Browsing by Author "Rodríguez Palomares, José"

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    Automated Segmentation of Thoracic Aortic Lumen and Vessel Wall on 3D Bright- and Black-Blood MRI using nnU-Net
    (2025) Cesario, Matteo; Littlewood, Simon J.; Nadel, James; Fletcher, Thomas J.; Fotaki, Anastasia; Castillo Passi, Carlos; Hajhosseiny, Reza; Pouliopoulos, Jim; Jabbour, Andrew; Olivero, Ruperto; Rodríguez Palomares, José; Kooi, M. Eline; Prieto Vásquez, Claudia; Botnar, René Michael
    BACKGROUND: Magnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation; a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for automatic segmentation of the aortic lumen and vessel wall in high-resolution ECG-triggered free-breathing respiratory motion-corrected 3D bright- and black-blood MRA images. METHODS: Manual segmentation, serving as the ground truth, was performed on 25 bright-blood and 15 black-blood 3D MRA image sets acquired with the iT2PrepIR-BOOST sequence (1.5T) in thoracic aortopathy patients. The training was performed with nnU-Net for bright-blood (lumen) and black-blood image sets (lumen and vessel wall). Training consisted of a 70:20:10% training: validation: testing split. Inference was run on datasets (single vendor) from different centres (UK, Spain, and Australia), sequences (iT2PrepIR-BOOST, T2 prepared CMRA, and TWIST MRA), acquired resolutions (from 0.9 mm 3 to 3 mm 3), and field strengths (0.55T, 1.5T, and 3T). Predictive measurements comprised Dice Similarity Coefficient (DSC), and Intersection over Union (IoU). Postprocessing (3D slicer) included centreline extraction, diameter measurement, and curved planar reformatting (CPR). RESULTS: The optimal configuration was the 3D U-Net. Bright blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm 3) and 3D CMRA datasets (0.9 mm 3) resulted in DSC ≥ 0.96 and IoU ≥ 0.92. For bright-blood segmentation on 3D CMRA at 0.55T, the nnUNet achieved DSC and IoU scores of 0.93 and 0.88 at 1.5 mm³, and 0.68 and 0.52 at 3.0 mm³, respectively. DSC and IoU scores of 0.89 and 0.82 were obtained for CMRA image sets (1 mm 3) at 1.5T (Barcelona dataset). DSC and IoU score of the BRnnUNet model were 0.90 and 0.82 respectively for the contrast-enhanced dataset (TWIST MRA). Lumen segmentation on black blood 1.5T iT2PrepIR-BOOST image sets achieved DSC ≥ 0.95 and IoU ≥ 0.90, and vessel wall segmentation resulted in DSC ≥ 0.80 and IoU ≥ 0.67. Automated centreline tracking, diameter measurement and CPR were successfully implemented in all subjects. CONCLUSION: Automated aortic lumen and wall segmentation on 3D bright- and black-blood image sets demonstrated excellent agreement with ground truth. This technique demonstrates a fast and comprehensive assessment of aortic morphology with great potential for future clinical application in various cardiovascular diseases.
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    The future of MRI in thoracic aortopathy: blueprint for the paradigm shift to improve management
    (2025) Nadel, James; Rodríguez Palomares, José; Phinikaridou, Alkystis; Prieto Vásquez, Claudia Del Carmen; Masci, Pier Giorgio; Botnar, René Michael
    Thoracic aortopathies result in aneurysmal expansion of the aorta that can lead to rapidly fatal aortic dissection or rupture. Despite the availability of abundant non-invasive imaging tools, the greatest contemporary challenge in the management of thoracic aortic aneurysm (TAA) is the lack of reliable metrics for risk stratification, with absolute aortic diameter, growth rate and syndromic factors remaining the primary determinants by which prophylactic surgical intervention is adjudged. Advanced MRI techniques present as a potential key to unlocking insights into TAA that could guide disease surveillance and surgical intervention. MRI has the capacity to encapsulate the aorta as a complex biomechanical structure, permitting the determination of aortic volume, morphology, composition, distensibility and fluid dynamics in a time-efficient manner. Nevertheless, current standard-of-care imaging protocols do not harness its full capacity. This state-of-the-art review explores the emerging role of MRI in the assessment of TAA and presents a blueprint for the required paradigm shift away from aortic size as the sole metric for risk stratifying TAA.