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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
Background: Traditional risk scores for recurrent atrial fibrillation (AF) following catheter ablation utilize readily available clinical and echocardiographic variables and yet have limited discriminatory capacity. Use of data from cardiac imaging and deep learning may help improve accuracy and prediction of recurrent AF after ablation. Methods: We evaluated patients with symptomatic, drug-refractory AF undergoing catheter ablation. All patients underwent pre-ablation cardiac computed tomography (cCT). LAVi was computed using a deep-learning algorithm. In a two-step analysis, random survival forest (RSF) was used to generate prognostic models with variables of highest importance, followed by Cox proportional hazard regression analysis of the selected variables. Events of interest included early and late recurrence. Results: Among 653 patients undergoing AF ablation, the most important factors associated with late recurrence by RSF analysis at 24 (+/-18) months follow-up included LAVi and early recurrence. In total, 5 covariates were identified as independent predictors of late recurrence: LAVi (HR per mL/m2 1.01 [1.01-1.02]; p < .001), early recurrence (HR 2.42 [1.90-3.09]; p < .001), statin use (HR 1.38 [1.09-1.75]; p = .007), beta-blocker use (HR 1.29 [1.01-1.65]; p = .043), and adjunctive cavotricuspid isthmus ablation [HR 0.74 (0.57-0.96); p = .02]. Survival analysis demonstrated that patients with both LAVi >66.7 mL/m2 and early recurrence had the highest risk of late recurrence risk compared with those with LAVi <66.7 mL/m2 and no early recurrence (HR 4.52 [3.36-6.08], p < .001). Conclusions: Machine learning-derived, full volumetric LAVi from cCT is the most important pre-procedural risk factor for late AF recurrence following catheter ablation. The combination of increased LAVi and early recurrence confers more than a four-fold increased risk of late recurrence.
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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
BACKGROUND: Coronary artery calcium (CAC) density is inversely associated with coronary heart disease (CHD) and cardiovascular disease (CVD) risk. We examined this relation in those with diabetes mellitus (DM) or metabolic syndrome (MetS). METHODS: We studied 3,818 participants with non-zero CAC scores from the Multiethnic Study of Atherosclerosis and classified them as DM, MetS (without DM) or neither DM/MetS. Risk factor-adjusted CAC density was calculated and examined in relation to incident CHD and CVD events over a median follow-up of 15 years among these three disease groups. RESULTS: Adjusted CAC density was 2.54, 2.61 and 2.69 among those with DM, MetS or neither DM/MetS. Hazard ratios (HRs) for CHD per 1 SD increase of CAC density was 0.91 (95% CI: 0.72-1.16), 0.70 (95% CI: 0.56-0.87) and 0.79 (95% CI: 0.66-0.95) for those with DM, MetS or neither DM/MetS groups and were 0.77 (95% CI: 0.64-0.94), 0.83 (95% CI: 0.70-0.99) and 0.82 (95% CI: 0.71-0.95) for CVD, respectively. Adjustment for CAC density increased the HRs of CAC volume for CHD/CVD events. Compared to prediction models with or without single CAC measures, c-statistics of models with CAC volume and density were the highest ranging 0.67-0.72. CONCLUSION: CAC density is lower among patients with DM or MetS than those with neither DM/MetS and is inversely associated with future CHD/CVD risk among them. Including CAC density in risk assessment among those with MetS may improve prediction of CHD and CVD.
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Aterosclerose , Doenças Cardiovasculares , Doença da Artéria Coronariana , Diabetes Mellitus , Síndrome Metabólica , Adulto , Humanos , Síndrome Metabólica/diagnóstico , Síndrome Metabólica/epidemiologia , Síndrome Metabólica/complicações , Cálcio/metabolismo , Doenças Cardiovasculares/complicações , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/epidemiologia , Aterosclerose/diagnóstico , Aterosclerose/epidemiologia , Aterosclerose/complicações , Diabetes Mellitus/diagnóstico , Diabetes Mellitus/epidemiologia , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/metabolismo , Fatores de Risco , Medição de RiscoRESUMO
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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BACKGROUND: Statin treatment is a potent lipid-lowering therapy associated with decreased cardiovascular risk and mortality. Recent studies including the PARADIGM trial have demonstrated the impact of statins on promoting calcified coronary plaque. HYPOTHESIS: The degree of systemic inflammation impacts the amount of increase in coronary plaque calcification over 2 years of statin treatment. METHODS: A subgroup of 142 participants was analyzed from the Risk Stratification with Image Guidance of HMG CoA Reductase Inhibitor Therapy (RIGHT) study (NCT01212900), who were on statin treatment and underwent cardiac computed tomography angiography (CCTA) at baseline and 2-year follow-up. This cohort was stratified by baseline median levels of high-sensitivity hs-CRP and analyzed with linear regressions using Stata-17 (StataCorp). RESULTS: In the high versus low hs-CRP group, patients with higher baseline median hs-CRP had increased BMI (median [IQR]; 29 [27-31] vs. 27 [24-28]; p < .001), hypertension (59% vs. 41%; p = .03), and LDL-C levels (97 [77-113] vs. 87 [75-97] mg/dl; p = .01). After 2 years of statin treatment, the high hs-CRP group had significant increase in dense-calcified coronary burden versus the low hs-CRP group (1.27 vs. 0.32 mm2 [100×]; p = .02), beyond adjustment (ß = .2; p = .03). CONCLUSIONS: Statin treatment over 2 years associated with a significant increase in coronary calcification in patients with higher systemic inflammation, as measured by hs-CRP. These findings suggest that systemic inflammation plays a role in coronary calcification and further studies should be performed to better elucidate these findings.
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Calcinose , Doença da Artéria Coronariana , Inibidores de Hidroximetilglutaril-CoA Redutases , Placa Aterosclerótica , Proteína C-Reativa , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/tratamento farmacológico , Progressão da Doença , Humanos , Inibidores de Hidroximetilglutaril-CoA Redutases/uso terapêutico , Inflamação/tratamento farmacológico , Estudos Prospectivos , Medição de RiscoRESUMO
BACKGROUND. Calibrated CT fat fraction (FFCT) measurements derived from un-enhanced abdominal CT reliably reflect liver fat content, allowing large-scale population-level investigations of steatosis prevalence and associations. OBJECTIVE. The purpose of this study was to compare the prevalence of hepatic steatosis, as assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). METHODS. This retrospective study included 3176 adults (1985 women and 1191 men) from seven Chinese provinces and 8748 adults (4834 women and 3914 men) from a single U.S. medical center, all drawn from previous studies. All participants were at least 40 years old and had undergone unenhanced abdominal CT in previous studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FFCT measurements. Mild, moderate, and severe steatosis were defined as adjusted FFCT of 5.0-14.9%, 15.0-24.9%, and 25.0% or more, respectively. The two cohorts were compared. RESULTS. In the Chinese and U.S. cohorts, the median adjusted FFCT for women was 4.7% and 4.8%, respectively, and that for men was 5.8% and 6.2%, respectively. In the Chinese and U.S. cohorts, steatosis prevalence for women was 46.3% and 48.7%, respectively, whereas that for men was 58.9% and 61.9%, respectively. Severe steatosis prevalence was 0.9% and 1.8% for women and 0.2% and 2.6% for men in the Chinese and U.S. cohorts, respectively. Adjusted FFCT did not vary across age decades among women or men in the Chinese cohort, although it increased across age decades among women and men in the U.S. cohort. Adjusted FFCT and BMI exhibited weak correlation (r = 0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts, respectively, had mild steatosis, and 3.0% and 1.5% of those in the Chinese and U.S. cohorts, respectively, had moderate or severe steatosis. Among U.S. participants with a BMI of 40.0 or greater, 17.7% had normal liver content. CONCLUSION. Steatosis and severe steatosis had higher prevalence in the U.S. cohort than in the Chinese cohort in both women and men. BMI did not reliably predict steatosis. CLINICAL IMPACT. The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.
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Fígado Gorduroso , Tomografia Computadorizada por Raios X , Adulto , Índice de Massa Corporal , China/epidemiologia , Fígado Gorduroso/complicações , Fígado Gorduroso/diagnóstico por imagem , Fígado Gorduroso/epidemiologia , Feminino , Humanos , Masculino , Prevalência , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodosRESUMO
The expression of immune-related genes in cancer cells can alter the anti-tumor immune response and thereby impact patient outcomes. Radiotherapy has been shown to modulate immune-related genes dependent on the fractionation regimen. To identify long-term changes in gene expression after irradiation, PC3 (p53 deleted) and LNCaP (p53 wildtype) prostate cancer cells were irradiated with either a single dose (SD, 10 Gy) or a fractionated regimen (MF) of 10 fractions (1 Gy per fraction). Whole human genome arrays were used to determine gene expression at 24 h and 2 months after irradiation. Immune pathway activation was analyzed with Ingenuity Pathway Analysis software. Additionally, 3D colony formation assays and T-cell cytotoxicity assays were performed. LNCaP had a higher basal expression of immunogenic genes and was more efficiently killed by cytotoxic T-cells compared to PC3. In both cell lines, MF irradiation resulted in an increase in multiple immune-related genes immediately after irradiation, while at 2 months, SD irradiation had a more pronounced effect on radiation-induced gene expression. Both immunogenic and immunosuppressive genes were upregulated in the long term in PC3 cells by a 10 Gy SD irradiation but not in LNCaP. T-cell-mediated cytotoxicity was significantly increased in 10 Gy SD PC3 cells compared to the unirradiated control and could be further enhanced by treatment with immune checkpoint inhibitors. Irradiation impacts the expression of immune-related genes in cancer cells in a fractionation-dependent manner. Understanding and targeting these changes may be a promising strategy for primary prostate cancer and recurrent tumors.
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Recidiva Local de Neoplasia , Neoplasias da Próstata , Apoptose , Linhagem Celular Tumoral , Humanos , Masculino , Neoplasias da Próstata/tratamento farmacológico , Neoplasias da Próstata/genética , Neoplasias da Próstata/radioterapiaRESUMO
[This corrects the article DOI: 10.1148/ryai.2021200218.].
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PURPOSE: To develop a deep learning model to detect incorrect organ segmentations at CT. MATERIALS AND METHODS: In this retrospective study, a deep learning method was developed using variational autoencoders (VAEs) to identify problematic organ segmentations. First, three different three-dimensional (3D) U-Nets were trained on segmented CT images of the liver (n = 141), spleen (n = 51), and kidney (n = 66). A total of 12 495 CT images then were segmented by the 3D U-Nets, and output segmentations were used to train three different VAEs for the detection of problematic segmentations. Automatic reconstruction errors (Dice scores) were then calculated. A random sampling of 2510 segmented images each for the liver, spleen, and kidney models were assessed manually by a human reader to determine problematic and correct segmentations. The ability of the VAEs to identify unusual or problematic segmentations was evaluated using receiver operating characteristic curve analysis and compared with traditional non-deep learning methods for outlier detection. Using the VAE outputs, passive and active learning approaches were performed on the original 3D U-Nets to determine if training could decrease segmentation error rates (15 CT scans were added to the original training data, according to each approach). RESULTS: The mean area under the receiver operating characteristic curve (AUC) for detecting problematic segmentations using the VAE method was 0.90 (95% CI: 0.89, 0.92) for kidney, 0.94 (95% CI: 0.93, 0.95) for liver, and 0.81 (95% CI: 0.80, 0.82) for spleen. The VAE performance was higher compared with traditional methods in most cases. For example, for liver segmentation, the highest performing non-deep learning method for outlier detection had an AUC of 0.83 (95% CI: 0.77, 0.90) compared with 0.94 (95% CI: 0.93, 0.95) using the VAE method (P < .05). Using the information on problematic segmentations for active learning approaches decreased 3D U-Net segmentation error rates (original error rate, 7.1%; passive learning, 6.0%; active learning, 5.7%). CONCLUSION: A method was developed to screen for unusual and problematic automatic organ segmentations using a 3D VAE.Keywords: Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Segmentation, CT© RSNA, 2021.
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Long non-coding RNAs (lncRNAs) have been shown to impact important biological functions such as proliferation, survival, and genomic stability. To analyze radiation-induced lncRNA expression in human tumors, we irradiated prostate cancer cells with a single dose of 10 Gy or a multifractionated radiotherapeutic regimen of 10 fractions with a dose of 1 Gy (10 × 1 Gy) during 5 days. We found a stable upregulation of the lncRNA LINC00261 and LINC00665 at 2 months after radiotherapy that was more pronounced after single-dose irradiation. Analysis of the The Cancer Genome Atlas (TCGA) and The Atlas of Non-coding RNAs in Cancer (TANRIC) databases showed that high expression of these two lncRNAs may be a potential negative prognostic marker for overall survival of prostate cancer patients. Knockdown of LINC00261 and LINC00665 in long-term survivors decreased survival after re-irradiation and affected DNA double-strand break repair. Mechanistically, both lncRNAs showed an interdependent expression and regulated expression of the DNA repair proteins CtIP (RBBP8) and XPC as well as the microRNA miR-329. Identifying long-term tumor adaptation mechanisms can lead to the discovery of new molecular targets, in effect opening up new research directions and improving multimodal radiation oncologic treatment.
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BACKGROUND: Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes. METHODS: Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events. RESULTS: Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction. CONCLUSION: Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.
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Doenças Cardiovasculares , Radiografia Abdominal , Adulto , Biomarcadores , Doenças Cardiovasculares/diagnóstico por imagem , Humanos , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Retrospectivos , Fatores de Risco , Tomografia Computadorizada por Raios XRESUMO
BACKGROUND: Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical. PURPOSE: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT. MATERIALS AND METHODS: The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations. RESULTS: On CTC scans, automated Agatston scoring correlated highly with manual assessment (R2â¯=â¯0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R2â¯=â¯0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments. CONCLUSION: Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.
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Aprendizado Profundo , Placa Aterosclerótica , Abdome , Aorta Abdominal/diagnóstico por imagem , Humanos , Placa Aterosclerótica/diagnóstico por imagem , Tomografia Computadorizada por Raios XRESUMO
PURPOSE: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures. MATERIALS AND METHODS: Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison. RESULTS: Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, - 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction. CONCLUSION: Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.
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Radiografia Abdominal , Tomografia Computadorizada por Raios X , Adulto , Biomarcadores , Densidade Óssea , Feminino , Humanos , Pessoa de Meia-Idade , MúsculosRESUMO
OBJECTIVE: Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms. MATERIALS AND METHODS: International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT. After patients with insufficient clinical data for diagnosis were excluded, the final cohort consisted of 7785 adults (mean age, 57.0 years; 4361 women and 3424 men). Previously validated and fully automated CT-based algorithms for quantifying muscle, visceral and subcutaneous fat, liver fat, and abdominal aortic calcification were applied to this final cohort. RESULTS: A total of 738 subjects (9.5% of all subjects; mean age, 56.7 years; 372 women and 366 men) met the clinical criteria for metabolic syndrome. Subsequent major cardiovascular events occurred more frequently in the cohort with metabolic syndrome (p < 0.001). Significant differences were observed between the two groups for all CT-based biomarkers (p < 0.001). Univariate L1-level total abdominal fat (area under the ROC curve [AUROC] = 0.909; odds ratio [OR] = 27.2), L3-level skeletal muscle index (AUROC = 0.776; OR = 5.8), and volumetric liver attenuation (AUROC = 0.738; OR = 5.1) performed well when compared with abdominal aortic calcification scoring (AUROC = 0.578; OR = 1.6). An L1-level total abdominal fat threshold of 460.6 cm2 was 80.1% sensitive and 85.4% specific for metabolic syndrome. For women, the AUROC was 0.930 when fat and muscle measures were combined. CONCLUSION: Fully automated quantitative tissue measures of fat, muscle, and liver derived from abdominal CT scans can help identify individuals who are at risk for metabolic syndrome. These visceral measures can be opportunistically applied to CT scans obtained for other clinical indications, and they may ultimately provide a more direct and useful definition of metabolic syndrome.
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Síndrome Metabólica/diagnóstico por imagem , Radiografia Abdominal , Tomografia Computadorizada por Raios X , Adulto , Idoso , Composição Corporal , Estudos de Coortes , Feminino , Humanos , Masculino , Programas de Rastreamento , Pessoa de Meia-Idade , Sensibilidade e EspecificidadeRESUMO
BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.
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Meios de Contraste , Fígado Gorduroso/diagnóstico por imagem , Intensificação de Imagem Radiográfica/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Aprendizado Profundo , Feminino , Humanos , Fígado/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Padrões de Referência , Estudos Retrospectivos , Sensibilidade e EspecificidadeRESUMO
OBJECTIVES: Cardiac magnetic resonance (CMR) first-pass perfusion is an established noninvasive diagnostic imaging modality for detecting myocardial ischemia. A CMR perfusion sequence provides a time series of 2D images for dynamic contrast enhancement of the heart. Accurate myocardial segmentation of the perfusion images is essential for quantitative analysis and it can facilitate automated pixel-wise myocardial perfusion quantification. METHODS: In this study, we compared different deep learning methodologies for CMR perfusion image segmentation. We evaluated the performance of several image segmentation methods using convolutional neural networks, such as the U-Net in 2D and 3D (2D plus time) implementations, with and without additional motion correction image processing step. We also present a modified U-Net architecture with a novel type of temporal pooling layer which results in improved performance. RESULTS: The best DICE scores were 0.86 and 0.90 for LV myocardium and LV cavity, while the best Hausdorff distances were 2.3 and 2.1 pixels for LV myocardium and LV cavity using 5-fold cross-validation. The methods were corroborated in a second independent test set of 20 patients with similar performance (best DICE scores 0.84 for LV myocardium). CONCLUSIONS: Our results showed that the LV myocardial segmentation of CMR perfusion images is best performed using a combination of motion correction and 3D convolutional networks which significantly outperformed all tested 2D approaches. Reliable frame-by-frame segmentation will facilitate new and improved quantification methods for CMR perfusion imaging. KEY POINTS: ⢠Reliable segmentation of the myocardium offers the potential to perform pixel level perfusion assessment. ⢠A deep learning approach in combination with motion correction, 3D (2D + time) methods, and a deep temporal connection module produced reliable segmentation results.