Browsing by Author "Qi, Haikun"
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- ItemA motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease(Elsevier B.V., 2024) Phair, Andrew; Fotaki, Anastasia; Felsner, Lina; Fletcher, Thomas J.; Qi, Haikun; Botnar, Rene Michael; Prieto Vásquez, Claudia del CarmenBackground: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. Methods: The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). Results: Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. Conclusion: The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.
- ItemEnd-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA(2021) Qi, Haikun; Hajhosseiny, Reza; Cruz, Gastao; Kuestner, Thomas; Kunze, Karl; Neji, Radhouene; Botnar, René Michael; Prieto Vásquez, ClaudiaPurpose: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA).
- ItemFree-running 3D whole heart myocardial T-1 mapping with isotropic spatial resolution(2019) Qi, Haikun; Jaubert, Olivier; Bustin, Aurélien; Cruz, Gastao; Chen, Huijun; Botnar, René Michael; Prieto Vásquez, Claudia
- ItemFree-running simultaneous myocardial T1/T2 mapping and cine imaging with 3D whole-heart coverage and isotropic spatial resolution(2019) Qi, Haikun; Bustin, Aurélien; Cruz, Gastao; Jaubert, Olivier; Chen, Huijun; Botnar, René Michael; Prieto Vásquez, Claudia
- ItemGeneralized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting(WILEY, 2021) Cruz, Gastao; Qi, Haikun; Jaubert, Olivier; Kuestner, Thomas; Schneider, Torben; Michael Botnar, Rene; Prieto, ClaudiaPurpose: Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF).
- ItemSelf-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography(ELSEVIER SCIENCE INC, 2022) Munoz, Camila; Qi, Haikun; Cruz, Gastao; Kuestner, Thomas; Botnar, Rene M.; Prieto, ClaudiaPurpose: To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction.
- ItemSelf-supervised motion-corrected image reconstruction network for 4D magnetic resonance imaging of the body trunk(Now Publishers Inc, 2022) Küstner, Thomas; Pan, Jiazhen; Gilliam, Christopher; Qi, Haikun; Cruz, Gastao; Hammernik, Kerstin; Blu, Thierry; Rueckert, Daniel; Botnar, René Michael; Prieto Vásquez, Claudia; Gatidis, SergiosRespiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with motion by fast imaging sequences under free-movement conditions followed by motion binning based on motion traces. These acquisitions yield sub-Nyquist sampled and motion-resolved k-space data. Motion states are linked to each other by non-rigid deformation fields. Usually, motion registration is formulated in image space which can however be impaired by aliasing artifacts or by estimation from low-resolution images. Subsequently, any motion-corrected reconstruction can be biased by errors in the deformation fields. In this work, we propose a deep-learning based motion-corrected 4D (3D spatial + time) image reconstruction which combines a non-rigid registration network and a 4D reconstruction network. Non-rigid motion is estimated in k-space and incorporated into the reconstruction network. The proposed method is evaluated on in-vivo 4D motion-resolved magnetic resonance images of patients with suspected liver or lung metastases and healthy subjects. The proposed approach provides 4D motion-corrected images and deformation fields. It enables a ∼ 14× accelerated acquisition with a 25- fold faster reconstruction than comparable approaches under consistent preservation of image quality for changing patients and motion patterns.
- ItemSynergistic multi-contrast cardiac magnetic resonance image reconstruction(ROYAL SOC, 2021) Qi, Haikun; Cruz, Gastao; Botnar, Rene; Prieto, ClaudiaCardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed.