Accuracy of a deep neural network for automated pulmonary embolism detection on dedicated CT pulmonary angiograms

PURPOSE

To assess the performance of a Deep Neural Network (DNN)-based prototype algorithm for automated PE detection on CTPA scans.

METHODS

Patients who had previously undergone CTPA with three different systems (SOMATOM Force, go.Top, and Definition AS; Siemens Healthineers, Forchheim, Germany) because of suspected PE from September 2022 to January 2023 were retrospectively enrolled in this study (n = 1,000, 58.8 % women). For detailed evaluation, all PE were divided into three location-based subgroups: central arteries, lobar branches, and peripheral regions. Clinical reports served as ground truth. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were determined to evaluate the performance of DNN-based PE detection.

RESULTS

Cases were excluded due to incomplete data (n = 32), inconclusive report (n = 17), insufficient contrast detected in the pulmonary trunk (n = 40), or failure of the preprocessing algorithms (n = 8). Therefore, the final cohort included 903 cases with a PE prevalence of 12 % (n = 110). The model achieved a sensitivity, specificity, PPV, and NPV of 84.6, 95.1, 70.5, and 97.8 %, respectively, and delivered an overall accuracy of 93.8 %. Among the false positive cases (n = 39), common sources of error included lung masses, pneumonia, and contrast flow artifacts. Common sources of false negatives (n = 17) included chronic and subsegmental PEs.

CONCLUSION

The proposed DNN-based algorithm provides excellent performance for the detection of PE, suggesting its potential utility to support radiologists in clinical reading and exam prioritization.

Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography

Purpose

To develop a deep learning-based algorithm that automatically and accurately classifies patients as either having pulmonary emboli or not in CT pulmonary angiography (CTPA) examinations.

Materials and methods

For model development, 700 CTPA examinations from 652 patients performed at a single institution were used, of which 149 examinations contained 1497 PE traced by radiologists. The nnU-Net deep learning-based segmentation framework was trained using 5-fold cross-validation. To enhance classification, we applied logical rules based on PE volume and probability thresholds. External model evaluation was performed in 770 and 34 CTPAs from two independent datasets.

Results

A total of 1483 CTPA examinations were evaluated. In internal cross-validation and test set, the trained model correctly classified 123 of 128 examinations as positive for PE (sensitivity 96.1 %; 95 % C.I. 91-98 %; P < .05) and 521 of 551 as negative (specificity 94.6 %; 95 % C.I. 92-96 %; P < .05), achieving an area under the receiver operating characteristic (AUROC) of 96.4 % (95 % C.I. 79-99 %; P < .05). In the first external test dataset, the trained model correctly classified 31 of 32 examinations as positive (sensitivity 96.9 %; 95 % C.I. 84-99 %; P < .05) and 2 of 2 as negative (specificity 100 %; 95 % C.I. 34-100 %; P < .05), achieving an AUROC of 98.6 % (95 % C.I. 83-100 %; P < .05). In the second external test dataset, the trained model correctly classified 379 of 385 examinations as positive (sensitivity 98.4 %; 95 % C.I. 97-99 %; P < .05) and 346 of 385 as negative (specificity 89.9 %; 95 % C.I. 86-93 %; P < .05), achieving an AUROC of 98.5 % (95 % C.I. 83-100 %; P < .05).

Conclusion

Our automatic pipeline achieved beyond state-of-the-art diagnostic performance of PE in CTPA using nnU-Net for segmentation and volume- and probability-based post-processing for classification.

Pixel-level annotated dataset of computed tomography angiography images of acute pulmonary embolism

Pulmonary embolism has a high incidence and mortality, especially if undiagnosed. The examination of choice for diagnosing the disease is computed tomography pulmonary angiography. As many factors can lead to misinterpretations and diagnostic errors, different groups are utilizing deep learning methods to help improve this process. The diagnostic accuracy of these methods tends to increase by augmenting the training dataset. Deep learning methods can potentially benefit from the use of images acquired with devices from different vendors. To the best of our knowledge, we have developed the first public dataset annotated at the pixel and image levels and the first pixel-level annotated dataset to contain examinations performed with equipment from Toshiba and GE. This dataset includes 40 examinations, half performed with each piece of equipment, representing samples from two medical services. We also included measurements related to the cardiac and circulatory consequences of pulmonary embolism. We encourage the use of this dataset to develop, evaluate and compare the performance of new AI algorithms designed to diagnose PE.

Pulmonary artery diameter correlates with echocardiographic parameters of right ventricular dysfunction in patients with acute pulmonary embolism

Introduction

Right ventricular dysfunction (RVD) is a key component in the process of risk stratification in patients with acute pulmonary embolism (PE). Echocardiography remains the gold standard for RVD assessment, however, measures of RVD may be seen on CTPA imaging, including increased pulmonary artery diameter (PAD). The aim of our study was to evaluate the association between PAD and echocardiographic parameters of RVD in patients with acute PE.

Methods

Retrospective analysis of patients diagnosed with acute PE was conducted at large academic center with an established pulmonary embolism response team (PERT). Patients with available clinical, imaging, and echocardiographic data were included. PAD was compared to echocardiographic markers of RVD. Statistical analysis was performed using the Student's t test, Chi-square test, or one-way analysis of variance (ANOVA); P < 0.05 was considered statistically significant.

Results

270 patients with acute PE were identified. Patients with a PAD >30 mm measured on CTPA had higher rates of RV dilation (73.1% vs 48.7%, P < 0.005), RV systolic dysfunction (65.4% vs 43.7%, P < 0.005), and RVSP >30 mmHg (90.2% vs 68%, P = 0.004), but not TAPSE ≤1.6 cm (39.1% vs 26.1%, P = 0.086). A weak increasing linear relationship between PAD and RVSP was noted (r = 0.379, P = 0.001).

Conclusions

Increased PAD in patients with acute PE was significantly associated with echocardiographic markers of RVD. Increased PAD on CTPA in acute PE can serve as a rapid prognostic tool and assist with PE risk stratification at the time of diagnosis, allowing rapid mobilization of a PERT team and appropriate resource utilization.

How artificial intelligence improves radiological interpretation in suspected pulmonary embolism

Objectives

To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice.

Methods

This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality.

Results

Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]).

Conclusion

Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists.

Key Points

• Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%).

• The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality.

• Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08645-2.

Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism

PURPOSE

 Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary angiograms regarding the presence of pulmonary embolism in the emergency room.

MATERIALS AND METHODS

 This retrospective study includes all pulmonary computed tomography angiogram studies performed in a large emergency department over a period of 36 months that were analyzed by two radiologists experienced in emergency radiology to set a reference standard. Original reports and computer-aided detection results were compared regarding the detection of lobar, segmental, and subsegmental pulmonary embolism. All computer-aided detection findings were analyzed concerning the underlying pathology. False-positive findings were correlated to the contrast-to-noise ratio.

RESULTS

 Expert reading revealed pulmonary embolism in 182 of 1229 patients (49 % men, 10-97 years) with a total of 504 emboli. The computer-aided detection algorithm reported 3331 findings, including 258 (8 %) true-positive findings and 3073 (92 %) false-positive findings. Computer-aided detection analysis showed a sensitivity of 47 % (95 %CI: 33-61 %) on the lobar level and 50 % (95 %CI 43-56 %) on the subsegmental level. On average, there were 2.25 false-positive findings per study (median 2, range 0-25). There was no significant correlation between the number of false-positive findings and the contrast-to-noise ratio (Spearman's Rank Correlation Coefficient = 0.09). Soft tissue (61.0 %) and pulmonary veins (24.1 %) were the most common underlying reasons for false-positive findings.

CONCLUSION

 Applied to a population at a large emergency room, the tested commercial computer-aided detection algorithm faced relevant performance challenges that need to be addressed in future development projects.

KEY POINTS

  · Computed tomography pulmonary angiograms are frequently acquired in emergency radiology.. · Computer-aided detection algorithms (CADs) can support image analysis.. · CADs face challenges regarding false-positive and false-negative findings.. · Radiologists using CADs need to be aware of these limitations.. · Further software improvements are necessary ahead of implementation in the daily routine..

CITATION FORMAT

· Müller-Peltzer K, Kretzschmar L, Negrão de Figueiredo G et al. Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1515-2923.

Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803-0.927) and 0.86 (95% CI 0.756-0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation

OBJECTIVES

Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in combination with worklist prioritization and an electronic notification system (ENS) can improve communication times and patient turnaround in the Emergency Department (ED).

METHODS

In 01/2019, an ENS allowing direct communication between radiology and ED was installed. Starting in 10/2019, CTPAs were processed by a deep learning (DL)-powered algorithm for detection of PE. CTPAs acquired between 04/2018 and 06/2020 (n = 1808) were analysed. To assess the impact of the ENS and the DL-algorithm, radiology report reading times (RRT), radiology report communication time (RCT), time to anticoagulation (TTA), and patient turnaround times (TAT) in the ED were compared for three consecutive time periods. Performance measures of the algorithm were calculated on a per exam level (sensitivity, specificity, PPV, NPV, F1-score), with written reports and exam review as ground truth.

RESULTS

Sensitivity of the algorithm was 79.6 % (95 %CI:70.8-87.2%), specificity 95.0 % (95 %CI:92.0-97.1%), PPV 82.2 % (95 %CI:73.9-88.3), and NPV 94.1 % (95 %CI:91.4-96 %). There was no statistically significant reduction of any of the observed times (RRT, RCT, TTA, TAT).

CONCLUSION

DL-assisted detection of PE in CTPAs and ENS-assisted communication of results to referring physicians technically work. However, the mere clinical introduction of these tools, even if they exhibit a good performance, is not sufficient to achieve significant effects on clinical performance measures.

Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study

Background

Acute pulmonary embolism is one of the most common cardiovascular diseases. Computer-aided technique is widely used in chest imaging, especially for assessing pulmonary embolism. The reliability and quantitative analyses of computer-aided technique are necessary. This study aimed to evaluate the reliability of geometry-based computer-aided detection and quantification for emboli morphology and severity of acute pulmonary embolism.

Material/Methods

Thirty patients suspected of acute pulmonary embolism were analyzed by both manual and computer-aided interpretation of vascular obstruction index and computer-aided measurements of emboli quantitative parameters. The reliability of Qanadli and Mastora scores was analyzed using computer-aided and manual interpretation.

Results

The time costs of manual and computer-aided interpretation were statistically different (374.90±150.16 versus 121.07±51.76, P<0.001). The difference between the computer-aided and manual interpretation of Qanadli score was 1.83±2.19, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (intraclass correlation coefficient, ICC=0.998). The difference between the computer-aided and manual interpretation of Mastora score was 1.46±1.62, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (ICC=0.997). The emboli quantitative parameters were moderately correlated with the Qanadli and Mastora scores (all P<0.001).

Conclusions

Computer-aided technique could reduce the time costs, improve the and reliability of vascular obstruction index and provided additional quantitative parameters for disease assessment.

Influence of Monoenergetic Images at Different Energy Levels in Dual-Energy Spectral CT on the Accuracy of Computer-Aided Detection for Pulmonary Embolism

PURPOSE

To investigate the influence of monoenergetic images of different energy levels in spectral computed tomography (CT) on the accuracy of computer aided detection (CAD) for pulmonary embolism (PE).

MATERIALS AND METHODS

CT images of 20 PE patients who underwent spectral CT pulmonary angiography were retrospectively analyzed. Nine sets of monochromatic images from 40 to 80 keV at 5 keV interval were reconstructed and then independently analyzed for detecting PE using a commercially available CAD software. Two experienced radiologists reviewed all images and recorded the number of emboli manually, which was used as the reference standard. The CAD findings for the number of PE at different energies were compared with the reference standard to determine the number of true positives and false positives with CAD and to calculate the sensitivity and false positive rate at different energies.

RESULT

There were 120 true emboli. The total numbers of CAD-detected PE at 40-80 keV were 48, 67, 63, 87, 106, 115, 138, 157, and 226. Images at low energies had low sensitivities and low false positive rates; images at high energies had high sensitivities and high false positive rates. At 60 keV and 65 keV, CAD achieved sensitivity at 81.67% and 84.17%, respectively and false positive rate at 7.55% and 12.17%, respectively to provide the optimum combination of high sensitivity and low false positive rate.

CONCLUSION

Monochromatic images of different energies in dual-energy spectral CT affect the accuracy of CAD for PE. The combination of CAD with images at 60-65 keV provides the optimum combination of high sensitivity and low false positive rate in detecting PE.

Machine learning-based dual-energy CT parametric mapping

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z_eff), relative electron density (ρ _e), mean excitation energy (I _x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Use of Model-based Iterative Reconstruction to Improve Detection of Congenital Cardiovascular Anomalies in Infants Undergoing Free-breathing Computed Tomographic Angiography

PURPOSE

The aim of the study was to assess the detection of congenital cardiovascular anomalies (congenital heart disease) in neonates and infants using model-based iterative reconstruction (MBIR) algorithm compared with hybrid iterative reconstruction (HIR) and filtered back projection (FBP) reconstructions on axial computed tomography (CT) performed at minimum scanner dose.

MATERIALS AND METHODS

Over 1 year, all CT angiographies performed in infants below 3 months of age with congenital heart disease were assessed retrospectively. All were scanned on a 256-slice CT (Brilliance iCT) using single axial rotation at minimum allowable scanner dose (80 kV/10 mAs), with patients free-breathing. Intravenous contrast was 1 mL/kg. Scan reconstruction was 0.9 mm/0.45 mm overlap, reconstructed with FBP, HIR (iDose5), and MBIR (IMR2). The 3 reconstructions per study were anonymized and randomized. Four cardiac radiologists (23, 9, 7, and 6 y experience) evaluated each reconstruction on a workstation for presence of an atrial septal defect, a ventricular septal defect, patent ductus arteriosus, and surgical shunt or anomalies of the aorta, pulmonary arteries, and pulmonary veins. Unevaluable structures were classified as nondiagnostic. Gold standard was surgery or both echocardiogram and cardiac catheterization. The sensitivity, specificity, and accuracy were determined for each reconstruction.

RESULTS

Fifteen scans in 14 infants met the inclusion criteria, with a total of 48 anomalies. Pooled sensitivity for MBIR of 0.82 (range, 0.75 to 0.9) was significantly better than those for FBP (0.58; range, 0.54 to 0.6; P<0.001) and HIR (0.67; range, 0.60 to 0.79; P<0.001). Pooled accuracy of MBIR, HIR, and FBP was 0.91, 0.84, and 0.81, respectively. Readers deemed 39 and 15 structures nondiagnostic with FBP and HIR, respectively, versus 2 with MBIR (MBIR-FBP, MBIR-HIR, P<0.0001). The CTDIvol, DLP, and estimated dose for all cases was 0.52 mGy, 4.2 mGy×cm, and 0.16 mSv.

CONCLUSIONS

MBIR significantly improves the detection of congenital anomalies in neonates and infants undergoing CT angiography at minimum allowable dose.

Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening

RATIONALE AND OBJECTIVES

This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening.

MATERIALS AND METHODS

We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms.

RESULTS

The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001).

CONCLUSIONS

The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.

Intra-individual diagnostic image quality and organ-specific-radiation dose comparison between spiral cCT with iterative image reconstruction and z-axis automated tube current modulation and sequential cCT

•

Superiority of spiral versus sequential cCT in image quality and organ-specific-radiation dose.

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Spiral cCT: lower organ-specific-radiation-dose in eye lense compared to tilted sequential cCT.

•

State-of-the-art IR spiral cCT techniques has significant advantages over sequential cCT techniques.

Objectives

To prospectively evaluate image quality and organ-specific-radiation dose of spiral cranial CT (cCT) combined with automated tube current modulation (ATCM) and iterative image reconstruction (IR) in comparison to sequential tilted cCT reconstructed with filtered back projection (FBP) without ATCM.

Methods

31 patients with a previous performed tilted non-contrast enhanced sequential cCT aquisition on a 4-slice CT system with only FBP reconstruction and no ATCM were prospectively enrolled in this study for a clinical indicated cCT scan. All spiral cCT examinations were performed on a 3rd generation dual-source CT system using ATCM in z-axis direction. Images were reconstructed using both, FBP and IR (level 1–5). A Monte-Carlo-simulation-based analysis was used to compare organ-specific-radiation dose. Subjective image quality for various anatomic structures was evaluated using a 4-point Likert-scale and objective image quality was evaluated by comparing signal-to-noise ratios (SNR).

Results

Spiral cCT led to a significantly lower (p < 0.05) organ-specific-radiation dose in all targets including eye lense. Subjective image quality of spiral cCT datasets with an IR reconstruction level 5 was rated significantly higher compared to the sequential cCT acquisitions (p < 0.0001). Consecutive mean SNR was significantly higher in all spiral datasets (FBP, IR 1–5) when compared to sequential cCT with a mean SNR improvement of 44.77% (p < 0.0001).

Conclusions

Spiral cCT combined with ATCM and IR allows for significant-radiation dose reduction including a reduce eye lens organ-dose when compared to a tilted sequential cCT while improving subjective and objective image quality.

Image Quality of 3rd Generation Spiral Cranial Dual-Source CT in Combination with an Advanced Model Iterative Reconstruction Technique: A Prospective Intra-Individual Comparison Study to Standard Sequential Cranial CT Using Identical Radiation Dose

Objectives

To prospectively intra-individually compare image quality of a 3^rd generation Dual-Source-CT (DSCT) spiral cranial CT (cCT) to a sequential 4-slice Multi-Slice-CT (MSCT) while maintaining identical intra-individual radiation dose levels.

Methods

35 patients, who had a non-contrast enhanced sequential cCT examination on a 4-slice MDCT within the past 12 months, underwent a spiral cCT scan on a 3^rd generation DSCT. CTDI_vol identical to initial 4-slice MDCT was applied. Data was reconstructed using filtered backward projection (FBP) and 3^rd-generation iterative reconstruction (IR) algorithm at 5 different IR strength levels. Two neuroradiologists independently evaluated subjective image quality using a 4-point Likert-scale and objective image quality was assessed in white matter and nucleus caudatus with signal-to-noise ratios (SNR) being subsequently calculated.

Results

Subjective image quality of all spiral cCT datasets was rated significantly higher compared to the 4-slice MDCT sequential acquisitions (p<0.05). Mean SNR was significantly higher in all spiral compared to sequential cCT datasets with mean SNR improvement of 61.65% (p*_{Bonferroni0.05}<0.0024). Subjective image quality improved with increasing IR levels.

Conclusion

Combination of 3^rd-generation DSCT spiral cCT with an advanced model IR technique significantly improves subjective and objective image quality compared to a standard sequential cCT acquisition acquired at identical dose levels.