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Background: In cardiac T1 mapping, a series of T1‐weighted (T1w) images are collected and numerically fitted to a two or three‐parameter model of the signal recovery to estimate voxel‐wise T1 values. To reduce the scan time, one can collect fewer T1w images, albeit at the cost of precision or/and accuracy. Recently, the feasibility of using a neural network instead of conventional two‐ or three‐parameter fit modeling has been demonstrated. However, prior studies used data from a single vendor and field strength; therefore, the generalizability of the models has not been established. Purpose: To develop and evaluate an accelerated cardiac T1 mapping approach based on MyoMapNet, a convolution neural network T1 estimator that can be used across different vendors and field strengths by incorporating the relevant scanner information as additional inputs to the model. Study Type: Retrospective, multicenter. Population: A total of 1423 patients with known or suspected cardiac disease (808 male, 57 ± 16 years), from three centers, two vendors (Siemens, Philips), and two field strengths (1.5 T, 3 T). The data were randomly split into 60% training, 20% validation, and 20% testing. Field Strength/Sequence: A 1.5 T and 3 T, Modified Look‐Locker inversion recovery (MOLLI) for native and postcontrast T1. Assessment: Scanner‐independent MyoMapNet (SI‐MyoMapNet) was developed by altering the deep learning (DL) architecture of MyoMapNet to incorporate scanner vendor and field strength as inputs. Epicardial and endocardial contours and blood pool (by manually drawing a large region of interest in the blood pool) of the left ventricle were manually delineated by three readers, with 2, 8, and 9 years of experience, and SI‐MyoMapNet myocardial and blood pool T1 values (calculated from four T1w images) were compared with conventional MOLLI T1 values (calculated from 8 to 11 T1w images). Statistical Tests: Equivalency test with 95% confidence interval (CI), linear regression slope, Pearson correlation coefficient (r), Bland–Altman analysis. Results: The proposed SI‐MyoMapNet successfully created T1 maps. Native and postcontrast T1 values measured from SI‐MyoMapNet were strongly correlated with MOLLI, despite using only four T1w images, at both field‐strengths and vendors (all r > 0.86). For native T1, SI‐MyoMapNet and MOLLI were in good agreement for myocardial and blood T1 values in institution 1 (myocardium: 5 msec, 95% CI [3, 8]; blood: −10 msec, 95%CI [−16, −4]), in institution 2 (myocardium: 6 msec, 95% CI [0, 11]; blood: 0 msec, [−18, 17]), and in institution 3 (myocardium: 7 msec, 95% CI [−8, 22]; blood: 8 msec, [−14, 30]). Similar results were observed for postcontrast T1. Data Conclusion: Inclusion of field strength and vendor as additional inputs to the DL architecture allows generalizability of MyoMapNet across different vendors or field strength. Evidence Level: 2. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Background: In cardiac T 1 mapping, a series of T 1 -weighted (T 1 w) images are collected and numerically fitted to a two or three-parameter model of the signal recovery to estimate voxel-wise T 1 values. To reduce the scan time, one can collect fewer T 1 w images, albeit at the cost of precision or/and accuracy. Recently, the feasibility of using a neural network instead of conventional two- or three-parameter fit modeling has been demonstrated. However, prior studies used data from a single vendor and field strength; therefore, the generalizability of the models has not been established., Purpose: To develop and evaluate an accelerated cardiac T 1 mapping approach based on MyoMapNet, a convolution neural network T 1 estimator that can be used across different vendors and field strengths by incorporating the relevant scanner information as additional inputs to the model., Study Type: Retrospective, multicenter., Population: A total of 1423 patients with known or suspected cardiac disease (808 male, 57 ± 16 years), from three centers, two vendors (Siemens, Philips), and two field strengths (1.5 T, 3 T). The data were randomly split into 60% training, 20% validation, and 20% testing., Field Strength/sequence: A 1.5 T and 3 T, Modified Look-Locker inversion recovery (MOLLI) for native and postcontrast T 1 ., Assessment: Scanner-independent MyoMapNet (SI-MyoMapNet) was developed by altering the deep learning (DL) architecture of MyoMapNet to incorporate scanner vendor and field strength as inputs. Epicardial and endocardial contours and blood pool (by manually drawing a large region of interest in the blood pool) of the left ventricle were manually delineated by three readers, with 2, 8, and 9 years of experience, and SI-MyoMapNet myocardial and blood pool T 1 values (calculated from four T 1 w images) were compared with conventional MOLLI T 1 values (calculated from 8 to 11 T 1 w images)., Statistical Tests: Equivalency test with 95% confidence interval (CI), linear regression slope, Pearson correlation coefficient (r), Bland-Altman analysis., Results: The proposed SI-MyoMapNet successfully created T 1 maps. Native and postcontrast T 1 values measured from SI-MyoMapNet were strongly correlated with MOLLI, despite using only four T 1 w images, at both field-strengths and vendors (all r > 0.86). For native T 1 , SI-MyoMapNet and MOLLI were in good agreement for myocardial and blood T 1 values in institution 1 (myocardium: 5 msec, 95% CI [3, 8]; blood: -10 msec, 95%CI [-16, -4]), in institution 2 (myocardium: 6 msec, 95% CI [0, 11]; blood: 0 msec, [-18, 17]), and in institution 3 (myocardium: 7 msec, 95% CI [-8, 22]; blood: 8 msec, [-14, 30]). Similar results were observed for postcontrast T 1 ., Data Conclusion: Inclusion of field strength and vendor as additional inputs to the DL architecture allows generalizability of MyoMapNet across different vendors or field strength., Evidence Level: 2., Technical Efficacy: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)