T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the ...automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset.
CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed.
DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1,
= 0.967 (95% confidence interval CI, 0.951-0.978) and bias of 9.5 msec (95% limits of agreement LOA, -23.6-42.6 msec); for ECV,
= 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient ICC of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively).
The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.
Contrast agents for magnetic resonance imaging (MRI) improve anatomical visualizations. However, owing to poor image resolution in whole-body MRI, resolving fine structures is challenging. Here, we ...report that a nanoparticle with a polysaccharide supramolecular core and a shell of amorphous-like hydrous ferric oxide generating strong T
MRI contrast (with a relaxivity coefficient ratio of ~1.2) facilitates the imaging, at resolutions of the order of a few hundred micrometres, of cerebral, coronary and peripheral microvessels in rodents and of lower-extremity vessels in rabbits. The nanoparticle can be synthesized at room temperature in aqueous solution and in the absence of surfactants, has blood circulation and renal clearance profiles that prevent opsonization, and leads to better imaging performance than Dotarem (gadoterate meglumine), a clinically approved gadolinium-based MRI contrast agent. The nanoparticle's biocompatibility and imaging performance may prove advantageous in a broad range of preclinical and clinical applications of MRI.
BackgroundThe reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional ...datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers.MethodsEighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment.ResultsThe average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values.ConclusionsThe DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.
This study examines the relationship between safety factors and health management systems based on accident statistics in the construction industry stipulated in the Serious Disaster Punishment Act. ...To determine the level of safety achieved by companies through their health management system, the top 1000 construction firms in the country were surveyed online. Four hundred sixty companies responded to the survey by providing their statistics on major accidents (mortality, accidental mortality, and injury rates). Statistical tests showed that companies with a team dedicated to the oversight of safety and health management had fewer accidents than those without one. Factor and regression analyses revealed that three factors affected the mortality and accident rates: safety and health plan, safety and health professionals, and safety and health activities. Moreover, two factors significantly influenced the injury rate: safety management supported by a cooperative company and implementation of on-site safety and health activities. The findings of this study can be used as a fundamental reference for further research and consultation on the formulation of safety and health management systems for construction companies.
Purpose: Myocardial T1 and T2 relaxation times are affected by technical factors such as cardiovascular magnetic resonance platform/vendor. We aimed to validate T1 and T2 mapping sequences using a ...phantom; establish reference T1, T2, and extracellular volume (ECV) measurements using two sequences at 3T in normal Koreans; and compare the protocols and evaluate the differences from previously reported measurements. Materials and Methods: Eleven healthy subjects underwent cardiac magnetic resonance imaging (MRI) using 3T MRI equipment (Verio, Siemens, Erlangen, Germany). We did phantom validation before volunteer scanning: T1 mapping with modified look locker inversion recovery (MOLLI) with 5(3)3 and 4(1)3(1)2 sequences, and T2 mapping with gradient echo (GRE) and TrueFISP sequences. We did T1 and T2 mappings on the volunteers with the same sequences. ECV was also calculated with both sequences after gadolinium enhancement. Results: The phantom study showed no significant differences from the gold standard T1 and T2 values in either sequence. Pre-contrast T1 relaxation times of the 4(1)3(1)2 protocol was 1142.27 ± 36.64 ms and of the 5(3)3 was 1266.03 ± 32.86 ms on the volunteer study. T2 relaxation times of GRE were 40.09 ± 2.45 ms and T2 relaxation times of TrueFISP were 38.20 ± 1.64 ms in each. ECV calculation was 24.42% ± 2.41% and 26.11% ± 2.39% in the 4(1)3(1)2 and 5(3)3 protocols, respectively, and showed no differences at any segment or slice between the sequences. We also calculated ECV from the pre-enhancement T1 relaxation time of MOLLI 5(3)3 and the post-enhancement T1 relaxation time of MOLLI 4(1)3(1)2, with no significant differences between the combinations. Conclusion: Using phantom-validated sequences, we reported the normal myocardial T1, T2, and ECV reference values of healthy Koreans at 3T. There were no statistically significant differences between the sequences, although it has limited statistical value due to the small number of subjects studied. ECV showed no significant differences between calculations based on various pre- and post-mapping combinations.
Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine ...images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.
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