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OBJECTIVE: To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice. METHODS: This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models. RESULTS: Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study. CONCLUSIONS: Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.
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BACKGROUND: The aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans. METHODS: In this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60-75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI. RESULTS: For CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671-0.853). CONCLUSION: In this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial. CLINICAL RELEVANCE STATEMENT: Photon counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice. KEY POINTS: Photon-counting CTA allows for excellent detectability of low-density calcifications at all heart rates. Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed. Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.
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Coronary CT angiography is increasingly used for cardiac diagnosis. Dose modulation techniques can reduce radiation dose, but resulting functional images are noisy and challenging for functional analysis. This retrospective study describes and evaluates a deep learning method for denoising functional cardiac imaging, taking advantage of multiphase information in a three-dimensional convolutional neural network. Coronary CT angiograms (n = 566) were used to derive synthetic data for training. Deep learning-based image denoising was compared with unprocessed images and a standard noise reduction algorithm (block-matching and three-dimensional filtering [BM3D]). Noise and signal-to-noise ratio measurements, as well as expert evaluation of image quality, were performed. To validate the use of the denoised images for cardiac quantification, threshold-based segmentation was performed, and results were compared with manual measurements on unprocessed images. Deep learning-based denoised images showed significantly improved noise compared with standard denoising-based images (SD of left ventricular blood pool, 20.3 HU ± 42.5 [SD] vs 33.4 HU ± 39.8 for deep learning-based image denoising vs BM3D; P < .0001). Expert evaluations of image quality were significantly higher in deep learning-based denoised images compared with standard denoising. Semiautomatic left ventricular size measurements on deep learning-based denoised images showed excellent correlation with expert quantification on unprocessed images (intraclass correlation coefficient, 0.97). Deep learning-based denoising using a three-dimensional approach resulted in excellent denoising performance and facilitated valid automatic processing of cardiac functional imaging. Keywords: Cardiac CT Angiography, Deep Learning, Image Denoising Supplemental material is available for this article. © RSNA, 2024.
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Angiografia por Tomografia Computadorizada , Aprendizado Profundo , Angiografia por Tomografia Computadorizada/métodos , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Angiografia CoronáriaRESUMO
Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (Ï,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.
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Dissecção Aórtica , Angiografia por Tomografia Computadorizada , Humanos , Estudos Retrospectivos , Incerteza , Aorta , Dissecção Aórtica/diagnóstico por imagemRESUMO
BACKGROUND: Substantial variation in Agatston scores (AS) acquired with different computed tomography (CT) scanners may influence patient risk classification. OBJECTIVES: This study sought to develop a calibration tool for state-of-the-art CT systems resulting in vendor-neutral AS (vnAS), and to assess the impact of vnAS on coronary heart disease (CHD) event prediction. METHODS: The vnAS calibration tool was derived by imaging 2 anthropomorphic calcium containing phantoms on 7 different CT and 1 electron beam tomography system, which was used as the reference system. The effect of vnAS on CHD event prediction was analyzed with data from 3,181 participants from MESA (Multi-Ethnic Study on Atherosclerosis). Chi-square analysis was used to compare CHD event rates between low (vnAS <100) and high calcium groups (vnAS ≥100). Multivariable Cox proportional hazard regression models were used to assess the incremental value of vnAS. RESULTS: For all CT systems, a strong correlation with electron beam tomography-AS was found (R2 >0.932). Of the MESA participants originally in the low calcium group (n = 781), 85 (11%) participants were reclassified to a higher risk category based on the recalculated vnAS. For reclassified participants, the CHD event rate of 15% was significantly higher compared with participants in the low calcium group (7%; P = 0.008) with a CHD HR of 3.39 (95% CI: 1.82-6.35; P = 0.001). CONCLUSIONS: The authors developed a calibration tool that enables calculation of a vnAS. MESA participants who were reclassified to a higher calcium category by means of the vnAS experienced more CHD events, indicating improved risk categorization.
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Aterosclerose , Doença da Artéria Coronariana , Calcificação Vascular , Humanos , Doença da Artéria Coronariana/diagnóstico por imagem , Cálcio , Valor Preditivo dos Testes , Tomografia Computadorizada por Raios X , Fatores de Risco , Medição de Risco , Vasos Coronários/diagnóstico por imagem , Calcificação Vascular/diagnóstico por imagemRESUMO
Background: On the basis of the hypothesis that virtual noniodine (VNI)-based coronary artery calcium scoring (CACS) is feasible at reduced radiation doses, this study assesses the impact of radiation dose reduction on the accuracy of this VNI algorithm on a photon-counting detector (PCD)-CT. Methods: In a systematic in vitro setting, a phantom for CACS simulating three chest sizes was scanned on a clinical PCD-CT. The standard radiation dose was chosen at volumetric CT dose indices (CTDIVol) of 1.5, 3.3, 7.0 mGy for small, medium-sized, and large phantoms, and was gradually reduced by adjusting the tube current resulting in 100, 75, 50, and 25%, respectively. VNI images were reconstructed at 55 keV, quantum iterative reconstruction (QIR)1, and at 60 keV/QIR4, and evaluated regarding image quality (image noise (IN), contrast-to-noise ratio (CNR)), and CACS. All VNI results were compared to true noncontrast (TNC)-based CACS at 70 keV and standard radiation dose (reference). Results: INTNC was significantly higher than INVNI, and INVNI at 55 keV/QIR1 higher than at 60 keV/QIR4 (100% dose: 16.7 ± 1.9 vs. 12.8 ± 1.7 vs. 7.7 ± 0.9; p < 0.001 for every radiation dose). CNRTNC was higher than CNRVNI, but it was better to use 60 keV/QIR4 (p < 0.001). CACSVNI showed strong correlation and agreement at every radiation dose (p < 0.001, r > 0.9, intraclass correlation coefficient > 0.9). The coefficients of the variation in root-mean squared error were less than 10% and thus clinically nonrelevant for the CACSVNI of every radiation dose. Conclusion: This phantom study suggests that CACSVNI is feasible on PCD-CT, even at reduced radiation dose while maintaining image quality and CACS accuracy.
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RATIONALE AND OBJECTIVES: Post-TAVR persistent pulmonary hypertension (PH) is a better predictor of poor outcome than pre-TAVR PH. In this longitudinal study we sought to evaluate whether pulmonary artery (distensibility (DPA) measured on preprocedural ECG-gated CTA is associated with persistent-PH and 2-year mortality after TAVR. MATERIALS AND METHODS: Three hundred and thirty-six patients undergoing TAVR between July 2012 and March 2016 were retrospectively included and followed for all-cause mortality until November 2017. All patients underwent retrospectively ECG-gated CTA prior to TAVR. Main pulmonary artery (MPA) area was measured in systole and in diastole. DPA was calculated as: [(area-MPAmax-area-MPAmin)/area-MPAmax]%. ROC analysis was performed to assess the AUC for persistent-PH. Youden Index was used to determine the optimal threshold of DPA for persistent-PH. Two groups were compared based on a DPA threshold of 8% (specificity of 70% for persistent-PH). Kaplan-Meier, Cox proportional-hazard, and logistic regression analyses were performed. The primary clinical endpoint was defined as persistent-PH post-TAVR. The secondary endpoint was defined as all-cause mortality 2 years after TAVR. RESULTS: Median follow-up time was 413 (interquartiles 339-757) days. A total of 183 (54%) had persistent-PH and 68 (20%) patients died within 2-years after TAVR. Patients with DPA<8% had significantly more persistent-PH (67% vs 47%, p<0.001) and 2-year deaths (28% vs 15%, p=0.006), compared to patients with DPA>8%. Adjusted multivariable regression analyses showed that DPA<8% was independently associated with persistent-PH (OR 2.10 [95%-CI 1.3-4.5], p=0.007) and 2-year mortality (HR 2.91 [95%-CI 1.5-5.8], p=0.002). Kaplan-Meier analysis showed that 2-year mortality of patients with DPA<8% was significantly higher compared to patients with DPA≥8% (mortality 28% vs 15%; log-rank p=0.003). CONCLUSION: DPA on preprocedural CTA is independently associated with persistent-PH and two-year mortality in patients who undergo TAVR.
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Estenose da Valva Aórtica , Hipertensão Pulmonar , Substituição da Valva Aórtica Transcateter , Humanos , Valva Aórtica , Artéria Pulmonar/diagnóstico por imagem , Estenose da Valva Aórtica/diagnóstico por imagem , Estenose da Valva Aórtica/cirurgia , Estenose da Valva Aórtica/complicações , Hipertensão Pulmonar/diagnóstico por imagem , Hipertensão Pulmonar/complicações , Resultado do Tratamento , Estudos Longitudinais , Estudos Retrospectivos , Fatores de Risco , Índice de Gravidade de DoençaRESUMO
OBJECTIVE: Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. METHODS: Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R2 value as a measure of goodness of fit. RESULTS: A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R2 decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. CONCLUSION: A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. CLINICAL RELEVANCE STATEMENT: Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. KEY POINTS: ⢠The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. ⢠A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. ⢠A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level.
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Neoplasias Pulmonares , Nódulos Pulmonares Múltiplos , Humanos , Tomografia Computadorizada por Raios X/métodos , Doses de Radiação , Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Prognóstico , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologiaRESUMO
ABSTRACT: Noninvasive cardiac imaging has rapidly evolved during the last decade owing to improvements in computed tomography (CT)-based technologies, among which we highlight the recent introduction of the first clinical photon-counting detector CT (PCD-CT) system. Multiple advantages of PCD-CT have been demonstrated, including increased spatial resolution, decreased electronic noise, and reduced radiation exposure, which may further improve diagnostics and may potentially impact existing management pathways. The benefits that can be obtained from the initial experiences with PCD-CT are promising. The implementation of this technology in cardiovascular imaging allows for the quantification of coronary calcium, myocardial extracellular volume, myocardial radiomics features, epicardial and pericoronary adipose tissue, and the qualitative assessment of coronary plaques and stents. This review aims to discuss these major applications of PCD-CT with a focus on cardiac and myocardial characterization.
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Coração , Tomografia Computadorizada por Raios X , Imagens de Fantasmas , Tomografia Computadorizada por Raios X/métodos , Miocárdio , FótonsRESUMO
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.
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Inteligência Artificial , Aprendizado Profundo , Humanos , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Processamento de Imagem Assistida por Computador/métodosRESUMO
OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: ⢠Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. ⢠A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. ⢠Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.
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Dissecção Aórtica , Humanos , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Estudos Retrospectivos , Dissecção Aórtica/diagnóstico por imagem , AortaRESUMO
Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension-capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 Keywords: Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN).
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All patients surviving an acute aortic dissection require continued lifelong surveillance of their diseased aorta. Late complications, driven predominantly by chronic false lumen degeneration and aneurysm formation, often require surgical, endovascular, or hybrid interventions to treat or prevent aortic rupture. Imaging plays a central role in the medical decision-making of patients with chronic aortic dissection. Accurate aortic diameter measurements and rigorous, systematic documentation of diameter changes over time with different imaging equipment and modalities pose a range of practical challenges in these complex patients. Currently, no guidelines or recommendations for imaging surveillance in patients with chronic aortic dissection exist. In this document, we present state-of-the-art imaging and measurement techniques for patients with chronic aortic dissection and clarify the need for standardized measurements and reporting for lifelong surveillance. We also examine the emerging role of imaging and computer simulations to predict aortic false lumen degeneration, remodeling, and biomechanical failure from morphological and hemodynamic features. These insights may improve risk stratification, individualize contemporary treatment options, and potentially aid in the conception of novel treatment strategies in the future.
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Aneurisma da Aorta Torácica , Dissecção Aórtica , Implante de Prótese Vascular , Procedimentos Endovasculares , American Heart Association , Dissecção Aórtica/cirurgia , Dissecção Aórtica/terapia , Aneurisma da Aorta Torácica/cirurgia , Aortografia/métodos , Prótese Vascular , Procedimentos Endovasculares/métodos , Humanos , Estudos Retrospectivos , Fatores de Tempo , Resultado do TratamentoRESUMO
In this prospective, pilot study, we tested a kV-independent coronary artery calcium scoring CT protocol, using a novel reconstruction kernel (Sa36f). From December 2018 to November 2019, we performed an additional research scan in 61 patients undergoing clinical calcium scanning. For the standard protocol (120 kVp), images were reconstructed with a standard, medium-sharp kernel (Qr36d). For the research protocol (automated kVp selection), images were reconstructed with a novel kernel (Sa36f). Research scans were sequentially performed using a higher (cohort A, n = 31) and a lower (cohort B, n = 30) dose optimizer setting within the automatic system with customizable kV selection. Agatston scores, coronary calcium volumes, and radiation exposure of the standard and research protocol were compared. A phantom study was conducted to determine inter-scan variability. There was excellent correlation for the Agatston score between the two protocols (r = 0.99); however, the standard protocol resulted in slightly higher Agatston scores (29.4 [0-139.0] vs 17.4 [0-158.2], p = 0.028). The median calcium volumes were similar (11.5 [0-109.2] vs 11.2 [0-118.0] mm3; p = 0.176), and the number of calcified lesions was not significantly different (p = 0.092). One patient was reclassified to another risk category. The research protocol could be performed at a lower kV and resulted in a substantially lower radiation exposure, with a median volumetric CT dose index of 4.1 vs 5.2 mGy, respectively (p < 0.001). Our results showed that a consistent coronary calcium scoring can be achieved using a kV-independent protocol that lowers radiation doses compared to the standard protocol. KEY POINTS: ⢠The Sa36f kernel enables kV-independent Agatston scoring without changing the original Agatston weighting threshold. ⢠Agatston scores and calcium volumes of the standard and research protocols showed an excellent correlation. ⢠The research protocol resulted in a significant reduction in radiation exposure with a mean reduction of 22% in DLP and 25% in CTDIvol.
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Cálcio , Doença da Artéria Coronariana , Angiografia Coronária/métodos , Doença da Artéria Coronariana/diagnóstico por imagem , Vasos Coronários/diagnóstico por imagem , Humanos , Projetos Piloto , Estudos Prospectivos , Doses de Radiação , Tomografia Computadorizada por Raios X/métodosRESUMO
Limited aortic intimal tear is an uncommon lesion of the dissection spectrum. The lesion has several imaging features that are not well known, including asymmetric aortic contour abnormalities, filling defects, and various morphologic patterns, such as linear, L-shaped, T-shaped, and stellate configurations. Hemorrhage of the aortic wall may also be present in patients with this rare entity. This imaging essay reviews the CT imaging findings and clinical characteristics of patients with limited intimal tears. Keywords: Aorta, CT © RSNA, 2022.
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Purpose: To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD). Materials and Methods: The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique. Results: The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling. Conclusion: This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue.
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OBJECTIVE: To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR). METHODS: PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made. RESULTS: Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence. CONCLUSIONS: There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR. KEY POINTS: CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.
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Aprendizado Profundo , Interpretação de Imagem Radiográfica Assistida por Computador , Abdome/diagnóstico por imagem , Algoritmos , Densitometria , Humanos , Doses de Radiação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodosRESUMO
Purpose: To compare coronary artery calcium volume and score (CACS) between photon-counting detector (PCD) and conventional energy integrating detector (EID) computed tomography (CT) in a phantom and prospective patient study. Methods: A commercially available CACS phantom was scanned with a standard CACS protocol (120 kVp, slice thickness/increment 3/1.5 mm, and a quantitative Qr36 kernel), with filtered back projection on the EID-CT, and with monoenergetic reconstruction at 70 keV and quantum iterative reconstruction off on the PCD-CT. The same settings were used to prospectively acquire data in patients (n = 23, 65 ± 12.1 years), who underwent PCD- and EID-CT scans with a median of 5.5 (3.0-12.5) days between the two scans in the period from August 2021 to March 2022. CACS was quantified using a commercially available software solution. A regression formula was obtained from the aforementioned comparison and applied to simulate risk reclassification in a pre-existing cohort of 514 patients who underwent a cardiac EID-CT between January and December 2021. Results: Based on the phantom experiment, CACS PCD-CT showed a more accurate measurement of the reference CAC volumes (overestimation of physical volumes: PCD-CT 66.1 ± 1.6% vs. EID-CT: 77.2 ± 0.5%). CACS EID-CT and CACS PCD-CT were strongly correlated, however, the latter measured significantly lower values in the phantom (CACS PCD-CT : 60.5 (30.2-170.3) vs CACS EID-CT 74.7 (34.6-180.8), p = 0.0015, r = 0.99, mean bias -9.7, Limits of Agreement (LoA) -36.6/17.3) and in patients (non-significant) (CACS PCD-CT : 174.3 (11.1-872.7) vs CACS EID-CT 218.2 (18.5-876.4), p = 0.10, r = 0.94, mean bias -41.1, LoA -315.3/232.5). The systematic lower measurements of Agatston score on PCD-CT system led to reclassification of 5.25% of our simulated patient cohort to a lower classification class. Conclusion: CACS PCD-CT is feasible and correlates strongly with CACS EID-CT , however, leads to lower CACS values. PCD-CT may provide results that are more accurate for CACS than EID-CT.