Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling

Hosseini, S. Amir; Zhang, Chi; Weingärtner, Sebastian; Moeller, Steen; Stuber, Matthias; Uğurbil, Kâmil; Akçakaya, Mehmet

doi:10.1371/journal.pone.0229418

Cited by 31 publications

(20 citation statements)

References 46 publications

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“…Three studies [70]- [72] operate on the undersampled k-space with higher reconstruction accuracy compared with the image-domain techniques, e.g., DC-CNN and VN [71]. Two studies [32] and [73] use a hybrid of k-space and image space. That is, for a 2-D undersampled k-space input, inverse Fourier transform was performed along the x-axis.…”

Section: Input Domainmentioning

confidence: 99%

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

et al. 2022

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Section: Input Domainmentioning

confidence: 99%

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

et al. 2022

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“…), raw k-space or noisy/aliased image input, the input dimensionality (2D, 3D, 2D+time, 3D+time, etc. ), single-or multiparametric input, complex-or real-valued processing of the complex-valued data, and single-coil (coil-combined) or multi-coil processing [20,21,25,26,[33][34][35][36][37][38][39][40][41][42][43]. To date, there are only a small number of published studies using DL methods on k-space data for MR image reconstruction in a clinical setting.…”

Section: Assessment Of Anatomical Structuresmentioning

confidence: 99%

Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging

et al. 2021

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Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSES) and DL-based TSE sequences (TSEDL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSES (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSES and TSEDL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

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“…DAGAN) 33 , 34 , manifold learning 35 , variational neural networks 36 – 38 and generalized PI reconstructions 39 – 41 . Network inputs differ from single-coil 2D magnitude image 27 – 29 and/or k-space 26 , 27 , 35 to multi-coil 2D magnitude/phase image 31 , 34 , 37 , 41 , 2D k-space 29 , 30 , 39 , 40 , 42 , 43 or low-resolution 3D k-space 44 and were studied for static imaging 24 , 26 , 27 , 29 – 31 , 33 – 38 , i.e. no temporal dynamics, or for 2D dynamic imaging 28 , 32 , 41 , i.e.…”

Section: Introductionmentioning

confidence: 99%

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions

Küstner

Fuin

Hammernik

et al. 2020

Sci Rep

178

135

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cardiac cine magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited to anisotropic resolution and long reconstruction times. Recently deep learning has shown promising results for computationally efficient reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging. CINENet is based on (3 + 1)D complex-valued spatio-temporal convolutions and multi-coil data processing. We trained and evaluated the proposed cinenet on in-house acquired 3D CINE data of 20 healthy subjects and 15 patients with suspected cardiovascular disease. The proposed cinenet network outperforms iterative reconstructions in visual image quality and contrast (+ 67% improvement). We found good agreement in LV function (bias ± 95% confidence) in terms of end-systolic volume (0 ± 3.3 ml), end-diastolic volume (− 0.4 ± 2.0 ml) and ejection fraction (0.1 ± 3.2%) compared to clinical gold-standard 2D CINE, enabling single breath-hold isotropic 3D CINE in less than 10 s scan and ~ 5 s reconstruction time. Cardiac CINE magnetic resonance imaging (MRI) is the gold standard for the assessment of cardiac morphology and function. Conventionally, multi-slice 2D CINE imaging is performed under multiple breath-holds to achieve left ventricular (LV) coverage. For fast LV coverage only a few (~ 12) short-axis 2D slices with anisotropic resolution in the slice direction are acquired throughout multiple breath-holds of < 15 s duration each. Imperfect (e.g. drifts) or varying breath-hold positions and the anisotropic image resolution can cause slice misalignments which may lead to staircasing artifacts and erroneous assessment of the ventricular volume. The LV function assessment is assessed by epicardial and endocardial segmentation of the images in short-axis orientation. Indeed, the anisotropic resolution of the short-axis 2D CINE does not allow for reformats to arbitrary orientations. Further images in other long axis orientations are required for a comprehensive assessment of cardiac morphology and function which in turn requires multiple acquisitions to be performed in several geometric views and thereby increasing overall planning and scan time. To overcome these limitations, 2D 1,2 and 3D 3-5 free-breathing cardiac CINE imaging with retrospective motion correction have been proposed to minimize slice misalignment and improve patient comfort. Data are acquired under free-breathing and respiratory and cardiac motion is resolved retrospectively which comes however at the expense of a prolonged scan time in the order of several minutes. Moreover, these approaches usually require long reconstruction times associated with the high-dimensional (spatial, respiratory temporal and cardiac temporal) data processing or with the nat...

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Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling

Cited by 31 publications

References 46 publications

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions

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