We sought to investigate the optimal method of quantifying late gadolinium enhancement (LGE) in cardiac sarcoidosis (CS) using cardiac magnetic resonance imaging (MRI). We retrospectively studied 53 patients with CS. LGE quantitation was performed using (a) semi-automated segmentation using Signal Threshold versus Reference Mean (STRM) cutoffs of > 2, > 3 and > 5 standard deviations (SD); (b) full-width-half-max (FWHM) method and (c) manual segmentation (MS) of affected myocardial segments. Primary outcome was a composite of cardiovascular death and ventricular tachyarrhythmia (VTA). A multivariate regression analysis was performed comparing the techniques adjusting for age, gender, NYHA class and LVEF. Mean age was 56.3 ± 12 years, 71.6% males, 66% white. Mean LVEF was 45.1% ± 14.7%. Over median follow-up of 28.1 months, 2 patients had cardiac death (3.7%) and 8 (15.1%) had VTA. On multivariate analysis, MS, > 2SD, > 3SD, > 5SD and FWHM had OR of 1.39 [CI 1.04-1.79], 1.09 [CI 0.99-1.21], 1.15 [CI 1.03-1.29], 1.16 [CI 1.04-1.27] and 1.08 [CI 0.96-1.21], respectively, for predicting the composite outcome. ROC curve analysis showed MS to have the highest AUC 0.89 followed by 0.81 for > 3SD and > 5SD, 0.75 for > 2SD and lowest 0.69 for FWHM method. Reproducibility was lower for manual method (ICC 0.7) than for > 3SD (ICC 0.991) and > 5SD (ICC 0.997). CS quantitation of LGE with MS or semi-automated quantitation with STRM > 3SD or > 5SD was significantly associated with the composite outcome of cardiac death and VTA. Semi-automated quantitation with STRM > 3SD provided the best combination of accuracy and reproducibility.
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http://dx.doi.org/10.1007/s10554-022-02751-4 | DOI Listing |
Eur Radiol
December 2024
Sorbonne Université, Laboratoire d'Imagerie Biomédicale, Inserm, CNRS, Paris, France.
Objectives: The aim of this study was to compare CMR imaging biomarkers between SLE patients and matched controls.
Materials And Methods: Electronic databases were systematically searched from inception until November 2023. All studies reporting CMR imaging data in SLE patients were included.
Radiol Cardiothorac Imaging
December 2024
From the Department of Radiology, Zhongshan Hospital, Fudan University, Xuhui District, Shanghai 200032, China (X.Q., S.W., Y.W., X.M., Y.C., H.L., H.J., M.Z.); Shanghai Institute of Medical Imaging, Shanghai, China (X.Q., S.W., Y.W., X.M., Y.C., H.L., H.J., M.Z.); and United Imaging Health Care Group, Shanghai, China (R.W., D.W., F.W., S.Z., J.H.).
Purpose To compare the acquisition time, image quality, and late gadolinium enhancement (LGE) visualization and quantification on phase-sensitive inversion recovery (PSIR) images using 5.0-T versus 3.0-T cardiac MRI.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Japan.
: This study investigated the relationship between LA (LA) enhancement on three-dimensional (3D) late gadolinium enhancement (LGE) MRI and recurrence after catheter ablation in patients with AF (AF). : A total of one hundred patients with AF (mean age: 68 ± 9 years, 50% with paroxysmal AF) were included in this study. Each patient underwent a high-resolution 3D LGE MRI prior to catheter ablation, allowing for detailed imaging of the LA wall.
View Article and Find Full Text PDFJ Cardiovasc Magn Reson
December 2024
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China. Electronic address:
Background: Cardiac involvement in light chain amyloidosis (AL) is the main determinant of prognosis. Amyloid can be deposited in the extracellular space and cause an increase in extracellular volume fraction (ECV). At the same time, amyloid can also be deposited in the wall of small vessels and cause microvascular dysfunction.
View Article and Find Full Text PDFMach Learn Med Imaging
October 2024
Scientific Computing and Imaging Institute, University of Utah, UT, USA.
The study of physiology demonstrates that the form (shape) of anatomical structures dictates their functions, and analyzing the form of anatomies plays a crucial role in clinical research. Statistical shape modeling (SSM) is a widely used tool for quantitative analysis of forms of anatomies, aiding in characterizing and identifying differences within a population of subjects. Despite its utility, the conventional SSM construction pipeline is often complex and time-consuming.
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