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).
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).
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.

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.
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.
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.
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.

暂无摘要。

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).

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate\'s arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
The heart usually beats with a regular rhythm to pump the blood that carries oxygen and nutrients to different organs. Sometimes, alterations in the heart’s rhythm known as arrhythmias can occur. Atrial fibrillation, also called AFib, is a type of arrhythmia in which the heart beats rapidly and irregularly, causing abnormal blood-flow that can lead to the formation of blood clots. If one of these blood clots travels to the brain, it can block a blood vessel, causing a stroke. However, many strokes occur without any evidence of AFib. One subset of strokes that are not associated with AFib are embolic strokes of undetermined source (ESUS), which account for 25% of all strokes. By definition ESUS and AFib do not occur together, but both are associated with similar elevated levels of disease-related remodeling (i.e., fibrosis) in the heart tissue, which appears when the heart is injured. Fibrosis impairs the heart’s normal electrical activity. Bifulco et al. wanted to determine whether there is some fundamental difference in fibrosis between people with AFib and those who have had an ESUS event. To do this, they used a computational approach to model the geometries and patterns of fibrosis of the hearts of 45 ESUS patients and 45 patients with AFib, essentially producing a virtual version of each patient’s heart. Bifulco et al. then applied a virtual pace-maker (working in overdrive mode) to each heart model to determine whether electrical inputs that can lead to AFib had different effects on ESUS and AFib patients. The results showed that the electrical inputs had similar effects in all of the heart models. This led Bifulco et al. to conclude that ESUS and AFib patients have indistinguishable patterns of fibrosis. The key difference is that ESUS patients are missing the trigger to initiate the fibrillation process – if atrial fibrosis is the proverbial tinderbox, these triggers are the spark needed to ignite a fire. Further research, including confirmation of Bifulco et al.’s findings in live patients, will be needed to confirm the hypothesis that ESUS patients lack AFib primarily due to an absence of triggers. If this is indeed the case, these findings may make it easier to identify ESUS patients at higher risk for AFib or further strokes. Additionally, a better understanding of fibrosis as a link between stroke and AFib will help clinicians provide better, more personalized treatments, for example guiding whether a patient should take blood thinners or undergo more rigorous cardiac monitoring.

BACKGROUND: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain.
OBJECTIVE: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance.
METHODS: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression.
RESULTS: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: -0.11 m/s per unit increase in NI (P < 0.001) and -0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR.
CONCLUSIONS: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.

To evaluate the risk for ventricular arrhythmia (VA) and sudden cardiac death (SCD) in patients with cardiac sarcoidosis (CS) and determine the prognostic factors.
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.
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].

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.
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).
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).
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.

Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.

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.
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.
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).
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.