Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi-level fusion framework.

The use of multi-modality non-contrast images (i.e., T1FS, T2FS and DWI) for segmenting liver tumors provides a solution by eliminating the use of contrast agents and is crucial for clinical diagnosis. However, this remains a challenging task to discover the most useful information to fuse multi-modality images for accurate segmentation due to inter-modal interference. In this paper, we propose a dual-stream multi-level fusion framework (DM-FF) to, for the first time, accurately segment liver tumors from non-contrast multi-modality images directly. Our DM-FF first designs an attention-based encoder-decoder to effectively extract multi-level feature maps corresponding to a specified representation of each modality. Then, DM-FF creates two types of fusion modules, in which a module fuses learned features to obtain a shared representation across multi-modality images to exploit commonalities and improve the performance, and a module fuses the decision evidence of segment to discover differences between modalities to prevent interference caused by modality's conflict. By integrating these three components, DM-FF enables multi-modality non-contrast images to cooperate with each other and enables an accurate segmentation. Evaluation on 250 patients including different types of tumors from two MRI scanners, DM-FF achieves a Dice of 81.20%, and improves performance (Dice by at least 11%) when comparing the eight state-of-the-art segmentation architectures. The results indicate that our DM-FF significantly promotes the development and deployment of non-contrast liver tumor technology.

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation from Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge. In this paper, we propose a constraint-aware learning framework to improve the estimation of the FFR pullback curve from invasive coronary imaging. It incorporates both geometrical and physical constraints to approximate the relationships between the geometric structure and FFR values along the coronary artery centerline. Our method also leverages the power of synthetic data in model training to reduce the collection costs of clinical data. Moreover, to bridge the domain gap between synthetic and real data distributions when testing on real-world imaging data, we also employ a diffusion-driven test-time data adaptation method that preserves the knowledge learned in synthetic data. Specifically, this method learns a diffusion model of the synthetic data distribution and then projects real data to the synthetic data distribution at test time. Extensive experimental studies on a synthetic dataset and a real-world dataset of 382 patients covering three imaging modalities have shown the better performance of our method for FFR estimation of stenotic coronary arteries, compared with other machine/deep learning-based FFR estimation models and computational fluid dynamics-based model. The results also provide high agreement and correlation between the FFR predictions of our method and the invasively measured FFR values. The plausibility of FFR predictions along the coronary artery centerline is also validated.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography.

BACKGROUND AND OBJECTIVE: Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value. METHODS: Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG. RESULTS: The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from -27.7 to 18.7, outperforming MPGEcho, which ranged from -40.1 to 18.0. CONCLUSIONS: The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

Non-invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

OBJECTIVE: Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow. METHODS: To overcome this challenge, we propose a vascular deformation-based flow estimation (VDFE) model to directly estimate coronary blood flow for reliable IMR[Formula: see text] computation. Specifically, we extract the vascular deformation of each vascular segment from multi-phase CTA. The concept of inverse problem solving is applied to implicitly derive coronary blood flow based on the physical constraint relationship between blood flow and vascular deformation. The vascular deformation constraints imposed on each segment within the vascular structure ensure sufficient individualization of coronary blood flow. RESULTS: Experimental studies on 106 vessels collected from 89 subjects demonstrate the validity of our VDFE, achieving an IMR[Formula: see text] accuracy of 82.08 %. The coronary blood flow estimated by VDFE has better reliability than the other four existing methods. CONCLUSION: Our proposed VDFE is an effective approach to non-invasively compute IMR[Formula: see text] with excellent diagnostic performance. SIGNIFICANCE: The VDFE has the potential to serve as a safe, effective, and cost-effective clinical tool for guiding CMD clinical treatment and assessing prognosis.

Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images.

Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features. This way limits the generalization of these methods. The existing semantic segmentation methods integrate low-level and high-level features to enhance generalization performance. But these methods also introduce additional interference, which is harmful to solving structural intrinsic distractions. Distraction cue methods attempt to address structural intrinsic distractions by removing interference from the features through a unique decoder. However, they tend to overlook the problem of inter-structural distractions. In this paper, we propose distraction-aware hierarchical learning (DHL) for vascular structure segmentation in IVUS images. Inspired by distraction cue methods for removing interference in a decoder, the DHL is designed as a hierarchical decoder that gradually removes structure-dependent distractions. The DHL includes global perception process, distraction perception process and structural perception process. The global perception process and distraction perception process remove structural intrinsic distractions then the structural perception process removes inter-structural distractions. In the global perception process, the DHL searches for the coarse structural region of the vascular structures on the slice of IVUS sequence. In the distraction perception process, the DHL progressively refines the coarse structural region of the vascular structures to remove structural distractions. In the structural perception process, the DHL detects regions of inter-structural distractions in fused structure features then separates them. Extensive experiments on 361 subjects show that the DHL is effective (e.g., the average Dice is greater than 0.95), and superior to ten state-of-the-art IVUS vascular structure segmentation methods.

Frequency-Enhanced Geometric-Constrained Reconstruction for Localizing Myocardial Infarction in 12-Lead Electrocardiograms.

Determining the location of myocardial infarction is crucial for clinical management and therapeutic stratagem. However, existing diagnostic tools either sacrifice ease of use or are limited by their spatial resolution. Addressing this, we aim to refine myocardial infarction localization via surface potential reconstruction of the ventricles in 12-lead electrocardiograms (ECG). A notable obstacle is the ill-posed nature of such reconstructions. To overcome this, we introduce the frequency-enhanced geometric-constrained iterative network (FGIN). FGIN begins by mining the latent features from ECG data across both time and frequency domains. Subsequently, it increases the data dimensionality of ECG and captures intricate features using convolutional layers. Finally, FGIN incorporates ventricular geometry as a constraint on surface potential distribution. It allocates variable weights to distinct edges. Experimental validation of FGIN confirms its efficacy over synthetic and clinical datasets. On the synthetic dataset, FGIN outperforms seven existing reconstruction methods, attaining the highest Pearson Correlation Coefficient of 0.8624, the lowest Root Mean Square Error of 0.1548, and the highest Structural Similarity Index Measure of 0.7988. On the clinical public dataset (2007 PhysioNet/Computers in Cardiology Challenge), FGIN achieves better localization results than other approaches, according to the clinical standard 17-segment model, achieving an average Segment Overlap of 87.2%. Clinical trials on 50 patients demonstrate FGIN's effectiveness, showing an average accuracy of 91.6% and an average Segment Overlap of 88.2%.

Diagnostic performance of angiography-derived fractional flow reserve analysis based on bifurcation fractal law for assessing hemodynamic significance of coronary stenosis.

OBJECTIVES: Blood flow into the side branch affects the calculation of coronary angiography-derived fractional flow reserve (FFR), called Angio-FFR. Neglecting or improperly compensating for the side branch flow may decrease the diagnostic accuracy of Angio-FFR. This study aims to evaluate the diagnostic accuracy of a novel Angio-FFR analysis that considers the side branch flow based on the bifurcation fractal law. METHODS: A one-dimensional reduced-order model based on the vessel segment was used to perform Angio-FFR analysis. The main epicardial coronary artery was divided into several segments according to the bifurcation nodes. Side branch flow was quantified using the bifurcation fractal law to correct the blood flow in each vessel segment. In order to verify the diagnostic performance of our Angio-FFR analysis, two other computational methods were taken as control groups: (i) FFR_s: FFR calculated by delineating the coronary artery tree to consider side branch flow, (ii) FFR_n: FFR calculated by just delineating the main epicardial coronary artery and neglecting the side branch flow. RESULTS: The analysis of 159 vessels from 119 patients showed that our Anio-FFR calculation method had comparable diagnostic accuracy to FFR_s and provided significantly higher diagnostic accuracy than that of FFR_n. In addition, using invasive FFR as a reference, the Pearson correlation coefficients of Angio-FFR and FFRs were 0.92 and 0.91, respectively, while that of FFR_n was only 0.85. CONCLUSIONS: Our Angio-FFR analysis has demonstrated good diagnostic performance in assessing the hemodynamic significance of coronary stenosis by using the bifurcation fractal law to compensate for side branch flow. CLINICAL RELEVANCE STATEMENT: Bifurcation fractal law can be used to compensate for side branch flow during the Angio-FFR calculation of the main epicardial vessel. Compensating for side branch flow can improve the ability of Angio-FFR to diagnose stenosis functional severity. KEY POINTS: • The bifurcation fractal law could accurately estimate the blood flow from the proximal main vessel into the main branch, thus compensating for the side branch flow. • Angiography-derived FFR based on the bifurcation fractal law is feasible to evaluate the target diseased coronary artery without delineating the side branch.

Motion estimation based on projective information disentanglement for 3D reconstruction of rotational coronary angiography.

The 2D projection space-based motion compensation reconstruction (2D-MCR) is a kind of representative method for 3D reconstruction of rotational coronary angiography owing to its high efficiency. However, due to the lack of accurate motion estimation of the overlapping projection pixels, existing 2D-MCR methods may still have a certain level of under-sampling artifacts or lose accuracy for cases with strong cardiac motion. To overcome this, in this study, we proposed a motion estimation approach based on projective information disentanglement (PID-ME) for 3D reconstruction of rotational coronary angiography. The reconstruction method adopts the framework of 2D-MCR, which is referred to as 2D-PID-MCR. The PID-ME consists of two parts: generation of the reference projection sequence based on the fast simplified distance driven projector (FSDDP) algorithm, motion estimation and correction based on the projective average minimal distance measure (PAMD) model. The FSDDP algorithm generates the reference projection sequence faster and accelerates the whole reconstruction greatly. The PAMD model can disentangle the projection information effectively and estimate the motion of both overlapping and non-overlapping projection pixels accurately. The main contribution of this study is the construction of 2D-PID-MCR to overcome the inherent limitations of the existing 2D-MCR method. Simulated and clinical experiments show that the PID-ME, consisting of FSDDP and PAMD, can estimate the motion of the projection sequence data accurately and efficiently. Our 2D-PID-MCR method outperforms the state-of-the-art approaches in terms of accuracy and real-time performance.

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.

Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images.

Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation.

Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher-student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve.

PURPOSE: Coronary outlet resistance is influenced by the quantification and distribution of resting coronary blood flow. It is crucial for a more physiologically accurate estimation of fractional flow reserve (FFR) derived from computed tomography angiography (CTA), referred to as FFRCT. This study presents a physiologically personalized (PP)-based coronary blood flow model involving the outlet boundary condition (BC) and a standardized outlet truncation strategy to estimate the outlet resistance and FFRCT. METHODS: In this study, a total of 274 vessels were retrospectively collected from 221 patients who underwent coronary CTA and invasive FFR within 14 days. For FFRCT determination, we have employed a PP-based outlet BC model involving personalized physiological parameters and left ventricular mass (LVM) to quantify resting coronary blood flow. We evaluated the improvement achieved in the diagnostic performance of FFRCT by using the PP-based outlet BC model relative to the LVM-based model, with respect to the invasive FFR. Additionally, in order to evaluate the impact of the outlet truncation strategy on FFRCT, 68 vessels were randomly selected and analyzed independently by two operators, by using two different outlet truncation strategies at 1-month intervals. RESULTS: The per-vessel diagnostic performance of the PP-based outlet BC model was improved, based on invasive FFR as reference, compared to the LVM-based model: (i) accuracy/sensitivity/specificity: 91.2%/90.4%/91.8% versus 86.5%/84.6%/87.6%, for the entire dataset of 274 vessels, (ii) accuracy/sensitivity/specificity: 88.7%/82.4%/90.4% versus 82.4%/ 76.5%/84.0%, for moderately stenosis lesions. The standardized outlet truncation strategy showed good repeatability with the Kappa coefficient of 0.908. CONCLUSIONS: It has been shown that our PP-based outlet BC model and standardized outlet truncation strategy can improve the diagnostic performance and repeatability of FFRCT.

Effect of plaque compositions on fractional flow reserve in a fluid-structure interaction analysis.

Coronary artery disease involves the reduction of blood flow to the myocardium due to atherosclerotic plaques. The findings of myocardial ischemia may indicate severe coronary stenosis, but many studies have demonstrated a mismatch between lumen stenosis and fractional flow reserve (FFR). Recently, some clinical studies have found that the composition of atherosclerotic plaques may be a potential missing link between stenosis and ischemia. To investigate the relationship between myocardial ischemia and plaque composition, we have developed and adopted a new fluid-structure interaction (FSI) patient-specific coronary plaque model, based on computed tomography angiography data, to assess the impact on FFR as a biomechanical indicator of ischemia. A total of 180 analyses have been performed in 3D-FSI coronary artery disease models based on plaque compositions, plaque location, and stenosis degree. Hemodynamic analysis of simulation results and comparisons with other methods has been conducted to validate our models. Our results have successfully verified that the different compositions of plaques have resulted in differences in the calculated FFR. The mean FFR values with lipid plaques are [Formula: see text] as compared to the mean FFR values in lesions with fibrous plaques [Formula: see text] and calcified plaques [Formula: see text]. Besides, FFR differences between the three different plaque compositions have been shown to increase as the diameter stenosis increased. Plaque composition affects vascular stiffness and vascular dilation ability, and thereby affects the stenosis degree, resulting in abnormal FFR leading to myocardial ischemia. This interrelationship can help to diagnose the cause of high-risk coronary artery disease, leading to myocardial ischemia.

A Personalized Pulmonary Circulation Model to Non-Invasively Calculate Fractional Flow Reserve for Artery Stenosis Detection.

OBJECTIVE: Fractional Flow Reserve (FFR) is regarded as a fundamental index to assess pulmonary artery stenosis. The application of FFR can increase the accuracy of detection of pulmonary artery stenosis. However, the invasive examination may carry a number of physiological risks for patients. Therefore, we propose a personalized pulmonary circulation model to non-invasively calculate FFR of pulmonary artery stenosis. METHODOLOGY: We have employed a personalized pulmonary circulation model to non-invasively calculate FFR. This model combines boundary condition estimation and 3D pulmonary artery morphology reconstruction for CFD simulation. Firstly, we obtain patient-specific boundary conditions by matching cardiac output and main pulmonary artery pressure. Secondly, the 3D pulmonary artery morphology is reconstructed by semi-automatic segmentation. The CFD simulation is performed to obtain the pressure distribution in the entire pulmonary artery. Finally, the FFR in pulmonary artery stenosis is calculated as the ratio of distal pressure and proximal pressure. RESULTS: To validate our model, we compare the simulated FFR with the measured FFR by pressure guide wires examination of 20 patients. The FFR simulated by our model shows a good agreement with the measured FFR by pressure guide wires examination ( r= 0.989, 0.001). CONCLUSION: Our proposed personalized pulmonary circulation model is shown to be capable of non-invasively calculating FFR with sufficient accuracy. SIGNIFICANCE: The FFR calculated by our personalized circulation model may effectively contribute to non-invasive detection of pulmonary artery stenosis and to a comprehensive evaluation of the entire pulmonary artery vascular tree.

DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.

Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly unsatisfactory with indistinguishable and blurred boundaries of segmentation results of tiny tumor volume. To address the problem, we propose a densely connected deep convolutional network consisting of an encoder network and a corresponding decoder network, which extracts high-level semantic features from different levels and uses low-level spatial features concurrently to obtain fine-grained segmented masks. Skip-connection architecture is involved and modified to propagate spatial information to the decoder network. Preliminary experiments are conducted on 30 patients. Experimental results show our model outperforms all baseline models, with improvements of 4.17%. An ablation study is performed, and the effectiveness of the novel loss function is validated.

AttR2U-Net: A Fully Automated Model for MRI Nasopharyngeal Carcinoma Segmentation Based on Spatial Attention and Residual Recurrent Convolution.

Radiotherapy is an essential method for treating nasopharyngeal carcinoma (NPC), and the segmentation of NPC is a crucial process affecting the treatment. However, manual segmentation of NPC is inefficient. Besides, the segmentation results of different doctors might vary considerably. To improve the efficiency and the consistency of NPC segmentation, we propose a novel AttR2U-Net model which automatically and accurately segments nasopharyngeal carcinoma from MRI images. This model is based on the classic U-Net and incorporates advanced mechanisms such as spatial attention, residual connection, recurrent convolution, and normalization to improve the segmentation performance. Our model features recurrent convolution and residual connections in each layer to improve its ability to extract details. Moreover, spatial attention is fused into the network by skip connections to pinpoint cancer areas more accurately. Our model achieves a DSC value of 0.816 on the NPC segmentation task and obtains the best performance compared with six other state-of-the-art image segmentation models.

Preclinical Study of Anticoagulation Regimens in Sheep After Implantation of CH-VAD Blood Pump.

Effective anticoagulation regimens are needed to reduce risks of thrombosis and bleeding in animal models of ventricular assist device to verify its hemocompatibility, biologic safety and reliability. This study is to develop a validated anticoagulation procedure for a sheep model to test the newly developed CH-VAD. CH-VAD models were established in six healthy sheep by constructing blood bypass of left ventricle → ventricular assist device → descending aorta. Heparin infusion was used during operation and in the prior 4 days to maintain activated clotting time 1.5-2.0 times the baseline. From the third day, proper dosage of warfarin was used orally to maintain international normalized ratio values within the range of 1.2-2.0. After termination, we examined whether there was thrombosis in the blood pump, grafts, and anastomotic stoma. Macroscopic and histopathologic examinations were performed in major organs to check for congestion and infarction. Bleeding complications were not found in any animals throughout the experiments. Activated clotting time values were 326 ± 33 s intraoperatively and 157 ± 28 s in the prior 4 days postoperatively. Activated partial thromboplastin time values increased slowly and reached the lower limit of the target range on the fourth day. Only in one of six cases was thrombus or fibrosis tissue found in the blood flow channel of the pump. Pathologic analysis showed no thrombosis, necrosis and microembolus in end-stage organs. Under the anticoagulation regimens, coagulation system could be well controlled to avoid thrombosis and bleeding complications in sheep models for CH-VAD.

Quantitative Evaluation of Coronary Plaque Progression by Computed Tomographic Angiography.

Understanding plaque formation in patients at risk for coronary artery disease-the leading cause of morbidity and death in the world-enables physicians to better determine whether and how to treat these individuals. We used computed tomographic angiography to quantitatively evaluate the progression of nonculprit coronary plaques along the full length of the right coronary artery in 21 patients with acute coronary syndrome. Each right coronary artery was analyzed in sequential, 3-mm-long segments, and the minimum luminal area, plaque burden, and plaque volume within each segment were evaluated at baseline and at 12-month follow-up. Serial remodeling of the right coronary artery was also evaluated. In total, 625 arterial segments were analyzed. At 12-month follow-up, the plaque burden had increased slightly by 0.34% (interquartile range [IQR], -4.32% to 6.35%; P=0.02), and the plaque volume was not significantly changed (0.33 mm3; IQR, -3.05 to 3.54; P=0.213). The minimum luminal area decreased 0.05 mm2 (IQR, -1.33 to 0.87 mm2; P=0.012), and this was accompanied by vessel reduction, as evidenced by negative remodeling in 43% of the 625 segments. We conclude that serial computed tomographic angiography can be used to quantitatively evaluate the morphologic progression of coronary plaques.

Prediction of coronary plaque progression using biomechanical factors and vascular characteristics based on computed tomography angiography.

OBJECTIVES: Coronary atherosclerotic plaques progress in a highly individual manner. Accurately predicting plaque progression will promote clinical management of atherosclerosis. The purpose of this study was to investigate the role of local biomechanics factors and vascular characteristics in coronary plaque progression and arterial remodeling. METHODS: Computed tomography angiography-based three-dimensional reconstruction of the native right coronary artery was performed in vivo in twelve patients with acute coronary syndrome at baseline and 12-month follow-up. The reconstructed arteries were divided into sequential 3-mm-long segments. Wall shear stress (WSS) and von Mises stress (VMS) were computed in all segments at baseline by applying fluid-structure interaction simulations. RESULTS: In total, 365 segments 3-mm long were analyzed. The decrease in minimal lumen area was independently predicted by low baseline VMS (-0.73 ± 0.13 mm2), increase in plaque burden was independently predicted by small minimal lumen area and low baseline WSS (6.28 ± 0.96%), and decrease in plaque volume was independently predicted by low baseline VMS (-0.99 ± 0.49 mm3). Negative remodeling was more likely to occur in low- (55%) and moderate-VMS (40%) segments, but expansive remodeling was more likely to occur in high-VMS (44%) segments. CONCLUSIONS: Local von Mises stress, wall shear stress, minimal lumen area, and plaque burden provide independent and additive prediction in identifying coronary plaque progression and arterial remodeling.

Initial In Vivo Evaluation of a Novel Left Ventricular Assist Device.

The aim of the study was to use the ovine model to evaluate the hemocompatibility and end-organ effects of a newly developed magnetic suspension centrifugal left ventricular assist device (LVAD) by CH Biomedical Inc., Jiangsu, China. The LVADs were implanted in 6 healthy sheep, where inflow was inserted into the left ventricular apex and outflow was anastomosed to the descending aorta. All sheep received anticoagulation and antiaggregation therapy during the study. Hematologic and biochemical tests were performed to evaluate anemia, hepatorenal function, and the extent of hemolysis. The experiments lasted for up to 30 days on the beating hearts. All sheep were humanely killed at the termination of the experiments, and the end-organs were examined macroscopically and histopathologically. Autopsy was performed in all animals and there was no thrombus formation observed inside the pump. The pump's inflow and outflow conduits were also free of thrombus. Hematologic and biochemical test results were within normal limits during the study period. Postmortem examination of the explanted organs revealed no evidence of ischemia or infarction. Based on the in vivo study, this LVAD is suitable for implantation and can provide efficient support with good biocompatibility. The encouraging results in this study suggest that it is feasible to evaluate the device's long-term durability and stability.