4.5 Article

Partial Fourier reconstruction of complex MR images using complex-valued convolutional neural networks

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Radiology, Nuclear Medicine & Medical Imaging

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

Dana J. Lin et al.

Summary: Artificial intelligence (AI) has shown great promise in the field of medical imaging, particularly in the application of deep learning to MR image reconstruction. Recent research indicates that deep-learning-based algorithms can compete with or even surpass traditional methods in a range of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Enhanced POCS reconstruction for partial Fourier imaging in multi-echo and time-series acquisitions

Peter J. Koopmans et al.

Summary: The study introduces a method to improve partial Fourier imaging reconstruction by using enhanced POCS algorithm, which reduces reconstruction errors and bias by obtaining full-resolution phase estimates in time-series or multi-echo acquisitions.

MAGNETIC RESONANCE IN MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Training a neural network for Gibbs and noise removal in diffusion MRI

Matthew J. Muckley et al.

Summary: The study developed and evaluated a neural network-based method for removing artifacts and noise in MRI images. Through machine learning, they were able to effectively mitigate artifacts in diffusion-weighted images, and the method can be independently applied to each imaging slice, providing flexibility.

MAGNETIC RESONANCE IN MEDICINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion

Yilong Liu et al.

Summary: The study introduces a method for joint calibrationless reconstruction of highly accelerated images in multislice MR data using tensor completion framework, which significantly reduces residual artifacts and root mean square error, offering a new and effective approach for acquiring and reconstructing highly undersampled multislice MR data.

MAGNETIC RESONANCE IN MEDICINE (2021)

Article Biophysics

Minimizing echo and repetition times in magnetic resonance imaging using a double half-echo k-space acquisition and low-rank reconstruction

Mark Bydder et al.

Summary: The study introduces a method for reconstructing images from exact half echoes using two separate acquisitions with reversed readout polarity, which effectively provides a full line of k-space without additional data around central k-space. This approach can benefit sequences or applications that prioritize short TE, short inter-echo spacing or short repetition time.

NMR IN BIOMEDICINE (2021)

Article Engineering, Electrical & Electronic

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

Vishal Monga et al.

Summary: Deep neural networks have shown remarkable performance gains in signal and image processing, but their black-box nature and dependency on large training sets hinder their future development. Algorithm unrolling offers a solution by connecting iterative algorithms to deep neural networks. Initially proposed for fast neural network approximations, unrolling methods are now rapidly growing and attracting significant attention for their potential in developing efficient, high-performance and interpretable network architectures.

IEEE SIGNAL PROCESSING MAGAZINE (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications

Elizabeth Cole et al.

Summary: This study aims to compare the performance of complex-valued convolutional neural networks (CNNs) with real-valued CNNs in MRI reconstruction and phase-based applications. Results demonstrate that complex-valued CNNs outperform real-valued CNNs in reconstruction and have better normalized RMS error, structural similarity index, and peak signal-to-noise ratio.

MAGNETIC RESONANCE IN MEDICINE (2021)

Article Multidisciplinary Sciences

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

N. Koonjoo et al.

Summary: Recent advancements in magnet, coil, and gradient set designs have sparked a renewed interest in inexpensive low magnetic field MRI systems. Our study demonstrates the effectiveness of using an end-to-end deep neural network approach (AUTOMAP) to significantly improve image quality in highly noise-corrupted low-field MRI data, outperforming contemporary denoising algorithms and suppressing noise-like artifacts. Domain-specific training corpora play an important role in the reconstruction performance of the AUTOMAP approach.

SCIENTIFIC REPORTS (2021)

Editorial Material Engineering, Electrical & Electronic

Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues

Florian Knoll et al.

IEEE SIGNAL PROCESSING MAGAZINE (2020)

Article Multidisciplinary Sciences

On instabilities of deep learning in image reconstruction and the potential costs of AI

Vegard Antun et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

Shanshan Wang et al.

MAGNETIC RESONANCE IMAGING (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of the generalization of learned image reconstruction and the potential for transfer learning

Florian Knoll et al.

MAGNETIC RESONANCE IN MEDICINE (2019)

Article Computer Science, Interdisciplinary Applications

MoDL: Model-Based Deep Learning Architecture for Inverse Problems

Hemant K. Aggarwal et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks

Qianqian Zhang et al.

MAGNETIC RESONANCE IN MEDICINE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Learning a variational network for reconstruction of accelerated MRI data

Kerstin Hammernik et al.

MAGNETIC RESONANCE IN MEDICINE (2018)

Article Multidisciplinary Sciences

Image reconstruction by domain-transform manifold learning

Bo Zhu et al.

NATURE (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Partial Fourier and Parallel MR Image Reconstruction with Integrated Gradient Nonlinearity Correction

Shengzhen Tao et al.

MAGNETIC RESONANCE IN MEDICINE (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Toward online reconstruction of quantitative susceptibility maps: Superfast dipole inversion

Ferdinand Schweser et al.

MAGNETIC RESONANCE IN MEDICINE (2013)

Article Computer Science, Artificial Intelligence

A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

Amir Beck et al.

SIAM JOURNAL ON IMAGING SCIENCES (2009)

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field

Ludovic de Rochefort et al.

MAGNETIC RESONANCE IN MEDICINE (2008)

Article Radiology, Nuclear Medicine & Medical Imaging

Improved image reconstruction for partial Fourier gradient-echo echo-planar imaging (EPI)

Nan-kuei Chen et al.

MAGNETIC RESONANCE IN MEDICINE (2008)

Article Radiology, Nuclear Medicine & Medical Imaging

Partial k-space reconstruction in single-shot diffusion-weighted echo-planar imaging

Pippa Storey et al.

MAGNETIC RESONANCE IN MEDICINE (2007)

Article Radiology, Nuclear Medicine & Medical Imaging

Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): Application with fast spin-echo imaging

SB Reeder et al.

MAGNETIC RESONANCE IN MEDICINE (2005)

Article Radiology, Nuclear Medicine & Medical Imaging

Susceptibility weighted imaging (SWI)

EM Haacke et al.

MAGNETIC RESONANCE IN MEDICINE (2004)

Article Radiology, Nuclear Medicine & Medical Imaging

Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA)

MA Griswold et al.

MAGNETIC RESONANCE IN MEDICINE (2002)