Quantitative inversion time prescription for myocardial late gadolinium enhancement using T1-mapping-based synthetic inversion recovery imaging: reducing subjectivity in the estimation of inversion time

Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment

PURPOSE

Joint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging.

METHODS

The proposed algorithm first localizes the left ventricle using a U-Net architecture. The localized left cavity centroid is extracted and a squared region of interest ("focus box") is created around the resulting pixel. The focus box is then propagated on each image and the sum of the pixel intensity inside is computed. The smallest sum corresponds to the image with the lowest intensity signal within the blood pool and healthy myocardium, which will provide an ideal scar-to-blood contrast. The image's corresponding TI is considered optimal. The U-Net was trained to segment the epicardium in 177 patients with binary cross-entropy loss. The algorithm was validated retrospectively in 152 patients, and the agreement between the algorithm and two magnetic resonance (MR) operators' prediction of TI values was calculated using the Fleiss' kappa coefficient. Thirty focus box sizes, ranging from 2.3mm2 to 20.3cm2, were tested. Processing times were measured.

RESULTS

The U-Net's Dice score was 93.0 ± 0.1%. The proposed algorithm extracted TI values in 2.7 ± 0.1 s per patient (vs. 16.0 ± 8.5 s for the operator). An agreement between the algorithm's prediction and the MR operators' prediction was found in 137/152 patients (κ= 0.89), for an optimal focus box of size 2.3cm2.

CONCLUSION

The proposed fully-automated algorithm has potential of reducing uncertainties, variability, and workload inherent to manual approaches with promise for future clinical implementation for joint bright- and black-blood MRI.

Fully-modelled blood-focused variable inversion times for 3D late gadolinium-enhanced imaging

PURPOSE

Variable heart rate during single-cycle inversion-recovery Late Gadolinium-Enhanced (LGE) scanning degrades image quality, which can be mitigated using Variable Inversion Times (VTIs) in real-time response to R-R interval changes. We investigate in vivo and in simulations an extension of a single-cycle VTI method previously applied in 3D LGE imaging, that now fully models the longitudinal magnetisation (fmVTI).

METHODS

The VTI and fmVTI methods were used to perform 3D LGE scans for 28 3D LGE patients, with qualitative image quality scores assigned for left atrial wall clarity and total ghosting. Accompanying simulations of numerical phantom images were assessed in terms of ghosting of normal myocardium, blood, and myocardial scar.

RESULTS

The numerical simulations for fmVTI showed a significant decrease in blood ghosting (VTI: 410 ± 710, fmVTI: 68 ± 40, p < 0.0005) and scar ghosting (VTI: 830 ± 1300, fmVTI: 510 ± 730, p < 0.02). Despite this, there was no significant change in qualitative image quality scores, either for left atrial wall clarity (VTI: 2.0 ± 1.0, fmVTI: 1.8 ± 1.0, p > 0.1) or for total ghosting (VTI: 1.9 ± 1.0, fmVTI: 2.0 ± 1.0, p > 0.7).

CONCLUSIONS

Simulations indicated reduced ghosting with the fmVTI method, due to reduced Mz variability in the blood signal. However, other sources of phase-encode ghosting and blurring appeared to dominate and obscure this finding in the patient studies available.

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET)

PURPOSE

Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TINP ) to suppress the background myocardial signal is required. The purpose of this study was to assess the feasibility of automated selection of TINP using a convolutional neural network (CNN). We hypothesized that a CNN may use spatial and temporal imaging characteristics from an inversion-recovery scout to select TINP , without the aid of a human observer.

METHODS

We retrospectively collected 425 clinically acquired cardiac MRI exams performed at 1.5 T that included inversion-recovery scout acquisitions. We developed a VGG19 classifier ensembled with long short-term memory to identify the TINP . We compared the performance of the ensemble CNN in predicting TINP against ground truth, using linear regression analysis. Ground truth was defined as the expert physician annotation of the optimal TI. In a backtrack approach, saliency maps were generated to interpret the classification outcome and to increase the model's transparency.

RESULTS

Prediction of TINP from our ensemble VGG19 long short-term memory closely matched with expert annotation (ρ = 0.88). Ninety-four percent of the predicted TINP were within ±36 ms, and 83% were at or after expert TI selection.

CONCLUSION

In this study, we show that a CNN is capable of automated prediction of myocardial TI from an inversion-recovery experiment. Merging the spatial and temporal characteristics of the VGG-19 and long short-term-memory CNN structures appears to be sufficient to predict myocardial TI from TI scout.