Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.

Background: The 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. Methods: Eighty-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. Results: The 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. Conclusions: The 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.

Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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, r = 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, r = 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). CONCLUSION: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

Myocardial T2* Imaging at 3T and 1.5T: A Pilot Study with Phantom and Normal Myocardium.

BACKGROUND: Myocardial T2* mapping at 1.5T remains the gold standard, but the use of 3T scanners is increasing. We aimed to determine the conversion equations in different scanners with clinically available, vendor-provided T2* mapping sequences using a phantom and evaluated the feasibility of the phantom-based conversion method. METHODS: T2* of a phantom with FeCl3 (five samples, 3.53-20.09 mM) were measured with 1.5T (MR-A1) and 3T scanners (MR-A2, A3, B), and the site-specific equation was determined. T2* was measured in the interventricular septum of three healthy volunteers at 1.5T (T2*1.5T, MR-A1) and 3T (T2*3.0T, MR-B). T2*3.0T was converted based on the equation derived from the phantom (T2*eq). RESULTS: R2* at 1.5T and 3T showed linear association, but a different relationship was observed according to the scanners (MR-A2, R2*1.5T = 0.76 × R2*3.0T - 2.23, R2 = 0.999; MR-A3, R2*1.5T = 0.95 × R2*3.0T - 34.28, R2 = 0.973; MR-B, R2*1.5T = 0.76 × R2*3.0T - 3.02, R2 = 0.999). In the normal myocardium, T2*eq and T2*1.5T showed no significant difference (35.5 ± 3.5 vs. 34.5 ± 1.2, p = 0.340). The mean squared error between T2*eq and T2*1.5T was 16.33, and Bland-Altman plots revealed a small bias (-0.94, 95% limits of agreement: -8.86-6.99). CONCLUSIONS: a phantom-based, site-specific equation can be utilized to estimate T2* values at 1.5T in centers where only 3T scanners are available.

Stiffness measurement using terahertz and acoustic waves for biological samples.

A method is proposed to measure sample stiffness using terahertz wave and acoustic stimulation. The stiffness-dependent vibration is measured using terahertz wave (T-ray) during an acoustic stimulation. To quantify the vibration, time of the peak amplitude of the reflected T-ray is measured. In our experiment, the T-ray is asynchronously applied during the period of the acoustic stimulation, and multiple measurements are taken to use the standard deviation and the maximum difference in the peak times to estimate the amplitude of the vibration. Some preliminary results are shown using biological samples.

Field inhomogeneity correction using partial differential phases in magnetic resonance imaging.

Correction of an inhomogeneous magnetic field is proposed using partial differential phases in magnetic resonance imaging. Estimation of the inhomogeneous magnetic field from a measured phase is not an easy task due to phase wrapping and chemical-dependent phase shifts. Using the proposed partial differential phase technique, such problems are resolved. The proposed technique uses most of the 3D pixel data regardless of chemical compounds for the estimation of the inhomogeneous magnetic field. A large number of partial difference data compared to the number of expansion terms for the model of inhomogeneous magnetic field provides a very stable estimation, robust to noise. The technique is applicable to in vivo shimming, water-fat imaging, eddy current compensation, and most phase-related measurements and imaging. The efficacy of the proposed technique is demonstrated with in vivo water-fat imaging.