Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched.This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar, and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail and summarize the validation strategies applied in each task as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.     

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.     

Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

Background

Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.

Methods

The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.

Results

Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.

Conclusions

The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.     

DCAN: Deep contour-aware networks for object instance segmentation from histology images

In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures. In order to meet these challenges, we propose a novel deep contour-aware network (DCAN) under a unified multi-task learning framework for more accurate detection and segmentation. In the proposed network, multi-level contextual features are explored based on an end-to-end fully convolutional network (FCN) to deal with the large appearance variation. We further propose to employ an auxiliary supervision mechanism to overcome the problem of vanishing gradients when training such a deep network. More importantly, our network can not only output accurate probability maps of histological objects, but also depict clear contours simultaneously for separating clustered object instances, which further boosts the segmentation performance. Our method ranked the first in two histological object segmentation challenges, including 2015 MICCAI Gland Segmentation Challenge and 2015 MICCAI Nuclei Segmentation Challenge. Extensive experiments on these two challenging datasets demonstrate the superior performance of our method, surpassing all the other methods by a significant margin.     

High-resolution 3-dimensional late gadolinium enhancement scar imaging in surgically corrected Tetralogy of Fallot: clinical feasibility of volumetric quantification and visualization

Background

The extent of surgical scarring in Tetralogy of Fallot (TOF) may be a marker of adverse outcomes and provide substrate for ventricular arrhythmia. In this study we evaluate the feasibility of high resolution three dimensional (3D) late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) for volumetric scar quantification in patients with surgically corrected TOF.

Methods

Fifteen consecutive patients underwent 3D LGE imaging with 3 Tesla CMR using a whole-heart, respiratory-navigated technique. A novel, signal-histogram based segmentation technique was tested for the quantification and modeling of surgical scar. Total scar volume was compared to the gold standard manual expert segmentation. The feasibility of segmented scar fusion to matched coronary CMR data for volumetric display was explored.

Results

Image quality sufficient for 3D scar segmentation was acquired in fourteen patients. Mean patient age was 32.2 ± 11.9 years (range 21 to 57 years) with mean right ventricle (RV) ejection fraction (EF) of 53.9 ± 9.2% and mean RV end diastolic volume of 117.0 ± 41.5 mL/m². The mean total scar volume was 11.1 ± 8.2 mL using semi-automated 3D segmentation with excellent correlation to manual expert segmentation (r = 0.99, bias = 0.89 mL, 95% CI -1.66 to 3.44). The mean segmentation time was significantly reduced using the novel semi-automated segmentation technique (10.1 ± 2.6 versus 45.8 ± 12.6 minutes). Excellent intra-observer and good inter-observer reproducibility was observed.

Conclusion

3D high resolution LGE imaging with semi-automated scar segmentation is clinically feasible among patients with surgically corrected TOF and shows excellent accuracy and reproducibility. This approach may offer a valuable clinical tool for risk prediction and procedural planning among this growing population.     

Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization

Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization.     

磁共振成像在房颤导管射频消融术中的应用

导管射频消融术是公认的症状性、药物难治性房颤的首选治疗方法。术前对肺静脉及左房解剖情况的准确评估至关重要,但却受限于传统的影像学方法。随着无创影像三维重建技术的日趋完善,尤其是核磁共振成像(MRI)技术,使得术前评估肺静脉及左房纤维化情况已成为现实。此外,MRI还可对术后左房损伤及瘢痕组织情况作出准确评估,可用于预测房颤的预后,亦可用于确定二次消融位点,达到彻底电隔离肺静脉。     

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.     

Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information

Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and four sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.     

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).     

缺血性心肌病患者左心房功能改变的超声研究

目的探讨超声对缺血性心肌病(ICM)患者左心房收缩及舒张功能改变的评价。方法对34例ICM患者(NYHA心功能Ⅱ~Ⅳ级),采用声学定量(AQ)技术测量左心室舒张末期左心房容积(EDV)、左心室收缩末期左心房容积(ESV)、快速排空末期左心房容积(EREV)、左心房存储器容积即左心房总排空容积(LARV)、左心房快速排空容积(LARE)、左心房主动收缩排空容积(LAAE)、左心房快速排空分数(REF)、左心房主动收缩排空分数(AEF)、峰值充盈率(PFR)、峰值快速排空率(PRER)、峰值左心房排空率(PAER),并与34例正常人的上述各参数测值进行对比分析。结果ICM组与正常组相比EDV、EREV、AEF有不同程度的升高(P<0.01),ESV、LARV、LARE、LAAE、REF、PFR、PRER、PAER有不同程度的减低(P<0.01或P<0.05)。结论ICM患者左心房存储器功能和管道功能减低,助力泵功能代偿性增强,AQ技术为左心房功能的评价提供了无创性新方法。     

CMR findings in patients with hypertrophic cardiomyopathy and atrial fibrillation

Objectives

We sought to evaluate the relation between atrial fibrillation (AF) and the extent of myocardial scarring together with left ventricular (LV) and atrial parameters assessed by late gadolinium-enhancement (LGE) cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM).

Background

AF is the most common arrhythmia in HCM. Myocardial scarring is also identified frequently in HCM. However, the impact of myocardial scarring assessed by LGE CMR on the presence of AF has not been evaluated yet.

Methods

87 HCM patients underwent LGE CMR, echocardiography and regular ECG recordings. LV function, volumes, myocardial thickness, left atrial (LA) volume and the extent of LGE, were assessed using CMR and correlated to AF. Additionally, the presence of diastolic dysfunction and mitral regurgitation were obtained by echocardiography and also correlated to AF.

Results

Episodes of AF were documented in 37 patients (42%). Indexed LV volumes and mass were comparable between HCM patients with and without AF. However, indexed LA volume was significantly higher in HCM patients with AF than in HCM patients without AF (68 ± 24 ml·m^-2 versus 46 ± 18 ml·m^-2, p = 0.0002, respectively). The mean extent of LGE was higher in HCM patients with AF than those without AF (12.4 ± 14.5% versus 6.0 ± 8.6%, p = 0.02). When adjusting for age, gender and LV mass, LGE and indexed LA volume significantly correlated to AF (r = 0.34, p = 0.02 and r = 0.42, p < 0.001 respectively). By echocardiographic examination, LV diastolic dysfunction was evident in 35 (40%) patients. Mitral regurgitation greater than II was observed in 12 patients (14%). Multivariate analysis demonstrated that LA volume and presence of diastolic dysfunction were the only independent determinant of AF in HCM patients (p = 0.006, p = 0.01 respectively). Receiver operating characteristic curve analysis indicated good predictive performance of LA volume and LGE (AUC = 0.74 and 0.64 respectively) with respect to AF.

Conclusion

HCM patients with AF display significantly more LGE than HCM patients without AF. However, the extent of LGE is inferior to the LA size for predicting AF prevalence. LA dilation is the strongest determinant of AF in HCM patients, and is related to the extent of LGE in the LV, irrespective of LV mass.     

四维自动左房定量技术评价尿毒症血液透析患者左房功能

目的：心血管损伤是尿毒症血液透析患者的主要并发症,左房形变与左室充盈压及功能障碍有关,目前量化左房功能仍有一定挑战。本研究探讨四维自动左房定量(LAQ)技术评估保留左室射血分数(LVEF)的尿毒症血液透析患者的左房功能。方法：选取LVEF正常的尿毒症血液透析患者37例和正常对照组34例,所有参与者行常规超声心动图和LAQ检查。主要采集胸骨旁左室长轴和心尖四腔心切面图像,通过脱机分析软件EchoPac 203测量左房直径、容积、射血分数和应变参数,并对两组进行分析比较。结果：与正常对照组相比,尿毒症组的左室扩大、左室壁肥厚,左房直径和容积增加,左房排空分数虽有一定降低,但仍在正常范围内。左房储存、管道及辅泵期的纵向应变均降低,周向应变仅储存和管道期降低,辅泵期无明显差异。结论：尿毒症血液透析患者的心肌损害早于LVEF异常,左房应变比常规超声参数更敏感、更早期发现左房结构和功能的损害。LAQ作为一种新方法,可早期检出LVEF正常的尿毒症患者的左房功能障碍,为临床早期诊疗提供有价值的信息。     

Improved border sharpness of post-infarct scar by a novel self-navigated free-breathing high-resolution 3D whole-heart inversion recovery magnetic resonance approach

The border zone of post-infarction myocardial scar as identified by late gadolinium enhancement (LGE) has been identified as a substrate for arrhythmias and consequently, high-resolution 3D scar information is potentially useful for planning of electrophysiological interventions. This study evaluates the performance of a novel high-resolution 3D self-navigated free-breathing inversion recovery magnetic resonance pulse sequence (3D-SN-LGE) vs. conventional 2D breath-hold LGE (2D-LGE) with regard to sharpness of borders (S_Border) of post-infarction scar. Patients with post-infarction scar underwent two magnetic resonance examinations for conventional 2D-LGE and high-resolution 3D-SN-LGE acquisitions (both 15 min after 0.2 mmol/kg Gadobutrol IV) at 1.5T. In the prototype 3D-SN-LGE sequence, each ECG-triggered radial steady-state-free-precession read-out segment is preceded by a non-slice-selective inversion pulse. Scar volume and S_Border were assessed on 2D-LGE and matching reconstructed high-resolution 3D-SN-LGE short-axis slices. In 16 patients (four females, 58?±?10y) all scars visualized by 2D-LGE could be identified on 3D-SN-LGE (time between 2D-LGE and 3D-SN-LGE 48?±?53 days). A good agreement of scar volume by 3D-SN-LGE vs. 2D-LGE was found (Bland–Altman: ?3.7?±?3.4 ml, correlation: r?=?0.987, p?<?0.001) with a small difference in scar volume (20.5 (15.8, 35.2) ml vs. 24.5 (20.0, 41.9)) ml, respectively, p?=?0.002] and a good intra- and interobserver variability (1.1?±?4.1 and ?1.1?±?11.9 ml, respectively). S_Border of border “scar to non-infarcted myocardium” was superior on 3D-SN-LGE vs. 2D-LGE: 0.180?±?0.044 vs. 0.083?±?0.038, p?<?0.001. Detection and quantification of myocardial scar by 3D-SN-LGE is feasible and accurate in comparison to 2D-LGE. The high spatial resolution of the 3D sequence improves delineation of scar borders.     

高血压病左心功能相关性的超声研究

目的 探讨高血压病左心房与左心室功能的相关关系。方法 对照组20例，高血压病左心室心肌质量指数(LVMI)正常组32例，高血压病左心室肥厚组16例，采用声学定量技术，测量左心房助力泵功能(LAEF、AE)、管道功能(CV)和储存器功能(RV)以及左心室舒张功能。结果 左心房助力泵功能与其主动收缩前容量、RV正相关，与CV及左心室舒张功能负相关；左心房管道功能与AE、LAEF、RV和左心房主动收缩前容量显著负相关，与左心室舒张功能正相关；左心房储存器功能与左心房主动收缩前容量、LAEF正相关，与CV负相关，与左心室舒张功能指标无显著相关。结论 高血压病左心室舒张功能减退导致左心房管道功能减低、肺静脉与左心房间压差增大、左心房储存器功能增强、左心室舒张早期充盈增加，而管道功能与储存器功能的改变导致左心房前负荷增加和左心房收缩能力增强、左心房助力泵功能增强、左心室舒张晚期充盈增加。     

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions.In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).     

Quantitative assessment of myocardial strain with displacement encoding with stimulated echoes MRI in patients with coronary artery disease

To determine the diagnostic performance and reproducibility of strain assessment with displacement encoding with stimulated echoes (DENSE) cardiovascular magnetic resonance (CMR) in identifying contractile abnormalities in myocardial segments with late gadolinium enhancement (LGE). DENSE CMR was obtained on short-axis planes of the left ventricle (LV) in 24 patients with suspected coronary artery disease. e1 and e2 strains of LV wall were quantified. Cine MRI was acquired to determine percent systolic wall thickening (%SWT), followed by (LGE) CMR. The diagnostic performance of e1, e2 and %SWT for predicting the presence of LGE was evaluated by receiver operating characteristics (ROC) analysis. Myocardial scar on LGE CMR was observed in 91 (24 %) of 384 segments. The area under ROC curve for predicting the segments with LGE was 0.874 by e1, 0.916 by e2 and 0.828 by %SWT (p = 0.001 between e2 and %SWT). Excellent inter-observer reproducibility was found for strain [Intraclass correlation coefficient (ICC) = 0.962 for e1, 0.955 for e2] as compared with %SWT (ICC = 0.790). DENSE CMR can be performed as a part of routine CMR study and allows for quantification of myocardial strain with high inter-observer reproducibility. Myocardial strain, especially e2 is useful in detecting altered abnormal systolic contraction in the segments with myocardial scar.     

Mapping Reentry Around Atriotomy Scars Using Double Potentials

Supraventricular arrhythmias, often seen in patients after cardiac surgery, may be associated with scars produced in the atria at the time of surgery. Double potentials, found in the presence of functional or anatomical block, can define the limits and critical regions of a reentrant circuit associated with the atriotomy scars. We describe six patients with seven distinct atrial tachycardias in whom atriotomy scars were successfully mapped during intraatrial reentry utilizing the presence and interelectrogram relationship of observed double potentials. The reentrant circuit was mapped in all patients by following the relationship between double potentials along the surgical scar, assuming that they would be widely split in the middle of the scar and merge into a single continuous fractionated potential at the apex of the scar. At this site, atrial pacing was performed to entrain the tachycardia and confirm the participation of the atriotomy scar in the clinically relevant atrial tachycardia. Radiofrequency ablation was performed from the site of electrogram fusion to the nearest anatomical obstacle. Five of seven atrial tachycardias were successfully ablated utilizing this technique over a mean follow-up of 10 months. We proposed that these double potentials and their interelectrogram relationship are an effective means of mapping atriotomy scars and guiding successful radiofrequency ablation.     

经导管线性消融术治疗阵发性房颤对左房近期功能的影响

目的应用超声心动图评价左房线性消融术治疗阵发性房颤对近期左房功能的影响。 方法28例因阵发性房颤行线性消融的患者，于术前和术后2～3个月行超声心动图检查。通过二维超声测量左房容积和排空能力，组织速度成像检测二尖瓣前瓣环舒张晚期峰值速度Va，应变率显像检测左房壁舒张晚期峰值应变率SRa，来观察左房储存功能、管道功能和辅泵功能的变化。 结果消融后左房收缩末容积和左房内径无明显改变；左房管道容积和舒张末容积增高；左房分数、左房射血力、二尖瓣血流VA、二尖瓣环Va显著降低；左房平均SRa和局部各壁SRa均显著降低，尤其以后壁和侧壁降低最明显。 结论线性消融术对近期左房局部和整体功能均存在一定程度的不利影响。     

Assessment of left atrial volume and function: a comparative study between echocardiography, magnetic resonance imaging and multi slice computed tomography

Measurement of left atrial (LA) maximal volume (LA(max)) using two-dimensional transthoracic echocardiography (TTE) provides prognostic information in several cardiac diseases. However, the relationship between LA(max) and LA function is poorly understood and TTE is less well suited for measuring dynamic LA volume changes. Conversely, cardiac magnetic resonance imaging (CMR) and multi-slice computed tomography (MSCT) appears more appropriate for such measures. We sought to determine the relationship between LA size assessed with TTE and LA size and function assessed with CMR and MSCT. Fifty-four patients were examined 3 months post myocardial infarction with echocardiography, CMR and MSCT. Left atrial volumes and LA reservoir function were assessed by TTE. LA time-volume curves were determined and LA reservoir function (cyclic change and fractional change), passive emptying function (reservoir volume) and pump function (left atrial ejection fraction-LAEF) were derived using CMR and MSCT. Left atrial fractional change and left atrial ejection fraction (LAEF) determined with CMR and MSCT were unrelated to LA(max) enlargement by echocardiography (P = NS). There was an overall good agreement between CMR and MSCT, with a small to moderate bias in LA(max) (4.9 ± 10.4 ml), CC (3.1 ± 9.1 ml) and reservoir volume (3.4 ± 9.1 ml). TTE underestimates LA(max) with up to 32% compared with CMR and MSCT (P < 0.001). Left atrial function assessed with MSCT and CMR as LA fractional change and LAEF is not significantly related to LA(max) measured by TTE. TTE systematically underestimated LA volumes, whereas there are good agreements between MSCT and CMR for volumetric and functional properties.