Browsing by Author "Munoz C."

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    Coronary Magnetic Resonance Angiography: Technical Innovations Leading Us to the Promised Land?
    (Elsevier Inc., 2020) Hajhosseiny R.; Bustin A.; Munoz C.; Rashid I.; Cruz G.; Prieto C.; Botnar R.M.; Hajhosseiny R.; Manning W.J.; Prieto C.; Botnar R.M.
    © 2020 American College of Cardiology FoundationCoronary artery disease remains the leading cause of cardiovascular morbidity and mortality. Invasive X-ray angiography and coronary computed tomography angiography are established gold standards for coronary luminography. However, they expose patients to invasive complications, ionizing radiation, and iodinated contrast agents. Among a number of imaging modalities, coronary cardiovascular magnetic resonance (CMR) angiography may be used in some cases as an alternative for the detection and monitoring of coronary arterial stenosis, with advantages including its versatility, excellent soft tissue characterization, and avoidance of ionizing radiation and iodinated contrast agents. In this review, we explore the recent advances in motion correction, image acceleration, and reconstruction technologies that are bringing coronary CMR angiography closer to widespread clinical implementation.
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    Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute
    (John Wiley and Sons Inc, 2021) Küstner T.; Munoz C.; Psenicny A.; Bustin A.; Fuin N.; Qi H.; Neji R.; Kunze K.; Hajhosseiny R.; Prieto C.; Botnar R.; Küstner T.; Bustin A.; Neji R.; Kunze K.; Prieto C.; Botnar R.
    © 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in MedicinePurpose: To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute. Methods: Undersampled motion-corrected reconstructions have enabled free-breathing isotropic 3D CMRA in ~5-10 min acquisition times. In this work, we propose a deep-learning–based SR framework, combined with non-rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16-fold increase in spatial resolution is achieved by reconstructing a high-resolution (HR) isotropic CMRA (0.9 mm3 or 1.2 mm3) from a low-resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm3 or 1.2 × 4.8 × 4.8 mm3). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch-level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR-CMRA in ~50 s under free-breathing. Vessel sharpness and length of the coronary arteries from the SR-CMRA is compared against the HR-CMRA. Results: SR-CMRA showed statistically significant (P <.001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR-CMRA. Good generalization to input resolution and image/patch-level processing was found. SR-CMRA enabled recovery of coronary stenosis similar to HR-CMRA with comparable qualitative performance. Conclusion: The proposed SR-CMRA provides a 16-fold increase in spatial resolution with comparable image quality to HR-CMRA while reducing the predictable scan time to <1 min.
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    News Gathering: Leveraging Transformers to Rank News
    (Springer Science and Business Media Deutschland GmbH, 2024) Munoz C.; Apolo M.J.; Ojeda M.; Lobel H.; Mendoza M.
    News media outlets disseminate information across various platforms. Often, these posts present complementary content and perspectives on the same news story. However, to compile a set of related news articles, users must thoroughly scour multiple sources and platforms, manually identifying which publications pertain to the same story. This tedious process hinders the speed at which journalists can perform essential tasks, notably fact-checking. To tackle this problem, we created a dataset containing both related and unrelated news pairs. This dataset allows us to develop information retrieval models grounded in the principle of binary relevance. Recognizing that many Transformer-based models might be suited for this task but could overemphasize relationships based on lexical connections, we tailored a dataset to fine-tune these models to focus on semantically relevant connections in the news domain. To craft this dataset, we introduced a methodology to identify pairs of news stories that are lexically similar yet refer to different events and pairs that discuss the same event but have distinct lexical structures. This design compels Transformers to recognize semantic connections between stories, even when their lexical similarities might be absent. Following a human-annotation assessment, we reveal that BERT outperformed other techniques, excelling even in challenging test cases. To ensure the reproducibility of our approach, we have made the dataset and top-performing models publicly available.