One-to-one methods take as input a single source contrast, and they learn a latent representation sensitive to unique features of the source. When a single target contrast is of interest, common approaches for multi-contrast MRI involve either one-to-one or many-to-one synthesis methods depending on their input. Synthesis of missing or corrupted contrasts from other high-quality ones can alleviate this limitation. Yet, the number and quality of contrasts are limited in practice by various factors including scan time and patient motion. Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. The proposed approach can improve the reliability of high-throughput image analysis in large-scale population studies, minimising the need for re-scanning patients or discarding incomplete acquisitions. For instance, the results obtained when compensating for the absence of two basal slices show that the mean differences to the reference of stroke volume and ejection fraction are only -1.3 mL and -1.0 %, respectively, which are significantly smaller than those calculated from the incomplete data (-26.8 mL and -6.7 %). The proposed network can infer multiple missing slices that are anatomically plausible and lead to improved accuracy of subsequent analyses on cardiac MRIs, e.g., ventricle segmentation, cardiac quantification compared to those derived from incomplete cardiac MR datasets. The imputation network is based on a dedicated conditional generative adversarial network (GAN) that helps retain key visual cues and fine structural details in the synthesised image slices. The detection model comprises several dense blocks containing convolutional long short-term memory (ConvLSTM) layers, to leverage through-plane contextual and sequential ordering information of slices in cine MR data and achieve reliable classification results. To address this problem, we propose an effective two-stage pipeline for detecting and synthesising absent slices in both the apical and basal region. However, this requirement cannot be guaranteed when acquiring images in the presence of motion induced by cardiac muscle contraction and respiration. Cardiac MR acquisition with complete coverage from base to apex is required to ensure accurate subsequent analyses, such as volumetric and functional measurements.
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