Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure.

INTRODUCTION: Late-gadolinium enhancement magnetic resonance (LGE-MRI) imaging is increasingly used in management of atrial fibrillation (AFib) patients. Here, we assess the usefulness of LGE-MRI-based fibrosis quantification to predict arrhythmia recurrence in patients undergoing cryoballoon ablation. Our secondary goal was to compare two widely used fibrosis quantification methods. METHODS: In 102 AF patients undergoing LGE-MRI and cryoballoon ablation (mean age 62 years; 64% male; 59% paroxysmal AFib), atrial fibrosis was quantified using the pixel intensity histogram (PIH) and image intensity ratio (IIR) methods. PIH segmentations were completed by a third-party provider as part of the standard of care at our hospital; Image intensity ratio (IIR) segmentations of the same scans were carried out in our lab using a commercially available software package. Fibrosis burdens and spatial distributions for the two methods were compared. Patients were followed prospectively for recurrent arrhythmia following ablation. RESULTS: Average PIH fibrosis was 15.6 ± 5.8% of the left atrial (LA) volume. Depending on threshold (IIRthr ), the average IIR fibrosis (% of LA wall surface area) ranged from 5.0 ± 7.2% (IIRthr = 1.2) to 37.4 ± 10.9% (IIRthr = 0.97). An IIRthr of 1.03 demonstrated the greatest agreement between the methods, but spatial overlap of fibrotic areas delineated by the two methods was modest (Sorenson Dice coefficient: 0.49). Fourty-two patients (41.2%) had recurrent arrhythmia. PIH fibrosis successfully predicted recurrence (HR 1.07; p = .02) over a follow-up period of 362 ± 149 days; regardless of IIRthr , IIR fibrosis did not predict recurrence. CONCLUSIONS: PIH-based volumetric assessment of atrial fibrosis was modestly predictive of arrhythmia recurrence following cryoballoon ablation in this cohort. IIR-based fibrosis was not predictive of recurrence for any of the IIRthr values tested, and the overlap in designated areas of fibrosis between the PIH and IIR methods was modest. Caution must therefore be exercised when interpreting LA fibrosis from LGE-MRI, since the values and spatial pattern are methodology-dependent.

Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillation.

AIMS: Electro-anatomical voltage, conduction velocity (CV) mapping, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. This study aims to (i) compare pathological substrate extent and location between current modalities, (ii) establish spatial histograms in a cohort, (iii) develop a new estimated optimized image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM, (iv) predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation (AF). METHODS AND RESULTS: Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR >1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas <0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at <0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P < 0.001) across the mapping modalities: 10% (interquartile range 0-14%) of the LA displayed LVS <0.5 mV vs. 7% (0-12%) slow conduction areas <0.2 m/s vs. 15% (8-23%) LGE with the UTAH method vs. 13% (2-23%) using IIR >1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient's mean blood pool intensity correlated linearly (R2 = 0.89, P < 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR >1.20 (75% sensitivity, 62% specificity, AUC: 0.67). CONCLUSION: Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.

Reproducibility of clinical late gadolinium enhancement magnetic resonance imaging in detecting left atrial scar after atrial fibrillation ablation.

INTRODUCTION: Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (MRI) can be used to detect postablation atrial scar (PAAS) but its reproducibility and reliability in clinical scans across different magnetic flux densities and scar detection methods are unknown. METHODS: Patients (n = 45) having undergone two consecutive MRIs (3 months apart) on 3T and 1.5T scanners were studied. We compared PAAS detection reproducibility using four methods of thresholding: simple thresholding, Otsu thresholding, 3.3 standard deviations (SD) above blood pool (BP) mean intensity, and image intensity ratio (IIR). We performed a texture study by dividing the left atrial wall intensity histogram into deciles and evaluated the correlation of the same decile of the two scans as well as to a randomized distribution of intensities, quantified using Dice Similarity Coefficient (DSC). RESULTS: The choice of scanner did not significantly affect the reproducibility. The scar detection performed by Otsu thresholding (DSC of 71.26 ± 8.34) resulted in a better correlation of the two scans compared with the methods of 3.3 SD above BP mean intensity (DSC of 57.78 ± 21.2, p < .001) and IIR above 1.61 (DSC of 45.76 ± 29.55, p <.001). Texture analysis showed that correlation only for voxels with intensities in deciles above the 70th percentile of wall intensity histogram was better than random distribution (p < .001). CONCLUSIONS: Our results demonstrate that clinical LGE-MRI can be reliably used for visualizing PAAS across different magnetic flux densities if the threshold is greater than 70th percentile of the wall intensity distribution. Also, atrial wall-based thresholding is better than BP-based thresholding for reproducible PAAS detection.

Left atrial fibrosis progression detected by LGE-MRI after ablation of atrial fibrillation.

BACKGROUND: Left atrial (LA) fibrosis is thought to be a substrate for atrial fibrillation (AF) and can be quantified by late gadolinium enhancement magnetic resonance imaging (LGE-MRI). Fibrosis formation in LA is a dynamic process and may either progress or regress following AF ablation. We examined the impact of postablation progression in LA fibrosis on AF recurrence. METHODS: LA enhancement in LGE-MRI was quantified in 127 consecutive patients who underwent first time AF ablation. Serial LGE-MRIs were done prior to AF ablation, 3 months postablation and at least 12 months after second LGE-MRI. Transient postablation lesion (TL) was defined as atrial enhancement caused by ablation lesions that was detected on the first (3 month) but not on the second postablation LGE-MRI. New fibrosis (NF) was defined as atrial enhancement detected on the most recent LGE-MRI, at least 15 months after the ablation procedure. AF recurrence and its correlation with TL and NF was assessed in all patients during the follow-up period. RESULTS: An increase of 1% NF increased the chance of postablation AF recurrence by 3% (hazard ratio [HR] 1.03, 95% CI 1-1.06, P = .05). TL had no significant impact on recurrence (P = .057). After adjusting for cardiovascular risk factors, HR increased as NF became greater. Greater volume of NF (≥21%) corresponded with lower arrhythmia-free survival (37% vs 62%, P = .01). CONCLUSION: NF formation postablation of AF is a novel marker of long-term procedural outcome. Extensive NF is associated with significantly higher risk of atrial arrhythmia recurrence.

Segmentation of left ventricle in late gadolinium enhanced MRI through 2D-4D registration for infarct localization in 3D patient-specific left ventricular model.

PURPOSE: To evaluate a 2D-4D registration-cum-segmentation framework for the delineation of left ventricle (LV) in late gadolinium enhanced (LGE) MRI and for the localization of infarcts in patient-specific 3D LV models. METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis. RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation. CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.

Durable lesion formation while avoiding esophageal injury during ablation of atrial fibrillation: Lessons learned from late gadolinium MR imaging.

INTRODUCTION: Adequate catheter/atrial tissue contact is critical for lesion formation during radiofrequency (RF) ablation of atrial fibrillation (AF). Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is a unique tool for the evaluation of lesion formation and detection of acute esophageal injury. METHODS: LGE-MRIs were obtained prior, within 24 hours of, and at 115 ± 62 days after first AF ablation in 36 patients. The Visitag module of CARTO3 was used to collect contact force (CF) and duration from a CF sensing ablation catheter for each registered ablation point. The minimum CF resulting in permanent lesions was determined. Esophageal enhancement detected by acute LGE-MRI was classified as mild, moderate, and severe. The CF resulting in esophageal enhancement was determined. RESULTS: A total of 4,642 registered ablation tags at 50 W power were analyzed. The mean RF duration (5.9 ± 3.7 vs. 5.6 ± 3.2 seconds, P < 0.05), CF (11.5 ± 5.6 vs. 10.9 ± 5.4 g, P < 0.001), and force time integral (FTI) (67.3 ± 54.5 vs. 62.2 ± 52.7 gs, P < 0.01) were significantly higher between ablation tags with and without associated LGE-MRI detected scar. The mean CF (15.7 ± 6.1 vs. 12.6 ± 5.9 g, P < 0.05, n  =  17 patients) in areas of esophageal enhancement was greater than areas without. CONCLUSION: Left atrial short duration ablation lesions with a CF greater than 12 g are more likely to be associated with permanent lesion formation. Ablating on top of the esophagus, CF less than 15 g would help minimize esophageal wall injury.

Acute noncontrast T1-weighted magnetic resonance imaging predicts chronic radiofrequency ablation lesions.

BACKGROUND: Magnetic resonance imaging (MRI) has been used to visualize radiofrequency (RF) ablation lesions but the relationship between volumes that enhance in acute MRI and the chronic lesion size is unknown. OBJECTIVES: The main goal was to use noncontrast (native) T1-weighted (T1w) MRI and late gadolinium enhancement (LGE)-MRI to visualize lesions acutely and chronically and correlate the acute area of enhancement with chronic lesion size in histology. MATERIALS AND METHODS: In a canine (n = 9) model RF ablation lesions were created in both ventricles. Native T1w MRI and LGE-MRI were acquired acutely after the ablation procedure. After 8 weeks, another set of RF ablations was performed, and the MRI study was repeated. Volume and depth of enhancement in native T1w MRI and LGE-MRI acquired after the initial ablation procedure were correlated with chronic lesion volume and depth in histology. RESULTS: Thirty-three lesions were analyzed. Native T1w MRI visualized the acute lesions but not the chronic lesions. LGE-MRI showed both acute and chronic lesions. Acute native T1w MRI volume (average of 102.1 ± 48.5 mm3 ) and depth (4.9 ± 1.2 mm) correlated well with chronic histological volume (105.9 ± 51.8 mm3 ) and depth (4.8 ± 1.3 mm) with R2 of 0.881 (P < 0.001) and 0.874 (P < 0.001), respectively. Acute LGE-MRI had a significantly higher volume of enhancement of 499.7 ± 214.4 mm3 (P < 0.001) and depth of 7.5 ± 1.8 mm ( P < 0.001) when compared with chronic histological lesion volume and depth. CONCLUSIONS: Native T1w MRI acquired acutely after RF ablation is a good predictor of chronic lesion size. Acute LGE-MRI significantly overestimates the chronic lesion size.

Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment.

PURPOSE: To realize objective atrial scar assessment, this study aimed to develop a fully automatic method to segment the left atrium (LA) and pulmonary veins (PV) from late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI). The extent and distribution of atrial scar, visualized by LGE-MRI, provides important information for clinical treatment of atrial fibrillation (AF) patients. MATERIALS AND METHODS: Forty-six AF patients (age 62 ± 8, 14 female) who underwent cardiac MRI prior to RF ablation were included. A contrast-enhanced MR angiography (MRA) sequence was acquired for anatomy assessment followed by an LGE sequence for LA scar assessment. A fully automatic segmentation method was proposed consisting of two stages: 1) global segmentation by multiatlas registration; and 2) local refinement by 3D level-set. These automatic segmentation results were compared with manual segmentation. RESULTS: The LA and PVs were automatically segmented in all subjects. Compared with manual segmentation, the method yielded a surface-to-surface distance of 1.49 ± 0.65 mm in the LA region when using both MRA and LGE, and 1.80 ± 0.93 mm when using LGE alone (P < 0.05). In the PV regions, the distance was 2.13 ± 0.67 mm and 2.46 ± 1.81 mm (P < 0.05), respectively. The difference between automatic and manual segmentation was comparable to the interobserver difference (P = 0.8 in LA region and P = 0.7 in PV region). CONCLUSION: We developed a fully automatic method for LA and PV segmentation from LGE-MRI, with comparable performance to a human observer. Inclusion of an MRA sequence further improves the segmentation accuracy. The method leads to automatic generation of a patient-specific model, and potentially enables objective atrial scar assessment for AF patients. J. Magn. Reson. Imaging 2016;44:346-354.

Automated left ventricle segmentation in late gadolinium-enhanced MRI for objective myocardial scar assessment.

PURPOSE: To develop and validate an objective and reproducible left ventricle (LV) segmentation method for late gadolinium enhanced (LGE) magnetic resonance imaging (MRI), which can facilitate accurate myocardial scar assessment. MATERIALS AND METHODS: A cohort of 25 ischemic patients and 25 nonischemic patients were included. A four-step algorithm was proposed: first, the Cine-MRI and LGE-MRI volume were globally registered; second, the registered Cine-MRI contours were fitted to each LGE-MRI slice via the constructed contour image; third, the fitting was optimized in full LGE-MRI stack; finally, the contours were refined by taking into account patient-specific scar patterns. The automated LV segmentation results were compared with that of manual segmentation from two experienced observers. RESULTS: The accuracy of automated segmentation, expressed as the average contour distances to manual segmentation, was 0.82 ± 0.19 pixels, in the same order as interobserver difference between manual results (0.90 ± 0.26 pixels), but with lower variability (0.60 ± 0.37 pixels, P < 0.05). The myocardial scar identification based on automated LV segmentation further demonstrated higher consistency than that of manual segmentation (Pearson correlation 0.97 vs. 0.84). CONCLUSION: An automated LV segmentation method for LGE-MRI was developed, providing high segmentation accuracy and lower interobserver variability compared to fully manual image analysis. The method facilitates objective assessment of myocardial scar.

Rapid single-breath-hold 3D late gadolinium enhancement cardiac MRI using a stack-of-spirals acquisition.

PURPOSE: To develop a rapid single-breath-hold 3D late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) method, and demonstrate its feasibility in cardiac patients. MATERIALS AND METHODS: An inversion recovery dual-density 3D stack-of-spirals imaging sequence was developed. The spiral acquisition was 2-fold accelerated by self-consistent parallel imaging reconstruction (SPIRiT), which resulted in a total scan time of 12 heartbeats. Field map-based linear off-resonance correction was incorporated to the SPIRiT reconstruction. The 3D spiral LGE scans were performed in 15 patients who were referred for clinically ordered cardiac MR examinations that included the standard 2D multislice LGE imaging. Image sharpness and overall quality were qualitatively assessed based on 5-point scales. RESULTS: Scar-induced hyper-LGE was identified in 4 out of the 15 patients by both 3D spiral and 2D multislice LGE tests. On average over all datasets (n = 15), the image sharpness scores were 3.9 (3D spiral) and 4.0 (2D multislice), and the image quality scores were 4.1 (3D spiral) and 4.0 (2D multislice) with no significant difference in both metrics (paired t-test; P > 0.1). The average scar contrast enhancement ratios were 0.72 and 0.75 in 3D and 2D images, respectively (n = 4). The average difference of fractional scar volumes measured in 3D and 2D images was 4.3% (n = 3). CONCLUSION: Stack-of-spiral acquisition combined with non-Cartesian SPIRiT parallel imaging enables rapid 3D LGE MRI in a 12 heartbeat-long breath-hold.J.

Unipolar voltage electroanatomic mapping detects structural atrial remodeling identified by LGE-MRI.

BACKGROUND: In atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomic mapping (EAM) and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) are accepted methods for assessing the atrial substrate and are associated with ablation outcome, recent findings have highlighted discrepancies between low-voltage areas (LVAs) in EAM and LGE areas. OBJECTIVE: The purpose of this study was to explore the relationship between LGE regions and unipolar and bipolar LVAs using multipolar high-density mapping. METHODS: Twenty patients scheduled for AF ablation underwent preablation LGE-MRI. LA segmentation was conducted using a deep learning approach, which subsequently generated a 3-dimensional mesh integrating the LGE data. High-density EAM was performed in sinus rhythm for each patient. The electroanatomic map and LGE-MRI mesh were coregistered. LVAs were defined using cutoffs of 0.5 mV for bipolar voltage and 2.5 mV for unipolar voltage. The correspondence between LGE areas and LVAs in the LA was analyzed using confusion matrices and performance metrics. RESULTS: A considerable 87.3% of LGE regions overlapped with unipolar LVAs, compared with only 16.2% overlap observed with bipolar LVAs. Across all performance metrics, unipolar LVAs outperformed bipolar LVAs in identifying LGE areas (precision: 78.6% vs 61.1%; sensitivity: 87.3% vs 16.2%; F1 score: 81.3% vs 26.0%; accuracy: 74.0% vs 35.3%). CONCLUSION: Our findings demonstrate that unipolar LVAs strongly correlate with LGE regions. These findings support the integration of unipolar mapping alongside bipolar mapping into clinical practice. This would offer a nuanced approach to diagnose and manage AF by revealing critical insights into the complex architecture of the atrial substrate.

Prognostic role of late gadolinium-enhanced MRI in confirmed and suspected cardiac sarcoidosis: meta-analysis.

The prognostic implications of late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) in the context of cardiac sarcoidosis (CS) have attracted considerable attention. Nevertheless, a subset of studies has undistinguished confirmed and suspected CS cases, thereby engendering interpretative ambiguities. In this meta-analysis, we evaluated the differences in cardiac MRI findings and their prognostic utility between confirmed and suspected CS. A literature search was conducted using PubMed, Web of Science, and Cochrane libraries to compare the findings of cardiac MRI and its prognostic value in CS and suspected CS. A meta-analysis was performed to compare the prevalence of LGE MRI, odds ratios, and hazard ratios for predicting cardiac events in both groups. A total of 21 studies encompassing 24 different populations were included in the meta-analysis (CS: 393 cases, suspected CS: 2151 cases). CS had a higher frequency of LGE of the left ventricle (87.2% vs. 36.4%, p < 0.0001) and right ventricle (62.1% vs. 23.8%, p = 0.04) than suspected CS. In patients with suspected CS, the presence of left ventricular LGE was associated with higher all-cause mortality [odds ratio: 5.70 (95%CI: 2.51-12.93), p < 0.0001, I2 = 8%, p for heterogeneity = 0.37] and ventricular arrhythmia [odds ratio: 15.51 (95%CI: 5.65-42.55), p < 0.0001, I2 = 0, p for heterogeneity = 0.94]. In contrast, in CS, not the presence but extent of left ventricular LGE was a significant predictor of outcome (hazard ratio = 1.83 per 10% increase of %LGE (95%CI: 1.43-2.34, p < 0.001, I2 = 15, p for heterogeneity = 0.31). The presence of left ventricular LGE was a strong prognostic factor in suspected sarcoidosis. However, the extremely high prevalence of left ventricular LGE in confirmed CS suggests that the quantitative assessment of LGE is useful for prognostic estimation.

Usformer: A small network for left atrium segmentation of 3D LGE MRI.

Left atrial (LA) fibrosis plays a vital role as a mediator in the progression of atrial fibrillation. 3D late gadolinium-enhancement (LGE) MRI has been proven effective in identifying LA fibrosis. Image analysis of 3D LA LGE involves manual segmentation of the LA wall, which is both lengthy and challenging. Automated segmentation poses challenges owing to the diverse intensities in data from various vendors, the limited contrast between LA and surrounding tissues, and the intricate anatomical structures of the LA. Current approaches relying on 3D networks are computationally intensive since 3D LGE MRIs and the networks are large. Regarding this issue, most researchers came up with two-stage methods: initially identifying the LA center using a scaled-down version of the MRIs and subsequently cropping the full-resolution MRIs around the LA center for final segmentation. We propose a lightweight transformer-based 3D architecture, Usformer, designed to precisely segment LA volume in a single stage, eliminating error propagation associated with suboptimal two-stage training. The transposed attention facilitates capturing the global context in large 3D volumes without significant computation requirements. Usformer outperforms the state-of-the-art supervised learning methods in terms of accuracy and speed. First, with the smallest Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD), it achieved a dice score of 93.1% and 92.0% in the 2018 Atrial Segmentation Challenge and our local institutional dataset, respectively. Second, the number of parameters and computation complexity are largely reduced by 2.8x and 3.8x, respectively. Moreover, Usformer does not require a large dataset. When only 16 labeled MRI scans are used for training, Usformer achieves a 92.1% dice score in the challenge dataset. The proposed Usformer delineates the boundaries of the LA wall relatively accurately, which may assist in the clinical translation of LA LGE for planning catheter ablation of atrial fibrillation.

Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle.

Patients with myocardial infarction are at elevated risk of sudden cardiac death, and scar tissue arising from infarction is known to play a role. The accurate identification of scars therefore is crucial for risk assessment, quantification and guiding interventions. Typically, core scars and grey peripheral zones are identified by radiologists and clinicians based on cardiac late gadolinium enhancement magnetic resonance images (LGE-MRI). Scar regions from LGE-MRI vary in size, shape, heterogeneity, artifacts, and image resolution. Thus, manual segmentation is time consuming, and influenced by the observer's experience (bias effect). We propose a fully automatic framework that develops 3D anatomical models of the left ventricle with border zone and core scar regions that are free from bias effect. Our myocardium (SOCRATIS), border scar and core scar (BZ-SOCRATIS) segmentation pipelines were evaluated using internal and external validation datasets. The automatic myocardium segmentation framework performed a Dice score of 81.9% and 70.0% in the internal and external validation dataset. The automatic scar segmentation pipeline achieved a Dice score of 60.9% for the core scar segmentation and 43.7% for the border zone scar segmentation in the internal dataset and in the external dataset a Dice score of 44.2% for the core scar segmentation and 54.8% for the border scar segmentation respectively. To the best of our knowledge, this is the first study outlining a fully automatic framework to develop 3D anatomical models of the left ventricle with border zone and core scar regions. Our method exhibits high performance without the need for training or tuning in an unseen cohort (unsupervised).

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.

Uncertainty-guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium-enhanced MRI.

PURPOSE: Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentation results. METHODS: In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on the surface. RESULTS: We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparable performance. CONCLUSIONS: We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on the surface.

Preoperative myocardial fibrosis is associated with worse survival after alcohol septal ablation in patients with hypertrophic obstructive cardiomyopathy: A delayed enhanced cardiac magnetic resonance study.

Background: Prior studies have shown that myocardial fibrosis can be detected by late gadolinium enhancement (LGE) of cardiac magnetic resonance (CMR) and might be associated with higher mortality risk in hypertrophic cardiomyopathy (HCM). The objective of this study was to examine the prognostic utility of CMR in patients with hypertrophic obstructive cardiomyopathy (HOCM) undergoing alcohol septal ablation (ASA). Materials and methods: We conducted a retrospective study which consisted of 183 consecutive patients with symptomatic drug-refractory HOCM who underwent CMR for assessment of myocardial fibrosis before ASA. The cardiovascular disease related survival was evaluated according to LGE-CMR status. Results: The cohort comprised 74 (40.4%) women with a mean age of 51 ± 8 years. Preoperative myocardial fibrosis was detected in 148 (80.9%) patients. After a median of 6 years (range 2-11 years) follow-up, adverse clinical events occurred in 14 (7.7%) patients. Multivariate-adjusted Cox regression analyses revealed that age [hazard ratio (HR) 1.142 (1.059-1.230), p = 0.001] and LGE [HR 1.170 (1.074-1.275), p < 0.001] were independent predictors of cardiovascular mortality during follow-up. Conclusion: Preoperative myocardial fibrosis measured by LGE-CMR was an independent predictor of increased adverse clinical outcomes in patients with HOCM undergoing ASA and could be used for the pre-operative evaluation of risk stratification and long-term prognosis after ASA in these patients.

Epicardial adipose tissue is associated with left atrial volume and fibrosis in patients with atrial fibrillation.

Background: Obesity is a risk factor for atrial fibrillation (AF) and strongly influences the response to treatment. Atrial fibrosis shows similar associations. Epicardial adipose tissue (EAT) may be a link between these associations. We sought to assess whether EAT is associated with body mass index (BMI), left atrial (LA) fibrosis and volume. Methods: LA fibrosis and EAT were assessed using late gadolinium enhancement, and Dixon MRI sequences, respectively. We derived 3D models incorporating fibrosis and EAT, then measured the distance of fibrotic and non-fibrotic areas to the nearest EAT to assess spatial colocalization. Results: One hundred and three AF patients (64% paroxysmal, 27% female) were analyzed. LA volume index was 54.9 (41.2, 69.7) mL/m2, LA EAT index was 17.4 (12.7, 22.9) mL/m2, and LA fibrosis was 17.1 (12.4, 23.1)%. LA EAT was significantly correlated with BMI (R = 0.557, p < 0.001); as well as with LA volume and LA fibrosis after BSA adjustment (R = 0.579 and R = 0.432, respectively, p < 0.001 for both). Multivariable analysis showed LA EAT to be independently associated with LA volume and fibrosis. 3D registration of fat and fibrosis around the LA showed no clear spatial overlap between EAT and fibrotic LA regions. Conclusion: LA EAT is associated with obesity (BMI) as well as LA volume and fibrosis. Regions of LA EAT did not colocalize with fibrotic areas, suggesting a systemic or paracrine mechanism rather than EAT infiltration of fibrotic areas.

Risk and predictors of sudden death in cardiac sarcoidosis: A systematic review and meta-analysis.

BACKGROUND: To evaluate the risk for ventricular arrhythmia (VA) and sudden cardiac death (SCD) in patients with cardiac sarcoidosis (CS) and determine the prognostic factors. METHODS AND RESULTS: PUBMED, EMBASE and SCOPUS were searched up to 14th April 2020. Studies reporting the incidence of SCD, appropriate ICD therapy in CS patients, or relevant prognostic information in patients having undergone MRI, PET, or programmed electrical stimulation (PES) were included. Nineteen studies consisting of 1247 patients, reported the risk of ICD therapies or SCD over a follow-up period of 1.7-7 years. 22.7% (n = 9; 22.7, 95%CI [16.10-29.36]) of patients in primary and 58.4% (n = 9; 58.42, 95% CI [38.61-78.22]) in secondary prevention cohorts experienced appropriate device therapy or SCD events. 18% (n = 2; 18, 95%CI [14-23]) of patients received ≥5 appropriate therapies. 9 out of 664 patients with confirmed cardiac sarcoidosis but without implanted ICDs died suddenly. 17.9% of patients (n = 4; 17.9, 95%CI [10.80-25.03]) experienced inappropriate device therapy. Positive LGE-MRI and PES were associated with an 8.6-fold (n = 6; RR = 8.60, 95%CI [3.80-19.48]) and 9-fold (n = 5; RR = 9.07, 95%CI [4.65-17.68]) increased risk of VA respectively. Positive LGE-MRI and PET with associated with a 6.8-fold (n = 12; RR = 6.82, 95%CI [4.57-10.18]) and 3.4-fold (n = 7; RR = 3.41, 95%CI [2.03-5.74]) respectively for increased risk of major adverse cardiac events. CONCLUSIONS: The risk of appropriate ICD therapy or sudden cardiac death is high in patients with CS. The presence of LGE-MRI and positive electrophysiology study identify patients at increased risk of ventricular arrhythmias. [CRD42019124220].