Computed Tomography Pulmonary Angiography Examinations Research Articles

Predicting the Prognosis of Acute Pulmonary Embolism Using Computed Tomography Pulmonary Angiography Measurable Parameters in Comparison to Pulmonary Embolism Severity Index (PESI)

Abstract Background Pulmonary embolism is a leading cause of cardiovascular death and with CTPA being the gold standard in its diagnosis, it is feasible with radiologic parameters to stratify and early identify patients at risk of death. Aim of the Work To detect the prognostic value of radiologic parameters of CT pulmonary angiography (CTPA) in comparison to pulmonary embolism severity index (PESI). Patients and Methods This was a prospective study done at Ain Shams University Hospitals and included 84 patients diagnosed with acute pulmonary embolism and referred to the radio-diagnosis department for CT pulmonary angiography examination. Results 3.6% of patients died within 24 hours and 44% died within 30 days of diagnosis of PE. All patients who died within 30 days had abnormal CTPA findings especially RV/LV ratio, PT diameter, septal bowing and contrast reflux in the IVC which revealed significant relationship between them and 30-day-mortality. Meanwhile PESI showed contradictory results and was less reliable than the radiologic parameters. Conclusion The CTPA radiological parameters appeared valuable for the prediction of 30-day mortality in patients with acute PE, and was superior to the PESI.

Estimation of maternal and foetal risk of radiation-induced cancer from a survey of computed tomography pulmonary angiography and ventilation/perfusion lung scanning for diagnosing pulmonary embolism during pregnancy.

While there are many papers on maternal and foetal radiation doses from computed tomography pulmonary angiography (CTPA) and ventilation/perfusion (V/Q) lung scanning examinations for diagnosing pulmonary embolism in pregnant patients, few have used clinical data to examine the patient lifetime attributable risk (LAR) of different cancer types. This paper aims to estimate the cancer risk from maternal radiation doses from CTPA and V/Q examinations and associated foetal doses. Dosimetric data were determined for 267 pregnant patients who received CTPA and/or V/Q examinations over 8 years. Organ and foetal doses were determined using software allowing patient size variations for CTPA and using two different activity-to-organ dose conversion methods for V/Q scans. The LAR of cancer incidence was estimated using International Commission on Radiological Protection (ICRP) modelling including estimates of detriment. Estimated total cancer incidence was 23 and 22 cases per 100,000 for CTPA and V/Q examinations, respectively, with detriment estimates of 18 and 20 cases. Cancer incidence was evenly divided between lung and breast cancer for CTPA with lung cancer being 80% of all cancer for V/Q. The median foetal doses were 0.03 mSv for CTPA and 0.29 mSv for V/Q. Significant differences in estimated foetal dose for V/Q scans were obtained by the two different methods used. The differences in dose between the modes of CTPA scan acquisition highlight the importance of optimisation. Maternal cancer incidence and detriment were remarkably similar for each examination. Optimisation of examinations is critical for low-dose outcomes, particularly for CTPA examination.

Comparison of Radiation Doses of Computed Tomography Brain and Pulmonary Angiography with International Commission on Radiological Protection Guidelines: A Descriptive Study

Introduction: Computed Tomography Angiography (CTA) is useful for evaluating and diagnosing conditions related to blood vessels, such as aneurysms, stenosis (narrowing of vessels), vascular malformations, and blockages. It provides valuable information for planning and guiding interventions or surgeries. It is also important to measure patient doses during CTA operations to evaluate and optimise the technique and balance the benefits compared to radiation hazards. Aim: To calculate the CT Dose Index (CTDI), Dose Length Product (DLP), and effective dose for CT brain angiography and CT pulmonary angiography, and also to compare whether the measured values are within the International Commission on Radiological Protection (ICRP) recommended levels. Materials and Methods: A retrospective descriptive study was conducted in the Department of Radiodiagnosis at Yenepoya Medical College Hospital Mangaluru, Karnataka, India, from September 2022 to September 2023. A total of 52 data points were collected for CT brain and CT pulmonary angiography examinations, which were acquired before October 2022. Information on CTDI and DLP was collected, and the effective dose was calculated using the conversion factor. The values were then compared with the ICRP reference level. Descriptive statistics, mean, and standard deviation for continuous variables, and frequency and percentage for categorical variables were used. Results: There was a significantly lower value of CTDI, DLP, and effective dose for CT brain angiography and CT pulmonary angiography compared to the ICRP recommended reference levels. The mean CTDI and DLP for CT brain angiography were 111.56 mGy and 1153.31 mGy·cm, and the mean CTDI and DLP for CT pulmonary angiography were 24.56 mGy and 713.74 mGy·cm, respectively. The mean effective dose for CT brain and CT pulmonary angiography was 2.46 mSv and 9.94 mSv, respectively. Conclusion: The measured values were within the recommended values of ICRP regulations. It is recommended that CT brain angiography and CT pulmonary angiography examinations are safer for diagnostic purposes. Optimising scanning protocols, utilising low-dose techniques, and implementing dose monitoring and control are important clinical aspects. Compliance with the guidelines helps to enhance patient care and reduce the risk of radiation-related complications.

Successful conservative treatment of pulmonary thromboembolism in unrepaired d-transposition of the great arteries with pulmonary hypertension: a case report

Background: Unrepaired dextro-transposition of great arteries (D-TGA) is well known for long-term complications, including tricuspid valve regurgitation, arrhythmias, and coronary artery abnormalities. Pulmonary hypertension (PH) is a frequent, progressive, and fatal complication in unrepaired CHD. This chronic condition can induce pulmonary thromboembolism (PTE). Aim: To report the management of PTE in unrepaired D-TGA with PH. Case Presentation: We present a rare case of an eleven-year-old male in unrepaired D-TGA with PH. He came with hemoptysis with no sign of acute or chronic infection. This condition was not associated with endocarditis or tuberculosis. The patient underwent a CT-pulmonary angiography examination and found a filling defect in the apical and posterior branches of the right superior pulmonary artery, suggesting a PTE that caused pulmonary parenchymal infarction. He was treated with intravenous low molecular weight heparin (LMWH) for ten days (1 mg/kg) and tranexamic acid. On follow-up, he did not experience cardiovascular collapse or hypotension; by the day, the hemoptysis had decreased in frequency and volume. He received long-term warfarin (2 mg/day) for three months. After three months of follow-up, the PTE was not visualized from CT-pulmonary angiography. Summary: Hemoptysis is rare and life-threatening in unrepaired CHD. Heparin administration continued with warfarin, and tranexamic acid provides a good prognosis in patients with PTE with D-TGA and PH.

Automated detection, segmentation and measurement of major vessels and the trachea in CT pulmonary angiography

Mediastinal structure measurements are important for the radiologist’s review of computed tomography pulmonary angiography (CTPA) examinations. In the reporting process, radiologists make measurements of diameters, volumes, and organ densities for image quality assessment and risk stratification. However, manual measurement of these features is time consuming. Here, we sought to develop a time-saving automated algorithm that can accurately detect, segment and measure mediastinal structures in routine clinical CTPA examinations. In this study, 700 CTPA examinations collected and annotated. Of these, a training set of 180 examinations were used to develop a fully automated deterministic algorithm. On the test set of 520 examinations, two radiologists validated the detection and segmentation performance quantitatively, and ground truth was annotated to validate the measurement performance. External validation was performed in 47 CTPAs from two independent datasets. The system had 86–100% detection and segmentation accuracy in the different tasks. The automatic measurements correlated well to those of the radiologist (Pearson’s r 0.68–0.99). Taken together, the fully automated algorithm accurately detected, segmented, and measured mediastinal structures in routine CTPA examinations having an adequate representation of common artifacts and medical conditions.

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution

PurposeThe purpose of this study was to propose a deep learning-based approach to detect pulmonary embolism and quantify its severity using the Qanadli score and the right-to-left ventricle diameter (RV/LV) ratio on three-dimensional (3D) computed tomography pulmonary angiography (CTPA) examinations with limited annotations. Materials and methodsUsing a database of 3D CTPA examinations of 1268 patients with image-level annotations, and two other public datasets of CTPA examinations from 91 (CAD-PE) and 35 (FUME-PE) patients with pixel-level annotations, a pipeline consisting of: (i), detecting blood clots; (ii), performing PE-positive versus negative classification; (iii), estimating the Qanadli score; and (iv), predicting RV/LV diameter ratio was followed. The method was evaluated on a test set including 378 patients. The performance of PE classification and severity quantification was quantitatively assessed using an area under the curve (AUC) analysis for PE classification and a coefficient of determination (R²) for the Qanadli score and the RV/LV diameter ratio. ResultsQuantitative evaluation led to an overall AUC of 0.870 (95% confidence interval [CI]: 0.850–0.900) for PE classification task on the training set and an AUC of 0.852 (95% CI: 0.810–0.890) on the test set. Regression analysis yielded R² value of 0.717 (95% CI: 0.668–0.760) and of 0.723 (95% CI: 0.668–0.766) for the Qanadli score and the RV/LV diameter ratio estimation, respectively on the test set. ConclusionThis study shows the feasibility of utilizing AI-based assistance tools in detecting blood clots and estimating PE severity scores with 3D CTPA examinations. This is achieved by leveraging blood clots and cardiac segmentations. Further studies are needed to assess the effectiveness of these tools in clinical practice.

A comparison of bolus track and test bolus computed tomography pulmonary angiography and the implications on pulmonary and aortic vessel enhancement, effective dose and suboptimal scan rate.

Bolus track and test bolus are the most commonly used contrast timing protocols to undertake computed tomography pulmonary angiography (CTPA). The aim of this study was to compare test bolus and bolus track contrast enhancement protocols in terms of enhancement of the pulmonary vessels and aorta, radiation dose and suboptimal scan rate to determine the optimal technique for CTPA. A total of 200 CTPA examinations (100 using each protocol) performed between January and February 2021 were assessed retrospectively. All scans were performed on a 2x128 Dual Source Siemens Drive Scanner. CT attenuation was measured in Hounsfield Units (HU), with measurements taken from the main pulmonary trunk, right pulmonary artery and left pulmonary artery, ascending and descending aorta. The mean effective dose was calculated from the dose-length product (DLP). The suboptimal scan rate was calculated as the percentage of examinations below 210HU. The average HU of the pulmonary arteries was 358 HU ± SD 129.2 in the test bolus group and increased to 394 HU ± SD 133.9 in the bolus track group with a P value of ≤0.05. The average HU of the aorta was 235 HU ± SD 82.8 in the test bolus group and increased to 319 HU ± SD 91.8 in the bolus track group with a P value of <0.001. Although not statistically significant, the mean effective dose was reduced by 4.2% for the bolus track protocol (2.4 mSv vs. 2.5 mSv, P > 0.05). Fewer suboptimal scans were performed with the bolus track protocol (5 scans <210HU Bolus Track vs. 9 scans <210HU Test Bolus). The bolus track protocol results in increased enhancement of the pulmonary arteries and aorta, with the added benefits of a lower suboptimal scan rate and lower effective dose.

Radiologist Worklist Reprioritization Using Artificial Intelligence: Impact on Report Turnaround Times for CTPA Examinations Positive for Acute Pulmonary Embolism.

BACKGROUND. In patients with acute pulmonary embolism (PE), timely intervention (e.g., initiation of anticoagulation) is critical for optimizing clinical outcomes. OBJECTIVE. The purpose of this study was to evaluate the effect of artificial intelligence (AI)-based radiologist worklist reprioritization on report turnaround times for pulmonary CTA (CTPA) examinations positive for acute PE. METHODS. This retrospective single-center study included patients who underwent CTPA before (October 1, 2018-March 31, 2019 [pre-AI period]) and after (October 1, 2019-March 31, 2020 [post-AI period]) implementation of an AI tool that reprioritized CTPA examinations to the top of radiologists' reading worklists if acute PE was detected. EMR and dictation system timestamps were used to determine the wait time (time from examination completion to report initiation), read time (time from report initiation to report availability), and report turnaround time (sum of wait and read times) for the examinations. Times for reports positive for PE, with final radiology reports as reference, were compared between periods. RESULTS. The study included 2501 examinations of 2197 patients (1307 women, 890 men; mean age, 57.4 ± 17.0 [SD] years), including 1335 examinations from the pre-AI period and 1166 from the post-AI period. The frequency of acute PE, based on radiology reports, was 15.1% (201/1335) during the pre-AI period and 12.3% (144/1166) during the post-AI period. During the post-AI period, the AI tool reprioritized 12.7% (148/1166) of examinations. For PE-positive examinations, the post-AI period, compared with the pre-AI period, had significantly shorter mean report turnaround time (47.6 vs 59.9 minutes; mean difference, 12.3 minutes [95% CI, 0.6-26.0 minutes]) and mean wait time (21.4 vs 33.4 minutes; mean difference, 12.0 minutes [95% CI, 0.9-25.3 minutes]) but no significant difference in mean read time (26.3 vs 26.5 minutes; mean difference, 0.2 minutes [95% CI, -2.8 to 3.2 minutes]). During regular operational hours, wait time was significantly shorter in the post-AI than in the pre-AI period for routine-priority examinations (15.3 vs 43.7 minutes; mean difference, 28.4 minutes [95% CI, 2.2-64.7 minutes]) but not for stat- or urgent-priority examinations. CONCLUSION. AI-driven worklist reprioritization yielded reductions in report turnaround time and wait time for PE-positive CTPA examinations. CLINICAL IMPACT. By assisting radiologists in providing rapid diagnoses, the AI tool has potential for enabling earlier interventions for acute PE.

A closer look at the utilized radiation doses during computed tomography pulmonary angiography (CTPA) for COVID-19 patients

CTPA stands for computed tomography pulmonary angiography. CTPA is an X-ray imaging that combines X-rays and computer technology to create detailed images of the pulmonary arteries and veins in the lungs. This test diagnoses and monitors conditions like pulmonary embolism, arterial blockages, and hypertension. Coronavirus (COVID-19) has threatened world health over the last three years. The number of (CT) scans increased and played a vital role in diagnosing COVID-19 patients, including life-threatening pulmonary embolism (PE). This study aimed to assess the radiation dose resulted from CTPA for COVID-19 patients. Data were collected retrospectively from CTPA examinations on a single scanner in 84 symptomatic patients. The data collected included the dose length product (DLP), volumetric computed tomography dose index (CTDIvol), and size-specific dose estimate (SSDE). The organ dose and effective dose were estimated using VirtualDose software. The study population included 84 patients, 52% male and 48% female, with an average age of 62. The average DLP, CTDIvol, and SSDE were 404.2 mGycm, 13.5 mGy, and 11.6 mGy\, respectively. The mean effective doses (mSv) for males and females were 3.01 and 3.29, respectively. The maximum to minimum organ doses (mGy) between patients was 0.8 for the male bladder and 7.33 for the female lung. The increase in CT scans during the COVID-19 pandemic required close dose monitoring and optimization. The protocol used during CTPA should guarantee a minimum radiation dose with maximum patient benefits.

The application value of deep learning image reconstruction on improving image quality and evaluating the Qanadli embolism index of dual low-dose CT pulmonary angiography

Objective: To compare the image quality and Qanadli embolism index between deep learning image reconstruction (DLR) and adaptive statistical iterative reconstruction-veo (ASiR-V) in dual low-dose CT pulmonary angiography (CTPA) with low contrast agent dose and low radiation dose. Methods: Eighty-eight patients who underwent dual low-dose CTPA in the radiology department of the affiliated hospital of Xuzhou Medical University from October 2020 to March 2021 were retrospectively analyzed, including 44 males and 44 females, aged from 11 to 87 years (61±15 years). The CTPA examination were performed using 80 kV tube voltage and 20 ml contrast agent. The raw data were reconstructed using standard kernel DLR high level (DL-H) and ASiR-V reconstruction, respectively. The patients were divided into standard kernel DL-H group (n=88, 33 cases of positive embolism) and ASiR-V group (n=88, 36 cases of positive embolism). The CT value, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality score, Qanadli embolism index, positive rate and positive Qanadli embolism index were compared between the two groups. Results: There were no statistically significant differences in CT values of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery between the standard kernel DL-H group and ASiR-V group [(405.8±111.7) vs (404.0±112.0) HU, (412.9±113.1) vs (411.5±112.2) HU, (418.1±119.9) vs (415.4±118.0) HU, respectively;all P>0.05)]. The image noise of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group was significantly lower than the ASiR-V group(16.6±4.7 vs 28.1±4.8, 18.3±6.1 vs 29.8±4.9, 17.6±5.6 vs 28.4±4.7, respectively;all P<0.001). The SNR and CNR of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group were significantly higher than the ASiR-V group(SNR: 25.5±7.1 vs 14.5±3.9, 23.9±7.2 vs 13.9±3.4, 24.9±7.4 vs 14.8±4.1, CNR: 21.6±6.6 vs 12.3±3.9, 20.2±6.7 vs 11.8±3.4, 21.2±6.9 vs 12.6±4.1, respectively;all P<0.001). The subjective image quality score of the standard kernel DL-H group was significantly higher than the ASiR-V group (4.6 vs 3.8, P<0.001). There were no significant difference in the Qanadli embolism index, positive rate and positive Qanadli embolism index between the two groups (all P>0.05). Conclusion: Compared with ASiR-V reconstruction algorithms group, standard kernel DL-H reconstruction algorithms can significantly improve the image quality of dual low-dose CTPA.

Machine Learning Based on Computed Tomography Pulmonary Angiography in Evaluating Pulmonary Artery Pressure in Patients with Pulmonary Hypertension

Right heart catheterization is the gold standard for evaluating hemodynamic parameters of pulmonary circulation, especially pulmonary artery pressure (PAP) for diagnosis of pulmonary hypertension (PH). However, the invasive and costly nature of RHC limits its widespread application in daily practice. To develop a fully automatic framework for PAP assessment via machine learning based on computed tomography pulmonary angiography (CTPA). A machine learning model was developed to automatically extract morphological features of pulmonary artery and the heart on CTPA cases collected between June 2017 and July 2021 based on a single center experience. Patients with PH received CTPA and RHC examinations within 1 week. The eight substructures of pulmonary artery and heart were automatically segmented through our proposed segmentation framework. Eighty percent of patients were used for the training data set and twenty percent for the independent testing data set. PAP parameters, including mPAP, sPAP, dPAP, and TPR, were defined as ground-truth. A regression model was built to predict PAP parameters and a classification model to separate patients through mPAP and sPAP with cut-off values of 40 mm Hg and 55 mm Hg in PH patients, respectively. The performances of the regression model and the classification model were evaluated by analyzing the intraclass correlation coefficient (ICC) and the area under the receiver operating characteristic curve (AUC). Study participants included 55 patients with PH (men 13; age 47.75 ± 14.87 years). The average dice score for segmentation increased from 87.3% ± 2.9 to 88.2% ± 2.9 through proposed segmentation framework. After features extraction, some of the AI automatic extractions (AAd, RVd, LAd, and RPAd) achieved good consistency with the manual measurements. The differences between them were not statistically significant (t = 1.222, p = 0.227; t = -0.347, p = 0.730; t = 0.484, p = 0.630; t = -0.320, p = 0.750, respectively). The Spearman test was used to find key features which are highly correlated with PAP parameters. Correlations between pulmonary artery pressure and CTPA features show a high correlation between mPAP and LAd, LVd, LAa (r = 0.333, p = 0.012; r = -0.400, p = 0.002; r = -0.208, p = 0.123; r = -0.470, p = 0.000; respectively). The ICC between the output of the regression model and the ground-truth from RHC of mPAP, sPAP, and dPAP were 0.934, 0.903, and 0.981, respectively. The AUC of the receiver operating characteristic curve of the classification model of mPAP and sPAP were 0.911 and 0.833. The proposed machine learning framework on CTPA enables accurate segmentation of pulmonary artery and heart and automatic assessment of the PAP parameters and has the ability to accurately distinguish different PH patients with mPAP and sPAP. Results of this study may provide additional risk stratification indicators in the future with non-invasive CTPA data.

CT pulmonary angiography: optimising acute thoracic imaging by fixed-timing contrast medium delivery with a modified breathing instruction

To compare the thoracic vascular opacification achieved using the standard bolus-tracking protocol (BTP) with a fixed-timing protocol (FTP) with a modified breathing instruction during computed tomography pulmonary angiography (CTPA) examinations. A single-centre review of CTPA examinations performed between July 2018 and January 2019 using the BTP or FTP and weight-based contrast dosing of 20 mg iodine/kg body weight/s for 20 seconds at 100 kV tube potential. Radiodensity (in Hounsfield units) was analysed in the right ventricle, main pulmonary artery (MPA), left atrium, left ventricle, and ascending and descending thoracic aorta (DTA). A p-value of <0.05 was considered significant. Of 782 examinations, 88 BTP and 90 FTP examinations were included. Mean attenuation of the MPA was similar in the FTP (396±106 HU) and BTP (362±119 HU; p=0.06); however, good-quality (≥250 HU) MPA opacification was achieved in more FTP examinations (87/90, 96.7%) compared to the BTP (73/88, 82.9%; p=0.002). Mean attenuation of the DTA was better in the FTP (325±72 HU) than the BTP (228±75 HU; p <0.0001), with good-quality opacification (≥250 HU) in 76/90 (84.4%) FTP examinations compared with 36/88 (40.9%) BTP examinations (p <0.001). The FTP achieves better opacification of the MPA and DTA compared to the BTP.

APPLICATION OF CT PULMONARY ANGIOGRAPHY WITH "ULTRA-DOUBLE-LOW" AND ITERATIVE MODEL RECONSTRUCTION FOR ACUTE PULMONARY EMBOLISM.

The study is to investigate the feasibility of computed tomography pulmonary angiography (CTPA) with iterative model reconstruction (IMR) and "Ultra-double-low" (Ultra-low dose, Ultra-low contrast agent volume). Thirty-six patients who tested positive for pulmonary embolism in CTPA were enrolled in the study. Another CTPA was performed 1 week after thrombolytic therapy. The first examination was routine CTPA (Routine Group) with the parameters as follows: automatic mA scanning, 120kV and image reconstruction by using iDose4 iterative reconstruction (Lever 4), iodine concentration and dose of contrast agent: 300mgI/ml and 0.5gI/kg, respectively. The latter one was ultra-low dose CTPA examination (Ultra-low Group): 40 mAs, 80kV and IMR (Lever 3), contrast agent: 300mgI/ml and 15mL, respectively. Effective dose (ED), CT dose index volume (CTDIvol), dose length product (DLP), attenuation of pulmonary artery, contrast noise ratio (CNR) and signal noise ratio (SNR) were recorded and calculated. The imaging qualities were subjectively assessed. The Eds/CTDIvols/DLPs of Ultra-low Group are lower than the Routine Group (P<0.05). The differences in attenuation between the two groups are not significant (P>0.05). The differences in CNR and SNR between the two groups are significant (P<0.05). The differences in imaging qualities between the two groups when subjectively assessed are not significant (P>0.05). The 256-slice spiral CT combined with IMR and "Ultra-double-low" is feasible for the acute pulmonary embolism examination and the radiation dose and the volume of contrast agent can be greatly reduced.

PrEdictive value of coMbined pre-test proBability and blOod gas anaLysis In pulmonary emboliSM—the EMBOLISM study

In patients with suspected pulmonary embolism (PE), the number of unnecessary computed tomography pulmonary angiography (CTPA) scans remains high, especially in patients with low pre-test probability (PTP). So far, no study showed any additional benefit of capillary blood gas analysis (BGA) in diagnostic algorithms for PE. In this retrospective analysis of patients with suspected PE and subsequent CTPA, clinical data, D-dimer levels and BGA parameters (including standardized PaO2) were analyzed. Logistic regression analyses were performed to identify independent predictors for PE and reduce unnecessary CTPA examinations in patients with low PTP according to Wells score. Of 1538 patients, PE was diagnosed in 433 patients (28.2%). The original Wells score (odds ratio: 1.381 [95% CI 1.300–1.467], p < 0.001) and standardized PaO2 (odds ratio: 0.987 [95% CI 0.978–0.996], p = 0.005) were independent predictors for PE. After cohort adjustment for low PTP a D-dimer cut-off < 1.5 mg/L (278 patients (18.1%) with 18 PE (6.5%)) was identified in which a standardized PaO2 > 65 mmHg reduced the number of unnecessary CTPA by 31.9% with a 100% sensitivity. This approach was further validated in additional 53 patients with low PTP. In this validation group CTPA examinations were reduced by 32.7%. No patient with PE was missed. With our novel algorithm combining BGA testing with low PTP according to Wells score, we were able to increase the D-Dimer threshold to 1.5 mg/L and reduce CTPA examinations by approximately 32%.

Predictive values of AI-based triage model in suboptimal CT pulmonary angiography

PurposeWe evaluated and compared performance of an acute pulmonary embolism (PE) triaging artificial intelligence (PE-AI) model in suboptimal and optimal CT pulmonary angiography (CTPA). MethodsIn an IRB approved, retrospective study we identified 104 consecutive, suboptimal CTPA which were deemed as suboptimal for PE evaluation in radiology reports due to motion, artifacts and/or inadequate contrast enhancement. We enriched this dataset, with additional 226 optimal CTPA (over same timeframe as suboptimal CTPA) with and without PE. Two thoracic radiologists (ground truth) independently reviewed all 330 CTPA for adequacy (to assess PE down to distal segmental level), reason for suboptimal CTPA (artifacts or poor contrast enhancement), as well as for presence and location of PE. CT values (HU) were measured in the main pulmonary artery. Same attributes were assessed in 80 patients who had repeat or follow-up CTPA following suboptimal CTPA. All CTPA were processed with the PE-AI (Aidoc). ResultsAmong 104 suboptimal CTPA (mean age ± standard deviation 56 ± 15 years), 18/104 (17%) were misclassified as suboptimal for PE evaluation in their radiology reports but relabeled as optimal on ground truth evaluation. Of 226 optimal CTPA, 47 (21%) were reclassified as suboptimal CTPA. PEs were present in 97/330 CTPA. PE-AI had similar performance on suboptimal CTPA (sensitivity 100%; specificity 89%; AUC 0.89, 95% CI 0.80–0.98) and optimal CTPA (sensitivity 96%; specificity 92%; AUC 0.87, 95% CI 0.81–0.93). ConclusionSuboptimal CTPA examinations do not impair the performance of PE-AI triage model; AI retains clinically meaningful sensitivity and high specificity regardless of diagnostic quality.

Variation in Positivity Rates of Computed Tomography Pulmonary Angiograms for the Evaluation of Acute Pulmonary Embolism Among Emergency Department Physicians.

Computed tomography pulmonary angiography (CTPA) is an imaging study for which there is substantial evidence for its overuse in the evaluation of acute pulmonary embolism (PE). Prior literature has reported low positive PE rates, but the variability in positive rates among the ordering physicians has not been as well studied. The purpose of this study was to evaluate variation in ordering and positive rates among physicians in an emergency department (ED) within an integrated health care system.This study was based in a single ED that is part of a geographically isolated integrated health care system. We reviewed the patient records for all patients who underwent a CTPA for the evaluation for acute PE in the ED between January 1, 2018, and December 31, 2019. For each CTPA examination, we recorded the ordering ED physician, serum d-dimer value (mcg/mL), if any, and the results of the CTPA.Review of CTPAs over the 2-year period revealed 1380 CTPAs ordered by 23 ED physicians with a range of 25-141 studies per physician (mean of 60 + 31 CTPAs). The overall positive rate for PE was 6.9%. Individual ED physician positivity rates showed wide variability ranging from 0% to 18.4% (mean positive rate 7.6 + 4.4%). The results of this study confirm the need for greater adherence to existing guidelines using clinical decision rules and d-dimer testing when appropriate among all ED physicians but especially those who order a greater number of studies and have low rates for positive PE.

Automated Deep Learning Analysis for Quality Improvement of CT Pulmonary Angiography.

CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r = 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively. The algorithm was further evaluated in a random sampling of 3195 CTPA examinations from January 2019 through May 2021. Beginning in January 2021, the scanning protocol was transitioned from bolus tracking to a timing bolus strategy. Automated analysis of these examinations showed that most suboptimal examinations following the change in protocol were performed using one scanner, highlighting the potential value of deep learning algorithms for quality improvement in the radiology department. Keywords: CT Angiography, Pulmonary Arteries © RSNA, 2022.

Diagnostic Performance of a Contrast-Enhanced Ultra-Low-Dose High-Pitch CT Protocol with Reduced Scan Range for Detection of Pulmonary Embolisms

(1) Background: To evaluate the diagnostic performance of a simulated ultra-low-dose (ULD), high-pitch computed tomography pulmonary angiography (CTPA) protocol with low tube current (mAs) and reduced scan range for detection of pulmonary embolisms (PE). (2) Methods: We retrospectively included 130 consecutive patients (64 ± 16 years, 69 female) who underwent clinically indicated high-pitch CTPA examination for suspected acute PE on a 3rd generation dual-source CT scanner (SOMATOM FORCE, Siemens Healthineers, Forchheim, Germany). ULD datasets with a realistic simulation of 25% mAs, reduced scan range (aortic arch—basal pericardium), and Advanced Modeled Iterative Reconstruction (ADMIRE®, Siemens Healthineers, Forchheim, Germany) strength 5 were created. The effective radiation dose (ED) of both datasets (standard and ULD) was estimated using a dedicated dosimetry software solution. Subjective image quality and diagnostic confidence were evaluated independently by three reviewers using a 5-point Likert scale. Objective image quality was compared using noise measurements. For assessment of diagnostic accuracy, patients and pulmonary vessels were reviewed binarily for affection by PE, using standard CTPA protocol datasets as the reference standard. Percentual affection of pulmonary vessels by PE was computed for disease severity (modified Qanadli score). (3) Results: Mean ED in ULD protocol was 0.7 ± 0.3 mSv (16% of standard protocol: 4.3 ± 1.7 mSv, p < 0.001, r > 0.5). Comparing ULD to standard protocol, subjective image quality and diagnostic confidence were comparably good (p = 0.486, r > 0.5) and image noise was significantly lower in ULD (p < 0.001, r > 0.5). A total of 42 patients (32.2%) were affected by PE. ULD protocol had a segment-based false-negative rate of only 0.1%. Sensitivity for detection of any PE was 98.9% (95% CI, 97.2–99.7%), specificity was 100% (95% CI, 99.8–100%), and overall accuracy was 99.9% (95% CI, 98.6–100%). Diagnoses correlated strongly between ULD and standard protocol (Chi-square (1) = 42, p < 0.001) with a decrease in disease severity of only 0.48% (T = 1.667, p = 0.103). (4) Conclusions: Compared to a standard CTPA protocol, the proposed ULD protocol proved reliable in detecting and ruling out acute PE with good levels of image quality and diagnostic confidence, as well as significantly lower image noise, at 0.7 ± 0.3 mSv (84% dose reduction).

Task-based assessment on various optimization protocols of computed tomography Pulmonary Angiography examination

This prospective study was designed to assess CT performance by using the task-based image quality assessment and to identify the influence of iterative reconstruction (IR) algorithm, tube potential and pitch factor selection on dose and image quality during Computed Tomography Pulmonary Angiography (CTPA) examination. An image quality phantom, Catphan-600 was utilized in this study for assessing the image quality of CTPA protocols under 128-multislice CT scanner (Philips Brilliance iCT, USA). The standard CTPA protocols were altered on its tube potential, pitch factor and iterative reconstruction algorithm (IR). Three different levels of iDose, an IR algorithm were measured in this study. The result was presented in term of signal to noise ratio (SNR), contrast to noise ratio (CNR), noise power spectrum (NPS) and target transfer function (TTF). A massive improvement of the CNR and distortion of noise texture were observed with increasing the IR algorithm level. Noise value reduction was significantly achieved by increasing the tube potential selection although there are no changes in TTF values. The alteration of pitch factor provides some fluctuation on both NPS and TTF values. In conclusion, the task-based measurement was performed, and this technique allow for characterizing the image quality with a different type of quantitative measurement for local CTPA protocols.

Deep Learning for Pulmonary Embolism Detection: Tackling the RSNA 2020 AI Challenge.

In 2020, the Radiological Society of North America and Society of Thoracic Radiology sponsored a machine learning competition to detect and classify pulmonary embolism (PE). This challenge was one of the largest of its kind, with more than 9000 CT pulmonary angiography examinations comprising almost 1.8 million expertly annotated images. More than 700 international teams competed to predict the presence of PE on individual axial images, the overall presence of PE in the CT examination (with chronicity and laterality), and the ratio of right ventricular size to left ventricular size. This article presents a detailed overview of the second-place solution. Source code and models are available at https://github.com/i-pan/kaggle-rsna-pe. Keywords: CT, Neural Networks, Thorax, Pulmonary Arteries, Embolism/Thrombosis, Supervised Learning, Convolutional Neural Networks (CNN), Machine Learning Algorithms © RSNA, 2021.