart rates as high as 100 bpm. [ABSTRACT FROM AUTHOR]

Diagnostic computed tomography (CT) images are usually acquired in both helical and axial scans in the clinical applications using cone beam volumetric CT. In addition to faster patient throughput, a helical scan in volumetric CT can provide better image quality because of the satisfaction of data sufficiency condition, and thus has been performed far more frequently so far in the clinic. However, the first and last images in a helical scan are usually prescribed at the locations that are half helical turn indented from the starting and ending points of the scan. Due to such an indention, the dose efficiency of helical scan deteriorates with increasing detector dimension along z direction. To improve the dose efficiency of helical scan in volumetric CT, a hybrid helical cone beam filtered backprojection (CB-FBP) algorithm is presented here to reconstruct helical images beyond the conventional indented image zone. The hybrid algorithm is a combination of the ray-wise three-dimensional (3D) weighted CB-FBP algorithms that are recently proposed for helical and axial CB image reconstructions. Through the hybridization, the ray-wise 3D weighting becomes dependent on both helical pitch and image slice location. Phantom study shows that the conventional indented image zone in helical scan can be extended substantially by using the hybrid algorithm. Consequently, the dose efficiency of volumetric CT in helical scan can be improved significantly. It is believed that, with increasing detector dimension along z direction in cone beam volumetric CT, the hybrid algorithm will become more attractive in clinical applications. [ABSTRACT FROM AUTHOR]

Provides information on a study which analyzed the root causes of three-dimensional image artifact in helical scans. Review of the helical data collection and the reconstruction algorithms; Response of a conventional computed tomography (CT) system to a cylindrical object; Artifact correction schemes; Conclusion.