OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

Chen, Chong; Liu, Yingmin; Schniter, Philip; Tong, Matthew; Zaręba, Karolina M.; Simonetti, Orlando P.; Potter, Lee C.; Ahmad, Rizwan

doi:10.48550/arxiv.2008.03410

Cited by 12 publications

(16 citation statements)

References 35 publications

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“…Experiments were performed by retrospectively undersampling fully sampled datasets in six dynamic MRI applications: (i) multi parameter brain sequence (brain) [45], (ii) free breathing un-gated cardiac perfusion (cardiac-fb) [46] (this had both perfusion dynamics and motion dynamics due to cardiac and breathing motion), (iii) free breathing PINCAT perfusion phantom (PINCAT) [4], [9] (containing dynamics due to perfusion uptake in the heart as well as heavy breathing motion), (iv) a short 59-frame speech sequence acquired on a University of Iowa volunteer speaking slowly (short-speech), (v) one breath-held cardiac sequence from the OCMR database [47] (cardiac-cine), and (vi) a high temporal resolution (2048frame) but low spatial resolution speech sequence acquired at the University of Iowa on a healthy volunteer (long-speech). Details of these datasets are provided in Appendix A.…”

Section: Methodsmentioning

confidence: 99%

Fast Low Rank Column-Wise Compressive Sensing For Accelerated Dynamic MRI

Babu

Nayer

Lingala

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work develops a fast, memory-efficient, and 'general' algorithm for accelerated/undersampled dynamic MRI by assuming an approximate LR model on the matrix formed by the vectorized images of the sequence. By 'general', we mean that our algorithm can be used for multiple accelerated dynamic MRI applications and multiple sampling rates (acceleration rates) without any parameter changes. We show that our proposed algorithm, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI), outperforms many existing approaches on 6 different dynamic MRI applications, while also being faster (or much faster) than all of them. Our second contribution is a fully online and mini-batch extensions of this algorithm that can process new measurements and return reconstructions either as soon as measurements of a new image frame arrive, or after a short mini-batch of measurement vectors arrives.

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Section: Methodsmentioning

confidence: 99%

Fast Low Rank Column-Wise Compressive Sensing For Accelerated Dynamic MRI

Babu

Nayer

Lingala

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The proposed DTLR-Net brings a new perspective of exploiting the tensor low-rank prior by enforcing a lowrank constraint on the feature domain learned by the neural network. Reconstruction results on a prospective Cine MR dataset (real-time OCMR [13]) demonstrate the superior performance of the proposed TLR-Net over state-of-art methods.…”

Section: Introductionmentioning

confidence: 94%

Dynamic MRI using Learned Transform-based Deep Tensor Low-Rank Network (DTLR-Net)

Zhang¹,

Hu²

2022

Preprint

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While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, low-rank tensors models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets. In this paper, we introduce a modelbased deep learning network by learning the tensor low-rank prior of the cardiac dynamic MR images. Instead of representing the dynamic dataset as a low-rank tensor directly, we propose a learned transformation operator to exploit the tensor low-rank property in a transform domain. In particular, by generalizing the t-SVD tensor decomposition into a unitary transformed t-SVD, we define a transformed tensor nuclear norm (TTNN) to enforce the tensor low-rankness. The dynamic MRI reconstruction problem is thus formulated using a TTNN regularized optimization problem. An iterative algorithm based on ADMM used to minimize the cost is unrolled into a deep network, where the transform is learned using convolutional neural networks (CNNs) to promote the reconstruction quality in the feature domain. Experimental results on cardiac cine MRI reconstruction demonstrate that the proposed framework is able to provide improved recovery results compared with the state-of-the-art algorithms.

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“…Open-access Multi-coil k-space Dataset for Cardiovascular Magnetic Resonance Imaging dataset (OCMR) 5 [33] is an open-access dataset that provides multi-coil k-space data for VII. COMMON PITFALLS Previous studies have shown several benefits of deep neural network based techniques over conventional constrained reconstruction using specified regularisers.…”

Section: Cardiac Mri (Eg Ocmr Dataset)mentioning

confidence: 99%

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

Huang¹,

Fang²,

Yang³

et al. 2022

Preprint

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Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions.

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OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

Cited by 12 publications

References 35 publications

Fast Low Rank Column-Wise Compressive Sensing For Accelerated Dynamic MRI

Fast Low Rank Column-Wise Compressive Sensing For Accelerated Dynamic MRI

Dynamic MRI using Learned Transform-based Deep Tensor Low-Rank Network (DTLR-Net)

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

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