Abstract
Identifying the optimal Inversion Time (TI) is pivotal to null the myocardium and obtain high quality cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging. Setting the optimal TI can be challenging in some diseases and for less experienced operators. We propose an Artificial Intelligence (AI) tool to automatically predict the optimal TI in CMR-LGE imaging.
The AI tool, named THAITI, consists of a Random Forest regression model whose hyperparametrs were optimized by means of evolutionary computation. The model considers as input parameters patient-specific TI determinants, such as age, gender, weight, height, kidney function, heart rate, contrast dose, and time from injection to image acquisition. THAITI was trained on 155 patients (2588 CMR-LGE images) with mixed cardiac conditions who underwent CMR (1.5T Siemens AvantoFit; Gadovist; averaged, motion-corrected, free-breathing true-FISP IR). Clinical testing was performed on 55 matched patients, randomized to experimental (THAITI-set TI) vs control (experienced operator-set TI) group. A user interface was developed for clinical testing. Image quality was assessed blindly by 2 independent experienced operators.
THAITI Mean Squared Error (MSE) in the validation set was 4.7 and percentage of mispredicted TI of 4.5%. During clinical testing, LGE quality did not differ between the experimental vs control group: quality was “optimal” or “good” in 96% vs 93%, “poor” in 4% vs 7%. The average number of LGE images acquired and LGE imaging duration were similar (experimental vs control group: 17 ± 3 vs 16 ± 3 LGE images per patient; 12:14 vs 12:20 mm:ss, respectively).
THAITI efficiently predicts optimal TI for CMR-LGE imaging. Further development is needed to increase generalizability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to improve LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top panel: THAITI interface. Bottom panel: examples of experimental group LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ±SD
