Introduction

Cardiac T₁ mapping using the Modified Look-Locker inversion recovery (MOLLI) allows non-invasive quantification of interstitial diffuse fibrosis (1). The original MOLLI-3(3)3(3)5 scheme consists of three LL experiments with a waiting period of 3 rest heartbeats between each LL for magnetization recovery (2). Subsequently, MOLLI-5(3)3 and MOLLI-4(1)3(1)2 were implemented to improve acquisition efficiency (3). Pixel-wise curve fitting algorithm using 3-parameter model is applied to estimate T₁ values at each pixel. In this study, we sought to develop a fully-connected neural network (MyoMapNet)-based rapid myocardial T₁ mapping approach using a single LL experiment with scan duration of 4-5 heartbeats and near-instantaneous (~2ms) reconstruction time.

Methods

The data acquisition for MyoMapNet consists of collecting 5 (native) or 4 (post-contrast) T₁ weighted images after application of an inversion pulse. Images are collected using a single-shot acquisition during the quiescent period in mid-diastole. Figure 1 shows the MyoMapNet architecture for T₁ estimation from T₁-weighted images. MyoMapNet is a fully connected neural network that consists of input, output, and 5 hidden layers. The input layer has 2N_t nodes for N_t T₁ weighted signals and their corresponding inversion times, where N_t=5 or 4 in native or post-contrast T₁ networks, respectively. The number of nodes in the five hidden layers are 400, 400, 200, 200, and 100, respectively, with a leaky-ReLU after each hidden layer. Stochastic gradient descent optimizer with learning-rate of 1E-8 and momentum of 0.8 was used to train the network for 2000 epochs with batch-size of 80 maps.
Simulation experiments: Simulated MOLLI-5(3)3 dataset (80,000 samples) was generated and divided into training (80%; 64,000 samples) and testing (20%; 16,000 samples) signal-intensity time courses. Simulated T₁­ ranged from 400ms to 2000ms with an increment of 0.1 ms, T₂ of 42 ms, and a fixed heart-rate of 60 bpm. Different Gaussian noise levels were added to the simulated signals for SNR of 20, 40, and 100.
In-vivo experiments: T₁ mapping using MOLLI was collected in 717 subjects (386 males, 55±16.5 years old) with a subset of 535 subjects (232 male, 56.5±15 years old) with both native and post-contrast T₁ maps using 3T scanner (MAGNETOM Vida, Siemens, Erlangen, Germany). All data were extracted retrospectively from clinical patients who were referred for a clinical CMR exam. Study protocol was approved by the Institutional Review Board and written consent was waived. MOLLI-5(3)3 and MOLLI-4(1)3(1)2 T₁ was calculated off-line using 3-parameter curve-fitting model. Training datasets of 573 patients (1719 native T₁ maps) and 428 patients (1281 post-contrast T₁ maps) were randomly selected for native and post-contrast maps reconstruction, respectively. The performance of MyoMapNet was evaluated using a testing dataset of native T₁ maps from 144 patients (432 MOLLI-5(3)3 T₁ maps) and post-contrast T₁ maps from 107 patients (324 MOLLI-4(1)3(1)2 T₁ maps).
Data Analysis: For each T₁ map, we calculated 3 sets of T₁ estimates: (a) standard MOLLI T₁ mapping using all collected images (8, or 9 images in native or post-contrast T₁ mapping, respectively), (b) abbreviated MOLLI using only 5 native (MOLLI-5) or 4 post-contrast MOLLI (MOLLI-4) images using 3-parameter curve-fitting, and (c) MyoMapNet using only 5 native or 4 post-contrast MOLLI images. The ECV values were calculated for 75 patients who had both native and post-contrast T₁ mapping in the testing dataset.

Results

Figure 2 shows Bland-Altman plots of MOLLI-5 and MyoMapNet at different SNR levels of simulated data. For higher SNR, MOLLI-5 exhibits systematic errors as a function of T₁ values while MyoMapNet shows no systematic error. As SNR decreases, MyoMapNet significantly reduce the estimation errors compared to MOLLI-5 (0±15ms vs. -3±32ms; p-value<0.001) at SNR=40.
Figure 3 shows example native T₁ maps reconstructed with the MOLLI-5(3)3, MOLLI-5, and MyoMapNet in three subjects. MyoMapNet showed sharp myocardial T₁ boundaries with more homogeneous T₁ estimations than MOLLI-5 method. MyoMapNet was less sensitive to motion artifacts in cases with breath-holding failure (Figure 4).
In patient data, the mean T₁ values estimated by MyoMapNet was similar to standard MOLLI methods (1200±45ms and 1199±46ms; p-value=0.3) in native T₁, (563±45ms and 565±47ms; p-value=0.07) in post-contrast T₁, and (27±4% and 27±4%; p-value=0.4) in ECV values. MyoMapNet estimated T₁ values had smaller error than MOLL-5 (1±17ms vs. 31±34ms, p-value<0.001) in native T₁, (-3±18ms vs. -17±23ms, p-value<0.001) for post-contrast, and (0.1±1.3% vs. 1.9±2.5%, p-value<0.001) for ECV values (Figure 5).
The average time for estimating T₁ per map was ~25sec by a 3-parameter fitting model without motion correction using Matlab on CPU and 2ms by MyoMapNet using Python on GPU.

Conclusion

MyoMapNet enables fast and precise T₁ mapping quantification from only 4-5 T₁-weighted images, leading to shorter scan time and breath-holds of 4-5 heartbeats.

Acknowledgements

No acknowledgement found.

References

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2. Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP. Modified Look-Locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn. Reson. Med. 2004;52:141–146.
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