Browsing by Author "Qi, Haikun"

Now showing 1 - 13 of 13
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    3D B1+corrected simultaneous myocardial T1 and T1ρ mapping with subject-specific respiratory motion correction and water-fat separation
    (2024) Qi, Haikun; Lv, Zhenfeng; Diao, Jiameng; Tao, Xiaofeng; Hu, Junpu; Xu, Jian; Botnar, Rene; Prieto, Claudia; Hu, Peng
    PurposeTo develop a 3D free-breathing cardiac multi-parametric mapping framework that is robust to confounders of respiratory motion, fat, and B1+ inhomogeneities and validate it for joint myocardial T1 and T1 rho mapping at 3T. MethodsAn electrocardiogram-triggered sequence with dual-echo Dixon readout was developed, where nine cardiac cycles were repeatedly acquired with inversion recovery and T1 rho preparation pulses for T1 and T1 rho sensitization. A subject-specific respiratory motion model relating the 1D diaphragmatic navigator to the respiration-induced 3D translational motion of the heart was constructed followed by respiratory motion binning and intra-bin 3D translational and inter-bin non-rigid motion correction. Spin history B1+ inhomogeneities were corrected with optimized dual flip angle strategy. After water-fat separation, the water images were matched to the simulated dictionary for T1 and T1 rho quantification. Phantoms and 10 heathy subjects were imaged to validate the proposed technique. ResultsThe proposed technique achieved strong correlation (T1: R-2 = 0.99; T1 rho: R-2 = 0.98) with the reference measurements in phantoms. 3D cardiac T1 and T1 rho maps with spatial resolution of 2 x 2 x 4 mm were obtained with scan time of 5.4 +/- 0.5 min, demonstrating comparable T1 (1236 +/- 59 ms) and T1 rho (50.2 +/- 2.4 ms) measurements to 2D separate breath-hold mapping techniques. The estimated B1+ maps showed spatial variations across the left ventricle with the septal and inferior regions being 10%-25% lower than the anterior and septal regions. ConclusionThe proposed technique achieved efficient 3D joint myocardial T1 and T1 rho mapping at 3T with respiratory motion correction, spin history B1+ correction and water-fat separation.
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    A 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 Carmen
    Background: 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.
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    Accelerated 3D free-breathing high-resolution myocardial T1ρ mapping at 3 Tesla
    (2022) Qi, Haikun; Lv, Zhenfeng; Hu, Junpu; Xu, Jian; Botnar, Rene; Prieto, Claudia; Hu, Peng
    Purpose: To develop a fast free-breathing whole-heart high-resolution myocardial T-1 rho mapping technique with robust spin-lock preparation that can be performed at 3 Tesla.
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    Accelerating 3D MTC-BOOST in patients with congenital heart disease using a joint multi-scale variational neural network reconstruction
    (2022) Fotaki, Anastasia; Fuin, Niccolo; Nordio, Giovanna; Jimeno, Carlos Velasco; Qi, Haikun; Emmanuel, Yaso; Pushparajah, Kuberan; Botnar, Rene M.; Prieto, Claudia
    Purpose: Free-breathing Magnetization Transfer Contrast Bright blOOd phase SensiTive (MTC-BOOST) is a pro-totype balanced-Steady-State Free Precession sequence for 3D whole-heart imaging, that employs the endoge-nous magnetisation transfer contrast mechanism. This achieves reduction of flow and off-resonance artefacts, that often arise with the clinical T2prepared balanced-Steady-State Free Precession sequence, enabling high quality, contrast-agent free imaging of the thoracic cardiovascular anatomy. Fully-sampled MTC-BOOST acquisition requires long scan times (~10-24 min) and therefore acceleration is needed to permit its clinical incorporation. The aim of this study is to enable and clinically validate the 5-fold accelerated MTC-BOOST acquisition with joint Multi-Scale Variational Neural Network (jMS-VNN) reconstruction. Methods: Thirty-six patients underwent free-breathing, 3D whole-heart imaging with the MTC-BOOST sequence, which is combined with variable density spiral-like Cartesian sampling and 2D image navigators for translational motion estimation. This sequence acquires two differently weighted bright-blood volumes in an interleaved fashion, which are then joined in a phase sensitive inversion recovery reconstruction to obtain a complementary fully co-registered black-blood volume. Data from eighteen patients were used for training, whereas data from the remaining eighteen patients were used for testing/evaluation. The proposed deep-learning based approach adopts a supervised multi-scale variational neural network for joint reconstruction of the two differently weighted bright-blood volumes acquired with the 5-fold accelerated MTC-BOOST. The two contrast images are stacked as different channels in the network to exploit the shared information. The proposed approach is compared to the fully-sampled MTC-BOOST and 5-fold undersampled MTC-BOOST acquisition with Compressed Sensing (CS) reconstruction in terms of scan/reconstruction time and bright-blood image quality. Comparison against conventional 2-fold undersampled T2-prepared 3D bright-blood whole-heart clinical sequence (T2prep-3DWH) is also included. Results: Acquisition time was 3.0 & PLUSMN; 1.0 min for the 5-fold accelerated MTC-BOOST versus 9.0 +/- 1.1 min for the fully-sampled MTC-BOOST and 11.1 +/- 2.6 min for the T2prep-3DWH (p < 0.001 and p < 0.001, respectively). Reconstruction time was significantly lower with the jMS-VNN method compared to CS (10 +/- 0.5 min vs 20 +/- 2 s, p < 0.001). Image quality was higher for the proposed 5-fold undersampled jMS-VNN versus conventional CS, comparable or higher to the corresponding T2prep-3DWH dataset and similar to the fully-sampled MTC-BOOST. Conclusion: The proposed 5-fold accelerated jMS-VNN MTC-BOOST framework provides efficient 3D whole-heart bright-blood imaging in fast acquisition and reconstruction time with concomitant reduction of flow and off-resonance artefacts, that are frequently encountered with the clinical sequence. Image quality of the cardiac anatomy and thoracic vasculature is comparable or superior to the clinical scan and 5-fold CS reconstruction in faster reconstruction time, promising potential clinical adoption.
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    End-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, Claudia
    Purpose: 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).
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    End-to-end deep learning-based motion correction and reconstruction for accelerated whole-heart joint T 1/T 2 mapping.
    (2025) Felsner, Lina; Velasco, Carlos; Phair, Andrew; Fletcher, Thomas J.; Qi, Haikun; Botnar, René Michael; Prieto Vásquez, Claudia
    PURPOSE: To accelerate 3D whole-heart joint T 1/T 2 mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data.METHODS: A free-breathing high-resolution motion-compensated 3D joint T 1/T 2 water/fat sequence is employed. The sequence consists of the acquisition of four interleaved volumes with 2-echo encoding, resulting in eight volumes with different contrasts. An end-to-end non-rigid motion-corrected reconstruction network is used to estimate high quality motion-corrected reconstructions from the eight multi-contrast undersampled data for subsequent joint T 1/T 2 mapping. Reconstruction with the proposed approach was compared against state-of-the-art motion-corrected HD-PROST reconstruction.RESULTS: The proposed approach yields images with good visual agreement compared to the reference reconstructions. The comparison of the quantitative values in the T 1 and T 2 maps showed the absence of systematic errors, and a small bias of -6.35 ms and -1.8 ms, respectively. The proposed reconstruction time was 24 seconds in comparison to 2.5 hours with motion-corrected HD-PROST, resulting in a reconstruction speed-up of over 370 times.CONCLUSION: In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T 1 and T 2 values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.
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    Free-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
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    Free-running 3D whole-heart T1 and T2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction
    (2023) Phair, Andrew; Cruz, Gastao; Qi, Haikun; Botnar, Rene M.; Prieto, Claudia
    Purpose: To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial T-1 and T-2 maps and cine images, enabling a similar to 3-min scan.
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    Free-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
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    Generalized 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, Claudia
    Purpose: Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF).
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    Self-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, Claudia
    Purpose: 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.
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    Self-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, Sergios
    Respiratory 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.
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    Synergistic multi-contrast cardiac magnetic resonance image reconstruction
    (ROYAL SOC, 2021) Qi, Haikun; Cruz, Gastao; Botnar, Rene; Prieto, Claudia
    Cardiac 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.