Polycaprolactone (PCL) nanofibers are widely used in the field of tissue regeneration as a biodegradable material. However, there is a limitation in that low hydrophilicity and tissue cannot be directly regenerated. Recent studies have shown that polypyrrole (PPy) has potential in the field of tissue engineering due to its electrical conductivity and biocompatibility. Meanwhile, the electrospinning has the advantage of using most polymers and of facilitating a porous structure suitable for tissue regeneration, so the nanofibers were fabricated through electrospinning. The purpose of this study is to evaluate the physicochemical properties and biocompatibility of PCL nanofibers coated with PPy for guided tissue regeneration. To this end, PCL nanofibers coated with four types of concentration groups were prepared. The group was named according to the concentration ratio of PPy, and the control pure PCL containing no PPy and 20PPy, 30PPy, and 40PPy containing 20 wt%, 30 wt%, and 40 wt%, respectively, consisted of the experimental group. The mixed solution of PCL and pyrrole monomer was electrospun. Then precipitate in an iron (III) chloride(FeCl3 ) solution as an oxidizing agent which contains pyrrole monomer and polymerized. A tensile test was performed to confirm mechanical properties, and surface hydrophilicity was confirmed through measurement of contact angle.Electrical conductivity was also confirmed through measurement of resistance values. Lastly, cytotoxicity evaluation was performed using fibroblast (L929) and preosteoblast (MC3T3-E1) cell lines to confirm biocompatibility. The results were evaluated as one-way ANOVA (p-value = 0.05), and post-analysis was performed using Tukey’s post-hoc test. PPycoated PCL nanofibers showed no statistically significant decrease in mechanical strength compared to PPy-uncoated PCL nanofibers, while electrical conductivity increased significantly at all concentrations. When 30wt% or more of PPy was coated, hydrophilicity was significantly increased compared to the PPy-uncoated PCL nanofibers. Regardless of the concentration of PPy, cytotoxicity was not shown in all groups. Accordingly, it is expected that the PPy-coated PCL fibers may be applied as a material for guided tissue regeneration. This is because of improved hydrophilicity and electrical conductivity without deteriorated physical properties and cytotoxicity.

Purpose: The aim of this study is to demonstrate the association between recurrent atrial fibrillation (AF) and left ventricular (LV) adverse remodeling after catheter ablation and to evaluate the change of myocardial extracellular volume fraction (ECV) by catheter ablation outcomes. Materials and Methods: We retrospectively recruited 60 patients (44 men and 16 women) with a median age of 57 years (range, 32–78 years) who underwent cardiac MRI before and at 6–12 months after catheter ablation of AF. Cardiac MRI quantified myocardial ECV (%) in the left ventricle. Depending on myocardial ECV after catheter ablation, patients were divided into two groups: 1) LV adverse remodeling with ECV ≥ 28%; and 2) no adverse LV remodeling with ECV < 28%. Multivariable analysis was performed to assess the association between recurrent AF and LV remodeling. Results: Of 60 patients, 21 (35%) were in the LV adverse remodeling group (mean ECV ± standard deviation [SD]: 29.8% ± 1.4%) and 39 (65%) were in the no adverse LV remodeling group (mean ECV ± SD: 24.7% ± 1.5%). The incidence of recurrent AF was significantly greater in the LV adverse remodeling group than in the no adverse LV remodeling group (81% vs. 13%, p < 0.001). In patients with recurrent AF, mean myocardial ECV significantly increased from 27.7% ± 2.3% to 29.2% ± 2.3% (p = 0.004) after catheter ablation. In a multivariable analysis after adjusting sex, age, and myocardial ECV before catheter ablation, recurrent AF was independently associated with LV adverse remodeling after catheter ablation (odds ratio: 28.9, 95% confidence interval: 6.8–121.7, p < 0.001). Conclusion When monitoring with cardiac MRI, sustained AF was significantly associated with LV adverse remodeling through an increase in myocardial ECV after catheter ablation of AF.

Background: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). Methods: An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. Results: The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (−14.90–27.61), 6.21% (−9.62–22.03), and 2.68% (−8.57–13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. Conclusion Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

Objective: To assess the effect of left atrial appendage (LAA) isolation on LAA emptying and left atrial (LA) function using cardiac MRI in patients who underwent successful catheter ablation of atrial fibrillation (AF). Materials and Methods: This retrospective study included 84 patients (mean age, 59 ± 10 years; 67 males) who underwent cardiac MRI after successful catheter ablation of AF. According to the electrical activity of LAA after catheter ablation, patients showed either LAA isolation or LAA normal activity. The LAA emptying phase (LAA-EP, in the systolic phase [SP] or diastolic phase), LAA emptying flux (LAA-EF, mL/s), and LA ejection fraction (LAEF, %) were evaluated by cardiac MRI. Results: Of the 84 patients, 61 (73%) and 23 (27%) patients showed LAA normal activity and LAA isolation, respectively.Incidence of LAA emptying in SP was significantly higher in LAA isolation (91% vs. 0%, p < 0.001) than in LAA normal activation. LAA-EF was significantly lower in LAA isolation (40.1 ± 16.2 mL/s vs. 80.2 ± 25.1 mL/s, pp < 0.001) than in LAA normal activity. Furthermore, LAEF was significantly lower in LAA isolation (23.7% ± 11.2% vs. 31.1% ± 16.6%, p = 0.04) than in LAA normal activity. Multivariate analysis demonstrated that the LAA-EP was independent from LAEF (p = 0.01). Conclusion LAA emptying in SP may be a critical characteristic of LAA isolation, and it may adversely affect the LAEF after catheter ablation of AF.

Objective: To assess the effect of left atrial appendage (LAA) isolation on LAA emptying and left atrial (LA) function using cardiac MRI in patients who underwent successful catheter ablation of atrial fibrillation (AF). Materials and Methods: This retrospective study included 84 patients (mean age, 59 ± 10 years; 67 males) who underwent cardiac MRI after successful catheter ablation of AF. According to the electrical activity of LAA after catheter ablation, patients showed either LAA isolation or LAA normal activity. The LAA emptying phase (LAA-EP, in the systolic phase [SP] or diastolic phase), LAA emptying flux (LAA-EF, mL/s), and LA ejection fraction (LAEF, %) were evaluated by cardiac MRI. Results: Of the 84 patients, 61 (73%) and 23 (27%) patients showed LAA normal activity and LAA isolation, respectively.Incidence of LAA emptying in SP was significantly higher in LAA isolation (91% vs. 0%, p < 0.001) than in LAA normal activation. LAA-EF was significantly lower in LAA isolation (40.1 ± 16.2 mL/s vs. 80.2 ± 25.1 mL/s, pp < 0.001) than in LAA normal activity. Furthermore, LAEF was significantly lower in LAA isolation (23.7% ± 11.2% vs. 31.1% ± 16.6%, p = 0.04) than in LAA normal activity. Multivariate analysis demonstrated that the LAA-EP was independent from LAEF (p = 0.01). Conclusion LAA emptying in SP may be a critical characteristic of LAA isolation, and it may adversely affect the LAEF after catheter ablation of AF.

OBJECTIVE: To simulate the B₁-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: B₁-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R₁, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (K(trans), v(e), and v(p)). RESULTS: B₁-inhomogeneity resulted in −23–5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: −16.7–61.8% and −16.7–61.8% for the pre-contrast R₁, −1.0–0.3% and −5.2–1.3% for the contrast-enhancement ratio, and −14.2–58.1% and −14.1–57.8% for the Gd-concentration, respectively. These resulted in −43.1–48.4% error for K(trans), −32.3–48.6% error for the v(e), and −43.2–48.6% error for v(p). The pre-contrast R₁ was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a −10% FA error led to a 23.6% deviation in the pre-contrast R₁, −0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a −10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were −23.7% for K(trans), −23.7% for v(e), and −23.7% for v(p). CONCLUSION: Even a small degree of B₁-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R₁ calculations to FA deviations is a significant cause of the miscalculation.
Brain ; Gray Matter ; Magnetic Resonance Imaging* ; Monte Carlo Method ; Phantoms, Imaging