Technical performance of a dual-energy CT system with a novel deep-learning based reconstruction process: Evaluation using an abdomen protocol.

BACKGROUND: A new tube voltage-switching dual-energy (DE) CT system using a novel deep-learning based reconstruction process has been introduced. Characterizing the performance of this DE approach can help demonstrate its benefits and potential drawbacks. PURPOSE: To evaluate the technical performance of a novel DECT system and compare it to that of standard single-kV CT and a rotate/rotate DECT, for abdominal imaging. METHODS: DE and single-kV images of four different phantoms were acquired on a kV-switching DECT system, and on a rotate/rotate DECT. The dose for the acquisitions of each phantom was set to that selected for the kV-switching DE mode by the automatic tube current modulation (ATCM) at manufacturer-recommended settings. The dose that the ATCM would have selected in single-kV mode was also recorded. Virtual monochromatic images (VMIs) from 40 to 130 keV, as well as iodine maps, were reconstructed from the DE data. Single-kV images, acquired at 120 kV, were reconstructed using body hybrid iterative reconstruction. All reconstructions were made at 0.5 mm section thickness. Task transfer functions (TTFs) were determined for a Teflon and LDPE rod. Noise magnitude (SD), and noise power spectrum (NPS) were calculated using 240 and 320 mm diameter water phantoms. Iodine quantification accuracy and contrast-to-noise ratios (CNRs) relative to water for 2, 5, 10, and 15 mg I/ml were determined using a multi-energy CT (MECT) phantom. Low-contrast visibility was determined and the presence of beam-hardening artifacts and inhomogeneities were evaluated. RESULTS: The TTFs of the kV-switching DE VMIs were higher than that of the single-kV images for Teflon (20% TTF: 6.8 lp/cm at 40 keV, 6.2 lp/cm for single-kV), while for LDPE the DE TTFs at 70 keV and above were equivalent or higher than the single-kV TTF. All TTFs of the kV-switching DECT were higher than for the rotate/rotate DECT. The SD was lowest in the 70 keV VMI (12.0 HU), which was lower than that of single-kV (18.3 HU). The average NPS frequency varied between 2.3 lp/cm and 4.2 lp/cm for the kV-switching VMIs and was 2.2 lp/cm for single-kV. The error in iodine quantification was at maximum 1 mg I/ml (at 5 mg I/ml). The highest CNR for all iodine concentrations was at 60 keV, 2.5 times higher than the CNR for single-kV. At 70-90 keV, the number of visible low contrast objects was comparable to that in single-kV, while other VMIs showed fewer objects. At manufacturer-recommended ATCM settings, the CTDIvol for the DE acquisitions of the water and MECT phantoms were 12.6 and 15.4 mGy, respectively, and higher than that for single-kV. The 70 keV VMI had less severe beam hardening artifacts than single-kV images. Hyper- and hypo-dense blotches may appear in VMIs when object attenuation exceeds manufacturer recommended limits. CONCLUSIONS: At manufacturer-recommended ATCM settings for abdominal imaging, this DE implementation results in higher CTDIvol compared to single-kV acquisitions. However, it can create sharper, lower noise VMIs with up to 2.5 times higher iodine CNR compared to single-kV images acquired at the same dose.

CT imaging with ultra-high-resolution: Opportunities for cardiovascular imaging in clinical practice.

Cardiovascular computed tomography (CT) angiography has become an established alternative to invasive catheter angiography. However, imaging artifacts due to partial volume effects with current systems hinder accurate evaluation of calcified or stented segments. Increased spatial resolution may allow to overcome these barriers to precise delineation of vascular disease. Recent developments in CT hardware and reconstruction have enabled CT angiography with ultra-high spatial resolution (UHRCT). In this review we aim to describe the methods to achieve greater spatial resolution in CT that are either in clinical or preclinical stage. In addition, we provide an overview of the available clinical evidence including diagnostic accuracy studies supporting improved vascular assessment with this technology. The benefits that can be gleaned from the initial experiences with UHRCT are promising. Using UHRCT, more patients may receive non-invasive characterization of coronary atherosclerosis by overcoming the limitations of current CT spatial resolution in visualizing and quantifying calcified, stented or small diameter segments. UHRCT may potentially impact existing management pathways as well as contribute to better understanding of the underlying pathophysiology of both macro- and microvascular disease.

Validation of synthesized normal-resolution image data generated from high-resolution acquisitions on a commercial CT scanner.

PURPOSE: To validate a normal-resolution (NR) simulation (NRsim) algorithm that uses high-resolution (HR) or super-high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim-generated images and actual NR images. NRsim is intended to allow direct comparison between normal-resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams. METHODS: The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x-y) and row (z) directions. For NR, each detector element is 0.5 mm × 0.5 mm along the channel and row directions, 0.25 mm × 0.5 mm for HR, and 0.25 mm × 0.25 mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NRHR and NRSHR , respectively) by downsampling the pre-log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three-dimensional noise power spectrum (NPS) was measured in water-equivalent phantoms for standard protocols across a range of size-specific dose estimates (SSDE): head (6.2-29.8 mGy), lung (2.2-18.2 mGy), and body (5.6-19.4 mGy). The MTF and NPS measurements were combined to estimate low-contrast detectability (LCD) using a non-prewhitening model observer with an eye filter for a 5-mm disk with 10 HU contrast. All metrics were compared for NR, NRHR , and NRSHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just-noticeable difference in image noise level for human observers is typically <15%. RESULTS: The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5 mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2 HU of NR. LCD for NRsim was within 15% of NR with the exception of NRSHR for the lung protocol SSDE levels below 3.7 mGy with FBP. CONCLUSIONS: NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD largely equivalent to scans acquired using an actual NR acquisition. At SSDE levels below ~5 mGy for the lung protocol, differences in noise magnitude and LCD for NRSHR were >15% which defines a region where NRsim degrades due to contributions from electronic noise.

Correction to: Physical evaluation of an ultra-high-resolution CT scanner.

The original version of this article, published on 10 February 2019, unfortunately contained a mistake. The axes of the graphs in Fig. 3 are incorrect. The correct figure is given below. Therefore, the last two sentences in "Results," section "Noise," should read: "The peak frequency of the HR and SHR was 0.21 lp/mm. For the NR mode and the MDCT, the peak frequencies were 0.17 lp/mm and 0.21 lp/mm, respectively."

Physical evaluation of an ultra-high-resolution CT scanner.

OBJECTIVES: To evaluate the technical performance of an ultra-high-resolution CT (UHRCT) system. METHODS: The physico-technical capabilities of a novel commercial UHRCT system were assessed and compared with those of a current-generation multi-detector (MDCT) system. The super-high-resolution (SHR) mode of the system uses 0.25 mm (at isocentre) detector elements (dels) in the in-plane and longitudinal directions, while the high-resolution (HR) mode bins two dels in the longitudinal direction. The normal-resolution (NR) mode bins dels 2 × 2, resulting in a del-size equivalent to that of the MDCT system. In general, standard procedures and phantoms were used to perform these assessments. RESULTS: The UHRCT MTF (10% MTF 4.1 lp/mm) is twice as high as that of the MDCT (10% MTF 1.9 lp/mm), which is comparable to the MTF in the NR mode (10% MTF 1.7 lp/mm). The width of the slice sensitivity profile in the SHR mode (FWHM 0.45 mm) is about 60% of that of the MDCT (FWHM 0.77 mm). Uniformity and CT numbers are within the expected range. Noise in the high-resolution modes has a higher magnitude and higher frequency components compared with MDCT. Low-contrast visibility is lower for the NR, HR and SHR modes compared with MDCT, but about a 14%, for NR, and 23%, for HR and SHR, dose increase gives the same results. CONCLUSIONS: HR and SHR mode scanning results in double the spatial resolution, with about a 23% increase in dose required to achieve the same low-contrast detectability. KEY POINTS: • Resolution on UHRCT is up to twice as high as for the tested MDCT. • With abdominal settings, UHRCT needs higher dose for the same low-contrast detectability as MDCT, but dose is still below achievable levels as defined by current diagnostic reference levels. • The UHRCT system used in normal-resolution mode yields comparable resolution and noise characteristics as the MDCT system.

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233.

BACKGROUND: The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. AIMS: This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. MATERIALS AND METHODS: The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. RESULTS: The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. DISCUSSION: In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). CONCLUSION: We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.

Emphysema: effect of reconstruction algorithm on CT imaging measures.

In the current study, the effects of reconstruction algorithms on quantitative measures derived from computed tomographic (CT) lung images were assessed in patients with emphysema. CT image data sets were reconstructed with a standard algorithm and alternative algorithm(s) for 42 subjects. Algorithms were grouped as overenhancing, sharp, standard, or smooth. Density mask and volume measurements from the alternative algorithm data sets were compared with standard algorithm data sets. The overenhancing category yielded an average shift of 9.4% (ie, a shift in average score from 35.5% to 44.9%); the sharp category, a shift of 2.4%; and the smooth category, a shift of -1.0%. Differences in total lung volume measurements were less than 1%. In conclusion, the CT reconstruction algorithm may strongly affect density mask results, especially for certain reconstruction algorithms.