RESUMO
PURPOSE: Wideband phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) enables myocardial scar imaging in implantable cardioverter defibrillators (ICD) patients, mitigating hyperintensity artifacts. To address subendocardial scar visibility challenges, a 2D breath-hold single-shot electrocardiography-triggered black-blood (BB) LGE sequence was integrated with wideband imaging, enhancing scar-blood contrast. METHODS: Wideband BB, with increased bandwidth in the inversion pulse (0.8-3.8 kHz) and T2 preparation refocusing pulses (1.6-5.0 kHz), was compared with conventional and wideband PSIR, and conventional BB, in a phantom and sheep with and without ICD, and in six patients with cardiac devices and known myocardial injury. ICD artifact extent was quantified in the phantom and specific absorption rate (SAR) was reported for each sequence. Image contrast ratios were analyzed in both phantom and animal experiments. Expert radiologists assessed image quality, artifact severity, and scar segments in patients and sheep. Additionally, histology was performed on the sheep's heart. RESULTS: In the phantom, wideband BB reduced ICD artifacts by 62% compared to conventional BB while substantially improving scar-blood contrast, but with a SAR more than 24 times that of wideband PSIR. Similarly, the animal study demonstrated a considerable increase in scar-blood contrast with wideband BB, with superior scar detection compared with wideband PSIR, the latter confirmed by histology. In alignment with the animal study, wideband BB successfully eliminated severe ICD hyperintensity artifacts in all patients, surpassing wideband PSIR in image quality and scar detection. CONCLUSION: Wideband BB may play a crucial role in imaging ICD patients, offering images with reduced ICD artifacts and enhanced scar detection.
RESUMO
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.