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Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm $^2$ for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.

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

Motion artifacts may occur in coronary computed tomography angiography (CCTA) due to the heartbeat and impede the clinician's diagnosis of coronary arterial diseases. Thus, motion artifact correction of the coronary artery is required to quantify the risk of disease more accurately. We present a novel method based on deep learning for motion artifact correction in CCTA. Because the image of the coronary artery without motion (the ground-truth data required in supervised deep learning) is medically unattainable, we apply a style transfer method to 2D image patches cropped from full-phase 4D computed tomography (CT) to synthesize these images. We then train a convolutional neural network (CNN) for motion artifact correction using this synthetic ground-truth (SynGT). During testing, the output motion-corrected 2D image patches of the trained network are reinserted into the 3D CT volume with volumetric interpolation. The proposed method is evaluated using both phantom and clinical data. A phantom study demonstrates comparable results to other methods in quantitative performance and outperforms those methods in computation time. For clinical data, a quantitative analysis based on metric measurements is presented that confirms the correction of motion artifacts. Moreover, an observer study finds that by applying the proposed method, motion artifacts are markedly reduced, and boundaries of the coronary artery are much sharper, with a strong inter-observer agreement (κ = 0.78). Finally, evaluations using commercial software on the original and resulting CT volumes of the proposed method reveal a considerable increase in tracked coronary artery length.

The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challenging to accurately segment arteries in X-ray images using only a single neural network model. Consequently, coronary artery images obtained by segmentation with a single model are often fragmented, with parts of the arteries missing. Sophisticated post-processing is then required to identify and reconnect the fragmented regions. In this paper, we propose a method to reconstruct the missing regions of coronary arteries using X-ray angiography images.We apply an independent convolutional neural network model considering local details, as well as a local geometric prior, for reconnecting the disconnected fragments. We implemented and compared the proposed method with several convolutional neural networks with customized encoding backbones as baseline models.When integrated with our method, existing models improved considerably in terms of similarity with ground truth, with a mean increase of 0.330 of the Dice similarity coefficient in local regions of disconnected arteries. The method is efficient and is able to recover missing fragments in a short number of iterations.Owing to the restoration of missing fragments of coronary arteries, the proposed method enables a significant enhancement of clinical impact. The method is general and can simply be integrated into other existing methods for coronary artery segmentation.

This study aimed to evaluate the clinical feasibility of catheter-directed selective computed tomography angiography (S-CTA) in patients with coronary artery disease (CAD). 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. 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

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.

Rationale and Objectives The study aimed to evaluate the usefulness of dual-energy computed tomography (DECT) before and after calcium subtraction in the diagnosis of spinal bone bruise. Materials and Methods Among the patients who visited our emergency department between January 2013 and July 2014 who underwent both spinal DECT and magnetic resonance imaging, 38 patients (men:women = 25:13; mean age: 55.6 years, range: 28–82) were included. The patients were divided into two groups, those with and without acute spinal compression fracture, based on magnetic resonance imaging findings. In the fracture group (n = 22), the ratio of Hounsfield unit (HU) values was calculated between the fracture level and the next normal inferior vertebra in the DECT before and after calcium subtraction. In the non-fracture group (n = 16), the ratios of HU values were calculated between two normal adjacent vertebrae. The mean HU ratios were compared between the two groups. Results The mean HU ratio was higher in the fracture group (calcium subtraction: before: 1.57 and 1.59; after: 1.74 and 1.76) than the non-fracture group (before: 1.07 and 1.08; after: 1.07 and 1.07) (P Conclusion The HU ratio between the fractured and next normal vertebra was diagnostic for spinal bone bruise on DECT images both before and after calcium subtraction.

Generating a 3D surface model from coronary artery segmentation helps to not only improve the rendering efficiency but also the diagnostic accuracy by providing physiological informations such as fractional flow reserve using computational fluid dynamics (CFD). This paper proposes a method to generate a triangular surface mesh using vessel structure information acquired with coronary artery segmentation. The marching cube algorithm is a typical method for generating a triangular surface mesh from a segmentation result as bit mask. But it is difficult for methods based on marching cube algorithm to express the lumen of thin, small and winding vessels because the algorithm only works in a three-dimensional (3D) discrete space. The proposed method generates a more accurate triangular surface mesh for each singular vessel using vessel centerlines, normal vectors and lumen diameters estimated during the process of coronary artery segmentation as the input. Then, the meshes that are overlapped due to branching are processed by mesh merging and merged into a coronary mesh.

To demonstrate the feasibility of foot blood flow measurement by using dynamic volume perfusion computed tomographic (CT) technique with the upslope method in an animal experiment and a human study.The human study was approved by the institutional review board, and written informed consent was obtained from all patients. The animal study was approved by the research animal care and use committee. A perfusion CT experiment was first performed by using rabbits. A color-coded perfusion map was reconstructed by using in-house perfusion analysis software based on the upslope method, and the measured blood flow on the map was compared with the reference standard microsphere method by using correlation analysis. A total of 17 perfusion CT sessions were then performed (a) once in five human patients and (b) twice (before and after endovascular revascularization) in six human patients. Perfusion maps of blood flow were reconstructed and analyzed. The Wilcoxon signed rank test was used to prove significant differences in blood flow before and after treatment.The animal experiment demonstrated a strong correlation (R(2) = 0.965) in blood flow between perfusion CT and the microsphere method. Perfusion maps were obtained successfully in 16 human clinical sessions (94%) with the use of 32 mL of contrast medium and an effective radiation dose of 0.31 mSv (k factor for the ankle, 0.0002). The plantar dermis showed the highest blood flow among all anatomic structures of the foot, including muscle, subcutaneous tissue, tendon, and bone. After a successful revascularization procedure, the blood flow of the plantar dermis increased by 153% (P = .031). The interpretations of the color-coded perfusion map correlated well with the clinical and angiographic findings.Perfusion CT could be used to measure foot blood flow in both animals and humans. It can be a useful modality for the diagnosis of peripheral arterial disease by providing quantitative information on foot perfusion status.

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation. In order to examine the valve and mark the landmarks, physicians prefer a view parallel to the hinge plane, instead of using the conventional axial, coronal or sagittal view. However, customizing the view is a difficult and time-consuming task because of unclear aorta pose and different artifacts of CCTA. Therefore, automatic localization of landmarks can serve as a useful guide to the physicians customizing the viewpoint. In this paper, we present an automatic method to localize the aortic valve landmarks using colonial walk, a regression tree-based machine-learning algorithm. For efficient learning from the training set, we propose a two-phase optimized search space learning model in which a representative point inside the valvular area is first learned from the whole CT volume. All eight landmarks are then learned from a smaller area around that point. Experiment with preprocedural CCTA images of TAVI undergoing patients showed that our method is robust under high stenotic variation and notably efficient, as it requires only 12 milliseconds to localize all eight landmarks, as tested on a 3.60 GHz single-core CPU.

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.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).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.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.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.

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.

A significant amount of research has been done on the segmentation of coronary arteries. However, the resulting automated boundary delineation is still not suitable for clinical utilization. The convolutional neural network was driving advances in the medical image processing. We propose the brief convolutional network (BCN) that automatically produces the labeled mask with the luminal and wall boundaries of the coronary artery. We utilized 50 patients of CCTA - intravascular ultrasound matched image data sets. Training and testing were performed on 40 and 10 patient data sets, respectively. The prediction of luminal and wall mask was performed using stacked BCN on the each image view: axial, coronal, and sagittal of straightened curved planar reformation. We defined the vector that includes probability from BCN result on each image view and proposed amplified probability. We used an Adaptive Boost regressor with an extremely randomized tree regressor to determine the label for unknown probability vector.

Purpose To evaluate the diagnostic performance of dual-energy computed tomography (DECT) for the assessment of myocardial viability compared with magnetic resonance imaging (MRI) in patients with chronic myocardial infarction (CMI). Methods and material Twenty-six patients were prospectively enrolled, who underwent DECT and MRI at delayed phase. The infarct volumes for DECT and MRI were measured. Results In per-segment and per-vessel analysis, DECT showed excellent diagnostic performance compared with MRI (diagnostic accuracy: 86.2%, 81.2% respectively). In volume analysis, DECT correlated well with MRI (r = 0.966, p Conclusions DECT has excellent diagnostic performance for detecting CMI.

The study aimed to evaluate the usefulness of dual-energy computed tomography (DECT) before and after calcium subtraction in the diagnosis of spinal bone bruise.Among the patients who visited our emergency department between January 2013 and July 2014 who underwent both spinal DECT and magnetic resonance imaging, 38 patients (men:women = 25:13; mean age: 55.6 years, range: 28-82) were included. The patients were divided into two groups, those with and without acute spinal compression fracture, based on magnetic resonance imaging findings. In the fracture group (n = 22), the ratio of Hounsfield unit (HU) values was calculated between the fracture level and the next normal inferior vertebra in the DECT before and after calcium subtraction. In the non-fracture group (n = 16), the ratios of HU values were calculated between two normal adjacent vertebrae. The mean HU ratios were compared between the two groups.The mean HU ratio was higher in the fracture group (calcium subtraction: before: 1.57 and 1.59; after: 1.74 and 1.76) than the non-fracture group (before: 1.07 and 1.08; after: 1.07 and 1.07) (P 0.001). The mean HU ratio between before and after calcium subtraction images was different only in the fracture group (P 0.05). There was no significant difference in the area under the curve, sensitivity, specificity, positive and negative predictive values, and accuracy (before: 0.846, 87.5%, 81.2%, 87.5%, 81.2%, 85%; after: 0.865, 91.7%, 81.2%, 88%, 86.7%, 87.5% in high energy) between the images before and after calcium subtraction.The HU ratio between the fractured and next normal vertebra was diagnostic for spinal bone bruise on DECT images both before and after calcium subtraction.