Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator.

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework's superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach's efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method's critical role in optimizing performance, and robustness to input variations underscores its practical utility.

Artificial intelligence-enhanced electrocardiography analysis as a promising tool for predicting obstructive coronary artery disease in patients with stable angina.

Aims: The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes. Methods and results: A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume. Conclusion: The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.

Background: Evaluating left ventricular diastolic function (LVDF) is crucial in echocardiography; however, the complexity and time demands of current guidelines challenge clinical use. This study aimed to develop an artificial intelligence (AI)-based framework for automatic LVDF assessment to reduce subjectivity and improve accuracy and outcome prediction. Methods: We developed an AI-based LVDF assessment framework using a nationwide echocardiographic dataset from five tertiary hospitals. This framework automatically identifies views, calculates diastolic parameters, including mitral inflow and annular velocities (E/A ratio, e' velocity, and E/e' ratio), maximal tricuspid regurgitation velocity, left atrial (LA) volume index, and left atrial reservoir strain (LARS). Subsequently, it grades LVDF according to guidelines. The AI-framework was validated on an external dataset composed of randomly screened 173 outpatients who underwent transthoracic echocardiography with suspicion for diastolic dysfunction and 33 individuals from medical check-ups with normal echocardiograms at Seoul National University Bundang Hospital, tertiary medical center in Korea, between May 2012 and June 2022. Additionally, we assessed the predictive value of AI-derived diastolic parameters and LVDF grades for a clinical endpoint, defined as a composite of all-cause death and hospitalization for heart failure, using Cox-regression risk modelling. Results: In an evaluation with 200 echocardiographic examinations (167 suspected diastolic dysfunction patients, 33 controls), it achieves an overall accuracy of 99.1% in identifying necessary views. Strong correlations (Pearson coefficient 0.901-0.959) were observed between AI-derived and manually-derived measurements of diastolic parameters, including LARS as well as conventional parameters. When following the guidelines, whether utilizing AI-derived or manually-derived parameters, the evaluation of LVDF consistently showed high concordance rates (94%). However, both methods exhibited lower concordance rates with the clinician's prior assessments (77.5% and 78.5%, respectively). Importantly, both AI-derived and manually-derived LVDF grades independently demonstrated significant prognostic value [adjusted hazard ratio (HR) =3.03; P=0.03 and adjusted HR =2.75; P=0.04, respectively] for predicting clinical outcome. In contrast, the clinician's prior grading lost its significance as a prognostic indicator after adjusting for clinical risk factors (adjusted HR =1.63; P=0.36). AI-derived LARS values significantly decreased with worsening LVDF (P for trend <0.001), and low LARS (<17%) was associated with increased risk for the clinical outcome (Log-rank P=0.04) relative to that for preserved LARS (≥17%). Conclusions: Our AI-based approach for automatic LVDF assessment on echocardiography is feasible, potentially enhancing clinical diagnosis and outcome prediction.

Fully Convolutional Hybrid Fusion Network with Heterogeneous Representations for Identification of S1 and S2 from Phonocardiogram.

Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized algorithm for PCG analysis can aid in detecting abnormal signal patterns and support the clinical use of auscultation. It is important to detect fundamental components, such as the first and second heart sounds (S1 and S2), to accurately diagnose heart abnormalities. In this study, we developed a fully convolutional hybrid fusion network to identify S1 and S2 locations in PCG. It enables timewise, high-level feature fusion from dimensionally heterogeneous features: 1D envelope and 2D spectral features. For the fusion of heterogeneous features, we proposed a novel convolutional multimodal factorized bilinear pooling approach that enables high-level fusion without temporal distortion. We experimentally demonstrated the benefits of the comprehensive interpretation of heterogeneous features, with the proposed method outperforming other state-of-the-art PCG segmentation methods. To the best of our knowledge, this is the first study to interpret heterogeneous features through a high level of feature fusion in PCG analysis.

Artificial intelligence-enhanced automation for M-mode echocardiographic analysis: ensuring fully automated, reliable, and reproducible measurements.

To enhance M-mode echocardiography's utility for measuring cardiac structures, we developed and evaluated an artificial intelligence (AI)-based automated analysis system for M-mode images through the aorta and left atrium [M-mode (Ao-LA)], and through the left ventricle [M-mode (LV)]. Our system, integrating two deep neural networks (DNN) for view classification and image segmentation, alongside an auto-measurement algorithm, was developed using 5,958 M-mode images [3,258 M-mode (LA-Ao), and 2,700 M-mode (LV)] drawn from a nationwide echocardiographic dataset collated from five tertiary hospitals. The performance of view classification and segmentation DNNs were evaluated on 594 M-mode images, while automatic measurement accuracy was tested on separate internal test set with 100 M-mode images as well as external test set with 280 images (140 sinus rhythm and 140 atrial fibrillation). Performance evaluation showed the view classification DNN's overall accuracy of 99.8% and segmentation DNN's Dice similarity coefficient of 94.3%. Within the internal test set, all automated measurements, including LA, Ao, and LV wall and cavity, resonated strongly with expert evaluations, exhibiting Pearson's correlation coefficients (PCCs) of 0.81-0.99. This performance persisted in the external test set for both sinus rhythm (PCC, 0.84-0.98) and atrial fibrillation (PCC, 0.70-0.97). Notably, automatic measurements, consistently offering multi-cardiac cycle readings, showcased a stronger correlation with the averaged multi-cycle manual measurements than with those of a single representative cycle. Our AI-based system for automatic M-mode echocardiographic analysis demonstrated excellent accuracy, reproducibility, and speed. This automated approach has the potential to improve efficiency and reduce variability in clinical practice.

Simultaneous Viability Assessment and Invasive Coronary Angiography Using a Therapeutic CT System in Chronic Myocardial Infarction Patients.

PURPOSE: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. MATERIALS AND METHODS: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. RESULTS: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p<0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. CONCLUSION: The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard.

Small Intercondylar Notch Size Is Not Associated with Poor Surgical Outcomes of Anatomical Single-Bundle Anterior Cruciate Ligament Reconstructions.

Background: Although many studies have been conducted on the association between the intercondylar notch size and the risk of anterior cruciate ligament (ACL) injury, few studies have examined its relationship with the condition after surgical treatment. Therefore, this study aimed to investigate the surgical outcomes of anatomical single-bundle ACL reconstruction according to intercondylar notch volumes. Methods: Medical records of patients who underwent anatomical single-bundle ACL reconstruction using a tibialis anterior allograft between 2015 and 2019 were retrospectively reviewed. For each sex, eligible patients were classified into two groups based on their percentile of intercondylar notch volumes, which were measured using postoperative three-dimensional computed tomography images (group S, ≤ 50th percentile of included patients; group L, > 50th percentile of included patients). Additional grouping was performed based on the group's percentiles of normalized values of intercondylar notch volumes to body heights. Between-group comparative analyses were performed on the perioperative data and surgical outcomes in both objective and subjective aspects. Results: One hundred patients were included in the study. For male patients, there were no differences in the overall surgical outcomes between groups, whereas group L showed a significantly greater knee anteroposterior (AP) laxity than group S at the final follow-up (p = 0.042 for the side-to-side differences [SSD] at the maximum manual force). Similarly, there were no differences in the female patients in the overall surgical results between the groups, whereas group L showed a significantly greater knee AP laxity at the final follow-up (p = 0.020 for the SSD at 134 N; p = 0.011 for the SSD at the maximum manual force). Additional analyses based on the normalized values of the intercondylar notch volume showed consistent results for male patients, and additional grouping for female patients was identical to the existing grouping. Conclusions: The surgical outcomes of anatomical single-bundle ACL reconstruction in patients with relatively small intercondylar notch volumes were comparable to those with large notch volumes, but rather showed favorable outcomes in postoperative knee AP laxity.

Prediction of the development of new coronary atherosclerotic plaques with radiomics.

BACKGROUND: Radiomics is expected to identify imaging features beyond the human eye. We investigated whether radiomics can identify coronary segments that will develop new atherosclerotic plaques on coronary computed tomography angiography (CCTA). METHODS: From a prospective multinational registry of patients with serial CCTA studies at ≥ 2-year intervals, segments without identifiable coronary plaque at baseline were selected and radiomic features were extracted. Cox models using clinical risk factors (Model 1), radiomic features (Model 2) and both clinical risk factors and radiomic features (Model 3) were constructed to predict the development of a coronary plaque, defined as total PV â€‹≥ â€‹1 â€‹mm3, at follow-up CCTA in each segment. RESULTS: In total, 9583 normal coronary segments were identified from 1162 patients (60.3 â€‹± â€‹9.2 years, 55.7% male) and divided 8:2 into training and test sets. At follow-up CCTA, 9.8% of the segments developed new coronary plaque. The predictive power of Models 1 and 2 was not different in both the training and test sets (C-index [95% confidence interval (CI)] of Model 1 vs. Model 2: 0.701 [0.690-0.712] vs. 0.699 [0.0.688-0.710] and 0.696 [0.671-0.725] vs. 0.0.691 [0.667-0.715], respectively, all p â€‹> â€‹0.05). The addition of radiomic features to clinical risk factors improved the predictive power of the Cox model in both the training and test sets (C-index [95% CI] of Model 3: 0.772 [0.762-0.781] and 0.767 [0.751-0.787], respectively, all p â€‹< â€‹00.0001 compared to Models 1 and 2). CONCLUSION: Radiomic features can improve the identification of segments that would develop new coronary atherosclerotic plaque. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT0280341.

Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score.

PURPOSE: To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR). MATERIALS AND METHODS: We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS. RESULTS: The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions. CONCLUSIONS: The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.

Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising.

BACKGROUND: Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. METHOD: We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. RESULTS: Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. CONCLUSION: We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.

OBJECTIVE: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. MATERIALS AND METHODS: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. RESULTS: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). CONCLUSION: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A Phantom Study.

OBJECTIVES: This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features. MATERIALS AND METHODS: This study was conducted using an abdominal phantom with liver nodules. We developed an image conversion algorithm using a residual feature aggregation network to reproduce radiomics features with CT images under various CT protocols and reconstruction kernels. External validation was performed using images from different scanners, consisting of 8 different protocols. To evaluate the variability of radiomics features, regions of interest (ROIs) were drawn by targeting the liver parenchyma, vessels, paraspinal area, and liver nodules. We extracted 18 first-order, 68 second-order, and 688 wavelet radiomics features. Measurement variability was assessed using the concordance correlation coefficient (CCC), compared with the ground-truth image. RESULTS: In the ROI-based analysis, there was an 83.3% improvement of CCC (80/96; 4 ROIs with 3 categories of radiomics features and 8 protocols) in synthetic images compared with the original images. Among them, the 56 CCC pairs showed a significant increase after image synthesis. In the radiomics feature-based analysis, 62.0% (3838 of 6192; 774 radiomics features with 8 protocols) features showed increased CCC after image synthesis, and a significant increase was noted in 26.9% (1663 of 6192) features. In particular, the first-order feature (79.9%, 115/144) showed better improvement in terms of the reproducibility of radiomics feature than the second-order (59.9%, 326/544) or wavelet feature (61.7%, 3397/5504). CONCLUSIONS: Our study demonstrated that a deep learning model for image conversion can improve the reproducibility of radiomics features across various CT protocols, reconstruction kernels, and CT scanners.

Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine.

PURPOSE: To compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). MATERIALS AND METHODS: Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. RESULTS: Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078). The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08). CONCLUSION: Our feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.

Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System.

PURPOSE: To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA). MATERIALS AND METHODS: We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75. RESULTS: The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75. CONCLUSION: Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.

Deep learning-based stenosis quantification from coronary CT Angiography.

BACKGROUND: Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. METHODS: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. RESULTS: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. CONCLUSIONS: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.

Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery disease.

OBJECTIVE: This study aimed to evaluate the clinical feasibility of catheter-directed selective computed tomography angiography (S-CTA) in patients with coronary artery disease (CAD). METHODS: We prospectively enrolled 65 patients diagnosed with CAD who underwent conventional computed tomography angiography (C-CTA). C-CTA was performed with 60-90 mL of contrast medium (370 mg iodine/mL), whereas S-CTA was performed with 15 mL of contrast medium and 17.19 mg iodine/mL. Luminal enhancement range, homogeneity of luminal enhancement, image quality, plaque volume (PV), and percent aggregate plaque volume (%APV) were measured. Paired Student's t test, Wilcoxon rank-sum test, and Pearson's correlation coefficient were used to compare two methods. RESULTS: Luminal enhancement was significantly higher on S-CTA than on C-CTA (324.4 ± 8.0 Hounsfield unit (HU) vs. 312.0 ± 8.0 HU, p < 0.0001 in the per-vessel analysis). Transluminal attenuation gradient showed a significantly slower reduction pattern on S-CTA than on C-CTA (-0.65 HU/10 mm vs. -0.89 HU/10 mm, p < 0.0001 in the per-vessel analysis). Image noise was significantly lower on S-CTA than on C-CTA (39.6 ± 10.0 HU vs. 43.9 ± 9.4 HU, p < 0.0001). There was excellent correlation between S-CTA and C-CTA with respect to PV and %APV (r = 0.99, r = 0.98, respectively). CONCLUSIONS: S-CTA might be useful in facilitating atherosclerotic plaque analysis and providing guidance for complex lesions such as chronic total occlusion, particularly in cases in which on-site procedure planning is required. KEY POINTS: • Selective computed tomography angiography (S-CTA) can serve as an intraprocedural computed tomography angiography protocol. • S-CTA was performed with low dose of iodine compared with conventional computed tomography angiography. • S-CTA enables on-site atherosclerotic plaque analysis.

Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography.

We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.

Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images.

This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements.

Viability assessment after conventional coronary angiography using a novel cardiovascular interventional therapeutic CT system: Comparison with gross morphology in a subacute infarct swine model.

BACKGROUND: Given the lack of promptness and inevitable use of additional contrast agents, the myocardial viability imaging procedures have not been used widely for determining the need to performing revascularization. OBJECTIVE: This study is aimed to evaluate the feasibility of myocardial viability assessment, consecutively with diagnostic invasive coronary angiography (ICA) without use of additional contrast agent, using a novel hybrid system comprising ICA and multislice CT (MSCT). METHODS: In all, 14 Yucatan miniature swine models (female; age, 3 months; weight, 28-30 kg) were subjected to ICA followed by balloon occlusion (90 minutes) and reperfusion of the left anterior descending coronary artery. Two weeks after induction of myocardial infarction, delayed hyperenhancement (DHE) images were obtained, using a novel combined machine comprising ICA and 320-channel MSCT scanner (Aquilion ONE, Toshiba), after 2, 5, 7, 10, 15, and 20 minutes after conventional ICA. The heart was sliced in 10-mm consecutive sections in the short-axis plane and was embedded in a solution of 1% triphenyltetrazolium chloride (TTC). Infarct size was determined as TTC-negative areas as a percentage of total left ventricular area. On MSCT images, infarct size per slice was calculated by dividing the DHE area by the total slice area (%) and compared with histochemical analyses. RESULTS: Serial MSCT scans revealed a peak CT attenuation of the infarct area (222.5 ± 36.5 Hounsfield units) with a maximum mean difference in CT attenuation between the infarct areas and normal myocardium of at 2 minutes after contrast injection (106.4; P for difference = 0.002). Furthermore, the percentage difference of infarct size by MSCT vs histopathologic specimen was significantly lower at 2 (8.5% ± 1.8%) and 5 minutes (9.5% ± 1.9%) than those after 7 minutes. Direct comparisons of slice-matched DHE area by MSCT demonstrated excellent correlation with TTC-derived infarct size (r = 0.952; P < .001). Bland-Altman plots of the differences between DHE by MSCT and TTC-derived infarct measurements plotted against their means showed good agreement between the 2 methods. CONCLUSION: The feasibility of myocardial viability assessment by DHE using MSCT after conventional ICA was proven in experimental models, and the optimal viability images were obtained after 2 to 5 minutes after the final intracoronary injection of contrast agent for conventional ICA.

Feasibility of Selective Catheter-Directed Coronary Computed Tomography Angiography Using Ultralow-Dose Intracoronary Contrast Injection in a Swine Model.

OBJECTIVE: Selective catheter-directed intracoronary contrast injected coronary computed tomography angiography (selective CCTA) has recently been introduced for on-site evaluation of coronary artery disease during coronary artery catheterization. In this study, we aimed to develop a feasible protocol for selective CCTA using ultralow-dose contrast medium as compared with conventional intravenous CCTA (IV CCTA). MATERIALS AND METHODS: A novel combined system incorporating coronary angiography and a 320-detector row computed tomographic scanner was used to study 4 swine (35-40 kg) under animal institutional review board approval. A selective CCTA scan was simultaneously performed with an injection of 13.13 mgI/mL of modulated contrast medium at multiple different injection rates including 2, 3, and 4 mL/s and different total injection volumes of either 20 or 30 mL. Intravenous CCTA was performed with 60 mL of contrast medium, followed by 30 mL of saline chaser at 5 mL/s. Coronary mean and peak intensity, transluminal attenuation gradient, as well as 3-dimensional maximum intensity projections were obtained. RESULTS: Attenuation values (mean ± standard error, in Hounsfield units [HUs]) of selective CCTA for the left anterior descending (LAD) and right coronary artery (RCA) using the various combinations of injection rates and total injection volumes were as follows: 20 mL at 2 mL/s (LAD, 270.3 ± 20.4 HU; RCA, 322.6 ± 7.4 HU), 20 mL at 3 mL/s (LAD, 262.9 ± 20.4 HU; RCA, 264.7 ± 7.4 HU), 30 mL at 3 mL/s (LAD, 276.8 ± 20.4 HU; RCA, 274.0 ± 7.4 HU), 20 mL at 4 mL/s (LAD, 268.0 ± 20.4 HU; RCA, 277.7 ± 7.4 HU), and 30 mL at 4 mL/s (LAD, 251.3 ± 20.4 HU; RCA, 334.7 ± 7.4 HU). The representative protocol of the selective CCTA studies produced results within the optimal enhancement range (approximately 250-350 HU) for all segments, and comparison of transluminal attenuation gradient data with selective CCTA and IV CCTA studies demonstrated that the former method was more homogenous (-1.5245 and -1.7558 for LAD as well as 0.0459 and 0.0799 for RCA, respectively). Notably, the volume of iodine contrast medium used for selective CCTA was reported to be 1.09% (0.2 g) of IV CCTA (24 g). CONCLUSIONS: The current findings demonstrate the feasibility of selective CCTA using ultralow-dose intracoronary contrast injection. This technique may provide additional means of coronary evaluation in patients who may require strategic planning before a procedure using a combined modality system.