155 research outputs found
AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI
Sparse reconstruction is an important aspect of MRI, helping to reduce
acquisition time and improve spatial-temporal resolution. Popular methods are
based mostly on compressed sensing (CS), which relies on the random sampling of
k-space to produce incoherent (noise-like) artefacts. Due to hardware
constraints, 1D Cartesian phase-encode under-sampling schemes are popular for
2D CS-MRI. However, 1D under-sampling limits 2D incoherence between
measurements, yielding structured aliasing artefacts (ghosts) that may be
difficult to remove assuming a 2D sparsity model. Reconstruction algorithms
typically deploy direction-insensitive 2D regularisation for these
direction-associated artefacts. Recognising that phase-encode artefacts can be
separated into contiguous 1D signals, we develop two decoupling techniques that
enable explicit 1D regularisation and leverage the excellent 1D incoherence
characteristics. We also derive a combined 1D + 2D reconstruction technique
that takes advantage of spatial relationships within the image. Experiments
conducted on retrospectively under-sampled brain and knee data demonstrate that
combination of the proposed 1D AliasNet modules with existing 2D deep learned
(DL) recovery techniques leads to an improvement in image quality. We also find
AliasNet enables a superior scaling of performance compared to increasing the
size of the original 2D network layers. AliasNet therefore improves the
regularisation of aliasing artefacts arising from phase-encode under-sampling,
by tailoring the network architecture to account for their expected appearance.
The proposed 1D + 2D approach is compatible with any existing 2D DL recovery
technique deployed for this application
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction
Magnetic resonance imaging (MRI) plays an important role in modern medical
diagnostic but suffers from prolonged scan time. Current deep learning methods
for undersampled MRI reconstruction exhibit good performance in image
de-aliasing which can be tailored to the specific kspace undersampling
scenario. But it is very troublesome to configure different deep networks when
the sampling setting changes. In this work, we propose a deep plug-and-play
method for undersampled MRI reconstruction, which effectively adapts to
different sampling settings. Specifically, the image de-aliasing prior is first
learned by a deep denoiser trained to remove general white Gaussian noise from
synthetic data. Then the learned deep denoiser is plugged into an iterative
algorithm for image reconstruction. Results on in vivo data demonstrate that
the proposed method provides nice and robust accelerated image reconstruction
performance under different undersampling patterns and sampling rates, both
visually and quantitatively.Comment: 5 pages, 3 figure
CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions
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
Edge-weighted pFISTA-Net for MRI Reconstruction
Deep learning based on unrolled algorithm has served as an effective method
for accelerated magnetic resonance imaging (MRI). However, many methods ignore
the direct use of edge information to assist MRI reconstruction. In this work,
we present the edge-weighted pFISTA-Net that directly applies the detected edge
map to the soft-thresholding part of pFISTA-Net. The soft-thresholding value of
different regions will be adjusted according to the edge map. Experimental
results of a public brain dataset show that the proposed yields reconstructions
with lower error and better artifact suppression compared with the
state-of-the-art deep learning-based methods. The edge-weighted pFISTA-Net also
shows robustness for different undersampling masks and edge detection
operators. In addition, we extend the edge weighted structure to joint
reconstruction and segmentation network and obtain improved reconstruction
performance and more accurate segmentation results
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