An improved algorithm for rotational motion artifact suppression in MRI

Tomographic phase microscopy: principles and applications in bioimaging [Invited]

Tomographic phase microscopy (TPM) is an emerging optical microscopic technique for bioimaging. TPM uses digital holographic measurements of complex scattered fields to reconstruct three-dimensional refractive index (RI) maps of cells with diffraction-limited resolution by solving inverse scattering problems. In this paper, we review the developments of TPM from the fundamental physics to its applications in bioimaging. We first provide a comprehensive description of the tomographic reconstruction physical models used in TPM. The RI map reconstruction algorithms and various regularization methods are discussed. Selected TPM applications for cellular imaging, particularly in hematology, are reviewed. Finally, we examine the limitations of current TPM systems, propose future solutions, and envision promising directions in biomedical research.

POCS-enhanced correction of motion artifacts in parallel MRI

A new method for correction of MRI motion artifacts induced by corrupted k-space data, acquired by multiple receiver coils such as phased arrays, is presented. In our approach, a projections onto convex sets (POCS)-based method for reconstruction of sensitivity encoded MRI data (POCSENSE) is employed to identify corrupted k-space samples. After the erroneous data are discarded from the dataset, the artifact-free images are restored from the remaining data using coil sensitivity profiles. The error detection and data restoration are based on informational redundancy of phased-array data and may be applied to full and reduced datasets. An important advantage of the new POCS-based method is that, in addition to multicoil data redundancy, it can use a priori known properties about the imaged object for improved MR image artifact correction. The use of such information was shown to improve significantly k-space error detection and image artifact correction. The method was validated on data corrupted by simulated and real motion such as head motion and pulsatile flow.

Automatic algorithm for correcting motion artifacts in time-resolved two-dimensional magnetic resoance angiography using convex projections

Time-resolved contrast enhanced magnetic resonance angiography (MRA) may suffer from involuntary patient motion. It is noted that while MR signal change associated with motion is large in magnitude and has smooth phase variation in k-phase, signal change associated with vascular enhancement is small in magnitude and has rapid phase variation in k-space. Based upon this observation, a novel projection onto convex sets (POCS) algorithm is developed as an automatic iterative method to remove motion artifacts. The presented POCS algorithm consists of high-pass phase filtering and convex projections in both k-space and image space. Without input of detailed motion knowledge, motion effects are filtered out, while vasculature information is preserved. The proposed method can be effective for a large class of nonrigid motions, including through-plane motion. The algorithm is stable and converges quickly, usually within five iterations. A double-blind evaluation on a set of clinical MRA cases shows that a completely unsupervised version of the algorithm produces significantly better rank scores (P=0.038) when compared to angiograms produced manually by an experienced radiologist.

Iterative Next-Neighbor Regridding (INNG): improved reconstruction from nonuniformly sampled k-space data using rescaled matrices

The reconstruction of MR images from nonrectilinearly sampled data is complicated by the fact that the inverse 2D Fourier transform (FT) cannot be performed directly on the acquired k-space data set. k-Space gridding is commonly used because it is an efficient reconstruction method. However, conventional gridding requires optimized density compensation functions (DCFs) to avoid profile distortions. Oftentimes, the calculation of optimized DCFs presents an additional challenge in obtaining an accurately gridded reconstruction. Another type of gridding algorithm, the block uniform resampling (BURS) algorithm, often requires singular value decomposition (SVD) regularization to avoid amplification of data imperfections, and under some conditions it is difficult to adjust the regularization parameters. In this work, new reconstruction algorithms for nonuniformly sampled k-space data are presented. In the newly proposed algorithms, high-quality reconstructed images are obtained from an iterative reconstruction that is performed using matrices scaled to sizes greater than that of the target image matrix. A second version partitions the sampled k-space region into several blocks to avoid limitations that could result from performing multiple 2D-FFTs on large data matrices. The newly proposed algorithms are a simple alternative approach to previously proposed optimized gridding algorithms.

POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging

A novel method for iterative reconstruction of images from undersampled MRI data acquired by multiple receiver coil systems is presented. Based on Projection onto Convex Sets (POCS) formalism, the method for SENSitivity Encoded data reconstruction (POCSENSE) can be readily modified to include various linear and nonlinear reconstruction constraints. Such constraints may be beneficial for reconstructing highly and overcritically undersampled data sets to improve image quality. POCSENSE is conceptually simple and numerically efficient and can reconstruct images from data sampled on arbitrary k-space trajectories. The applicability of POCSENSE for image reconstruction with nonlinear constraining was demonstrated using a wide range of simulated and real MRI data.

Retrospective intra-scan motion correction

This paper analyzes the effects of intra-scan motion and demonstrates the possibility of correcting them directly in k-space with a new automatic retrospective method. The method is presented for series of 2D acquisitions with Cartesian sampling. Using a reference k-space acquisition (corrected for translations) within the series, intra-scan motion parameters are accurately estimated for each trajectory in k-space of each data set in the series resulting in pseudo-random sample positions. The images are reconstructed with a Bayesian estimator that can handle sparse arbitrary sampling in k-space and reduces intra-scan rotation artefacts to the noise level. The method has been assessed by means of a Monte Carlo study on axial brain images for different signal-to-noise ratios. The accuracy of motion estimates is better than 0.1 degrees for rotation, and 0.1 and 0.05 pixel, respectively, for translation along the read and phase directions for signal-to-noise ratios higher than 6 of the signals on each trajectory. An example of reconstruction from experimental data corrupted by head motion is also given.

Respiratory motion in coronary magnetic resonance angiography: a comparison of different motion models

PURPOSE

To assess respiratory motion models for coronary magnetic resonance angiography (CMRA). In this study various motion models that describe the respiration-induced 3D displacements and deformations of the main coronary arteries were compared.

MATERIALS AND METHODS

Multiple high-resolution 3D coronary MR images were acquired in healthy volunteers using navigator-based respiratory gating, each depicting the coronary vessels at different respiratory motion states. In the images representing the different inspiratory states the displacements and deformations of the main coronary vessels with respect to the end-expiratory state were determined, by means of elastic registration. Several correction models (superior-inferior (SI) translation, 3D translation, and 3D affine transformation) were tested and compared with respect to their ability to map a selected inspiratory to the end-expiratory motion state.

RESULTS

3D translation was found to be superior over SI translation, which is commonly used for prospective motion correction in CMRA. The 3D affine transformation was found to be the best correction model considered in this study. Furthermore, a large intersubject variability of the model parameters was observed.

CONCLUSION

The results of this study indicate that a patient-adapted 3D correction model (3D translation or better 3D affine) will considerably improve prospective motion correction in CMRA.