Reconstructing MR images from undersampled data: data-weighting considerations.
نویسنده
چکیده
Data which are sampled more densely than the Nyquist limit in k-space are weighted prior to reconstruction by the inverse of the local sampling density. This work considers the effects of weighting data that are sampled less densely than the Nyquist limit. It specifically analyzes azimuthally undersampled projection reconstruction, variable density spirals, and variable density phase encoding. Effects on resolution, aliasing, and SNR are given. Higher resolution is obtained by weighting undersampled data according to the inverse of sampling density, while better SNR and less aliasing artifact are obtained by weighting undersampled data uniformly. Magn Reson Med 43:867-875, 2000.
منابع مشابه
Data reordering for improved constrained reconstruction from undersampled k-space data
Introduction: There has always been interest in speeding the acquisition of MRI data by acquiring fewer data in k-space. Recently there has been a significant interest in applying inverse problem techniques to reconstructing images from undersampled k-space MRI data [1-6]. One class of methods, from the nascent field of compressed sensing, is based on the sparse representation of images. As an ...
متن کاملMotion corrected compressed sensing for free-breathing dynamic cardiac MRI.
Compressed sensing (CS) has been demonstrated to accelerate MRI acquisitions by reconstructing sparse images of good quality from highly undersampled data. Motion during MR scans can cause inconsistencies in k-space data, resulting in strong motion artifacts in the reconstructed images. For CS to be useful in these applications, motion correction techniques need to be combined with the undersam...
متن کاملMR Image Reconstruction from Undersampled k-Space with Bayesian Dictionary Learning
We develop an algorithm for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. While existing methods focus on either image-level or patch-level sparse regularization strategies, we present a regularization framework that uses both image and patch-level sparsity constraints. The proposed regularization enforces image-level sparsity in terms of spatial finite d...
متن کاملUndersampled Projection Imaging for Time-Resolved Contrast-Enhanced 3D MR Angiography (PR-TRICKS)
Contrast-enhanced MRA methods which provide temporal information, such as the 3D-TRICKS technique[1], have tradeoffs between temporal resolution and spatial resolution[2]. Acquiring data with adequate temporal resolution often limits the achievable spatial resolution. It is desirable to have high in-plane resolution along with good temporal resolution. Recently we have shown that angularly unde...
متن کاملA Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the stat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Magnetic resonance in medicine
دوره 43 6 شماره
صفحات -
تاریخ انتشار 2000