Feasibility of capturing vessel expansion with 4D-CTA: Phantom study to determine reproducibility, spatial and temporal resolution.

BACKGROUND: Dynamic Computed Tomography Angiography (4D CTA) has the potential of providing insight into the biomechanical properties of the vessel wall, by capturing motion of the vessel wall. For vascular pathologies, like intracranial aneurysms, this could potentially refine diagnosis, prognosis, and treatment decision-making. PURPOSE: The objective of this research is to determine the feasibility of a 4D CTA scanner for accurately measuring harmonic diameter changes in an in-vitro simulated vessel. METHODS: A silicon tube was exposed to a simulated heartbeat. Simulated heart rates between 40 and 100 beats-per-minute (bpm) were tested and the flow amplitude was varied, resulting in various changes of tube diameter. A 320-detector row CT system with ECG-gating captured three consecutive cycles of expansion. Image registration was used to calculate the diameter change. A vascular echography set-up was used as a reference, using a 9 MHz linear array transducer. The reproducibility of 4D CTA was represented by the Pearson correlation (r) between the three consecutive diameter change patterns, captured by 4D CTA. The peak value similarity (pvs) was calculated between the 4D CTA and US measurements for increasing frequencies and was chosen as a measure of temporal resolution. Spatial resolution was represented by the Sum of the Relative Percentual Difference (SRPD) between 4D CTA and US diameter change patterns for increasing amplitudes. RESULTS: The reproducibility of 4D CTA measurements was good (r ≥ 0.9) if the diameter change was larger than 0.3 mm, moderate (0.7 ≤ r < 0.9) if the diameter change was between 0.1 and 0.3 mm, and low (r < 0.7) if the diameter change was smaller than 0.1 mm. Regarding the temporal resolution, the amplitude of 4D CTA was similar to the US measurements (pvs ≥ 90%) for the frequencies of 40 and 50 bpm. Frequencies between 60 and 80 bpm result in a moderate similarity (70% ≤ pvs < 90%). A low similarity (pvs < 70%) is observed for 90 and 100 bpm. Regarding the spatial resolution, diameter changes above 0.30 mm result in SRPDs consistently below 50%. CONCLUSION: In a phantom setting, 4D CTA can be used to reliably capture reproducible tube diameter changes exceeding 0.30 mm. Low pulsation frequencies (40 or 50 bpm) provide an accurate measurement of the maximum tube diameter change.

Perceptual thresholds for differences in CT noise texture.

Purpose: The average (fav) or peak (fpeak) noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it. Approach: A model of CT NPS was created based on its fpeak and a half-Gaussian fit (σ) to the downslope. Two-alternative forced-choice staircase studies were used to determine perceptual thresholds for noise texture, defined as parameter differences with a predetermined level of discrimination performance (80% correct). Five imaging scientist observers performed the forced-choice studies for eight directions in the fpeak/σ-space, for two reference NPSs (corresponding to body and lung kernels). The experiment was repeated with 32 radiologists, each evaluating a single direction in the fpeak/σ-space. NPS differences were quantified by the noise texture contrast (Ctexture), the integral of the absolute NPS difference. Results: The two-parameter NPS model was found to be a good representation of various clinical CT reconstructions. Perception thresholds for fpeak alone are 0.2 lp/cm for body and 0.4 lp/cm for lung NPSs. For σ, these values are 0.15 and 2 lp/cm, respectively. Thresholds change if the other parameter also changes. Different NPSs with the same fpeak or fav can be discriminated. Nonradiologist observers did not need more Ctexture than radiologists. Conclusions: fpeak or fav is insufficient to describe noise texture completely. The discrimination of noise texture changes depending on its frequency content. Radiologists do not discriminate noise texture changes better than nonradiologists.

Radiation dose reduction of 50% in dynamic myocardial CT perfusion with skipped beat acquisition: a retrospective study.

BACKGROUND: Dynamic myocardial computed tomography perfusion (CTP) is a novel imaging technique that increases the applicability of CT for cardiac imaging; however, the scanning requires a substantial radiation dose. PURPOSE: To investigate the feasibility of dose reduction in dynamic CTP by comparing all-heartbeat acquisitions to periodic skipping of heartbeats. MATERIAL AND METHODS: We retrieved imaging data of 38 dynamic CTP patients and created new datasets with every fourth, third or second beat (Skip1:4, Skip1:3, Skip1:2, respectively) removed. Seven observers evaluated the resulting images and perfusion maps for perfusion deficits. The mean blood flow (MBF) in each of the 16 myocardial segments was compared per skipped-beat level, normalized by the respective MBF for the full dose, and averaged across patients. The number of segments/cases whose MBF was <1.0 mL/g/min were counted. RESULTS: Out of 608 segments in 38 cases, the total additional number of false-negative (FN) segments over those present in the full-dose acquisitions and the number of additional false-positive cases were shown as acquisition (segment [%], case): Skip1:4: 7 (1.2%, 1); Skip1:3: 12 (2%, 3), and Skip1:2: 5 (0.8%, 2). The variability in quantitative MBF analysis in the repeated analysis for the reference condition resulted in 8 (1.3%) additional FN segments. The normalized results show a comparable MBF across all segments and patients, with relative mean MBFs as 1.02 ± 0.16, 1.03 ± 0.25, and 1.06 ± 0.30 for the Skip1:4, Skip1:3, and Skip1:2 protocols, respectively. CONCLUSION: Skipping every second beat acquisition during dynamic myocardial CTP appears feasible and may result in a radiation dose reduction of 50%. Diagnostic performance does not decrease after removing 50% of time points in dynamic sequence.

Development, validation, and simplification of a scanner-specific CT simulator.

BACKGROUND: Simulated computed tomography (CT) images allow for knowledge of the underlying ground truth and for easy variation of imaging conditions, making them ideal for testing and optimization of new applications or algorithms. However, simulating all processes that affect CT images can result in simulations that are demanding in terms of processing time and computer memory. Therefore, it is of interest to determine how much the simulation can be simplified while still achieving realistic results. PURPOSE: To develop a scanner-specific CT simulation using physics-based simulations for the position-dependent effects and shift-invariant image corruption methods for the detector effects. And to investigate the impact on image realism of introducing simplifications in the simulation process that lead to faster and less memory-demanding simulations. METHODS: To make the simulator realistic and scanner-specific, the spatial resolution and noise characteristics, and the exposure-to-detector output relationship of a clinical CT system were determined. The simulator includes a finite focal spot size, raytracing of the digital phantom, gantry rotation during projection acquisition, and finite detector element size. Previously published spectral models were used to model the spectrum for the given tube voltage. The integrated energy at each element of the detector was calculated using the Beer-Lambert law. The resulting angular projections were subsequently corrupted by the detector modulation transfer function (MTF), and by addition of noise according to the noise power spectrum (NPS) and signal mean-variance relationship, which were measured for different scanner settings. The simulated sinograms were reconstructed on the clinical CT system and compared to real CT images in terms of CT numbers, noise magnitude using the standard deviation, noise frequency content using the NPS, and spatial resolution using the MTF throughout the field of view (FOV). The CT numbers were validated using a multi-energy CT phantom, the noise magnitude and frequency were validated with a water phantom, and the spatial resolution was validated with a tungsten wire. These metrics were compared at multiple scanner settings, and locations in the FOV. Once validated, the simulation was simplified by reducing the level of subsampling of the focal spot area, rotation and of detector pixel size, and the changes in MTFs were analyzed. RESULTS: The average relative errors for spatial resolution within and across image slices, noise magnitude, and noise frequency content within and across slices were 3.4%, 3.3%, 4.9%, 3.9%, and 6.2%, respectively. The average absolute difference in CT numbers was 10.2 HU and the maximum was 22.5 HU. The simulation simplification showed that all subsampling can be avoided, except for angular, while the error in frequency at 10% MTF would be maximum 16.3%. CONCLUSION: The simulation of a scanner-specific CT allows for the generation of realistic CT images by combining physics-based simulations for the position-dependent effects and image-corruption methods for the shift-invariant ones. Together with the available ground truth of the digital phantom, it results in a useful tool to perform quantitative analysis of reconstruction or post-processing algorithms. Some simulation simplifications allow for reduced time and computer power requirements with minimal loss of realism.

Dynamic Computed Tomography Angiography for capturing vessel wall motion: A phantom study for optimal image reconstruction.

BACKGROUND: Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis, and treatment decision making in vascular pathologies. PURPOSE: The aim of this study is to determine the most suitable image reconstruction method for 4D CTA to accurately assess harmonic diameter changes of vessels. METHODS: An elastic tube (inner diameter 6 mm, wall thickness 2 mm) was exposed to sinusoidal pressure waves with a frequency of 70 beats-per-minute. Five flow amplitudes were set, resulting in increasing sinusoidal diameter changes of the elastic tube, measured during three simulated pulsation cycles, using ECG-gated 4D CTA on a 320-detector row CT system. Tomographic images were reconstructed using one of the following three reconstruction methods: hybrid iterative (Hybrid-IR), model-based iterative (MBIR) and deep-learning based (DLR) reconstruction. The three reconstruction methods where based on 180 degrees (half reconstruction mode) and 360 degrees (full reconstruction mode) raw data. The diameter change, captured by 4D CTA, was computed based on image registration. As a reference metric for diameter change measurement, a 9 MHz linear ultrasound transducer was used. The sum of relative absolute differences (SRAD) between the ultrasound and 4D CTA measurements was calculated for each reconstruction method. The standard deviation was computed across the three pulsation cycles. RESULTS: MBIR and DLR resulted in a decreased SRAD and standard deviation compared to Hybrid-IR. Full reconstruction mode resulted in a decreased SRAD and standard deviations, compared to half reconstruction mode. CONCLUSIONS: 4D CTA can capture a diameter change pattern comparable to the pattern captured by US. DLR and MBIR algorithms show more accurate results than Hybrid-IR. Reconstruction with DLR is >3 times faster, compared to reconstruction with MBIR. Full reconstruction mode is more accurate than half reconstruction mode.

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.

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.

Coronary Artery Calcium Scoring: Toward a New Standard.

OBJECTIVES: Although the Agatston score is a commonly used quantification method, rescan reproducibility is suboptimal, and different CT scanners result in different scores. In 2007, McCollough et al (Radiology 2007;243:527-538) proposed a standard for coronary artery calcium quantification. Advancements in CT technology over the last decade, however, allow for improved acquisition and reconstruction methods. This study aims to investigate the feasibility of a reproducible reduced dose alternative of the standardized approach for coronary artery calcium quantification on state-of-the-art CT systems from 4 major vendors. MATERIALS AND METHODS: An anthropomorphic phantom containing 9 calcifications and 2 extension rings were used. Images were acquired with 4 state-of-the-art CT systems using routine protocols and a variety of tube voltages (80-120 kV), tube currents (100% to 25% dose levels), slice thicknesses (3/2.5 and 1/1.25 mm), and reconstruction techniques (filtered back projection and iterative reconstruction). Every protocol was scanned 5 times after repositioning the phantom to assess reproducibility. Calcifications were quantified as Agatston scores. RESULTS: Reducing tube voltage to 100 kV, dose to 75%, and slice thickness to 1 or 1.25 mm combined with higher iterative reconstruction levels resulted in an on average 36% lower intrascanner variability (interquartile range) compared with the standard 120 kV protocol. Interscanner variability per phantom size decreased by 34% on average. With the standard protocol, on average, 6.2 ± 0.4 calcifications were detected, whereas 7.0 ± 0.4 were detected with the proposed protocol. Pairwise comparisons of Agatston scores between scanners within the same phantom size demonstrated 3 significantly different comparisons at the standard protocol (P < 0.05), whereas no significantly different comparisons arose at the proposed protocol (P > 0.05). CONCLUSIONS: On state-of-the-art CT systems of 4 different vendors, a 25% reduced dose, thin-slice calcium scoring protocol led to improved intrascanner and interscanner reproducibility and increased detectability of small and low-density calcifications in this phantom. The protocol should be extensively validated before clinical use, but it could potentially improve clinical interscanner/interinstitutional reproducibility and enable more consistent risk assessment and treatment strategies.

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms.

OBJECTIVES: To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). METHODS: Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. RESULTS: For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. CONCLUSIONS: With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. KEY POINTS: • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Pulmonary nodule enhancement in subtraction CT and dual-energy CT: A comparison study.

OBJECTIVE: To compare nodule enhancement on subtraction CT iodine maps to that on dual-energy CT iodine maps using CT datasets acquired simultaneously. METHODS: A previously-acquired set of lung subtraction and dual-energy CT maps consisting of thirty patients with 95 solid pulmonary nodules (≥4 mm diameter) was used. Nodules were annotated and segmented on CT angiography, and mean nodule enhancement in the iodine maps calculated. Three radiologists scored nodule visibility with both techniques on a 4-point scale. RESULTS: Mean nodule enhancement was higher (p < 0.001) at subtraction CT (34.9 ±â€¯12.9 HU) than at dual-energy CT (25.4 ±â€¯21.0 HU). Nodule enhancement at subtraction CT was judged more often to be "highly visible" for each observers (p < 0.001) with an area under the curve of 0.81. CONCLUSIONS: Subtraction CT is able to depict iodine enhancement in pulmonary nodules better than dual-energy CT.

Measurement of coronary artery calcium volume using ultra-high-resolution computed tomography: A preliminary phantom and cadaver study.

OBJECTIVES: In this phantom- and cadaver study we investigated the differences of coronary artery calcium (CAC) volume on ultra-high-resolution computed tomography (U-HRCT) scans and conventional CT. METHODS: We scanned a coronary calcium phantom and the coronary arteries of five cadavers using U-HRCT in normal- and super-high resolution (NR, SHR) mode. The NR mode was similar to conventional CT; 896 detector channels, a matrix size of 512, and a slice thickness of 0.5 mm were applied. In SHR mode, we used 1792 detector channels, a matrix size of 1024, and a slice thickness of 0.25 mm. The CAC volume on NR- and SHR images were recorded. Differences in the physical- and the calculated CAC volume were defined as the error value and compared between NR- and SHR images of the phantom. Differences between the CAC volume on NR- and SHR scans of the cadavers were also recorded. RESULTS: The mean error value was lower on SHR- than NR images of the phantom (14.0 %, SD 11.1 vs 20.1 %, SD 15.2, p = 0.01). The mean CAC volume was significantly higher on SHR- than NR images of the cadavers (153.4 mm3, SD 161.0 vs 144.7 mm3, SD 164.8, p < 0.01). CONCLUSIONS: As small calcifications were more clearly visualized on U-HRCT images in SHR mode than on conventional (NR) CT scans, SHR imaging may facilitate the accurate quantification of the CAC.

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.

Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study.

Background Dual-energy CT iodine maps are used to detect pulmonary embolism (PE) with CT angiography but require dedicated hardware. Subtraction CT, a software-only solution, results in iodine maps with high contrast-to-noise ratios. Purpose To compare the use of subtraction CT versus dual-energy CT iodine maps to CT angiography for PE detection. Materials and Methods In this prospective study ( https://clinicaltrials.gov , NCT02890706), 274 participants suspected of having PE underwent precontrast CT followed by contrast material-enhanced dual-energy CT angiography between July 2016 and April 2017. Iodine maps from dual-energy CT were derived. Subtraction maps (contrast-enhanced CT minus precontrast CT) were calculated after motion correction. Truth was established by expert consensus. A total of 75 randomly selected participants with and without PE (1:1 ratio) were evaluated by three radiologists and six radiology residents (blinded to final diagnosis) for the presence of PE using three types of CT: CT angiography alone, dual-energy CT, and subtraction CT. The partial area under the receiver operating characteristic curve (AUC) for the clinically relevant specificity region (maximum partial AUC, 0.11) was compared by using multireader multicase variance. A P value less than or equal to .025 was considered indicative of a significant difference due to multiple comparisons. Results There were 35 men and 40 women in the reader study (mean age, 63 years ± 12 [standard deviation]). The pooled sensitivities were not different (P ≥ .31 among techniques) (95% confidence intervals [CIs]: 67%, 89% for CT angiography; 72%, 91% for dual-energy CT; 70%, 91% for subtraction CT). However, pooled specificity was higher for subtraction CT (95% CI: 100%, 100%) than for CT angiography (95% CI: 89%, 97%) or dual-energy CT (95% CI: 89%, 98%) (P < .001). Partial AUCs for the average observer improved equally when adding iodine maps (subtraction CT [0.093] vs CT angiography [0.088], P = .03; dual-energy CT [0.094] vs CT angiography, P = .01; dual-energy CT vs subtraction CT, P = .68). Average reading times were equivalent (range, 97-101 seconds; P ≥ .41) among techniques. Conclusion Subtraction CT iodine maps had greater specificity than CT angiography alone in pulmonary embolism detection. Subtraction CT had comparable diagnostic performance to that of dual-energy CT, without the need for dedicated hardware. © RSNA, 2019 Online supplemental material is available for this article.

Image Quality of Iodine Maps for Pulmonary Embolism: A Comparison of Subtraction CT and Dual-Energy CT.

OBJECTIVE. The objective of this study was to compare the image quality of iodine maps derived from subtraction CT and from dual-energy CT (DECT) in patients with suspected pulmonary embolism (PE). SUBJECTS AND METHODS. In this prospective study conducted between July 2016 and April 2017, consecutive patients with suspected PE underwent unenhanced CT at 100 kV and dual-energy pulmonary CT angiography at 100 and 140 kV on a dual-source scanner. The scanner was set to generate subtraction and DECT iodine maps at similar radiation doses. In 55 patients (30 women, 25 men; mean age ± SD, 63.4 ± 11.9 years old), various subjective image quality criteria including diagnostic acceptability were rated on a 5-point scale by four radiologists and a radiology resident. In 29 patients (17 women, 12 men; mean age, 62.4 ± 11.7 years old) with confirmed perfusion defects, the signal-difference-to-noise ratio (SDNR) between perfusion defects and adjacent normally perfused parenchyma was measured in corresponding ROIs on subtraction and DECT iodine maps. McNemar and Wilcoxon signed-rank tests were used for statistical comparisons. RESULTS. Diagnostic acceptability was rated excellent or good in a mean of 67% (range, 31-80%) of subtraction CT studies and 36% (5-69%) of DECT studies (p < 0.05 for four of the five radiologists), mainly because of fewer artifacts on subtraction CT. Mean SDNR was marginally higher for subtraction CT than for DECT (18.6 vs 17.1, p = 0.06) and was significantly higher in the upper lobes (21.8 vs 17.9, p < 0.05). CONCLUSION. Radiologist-judged image quality of pulmonary iodine maps was higher for subtraction CT than for DECT with similar to higher SDNR. Subtraction CT is a software-only solution, so it may be an attractive alternative to DECT for depicting perfusion defects.

Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study.

PURPOSE: The purpose of this study was to assess, using an anthropomorphic digital phantom, the accuracy of algorithms in registering precontrast and contrast-enhanced computed tomography (CT) chest images for generation of iodine maps of the pulmonary parenchyma via temporal subtraction. MATERIALS AND METHODS: The XCAT phantom, with enhanced airway and pulmonary vessel structures, was used to simulate precontrast and contrast-enhanced chest images at various inspiration levels and added CT simulation for realistic system noise. Differences in diaphragm position were varied between 0 and 20 mm, with the maximum chosen to exceed the 95th percentile found in a dataset of 100 clinical subtraction CTs. In addition, the influence of whole body movement, degree of iodine enhancement, beam hardening artifacts, presence of nodules and perfusion defects in the pulmonary parenchyma, and variation in noise on the registration were also investigated. Registration was performed using three lung registration algorithms - a commercial (algorithm A) and a prototype (algorithm B) version from Canon Medical Systems and an algorithm from the MEVIS Fraunhofer institute (algorithm C). For each algorithm, we calculated the voxel-by-voxel difference between the true deformation and the algorithm-estimated deformation in the lungs. RESULTS: The median absolute residual error for all three algorithms was smaller than the voxel size (1.0 × 1.0 × 1.0 mm3 ) for up to an 8 mm diaphragm difference, which is the average difference in diaphragm levels found clinically, and increased with increasing difference in diaphragm position. At 20 mm diaphragm displacement, the median absolute residual error after registration was 0.85 mm (interquartile range, 0.51-1.47 mm) for algorithm A, 0.82 mm (0.50-1.40 mm) for algorithm B, and 0.91 mm (0.54-1.52 mm) for algorithm C. The largest errors were seen in the paracardiac regions and close to the diaphragm. The impact of all other evaluated conditions on the residual error varied, resulting in an increase in the median residual error lower than 0.1 mm for all algorithms, except in the case of whole body displacements for algorithm B, and with increased noise for algorithm C. CONCLUSION: Motion correction software can compensate for respiratory and cardiac motion with a median residual error below 1 mm, which was smaller than the voxel size, with small differences among the tested registration algorithms for different conditions. Perfusion defects above 50 mm will be visible with the commercially available subtraction CT software, even in poorly registered areas, where the median residual error in that area was 7.7 mm.

Imaging of pulmonary perfusion using subtraction CT angiography is feasible in clinical practice.

Subtraction computed tomography (SCT) is a technique that uses software-based motion correction between an unenhanced and an enhanced CT scan for obtaining the iodine distribution in the pulmonary parenchyma. This technique has been implemented in clinical practice for the evaluation of lung perfusion in CT pulmonary angiography (CTPA) in patients with suspicion of acute and chronic pulmonary embolism, with acceptable radiation dose. This paper discusses the technical principles, clinical interpretation, benefits and limitations of arterial subtraction CTPA. KEY POINTS: • SCT uses motion correction and image subtraction between an unenhanced and an enhanced CT scan to obtain iodine distribution in the pulmonary parenchyma. • SCT could have an added value in detection of pulmonary embolism. • SCT requires only software implementation, making it potentially more widely available for patient care than dual-energy CT.

Author Correction: White Matter and Gray Matter Segmentation in 4D Computed Tomography.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Comparing dual energy CT and subtraction CT on a phantom: which one provides the best contrast in iodine maps for sub-centimetre details?

OBJECTIVES: To compare contrast-to-noise ratios (CNRs) and iodine discrimination thresholds on iodine maps derived from dual energy CT (DECT) and subtraction CT (SCT). METHODS: A contrast-detail phantom experiment was performed with 2 to 15 mm diameter tubes containing water or iodinated contrast concentrations ranging from 0.5 mg/mL to 20 mg/mL. DECT scans were acquired at 100 kVp and at 140 kVp+Sn filtration. SCT scans were acquired at 100 kVp. Iodine maps were created by material decomposition (DECT) or by subtraction of water scans from iodine scans (SCT). Matched exposure levels varied from 8 to 15 mGy. Iodine discrimination thresholds (Cr) and response times were determined by eight observers. RESULTS: The adjusted mean CNR was 1.9 times higher for SCT than for DECT. Exposure level had no effect on CNR. All observers discriminated all details ≥10 mm at 12 and 15 mGy. For sub-centimetre details, the lowest calculated Cr was ≤ 0.50 mg/mL for SCT and 0.64 mg/mL for DECT. The smallest detail was discriminated at ≥4.4 mg/mL with SCT and at ≥7.4 mg/mL with DECT. Response times were lower for SCT than DECT. CONCLUSIONS: SCT results in higher CNR and reduced iodine discrimination thresholds compared to DECT for sub-centimetre details. KEY POINTS: • Subtraction CT iodine maps exhibit higher CNR than dual-energy iodine maps • Lower iodine concentrations can be discriminated for sub-cm details with SCT • Response times are lower using SCT compared to dual-energy CT.

White Matter and Gray Matter Segmentation in 4D Computed Tomography.

Modern Computed Tomography (CT) scanners are capable of acquiring contrast dynamics of the whole brain, adding functional to anatomical information. Soft tissue segmentation is important for subsequent applications such as tissue dependent perfusion analysis and automated detection and quantification of cerebral pathology. In this work a method is presented to automatically segment white matter (WM) and gray matter (GM) in contrast- enhanced 4D CT images of the brain. The method starts with intracranial segmentation via atlas registration, followed by a refinement using a geodesic active contour with dominating advection term steered by image gradient information, from a 3D temporal average image optimally weighted according to the exposures of the individual time points of the 4D CT acquisition. Next, three groups of voxel features are extracted: intensity, contextual, and temporal. These are used to segment WM and GM with a support vector machine. Performance was assessed using cross validation in a leave-one-patient-out manner on 22 patients. Dice coefficients were 0.81 ± 0.04 and 0.79 ± 0.05, 95% Hausdorff distances were 3.86 ± 1.43 and 3.07 ± 1.72 mm, for WM and GM, respectively. Thus, WM and GM segmentation is feasible in 4D CT with good accuracy.