5,316 research outputs found
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multi-coil data by extending
deep cascade of CNN's and exploiting the data consistency layer. In particular,
we propose two variants, where one is inspired by POCSENSE and the other is
calibration-less. We show that the proposed approaches are competitive relative
to the state of the art both quantitatively and qualitatively.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #4663
Accelerated Coronary MRI with sRAKI: A Database-Free Self-Consistent Neural Network k-space Reconstruction for Arbitrary Undersampling
This study aims to accelerate coronary MRI using a novel reconstruction
algorithm, called self-consistent robust artificial-neural-networks for k-space
interpolation (sRAKI). sRAKI performs iterative parallel imaging reconstruction
by enforcing coil self-consistency using subject-specific neural networks. This
approach extends the linear convolutions in SPIRiT to nonlinear interpolation
using convolutional neural networks (CNNs). These CNNs are trained individually
for each scan using the scan-specific autocalibrating signal (ACS) data.
Reconstruction is performed by imposing the learned self-consistency and
data-consistency enabling sRAKI to support random undersampling patterns.
Fully-sampled targeted right coronary artery MRI was acquired in six healthy
subjects for evaluation. The data were retrospectively undersampled, and
reconstructed using SPIRiT, -SPIRiT and sRAKI for acceleration rates of
2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was
acquired to further evaluate performance. The results indicate that sRAKI
reduces noise amplification and blurring artifacts compared with SPIRiT and
-SPIRiT, especially at high acceleration rates in targeted data.
Quantitative analysis shows that sRAKI improves normalized mean-squared-error
(~44% and ~21% over SPIRiT and -SPIRiT at rate 5) and vessel sharpness
(~10% and ~20% over SPIRiT and -SPIRiT at rate 5). In addition,
whole-heart data shows the sharpest coronary arteries when resolved using
sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and
-SPIRiT, respectively. Thus, sRAKI is a database-free neural
network-based reconstruction technique that may further accelerate coronary MRI
with arbitrary undersampling patterns, while improving noise resilience over
linear parallel imaging and image sharpness over regularization
techniques.Comment: This work has been partially presented at ISMRM Workshop on Machine
Learning Part 2 (October 2018), SCMR/ISMRM Co-Provided Workshop (February
2019), IEEE International Symposium on Biomedical Imaging (April 2019) and
ISMRM 27 Annual Meeting and Exhibition (May 2019
Complexities of deep learning-based undersampled MR image reconstruction
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statementIncreasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.</p
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent
years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still
has some limitations, such as the robustness and flexibility of existing
methods have great deficiency. In this work, we propose a method to explore the
k-space domain learning via robust generative modeling for flexible
calibration-less PI reconstruction, coined weight-k-space generative model
(WKGM). Specifically, WKGM is a generalized k-space domain model, where the
k-space weighting technology and high-dimensional space augmentation design are
efficiently incorporated for score-based generative model training, resulting
in good and robust reconstructions. In addition, WKGM is flexible and thus can
be synergistically combined with various traditional k-space PI models, which
can make full use of the correlation between multi-coil data and
realizecalibration-less PI. Even though our model was trained on only 500
images, experimental results with varying sampling patterns and acceleration
factors demonstrate that WKGM can attain state-of-the-art reconstruction
results with the well-learned k-space generative prior.Comment: 11pages, 12 figure
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled -net for fast parallel MR imaging, including
parallel coil networks, which perform implicit coil weighting, and sensitivity
networks, involving explicit sensitivity maps. The networks in -net are
trained in a supervised way, including content and GAN losses, and with various
ways of data consistency, i.e., proximal mappings, gradient descent and
variable splitting. A semi-supervised finetuning scheme allows us to adapt to
the k-space data at test time, which, however, decreases the quantitative
metrics, although generating the visually most textured and sharp images. For
this challenge, we focused on robust and high SSIM scores, which we achieved by
ensembling all models to a -net.Comment: fastMRI challenge submission (team: holykspace
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