Task-Based Model Observer Assessment of A Partial Model-Based Iterative Reconstruction Algorithm in Thoracic Oncologic Multidetector CT

Optimization of CT pulmonary angiography for pulmonary embolism using task-based image quality assessment and diagnostic reference levels: A multicentric study

PURPOSE

To establish size-specific diagnostic reference levels (DRLs) for pulmonary embolism (PE) based on patient CT examinations performed on 74 CT devices. To assess task-based image quality (IQ) for each device and to investigate the variability of dose and IQ across different CTs. To propose a dose/IQ optimization.

METHODS

1051 CT pulmonary angiography dose data were collected. DRLs were calculated as the 75th percentile of CT dose index (CTDI) for two patient categories based on the thoracic perimeters. IQ was assessed with two thoracic phantom sizes using local acquisition parameters and three other dose levels. The area under the ROC curve (AUC) of a 2 mm low perfused vessel was assessed with a non-prewhitening with eye-filter model observer. The optimal IQ-dose point was mathematically assessed from the relationship between IQ and dose.

RESULTS

The DRLs of CTDIvol were 6.4 mGy and 10 mGy for the two patient categories. 75th percentiles of phantom CTDIvol were 6.3 mGy and 10 mGy for the two phantom sizes with inter-quartile AUC values of 0.047 and 0.066, respectively. After the optimization, 75th percentiles of phantom CTDIvol decreased to 5.9 mGy and 7.55 mGy and the interquartile AUC values were reduced to 0.025 and 0.057 for the two phantom sizes.

CONCLUSION

DRLs for PE were proposed as a function of patient thoracic perimeters. This study highlights the variability in terms of dose and IQ. An optimization process can be started individually and lead to a harmonization of practice throughout multiple CT sites.

Assessment of temporal resolution and detectability of moving objects in CT: A task-based image quality study

The metrics used for assessing image quality in computed tomography (CT) do not integrate the influence of temporal resolution. A shortcoming in the assessment of image quality for imaging protocols where motion blur can therefore occur. We developed a method to calculate the temporal resolution of standard CT protocols and introduced a specific spatiotemporal formulation of the non-prewhitening with eye filter (NPWE) model observer to assess the detectability of moving objects as a function of their speed. We scanned a cubic water phantom with a plexiglass cylindrical insert (120 HU) using a large panel of acquisition parameters (rotation times, pitch factors and collimation widths) on two systems (GE Revolution Apex and Siemens SOMATOM Force) to determine the in-plane task-based transfer functions (TTF) and noise power spectra (NPS). The phantom set in a uniform rectilinear motion in the transverse plane allowed the temporal modulation transfer function (MTF) calculation. The temporal MTF appropriately compared the temporal resolution of the various acquisition protocols. The longitudinal TTF was measured using a thin tungsten wire. The detectability index showed the advantage of applying high rotation speed, wide collimations and high pitch for object detection in the presence of motion. No counterpart to the increase in these three parameters was found in the in-plane and longitudinal image quality.

Coronary CT angiography for the assessment of atherosclerotic plaque inflammation: postmortem proof of concept with histological validation

OBJECTIVES

To evaluate the diagnostic utility of multiphase postmortem CT angiography (PMCTA) to detect plaque enhancement as a surrogate marker of inflammation, using fatal coronary plaques obtained from autopsies following sudden cardiac death.

METHODS

In this retrospective study, we included 35 cases (12 women, 34%; median [IQR] age, 52 [11] years), with autopsy-proven coronary thrombosis, histological examination, and multiphase PMCTA. Two radiologists blinded towards histological findings assessed PMCTA for plaque enhancement of the culprit lesion in consensus. Two forensic pathologists determined the culprit lesion and assessed histological samples in consensus. Cases with concomitant vasa vasorum density increase and intraplaque and periadventital inflammation were considered positive for plaque inflammation. Finally, we correlated radiology and pathology findings.

RESULTS

All 35 cases had histological evidence of atherosclerotic plaque disruption and thrombosis; 30 (85.7%) had plaque inflammation. Plaque enhancement at multiphase PMCTA was reported in 21 (60%) and resulted in a PPV of 95.2% (77.3-99.2%) and an NPV of 28.6% (17-43.9%). Median histological ratings indicated higher intraplaque inflammation (p = .024) and vasa vasorum density (p = .032) in plaques with enhancement. We found no evidence of a difference in adventitial inflammation between CT-negative and CT-positive plaques (p = .211).

CONCLUSIONS

Plaque enhancement was found in 2/3 of fatal atherothrombotic occlusions at coronary postmortem CT angiography. Furthermore, plaque enhancement correlated with histopathological plaque inflammation and increased vasa vasorum density. Plaque enhancement on multiphase CT angiography could potentially serve as a noninvasive marker of inflammation in high-risk populations.

CLINICAL RELEVANCE STATEMENT

Phenotyping coronary plaque more comprehensively is one of the principal challenges cardiac imaging is facing. Translating our ex vivo findings of CT-based plaque inflammation assessment into clinical studies might help pave the way in defining high-risk plaque better.

KEY POINTS

• Most thrombosed coronary plaques leading to fatality in our series had histological signs of inflammation. • Multiphase postmortem CT angiography can provide a noninvasive interrogation of plaque inflammation through contrast enhancement. • Atherosclerotic plaque enhancement at multiphase postmortem CT angiography correlated with histopathological signs of plaque inflammation and could potentially serve as an imaging biological marker of plaque vulnerability.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT

PURPOSE

To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).

METHODS

The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool.

RESULTS

DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65).

CONCLUSION

DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice

Background

This study investigated the feasibility of channelized hoteling observer (CHO) model in computed tomography (CT) protocol optimization regarding the image quality and patient exposure. While the utility of using model observers such as to optimize the clinical protocol is evident, the pitfalls associated with the use of this method in practice require investigation.

Materials and Methods

This study was performed using variable tube current and adaptive statistical iterative reconstruction (ASIR) level (ASIR 10% to ASIR 100%). Various criteria including noise, high-contrast spatial resolution, CHOs model were used to compare image quality at different captured levels. For the implementation of CHO, we first tuned the model in a restricted dataset and then it to the evaluation of a large dataset of images obtained with different reconstruction ASIR and filtered back projection (FBP) levels.

Results

The results were promising in terms of CHO use for the stated purposes. Comparisons of the noise of reconstructed images with 30% ASIR and higher levels of noise in rebuilding images using the FBP approach showed a significant difference (P < 0.05). The spatial resolution obtained using various ASIR levels and tube currents were 0.8 pairs of lines per millimeter, which did not differ significantly from the FBP method (P > 0.05).

Conclusions

Based on the results, using 80% ASIR can reduce the radiation dose on lungs, abdomen, and pelvis CT scans while maintaining image quality. Furthermore using ASIR 60% only for the reconstruction of lungs, abdomen, and pelvis images at standard radiation dose leads to optimal image quality.

Virtual Noncontrast Abdominal Imaging with Photon-counting Detector CT

Background Accurate CT attenuation and diagnostic quality of virtual noncontrast (VNC) images acquired with photon-counting detector (PCD) CT are needed to replace true noncontrast (TNC) scans. Purpose To assess the attenuation errors and image quality of VNC images from abdominal PCD CT compared with TNC images. Materials and Methods In this retrospective study, consecutive adult patients who underwent a triphasic examination with PCD CT from July 2021 to October 2021 were included. VNC images were reconstructed from arterial and portal venous phase CT. The absolute attenuation error of VNC compared with TNC images was measured in multiple structures by two readers. Then, two readers blinded to image reconstruction assessed the overall image quality, image noise, noise texture, and delineation of small structures using five-point discrete visual scales (5 = excellent, 1 = nondiagnostic). Overall image quality greater than or equal to 3 was deemed diagnostic. In a phantom, noise texture, spatial resolution, and detectability index were assessed. A detectability index greater than or equal to 5 indicated high diagnostic accuracy. Interreader agreement was evaluated using the Krippendorff α coefficient. The paired t test and Friedman test were applied to compare objective and subjective results. Results Overall, 100 patients (mean age, 72 years ± 10 [SD]; 81 men) were included. In patients, VNC image attenuation values were consistent between readers (α = .60), with errors less than 5 HU in 76% and less than 10 HU in 95% of measurements. There was no evidence of a difference in error of VNC images from arterial or portal venous phase CT (3.3 HU vs 3.5 HU, P = .16). Subjective image quality was rated lower in VNC images for all categories (all, P < .001). Diagnostic quality of VNC images was reached in 99% and 100% of patients for readers 1 and 2, respectively. In the phantom, VNC images exhibited 33% higher noise, blotchier noise texture, similar spatial resolution, and inferior but overall good image quality (detectability index >20) compared with TNC images. Conclusion Abdominal virtual noncontrast images from the arterial and portal venous phase of photon-counting detector CT yielded accurate CT attenuation and good image quality compared with true noncontrast images. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Sosna in this issue.

Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung

The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDIvol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: -849 ± 53 HU to QIR-4: -853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.

Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction

Background

To assess the spectral performance of rapid kV switching dual-energy CT (KVSCT-Canon) equipped with a Deep-Learning spectral reconstruction algorithm on virtual-monoenergetic images at low-energy levels and to compare its performances with four other dual-energy CT (DECT) platforms equipped with iterative reconstruction algorithms.

Methods

Two CT phantoms were scanned on five DECT platforms: KVSCT-Canon, fast kV-switching CT (KVSCT-GE), split filter CT, dual-source CT (DSCT), and dual-layer CT (DLCT). The classical parameters of abdomen-pelvic examinations were used for all phantom acquisitions, and a CTDI_vol close to 10 mGy. For KVSCT-Canon, virtual-monoenergetic images were reconstructed with a clinical slice thickness of 0.5 and 1.5 mm to be close to other platforms. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 80 keV of virtual-monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV.

Results

For KVSCT-Canon, the noise magnitude and average NPS spatial frequency (f_av) decreased from 40 to 70 keV and increased thereafter. Similar noise magnitude outcomes were found for KVSCT-GE but the opposite for f_av. For the other DECT platforms, the noise magnitude decreased as the keV increased. For split filter CT, DSCT and DLCT, the f_av values increased from 40 to 80 keV. For all DECT platforms, TTF at 50% (f₅₀) decreased as the keV increased, decreasing spatial resolution. For KVSCT-Canon, d' values peaked at 60 and 70 keV for both simulated lesions and from 50 to 70 keV for KVSCT-GE. d' decreased between 40 and 70 keV for DSCT, DLCT and split filter CT. For KVSCT-Canon, the increase in slice thickness decreases noise magnitude, f_av and f₅₀ and increases d' values. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.

Conclusions

For KVSCT-Canon, the detectability of contrast-enhanced lesions was highest at 60 keV. The highest d' values were found for DLCT at 40 and 50 keV and for KVSCT-Canon at 1.5 mm for other keV.

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Background

New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.

Methods

Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).

Results

NPS peaks were lower with AiCE than with AIDR 3D (-41%±6% for all levels) or FIRST (-15%±6% for Strong level and -41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (-9%±2% using Mild and -3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (-6%±6% and -13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (-19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; -21%±14%). Using the Standard level, dose could be reduced by -79% for the small calcification and -57% for the large mass using AICE compared to AIDR 3D.

Conclusions

The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

AIMS

To evaluate spectral photon-counting CT's (SPCCT) objective image quality characteristics in vitro, compared with standard-of-care energy-integrating-detector (EID) CT.

METHODS

We scanned a thorax phantom with a coronary artery module at 10 mGy on a prototype SPCCT and a clinical dual-layer EID-CT under various conditions of simulated patient size (small, medium, and large). We used filtered back-projection with a soft-tissue kernel. We assessed noise and contrast-dependent spatial resolution with noise power spectra (NPS) and target transfer functions (TTF), respectively. Detectability indices (d') of simulated non-calcified and lipid-rich atherosclerotic plaques were computed using the non-pre-whitening with eye filter model observer.

RESULTS

SPCCT provided lower noise magnitude (9-38% lower NPS amplitude) and higher noise frequency peaks (sharper noise texture). Furthermore, SPCCT provided consistently higher spatial resolution (30-33% better TTF10). In the detectability analysis, SPCCT outperformed EID-CT in all investigated conditions, providing superior d'. SPCCT reached almost perfect detectability (AUC ≈ 95%) for simulated 0.5-mm-thick non-calcified plaques (for large-sized patients), whereas EID-CT had lower d' (AUC ≈ 75%). For lipid-rich atherosclerotic plaques, SPCCT achieved 85% AUC vs. 77.5% with EID-CT.

CONCLUSIONS

SPCCT outperformed EID-CT in detecting simulated coronary atherosclerosis and might enhance diagnostic accuracy by providing lower noise magnitude, markedly improved spatial resolution, and superior lipid core detectability.

The Discriminative Power and Stability of Radiomics Features With Computed Tomography Variations: Task-Based Analysis in an Anthropomorphic 3D-Printed CT Phantom

OBJECTIVES

The aims of this study were to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data.

MATERIALS AND METHODS

A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. The stability and discriminative power of 86 radiomics features were assessed in measurements taken from 240 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing. Pairwise parameter group and pairwise tissue class comparisons were performed using Wilcoxon signed rank tests.

RESULTS

In total, 19,264 feature stability tests and 8256 discriminative power tests were performed. The 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. The 86 radiomics features were ranked according to the cumulative sum of successful stability and discriminative power tests.

CONCLUSIONS

The differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.

Effects of Adaptive Statistical Iterative Reconstruction-V Technology on the Image Quality and Radiation Dose of Unenhanced and Enhanced CT Scans of the Piglet Abdomen

To investigate the optimal pre- and post-adaptive statistical iterative reconstruction-V (ASiR-V) levels in pediatric abdominal computed tomography (CT) to minimize radiation exposure and maintain image quality using an animal model. A total of 10 standard piglets were selected and scanned to obtain unenhanced and enhanced images under different pre-ASiR-V conditions. The corresponding images were obtained using ASiR-V algorithm at different post-ASiR-V levels. CT value, signal-to-noise ratio (SNR), contrast noise ratio (CNR) of abdominal tissues, subjective image score, and radiation dose of unenhanced and enhanced scans were analyzed. With the increase of pre-ASiR-V level, the radiation dose in piglets gradually decreased (P < 0.05). Within the same group of pre-ASiR-V, the image noise was decreased (P < 0.05) by increasing post-ASiR-V level. There was no statistical difference between SNR and CNR values. In unenhanced CT, the subjective score of the images with the combination of 40% pre- and 60% post-ASiR-V levels had no statistical difference compared to the combination of 0% pre- and 60% post-ASiR-V levels, while the radiation dose decreased by 31.6%. In the enhanced CT, the subjective image score with the 60% pre- and 60% post-ASiR-V combination had no statistical difference compared to the 0% pre- and 60% post-ASiR-V combination, while the radiation dose was reduced by 48.9%. The combined use of pre- and post-ASiR-V maintains image quality at the reduced radiation dose. The optimal level for unenhanced CT is 40% pre-combined with 60% post-ASiR-V, while that for enhanced CT is 60% pre- combined with 60% post-ASiR-V in pediatric abdominal CT.

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis

BACKGROUND

Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.

PURPOSE

To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT.

MATERIAL AND METHODS

A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers.

RESULTS

Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86).

CONCLUSION

For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

Comparison of image quality between spectral photon-counting CT and dual-layer CT for the evaluation of lung nodules: a phantom study

OBJECTIVES

To evaluate the image quality (IQ) of a spectral photon-counting CT (SPCCT) using filtered back projection (FBP) and hybrid iterative reconstruction (IR) algorithms (iDose⁴), in comparison with a dual-layer CT (DLCT) system, and to choose the best image quality according to the IR level for SPCCT.

METHODS

Two phantoms were scanned using a standard lung protocol (120 kVp, 40 mAs) with SPCCT and DLCT systems. Raw data were reconstructed using FBP and 9 iDose⁴ levels (i1/i2/i3/i4/i5/i6/i7/i9/i11) for SPCCT and 7 for DLCT (i1/i2/i3/i4/i5/i6/i7). Noise power spectrum and task-based transfer function (TTF) were computed. Detectability index (d') was computed for detection of 4 mm ground-glass nodule (GGN) and solid nodule. Two chest radiologists performed an IQ evaluation (noise/nodule sharpness/nodule conspicuity/overall IQ) in consensus, and chose the best image for SPCCT.

RESULTS

Noise magnitude was -47% ± 2% lower on average with SPCCT than with DLCT for iDose⁴ range from i1 to i6. Average NPS spatial frequencies increased for SPCCT in comparison with DLCT. TTF also increased, except for the air insert with FBP, and i1/i2/i3. Higher detectability was found for SPCCT for both GGN and solid nodules. IQ for both types of nodule was rated consistently higher with SPCCT than with DLCT for the same iDose⁴ level. For SPCCT and both nodules, the scores for noise and conspicuity improved with increasing iDose⁴ level. iDose⁴ level 6 provided the best subjective IQ for both types of nodule.

CONCLUSIONS

Higher IQ for GGN and solid nodules was demonstrated with SPCCT compared with DLCT with better detectability using iDose⁴.

KEY POINTS

Using spectral photon-counting CT compared with dual-layer CT, noise magnitude was reduced with improvements in spatial resolution and detectability of ground-glass nodules and solid lung nodules. As the iDose⁴ level increased, noise magnitude was reduced and detectability of ground-glass and solid lung nodules was better for both CT systems. For spectral photon-counting CT imaging, two chest radiologists determined iDose⁴ level 6 as the best image quality for detecting ground-glass nodules and solid lung nodules.

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

OBJECTIVES

To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.

METHODS

Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V.

RESULTS

FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively.

CONCLUSION

Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

Reduced-iodine-dose dual-energy coronary CT angiography: qualitative and quantitative comparison between virtual monochromatic and polychromatic CT images

Objectives

To quantitatively evaluate the impact of virtual monochromatic images (VMI) on reduced-iodine-dose dual-energy coronary computed tomography angiography (CCTA) in terms of coronary lumen segmentation in vitro, and secondly to assess the image quality in vivo, compared with conventional CT obtained with regular iodine dose.

Materials and methods

A phantom simulating regular and reduced iodine injection was used to determine the accuracy and precision of lumen area segmentation for various VMI energy levels. We retrospectively included 203 patients from December 2017 to August 2018 (mean age, 51.7 ± 16.8 years) who underwent CCTA using either standard (group A, n = 103) or reduced (group B, n = 100) iodine doses. Conventional images (group A) were qualitatively and quantitatively compared with 55-keV VMI (group B). We recorded the location of venous catheters.

Results

In vitro, VMI outperformed conventional CT, with a segmentation accuracy of 0.998 vs. 1.684 mm², respectively (p < 0.001), and a precision of 0.982 vs. 1.229 mm², respectively (p < 0.001), in simulated overweight adult subjects. In vivo, the rate of diagnostic CCTA in groups A and B was 88.4% (n = 91/103) vs. 89% (n = 89/100), respectively, and noninferiority of protocol B was inferred. Contrast-to-noise ratios (CNR) of lumen versus fat and muscle were higher in group B (p < 0.001) and comparable for lumen versus calcium (p = 0.423). Venous catheters were more often placed on the forearm or hand in group B (p < 0.001).

Conclusion

In vitro, low-keV VMI improve vessel area segmentation. In vivo, low-keV VMI allows for a 40% iodine dose and injection rate reduction while maintaining diagnostic image quality and improves the CNR between lumen versus fat and muscle.

Key Points

• Dual-energy coronary CT angiography is becoming increasingly available and might help improve patient management.

• Compared with regular-iodine-dose coronary CT angiography, reduced-iodine-dose dual-energy CT with low-keV monochromatic image reconstructions performed better in phantom-based vessel cross-sectional segmentation and proved to be noninferior in vivo.

• Patients receiving reduced-iodine-dose dual-energy coronary CT angiography often had the venous catheter placed on the forearm or wrist without compromising image quality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07809-w.

Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data

OBJECTIVES

To compare the spectral performance of dual-energy CT (DECT) platforms using task-based image quality assessment based on phantom data.

MATERIALS AND METHODS

Two CT phantoms were scanned on four DECT platforms: fast kV-switching CT (KVSCT), split filter CT (SFCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Acquisitions on each phantom were performed using classical parameters of abdomen-pelvic examination and a CTDI_vol at 10 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated from 40 to 140 keV of virtual monoenergetic images. A detectability index (d') was computed to model the detection task of two contrast-enhanced lesions as function of keV.

RESULTS

The noise magnitude decreased from 40 to 70 keV for all DECT platforms, and the highest noise magnitude values were found for KVSCT and SFCT and the lowest for DSCT and DLCT. The average NPS spatial frequency shifted towards lower frequencies as the energy level increased for all DECT platforms, smoothing the image texture. TTF values decreased with the increase of keV deteriorating the spatial resolution. For both simulated lesions, higher detectability (d' value) was obtained at 40 keV for DLCT, DSCT, and SFCT but at 70 keV for KVSCT. The detectability of both simulated lesions was highest for DLCT and DSCT.

CONCLUSION

Highest detectability was found for DLCT for the lowest energy levels. The task-based image quality assessment used for the first time for DECT acquisitions showed the benefit of using low keV for the detection of contrast-enhanced lesions.

KEY POINTS

• Detectability of both simulated contrast-enhanced lesions was higher for dual-layer CT for the lowest energy levels. • The image noise increased and the image texture changed for the lowest energy levels. • The detectability of both simulated contrast-enhanced lesions was highest at 40 keV for all dual-energy CT platforms except for fast kV-switching platform.

A comprehensive assessment of physical image quality of five different scanners for head CT imaging as clinically used at a single hospital centre-A phantom study

Nowadays, given the technological advance in CT imaging and increasing heterogeneity in characteristics of CT scanners, a number of CT scanners with different manufacturers/technologies are often installed in a hospital centre and used by various departments. In this phantom study, a comprehensive assessment of image quality of 5 scanners (from 3 manufacturers and with different models) for head CT imaging, as clinically used at a single hospital centre, was hence carried out. Helical and/or sequential acquisitions of the Catphan-504 phantom were performed, using the scanning protocols (CTDI_vol range: 54.7–57.5 mGy) employed by the staff of various Radiology/Neuroradiology departments of our institution for routine head examinations. CT image quality for each scanner/acquisition protocol was assessed through noise level, noise power spectrum (NPS), contrast-to-noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD) and non-uniformity index analyses. Noise values ranged from 3.5 HU to 5.7 HU across scanners/acquisition protocols. NPS curves differed in terms of peak position (range: 0.21–0.30 mm^-1). A substantial variation of CNR values with scanner/acquisition protocol was observed for different contrast inserts. The coefficient of variation (standard deviation divided by mean value) of CNR values across scanners/acquisition protocols was 18.3%, 31.4%, 34.2%, 30.4% and 30% for teflon, delrin, LDPE, polystyrene and acrylic insert, respectively. An appreciable difference in MTF curves across scanners/acquisition protocols was revealed, with a coefficient of variation of f_50%/f_10% of MTF curves across scanners/acquisition protocols of 10.1%/7.4%. A relevant difference in LCD performance of different scanners/acquisition protocols was found. The range of contrast threshold for a typical object size of 3 mm was 3.7–5.8 HU. Moreover, appreciable differences in terms of NUI values (range: 4.1%-8.3%) were found. The analysis of several quality indices showed a non-negligible variability in head CT imaging capabilities across different scanners/acquisition protocols. This highlights the importance of a physical in-depth characterization of image quality for each CT scanner as clinically used, in order to optimize CT imaging procedures.

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography

OBJECTIVE

To determine the optimal pre-adaptive and post-adaptive level statistical iterative reconstruction V (ASiR-V) for improving image quality and reducing radiation dose in coronary computed tomography angiography (CCTA).

METHODS

The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into 5 groups (A, B, C, D, and E) with progressive scanning from 40% to 80% pre-ASiR-V with 10% intervals and reconstructing with 70% post-ASiR-V. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was assessed using a 5-point scale. The CT dose index volume (CTDIvol) and dose-length product (DLP) of each patient were recorded and the effective radiation dose (ED) was calculated after statistical analysis by optimizing for the best pre-ASiR-V value with the lowest radiation dose while maintaining overall image quality. In part II, the images were reconstructed with the recommended optimal pre-ASiR-V values in part I (D group) and 40%-90% of post-ASiR-V. The reconstruction group (D group) was divided into 6 subgroups (interval 10%, D0:40% post-ASiR-V, D1:50% post - ASiR-V, D2:60% post-ASiR-V, D3:70% post-ASiR-V, D4:80% post-ASiR-V, and D5:90% post-ASiR-V).The SNR and CNR of D0-D5 subgroups were calculated and analyzed using one-way analysis of variance, and the consistency of the subjective scores used the k test.

RESULTS

There was no significant difference in the SNRs, CNRs, and image quality scores among A, B, C, and D groups (P > 0.05). The SNR, CNR, and image quality scores of the E group were lower than those of the A, B, C, and D groups (P < 0.05). The mean EDs in the B, C, and D groups were reduced by 7.01%, 13.37%, and 18.87%, respectively, when compared with that of the A group. The SNR and CNR of the D4-D5 subgroups were higher than the D0-D3 subgroups, and the image quality scores of the D4 subgroups were higher than the other subgroups (P < 0.05).

CONCLUSION

The wide-detector combined with 70% pre-ASiR-V and 80% post-ASiR-V significantly reduces the radiation dose of CCTA while maintaining overall image quality as compared with the manufacture's recommendation of 40% pre-ASiR-V.