Browsing by Author "Littlewood, Simon J."

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    3D joint T 1/T 1 ρ/T 2 mapping and water-fat imaging for contrast-agent free myocardial tissue characterization at 1.5T.
    (2025) Crabb, Michael G.; Kunze, Karl P.; Littlewood, Simon J.; Tripp, Donovan; Fotaki, Anastasia; Prieto Vásquez, Claudia; Botnar, René Michael
    PURPOSE: To develop a novel, free-breathing, 3D joint T 1 $$ {T}_1 $$ / T 1 ρ $$ {T}_{1\rho } $$ / T 2 $$ {T}_2 $$ mapping sequence with Dixon encoding to provide co-registered 3D T 1 $$ {T}_1 $$ , T 1 ρ $$ {T}_{1\rho } $$ , and T 2 $$ {T}_2 $$ maps and water-fat volumes with isotropic spatial resolution in a single ≈ 7 $$ \approx 7 $$ min scan for comprehensive contrast-agent-free myocardial tissue characterization and simultaneous evaluation of the whole-heart anatomy. METHODS: An interleaving sequence over 5 heartbeats is proposed to provide T 1 $$ {T}_1 $$ , T 1 ρ $$ {T}_{1\rho } $$ , and T 2 $$ {T}_2 $$ encoding, with 3D data acquired with Dixon gradient-echo readout and 2D image navigators to enable 100 % $$ 100\% $$ respiratory scan efficiency. Images were reconstructed with a non-rigid motion-corrected, low-rank patch-based reconstruction, and maps were generated through dictionary matching. The proposed sequence was compared against conventional 2D techniques in phantoms, 10 healthy subjects, and 1 patient. RESULTS: The proposed 3D T 1 $$ {T}_1 $$ , T 1 ρ $$ {T}_{1\rho } $$ , and T 2 $$ {T}_2 $$ measurements showed excellent correlation with 2D reference measurements in phantoms. For healthy subjects, the mapping values of septal myocardial tissue were T 1 = 1060 ± 48 ms $$ {T}_1=1060\pm 48\kern0.2778em \mathrm{ms} $$ , T 1 ρ = 48 . 1 ± 3 . 9 ms $$ {T}_{1\rho }=48.1\pm 3.9\kern0.2778em \mathrm{ms} $$ , and T 2 = 44 . 2 ± 3 . 2 ms $$ {T}_2=44.2\pm 3.2\kern0.2778em \mathrm{ms} $$ for the proposed sequence, against T 1 = 959 ± 15 ms $$ {T}_1=959\pm 15\kern0.2778em \mathrm{ms} $$ , T 1 ρ = 56 . 4 ± 1 . 9 ms $$ {T}_{1\rho }=56.4\pm 1.9\kern0.2778em \mathrm{ms} $$ , and T 2 = 47 . 3 ± 1 . 5 ms $$ {T}_2=47.3\pm 1.5\kern0.2778em \mathrm{ms} $$ for 2D MOLLI, 2D T 1 ρ $$ {T}_{1\rho } $$ -prep bSSFP and 2D T 2 $$ {T}_2 $$ -prep bSSFP, respectively. Promising results were obtained when comparing the proposed mapping to 2D references in 1 patient with active myocarditis. CONCLUSION: The proposed approach enables simultaneous 3D whole-heart joint T 1 $$ {T}_1 $$ / T 1 ρ $$ {T}_{1\rho } $$ / T 2 $$ {T}_2 $$ mapping and water/fat imaging in ≈ $$ \approx $$ 7 min scan time, demonstrating good agreement with conventional mapping techniques in phantoms and healthy subjects and promising results in 1 patient with suspected cardiovascular disease.
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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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    Cardiovascular magnetic resonance imaging: Principles and advanced techniques
    (2025) Si, Dongyue; Littlewood, Simon J.; Crabb, Michael G.; Phair, Andrew; Prieto Vásquez, Claudia; Botnar, René Michael
    Cardiovascular magnetic resonance (CMR) imaging is an established non-invasive tool for the assessment of cardiovascular diseases, which are the leading cause of death globally. CMR provides dynamic and static multi-contrast and multi-parametric images, including cine for functional evaluation, contrast-enhanced imaging and parametric mapping for tissue characterization, and MR angiography for the assessment of the aortic, coronary and pulmonary circulation. However, clinical CMR imaging sequences still have some limitations such as the requirement for multiple breath-holds, incomplete spatial coverage, complex planning and acquisition, low scan efficiency and long scan times. To address these challenges, novel techniques have been developed during the last two decades, focusing on automated planning and acquisition timing, improved respiratory and cardiac motion handling strategies, image acceleration algorithms employing undersampled reconstruction, all-in-one imaging techniques that can acquire multiple contrast/parameters in a single scan, deep learning based methods applied along the entire CMR imaging pipeline, as well as imaging at high- and low-field strengths. In this article, we aim to provide a comprehensive review of CMR imaging, covering both established and emerging techniques, to give an overview of the present and future applications of CMR.
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    Free‐breathing 3D whole‐heart joint T1/T2 mapping and water/fat imaging at 0.55 T
    (2024) Si, Dongyue; Crabb, Michael G.; Kunze, Karl P.; Littlewood, Simon J.; Prieto Vasquez, Claudia Del Carmen; Botnar, René M.
    To develop and validate a highly efficient motion compensated free-breathingisotropic resolution 3D whole-heart joint T 1 /T2 mapping sequence with anatomicalwater/fat imaging at 0.55 T.Methods: The proposed sequence takes advantage of shorter T1 at 0.55 T to acquirethree interleaved water/fat volumes with inversion-recovery preparation, no prepara-tion, and T 2 preparation, respectively. Image navigators were used to facilitate nonrigidmotion-compensated image reconstruction. T1 and T2 maps were jointly calculated bya dictionary matching method. Validations were performed with simulation, phantom,and in vivo experiments on 10 healthy volunteers and 1 patient. The performance ofthe proposed sequence was compared with conventional 2D mapping sequences includ-ing modified Look-Locker inversion recovery and T2 -prepared balanced steady-SSFPsequence.Results: The proposed sequence has a good T1 and T2 encoding sensitivity in simula-tion, and excellent agreement with spin-echo reference T 1 and T2 values was observedin a standardized T1 /T2 phantom (R2 = 0.99). In vivo experiments provided good-qualityco-registered 3D whole-heart T1 and T2 maps with 2-mm isotropic resolution in ashort scan time of about 7 min. For healthy volunteers, left-ventricle T1 mean andSD measured by the proposed sequence were both comparable with those of modi-fied Look-Locker inversion recovery (640 ± 35 vs. 630 ± 25 ms [p = 0.44] and 49.9 ± 9.3vs. 54.4 ± 20.5 ms [p = 0.42]), whereas left-ventricle T2 mean and SD measured by theproposed sequence were both slightly lower than those of T2 -prepared balanced SSFP(53.8 ± 5.5 vs. 58.6 ± 3.3 ms [p < 0.01] and 5.2 ± 0.9 vs. 6.1 ± 0.8 ms [p = 0.03]). MyocardialT 1 and T2 in the patient measured by the proposed sequence were in good agreementwith conventional 2D sequences and late gadolinium enhancement.Conclusion: The proposed sequence simultaneously acquires 3D whole-heart T1 and T2mapping with anatomical water/fat imaging at 0.55 T in a fast and efficient 7-min scan.Further investigation in patients with cardiovascular disease is now warranted