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Purpose: To investigate the effect of different pitches and corresponding scan fields of view (SFOVs) on the image quality in the ultrafast, high-pitch turbo FLASH mode of the third-generation dual-source CT using an anthropomorphic phantom. Methods: The phantom was scanned using the ultrafast, high-pitch turbo FLASH protocols of the third-generation dual-source CT with the different pitches and corresponding SFOVs (pitches: 1.55 to 3.2 with increments of 0.1, SFOVs: 50 cm to 35.4 cm). The objective parameters such as the CT number, image noises, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifacts index (AI),and image features from the head,chest,and abdomen were compared between the CT images with a pitch of 1.55 and SFOV of Ø 50 cm and a pitch of 3.2 and SFOV of Ø 35.4 cm. Then, the 18 series of CT images of the head, chest, and abdomen were evaluated by three radiologists independently. Results: The differences in the CT numbers were not statically significant between the CT images with a pitch of 1.55 and SFOV of Ø 50 cm and a pitch of 3.2 and SFOV of Ø 35.4 cm from most body parts and potential combinations (p > 0.05),Most of the image noises and the AI from the images with the pitch of 1.55 were significantly lower than those with the pitch of 3.2 (p < 0.05), and the SNR and CNR from the images with the pitch of 1.55 were higher than those with the pitch of 3.2. There were significant differences in the first-order features and texture features of the head (59.3%, 28.3%), chest (66%, 35.7%), and abdomen (71.6%,64.7%) (p < 0.05).The subjective image quality was excellent when the pitch was less than 2.0 and gradually decreased with the increasing pitch. In addition, the image quality decreased significantly when the pitch was higher than 3.0 (all k≥0.69), especially in the head and chest. Conclusions: In the ultrafast, high-pitch turbo FLASH mode of the thirdgeneration DSCT, increasing the pitch and lowering the corresponding SFOV will change the image features and cause more artifacts degrading the image quality. Specific to the clinical needs, decreasing the pitch not only can expand the SFOV but also can improve the image quality. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) patients without coronary artery stenosis often show clinical evidence of ischemia. However myocardial perfusion in AF patients has been poorly studied. The purposes of this study were to investigate altered hyperemic myocardial blood flow (MBF) in patients with AF compared with risk‐matched controls in sinus rhythm (SR), and to evaluate hyperemic MBF before and after catheter ablation using dynamic CT perfusion. Methods: Hyperemic MBF was quantified in 87 patients with AF (44 paroxysmal, 43 persistent) scheduled for catheter ablation using dynamic CT perfusion, and compared with hyperemic MBF in 87 risk‐matched controls in SR. Follow‐up CT after ablation was performed in 49 AF patients. Results: Prior to ablation, hyperemic MBF of patients in AF during the CT (1.29 ± 0.34 ml/mg/min) was significantly lower than in patients in SR (1.49 ± 0.26 ml/g/min, p = 0.002) or matched controls (1.65 ± 0.32 ml/g/min, p < 0.001); no significant difference was seen between patients in SR during the CT and matched controls (vs. 1.50 ± 0.31 ml/g/min, p = 0.815). In patients in AF during the pre‐ablation CT (n = 24), hyperemic MBF significantly increased after ablation from 1.30 ± 0.35 to 1.53 ± 0.17 ml/g/min (p = 0.004); whereas in patients in SR during the pre‐ablation CT (n = 25), hyperemic MBF did not change significantly after ablation (from 1.46 ± 0.26 to 1.49 ± 0.27 ml/g/min, p = 0.499). Conclusion: In the current study using stress perfusion CT, hyperemic MBF in patients with AF during pre‐ablation CT was significantly lower than that in risk‐matched controls, and improved significantly after restoration of SR by catheter ablation, indicating that MBF abnormalities in AF patients are caused primarily by AF itself. [ABSTRACT FROM AUTHOR]

Purpose: For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods: Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results: We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTFtask) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions: By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR. [ABSTRACT FROM AUTHOR]

Purpose: Data completion is commonly employed in dual‐source, dual‐energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual‐energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)—e.g. model‐based iterative reconstruction, contrast‐enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging. Methods: To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual‐source, dual‐energy abdominal x‐ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual‐Energy ‐ WFBP" reconstruction mode with dual‐source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel‐wise, performing an image domain analog of dual‐source data completion with approximate spectral reweighting. The CNN with its U‐net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature‐contrast associations. Training was supervised, using data from within the 33‐cm chain B FoV to optimize and assess network performance. Results: Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature‐contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual‐source, dual‐energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30‐cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation. Conclusions: Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature‐contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases. [ABSTRACT FROM AUTHOR]

Purpose: Photon‐counting‐detector‐computed tomography (PCD‐CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K‐edge escape, and pulse pile‐up, which compromise spectral separation of multi‐energy data and degrade multi‐contrast imaging performance. The purpose of this work was to determine the performance of a dual‐source (DS) PCD‐CT relative to a single‐source (SS) PCD‐CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents. Methods: Phantom experiments were performed using a research whole‐body PCD‐CT and head/abdomen‐sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS‐PCD‐CT, the phantoms were scanned twice on the SS‐PCD‐CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low‐energy tube, while the high‐energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS‐PCD‐CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess‐board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy‐bin data sets, which permits simultaneous multi‐contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre‐defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS‐PCD‐CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K‐edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy‐bin images obtained from each scan (scan pair) were used to generate I‐, Gd‐, Bi‐specific image via material decomposition. Root‐mean‐square‐error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20‐cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi‐specific images. Results: Phantom results showed that DS‐PCD‐CT reduced noise in material‐specific images for both head and body phantoms compared to SS‐PCD‐CT. The noise level of SS‐PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS‐PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS‐PCD‐CT was reduced relative to DS‐PCD‐CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS‐PCD) to 0.72 (DS‐PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi). Conclusion: We demonstrate the feasibility of performing simultaneous imaging of I, Gd, and Bi materials on DS‐PCD‐CT. Under the condition without cross scattering, DS‐PCD reduced the RMSE for quantification of material concentration in relative to a SS‐PCD‐CT system using chess mode. [ABSTRACT FROM AUTHOR]

Objective To explore the feasibility of dual-source computed tomography (DSCT) in the evaluation of coronary in-stent restenosis (ISR) by comparing the results of DSCT and selective coronary angiography (CAG). Methods In-stent restenosis examination results from DSCT were compared with those obtained using CAG. Results Among 173 stents studied, 156 yielded good quality images when evaluated with DSCT. CAG identified 38 ISR cases, while DSCT found 40. Among the 112 stents in the study with an inner diameter ≥3.0 mm, CAG identified 29 as having ISR, while DSCT reported the same finding in 30; among the 44 stents with inner diameter <3.0 mm, CAG identified ISR in 9, while DSCT found ISR in 10. Conclusions Stent inner diameter is a key factor influencing the imaging of the stent lumen. DSCT demonstrated a higher negative predictive value in ISR assessment, suggesting that it could replace CAG for assessing the patency of stents with a larger inner diameter (≥3 mm). [ABSTRACT FROM AUTHOR]

In principle, dual-energy CT can only accurately decompose a mixture into two materials. To decompose a mixture into three constitute materials using dual-energy CT measurements, a third criteria must be provided to solve for three unknowns with only two spectral measurements. One solution is to assume that the sum of the volumes of three constituent materials is equivalent to the volume of the mixture (i.e., volume conservation), but this is not always true. A more generalized solution is to use the principle of mass conservation, which assumes that the sum of the masses of the three constituent materials is equivalent to the mass of the mixture. In this article, a mass-conservation based, three-material decomposition dual-energy CT algorithm is described and experimental validation of the accuracy of the technique presented. The results demonstrate that the proposed method can accurately measure elemental concentrations under low noise imaging conditions. Clinically, this may be applied to measure the mass fraction of any chemical element in a three-material mixture of solutions without the requirement of volume conservation. [ABSTRACT FROM AUTHOR]

The use of additional spectral filtration for dual-energy (DE) imaging using a dual-source CT (DSCT) system was investigated and its effect on the material-specific DEratio was evaluated for several clinically relevant materials. The x-ray spectra, data acquisition, and reconstruction processes for a DSCT system (Siemens Definition) were simulated using information provided by the system manufacturer, resulting in virtual DE images. The factory-installed filtration for the 80 kV spectrum was left unchanged to avoid any further reductions in tube output, and only the filtration for the high-energy spectrum was modified. Only practical single-element filter materials within the atomic number range of 40≤Z≤83 were evaluated, with the aim of maximizing the separation between the two spectra, while maintaining similar noise levels for high- and low-energy images acquired at the same tube current. The differences between mean energies and the ratio of the 140 and 80 kV detector signals, each integrated below 80 keV, were evaluated. The simulations were performed for three attenuation scenarios: Head, body, and large body. The large body scenario was evaluated for the DE acquisition mode using the 100 and 140 kV spectra. The DEratio for calcium hydroxyapatite (simulating bone or calcifications), iodine, and iron were determined for CT images simulated using the modified and factory-installed filtration. Several filter materials were found to perform well at proper thicknesses, with tin being a good practical choice. When image noise was matched between the low- and high-energy images, the spectral difference in mean absorbed energy using tin was increased from 25.7 to 42.7 keV (head), from 28.6 to 44.1 keV (body), and from 20.2 to 30.2 keV (large body). The overlap of the signal spectra for energies below 80 keV was reduced from 78% to 31% (head), from 93% to 27% (body), and from 106% to 79% (large body). The DEratio for the body attenuation scenario increased from 1.45 to 1.91 (calcium), from 1.84 to 3.39 (iodine), and from 1.73 to 2.93 (iron) with the additional tin filtration compared to the factory filtration. This use of additional filtration for one of the x-ray tubes used in dual-source DECT dramatically increased the difference between material-specific DE ratios, e.g., from 0.39 to 1.48 for calcium and iodine or from 0.28 to 1.02 for calcium and iron. Because the ability to discriminate between different materials in DE imaging depends primarily on the differences in DE ratios, this increase is expected to improve the performance of any material-specific DECT imaging task. Furthermore, for the large patient size and in conjunction with a 100/140 kV acquisition, the use of additional filtration decreased noise in the low-energy images and increased contrast in the DE image relative to that obtained with 80/140 kV and no additional filtration. [ABSTRACT FROM AUTHOR]

Over the past decade, computed tomography (CT) theory, techniques and applications have undergone a rapid development. Since CT is so practical and useful, undoubtedly CT technology will continue advancing biomedical and non-biomedical applications. In this outlook article, we share our opinions on the research and development in this field, emphasizing 12 topics we expect to be critical in the next decade: analytic reconstruction, iterative reconstruction, local/interior reconstruction, flat-panel based CT, dual-source CT, multi-source CT, novel scanning modes, energy-sensitive CT, nano-CT, artifact reduction, modality fusion, and phase-contrast CT. We also sketch several representative biomedical applications. [ABSTRACT FROM AUTHOR]