83 research outputs found
Advanced myocardial magnetic resonance imaging in patients with cardiac implantable electronic devices
No full textCardiac magnetic resonance imaging (CMR) is a gold standard technique for non-invasive assessment of cardiac structure, lesions, function, and tissue composition. However, in patients with cardiac implantable electronic devices (CIEDs), its diagnostic use remains limited due to severe device-related artefacts. With implantation rates increasing worldwide and the majority of patients expected to require a CMR during their lifetime, dedicated solutions are required to extend the benefits of CMR to this growing population.
The objective of this thesis was to develop and evaluate CMR methods using wideband radiofrequency pulses in patients with CIEDs in order to reduce device-related artefacts, improve the detection of myocardial lesions and improve diagnostic confidence compared to available reference techniques. Several complementary approaches were investigated and validated in phantoms, animals, healthy volunteers, and patients.
First, wideband black-blood late gadolinium enhancement (LGE) imaging was introduced to improve scar detection and reduce hyperintensity artefacts introduced by devices. Second, a free-breathing, motion-corrected implementation of wideband black-blood LGE was developed to facilitate imaging in patients unable to perform repeated breath-holds and to speed up the examination time. Third, a joint wideband bright- and black-blood technique (wideband SPOT) was proposed, combining improved scar-to-blood contrast with preserved anatomical reference to enable the localization of scars within the myocardial wall in the presence of CIEDs. Finally, wideband T2 mapping with advanced denoising was implemented to enable quantitative assessment of myocardial oedema and inflammation in the presence of CIEDs.
This work has established a more comprehensive framework of cardiac MRI techniques that are less prone to artefacts for the assessment of myocardial scar using LGE, and oedema and inflammation using T2 mapping in patients with implantable cardiac devices. These developments contribute to extending the role of CMR to a patient group that has long been excluded, with potential impact on diagnosis, risk stratification, and ablation guidance
Weiterentwickeltes Rekonstruktionsverfahren bei hochauflösenden Multi-Kontrast Kardio-Magnetresonanztomographie bei freier Atmung
No full textCardiovascular magnetic resonance (CMR) imaging is a valuable tool for high-resolution myocardial structure function and tissue assessment, providing essential information for clinical diagnosis and treatment decisions in cardiovascular disease. The unique ability of CMR to manipulate contrast and morphological information is however challenged by long scan time and physiological motion artifacts, and is strongly influenced by system imperfections leading to noisy acquisition and poor through-plane resolution.
In this thesis, we aim to bridge the gap between remote denoising and motion-correction approaches by developing efficient and reliable reconstruction techniques that jointly reconstruct high-resolution cardiac images free of motion and noise artifacts. Proposed techniques are applied in myocardial T1 mapping denoising, high-resolution 2D motion correction, and isotropic 3D MRI experiments, providing improved information and image quality to the cardiologist at no cost for assessing myocardial diseases.
In conclusion, the set of reconstruction methods described in this thesis, as well as their applications, address several challenges in typical image acquisitions that compose routine cardiac MR examinations. These new advances might enable the acceleration of MRI scan, reduction of motion and noise artifact, better visualization of cardiac images, and ultimately improve clinical diagnosis and patient comfort.Die kardiovaskuläre Magnetresonanz (CMR) -Bildgebung ist ein wertvolles Instrument für die hochauflösende myokardiale Strukturfunktion und die Gewebebewertung, die wesentliche Informationen für klinische Diagnose- und Therapieentscheidungen bei kardiovaskulären Erkrankungen liefert. Die einzigartige Fähigkeit von CMR, Kontrast und morphologische Informationen zu manipulieren, wird jedoch durch lange Abtastzeit und physiologische Bewegungsartefakte erkauft und wird stark durch Systemunvollkommenheiten beeinflusst, was zu einer lauten Akquisition und einer schlechten Zwischenschichtauflösung führt.
In dieser Arbeit wollen wir die Lücke zwischen Lämreduktions- und Bewegungskorrekturansätzen überbrücken, indem wir effiziente und zuverlässige Rekonstruktionstechniken entwickeln, die hochauflösende Bilder vom Herzen rekonstruieren, die frei von Bewegungs- und Rauschenartefakten sind. Vorgeschlagene Techniken werden in der myokardialen T1-Kartierung verwendet, die eine hochauflösende 2D-Bewegungskorrektur und isotrope 3D-MRI-Experimente ermöglicht und dem Kardiologen eine verbesserte Information und Bildqualität für die Beurteilung von Herzmuskelerkrankungen bietet.
Zusammenfassend lässt sich sagen, dass die in dieser Arbeit beschriebenen Rekonstruktionsmethoden, sowie ihre Anwendungen, einige Herausforderungen in typischen Bildakquisitionen angehen, die in routinemäßige kardiale MR-Untersuchungen entsehen. Diese neuen Fortschritte ermöglichen die Beschleunigung des MRT-Scans, die Verminderung von Bewegungs- und Rauschartefakten, eine bessere Visualisierung von Herzbildern und letztlich die Verbesserung der klinischen Diagnose und des Patientenkomforts
A MIQE-Compliant Real-Time PCR Assay for Aspergillus Detection
No full textPMCID: PMC3393739This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Recommended guidelines for sharing details of survivors' experiences in training or educational experiences
No full textTitle from PDF caption (viewed on May 15, 2019).This archived document is maintained by the State Library of Oregon as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Mode of access: Internet from the Oregon Government Publications Collection.Text in English
mapping and water/fat imaging
No full textPurpose
To develop a free-breathing isotropic-resolution whole-heart joint T1 and T2 mapping sequence with Dixon-encoding that provides coregistered 3D T1 and T2 maps and complementary 3D anatomical water and fat images in a single ~9 min scan.
Methods
Four interleaved dual-echo Dixon gradient echo volumes are acquired with a variable density Cartesian trajectory and different preparation pulses: 1) inversion recovery-preparation, 2) and 3) no preparations, and 4) T2 preparation. Image navigators are acquired to correct each echo for 2D translational respiratory motion; the 8 echoes are jointly reconstructed with a low-rank patch-based reconstruction. A water/fat separation algorithm is used to obtain water and fat images for each acquired volume. T1 and T2 maps are generated by matching the signal evolution of the water images to a simulated dictionary. Complementary bright-blood and fat volumes for anatomical visualization are obtained from the T2-prepared dataset. The proposed sequence was tested in phantom experiments and 10 healthy subjects and compared to standard 2D MOLLI T1 mapping, 2D balance steady-state free precession T2 mapping, and 3D T2-prepared Dixon coronary MR angiography.
Results
High linear correlation was found between T1 and T2 quantification with the proposed approach and phantom spin echo measurements (y = 1.1 × −11.68, R2 = 0.98; and y = 0.85 × +5.7, R2 = 0.99). Mean myocardial values of T1/T2 = 1116 ± 30.5 ms/45.1 ± 2.38 ms were measured in vivo. Biases of T1/T2 = 101.8 ms/−0.77 ms were obtained compared to standard 2D techniques.
Conclusion
The proposed joint T1/T2 sequence permitted the acquisition of motion-compensated isotropic-resolution 3D T1 and T2 maps and complementary coronary MR angiography and fat volumes, showing promising results in terms of T1 and T2 quantification and visualization of cardiac anatomy and pericardial fat
A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T1 mapping MRI
No full textInternational audiencePurpose: T1 mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm.Methods: After nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the T1 value. The algorithm was validated with cardiac T1 mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T1 relaxation with 2 and 3 parameters were tested.Results: The vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T1 values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T1.Conclusions: We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T1 mapping with SR sequences
A fully automated binning method for improved SHARP reconstruction of free-breathing cardiac images
No full textFrom Compressed-Sensing to Artificial Intelligence-based Cardiac MRI Reconstruction
No full textCardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive assessment of cardiovascular disease. However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage. In addition, both cardiac and respiratory-induced motion of the heart during the acquisition need to be accounted for, further increasing the scan time. Several undersampling reconstruction techniques have been proposed during the last decades to speed up CMR acquisition. These techniques rely on acquiring less data than needed and estimating the non-acquired data exploiting some sort of prior information. Parallel imaging and compressed sensing undersampling reconstruction techniques have revolutionized the field, enabling 2- to 3-fold scan time accelerations to become standard in clinical practice. Recent scientific advances in CMR reconstruction hinge on the thriving field of artificial intelligence. Machine learning reconstruction approaches have been recently proposed to learn the non-linear optimization process employed in CMR reconstruction. Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction parameters and priors. In particular, deep learning techniques promise to use deep neural networks (DNN) to learn the reconstruction process from existing datasets in advance, providing a fast and efficient reconstruction that can be applied to all newly acquired data. However, before machine learning and DNN can realize their full potentials and enter widespread clinical routine for CMR image reconstruction, there are several technical hurdles that need to be addressed. In this article, we provide an overview of the recent developments in the area of artificial intelligence for CMR image reconstruction. The underlying assumptions of established techniques such as compressed sensing and low-rank reconstruction are briefly summarized, while a greater focus is given to recent advances in dictionary learning and deep learning based CMR reconstruction. In particular, approaches that exploit neural networks as implicit or explicit priors are discussed for 2D dynamic cardiac imaging and 3D whole-heart CMR imaging. Current limitations, challenges, and potential future directions of these techniques are also discussed
The beating heart: artificial intelligence for cardiovascular application in the clinic
No full textArtificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field
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