Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.     

Time-resolved contrast-enhanced coronary magnetic resonance angiography with highly constrained projection reconstruction

Contrast-enhanced magnetic resonance angiography (MRA) is a promising technique for coronary artery imaging. The blood signal changes during the contrast injection will result in image artifacts, blurring and relatively low signal-to-noise ratio, when the k-space segments from different cardiac cycles are combined to reconstruct the final image as “time averaged.” Thus, it is important to acquire data during maximal blood signal enhancement for first-pass, contrast-enhanced MRA, and relatively high temporal resolution is required. This work demonstrated the feasibility of highly constrained backprojection reconstruction for time-resolved, contrast-enhanced coronary MRA. With this method, the temporal resolution can be increased. In addition, coronary artery images around blood signal enhancement peak have significantly improved contrast-to-noise ratio and suppressed artifacts compared to the composite images which were collected during a much longer acquisition time during substantial blood signal changes.     

Exploiting rank deficiency and transform domain sparsity for MR image reconstruction

The reconstruction of magnetic resonance (MR) images from the partial samples of their k-space data using compressed sensing (CS)-based methods has generated a lot of interest in recent years. To reconstruct the MR images, these techniques exploit the sparsity of the image in a transform domain (wavelets, total variation, etc.). In a recent work, it has been shown that it is also possible to reconstruct MR images by exploiting their rank deficiency. In this work, it will be shown that, instead of exploiting the sparsity of the image or rank deficiency alone, better reconstruction results can be achieved by combining transform domain sparsity with rank deficiency.To reconstruct an MR image using its transform domain sparsity and its rank deficiency, this work proposes a combined l₁-norm (of the transform coefficients) and nuclear norm (of the MR image matrix) minimization problem. Since such an optimization problem has not been encountered before, this work proposes and derives a first-order algorithm to solve it.The reconstruction results show that the proposed approach yields significant improvements, in terms of both visual quality as well as the signal to noise ratio, over previous works that reconstruct MR images either by exploiting rank deficiency or by the standard CS-based technique popularly known as the ‘Sparse MRI.’     

Image registration guided,sparsity constrained reconstructions for dynamic MRI

It is generally a challenging task to reconstruct dynamic magnetic resonance (MR) images with high spatial and high temporal resolutions, especially with highly incomplete k-space sampling. In this work, a novel method that combines a non-rigid image registration technique with sparsity-constrained image reconstruction is introduced. Employing a multi-resolution free-form deformation technique with B-spline interpolations, the non-rigid image registration accurately models the complex deformations of the physiological dynamics, and provides artifact-suppressed high spatial-resolution predictions. Based on these prediction images, the sparsity-constrained data fidelity-enforced image reconstruction further improves the reconstruction accuracy. When compared with the k-t FOCUSS with motion estimation/motion compensation (MEMC) technique on volunteer scans, the proposed method consistently outperforms in both the spatial and the temporal accuracy with variously accelerated k-space sampling. High fidelity reconstructions for dynamic systolic phases with reduction factor of 10 and cardiac perfusion series with reduction factor of 3 are presented.     

Joint reconstruction of multiecho MR images using correlated sparsity

This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in wavelet domain). Such techniques can be employed to reconstruct the individual T1- or T2-weighted images. However, in the current context, the different images are not really independent; they are images of the same cross section and, hence, are highly correlated. We exploit the correlation between the images, along with the spatial correlation within the images to achieve better reconstruction results than exploiting spatial correlation only.For individual MR images, CS-based techniques lead to a sparsity-promoting optimization problem in the wavelet domain. In this article, we show that the same framework can be extended to incorporate correlation between images leading to group/row sparsity-promoting optimization. Algorithms for solving such optimization problems have already been developed in the CS literature. We show that significant improvement in reconstruction accuracy can be achieved by considering the correlation between different T1- and T2-weighted images. If the reconstruction accuracy is considered to be constant, our proposed group sparse formulation can yield the same result with 33% less K-space samples compared with simple sparsity-promoting reconstruction. Moreover, the reconstruction time by our proposed method is about two to four times less than the previous method.     

Self-gated Fourier velocity encoding

Self-gating is investigated to improve the velocity resolution of real-time Fourier velocity encoding measurements in the absence of a reliable electrocardiogram waveform (e.g., fetal magnetic resonance or severe arrhythmia). Real-time flow data are acquired using interleaved k-space trajectories which share a common path near the origin of k-space. These common data provide a rapid self-gating signal that can be used to combine the interleaved data. The combined interleaves cover a greater area of k-space than a single real-time acquisition, thereby providing higher velocity resolution for a given aliasing velocity and temporal resolution. For example, this approach provided velocity spectra with a temporal resolution of 19 ms and velocity resolution of 22 cm/s over an 818 cm/s field-of-view. The method was validated experimentally using a computer-controlled pulsatile flow apparatus and applied in vivo to measure aortic-valve flow in a healthy volunteer.     

基于Hartley变换的磁共振成象

许多磁共振成象的应用场合需要利用正交双通道来采集一个具有时间和空间分辨力的对象序列。传统的基于Ｆｏｕｒｉｅｒ变换的成象方法，一方面，图象序列的重建时各帧图象是独立地进行重建的，因而图象序列的时间分辨力受到编码的限制；另一方面，来自两个通道之间的Ｆａｒｔｌｅｙ变换的磁共振成象技术。     

3D sensitivity encoded ellipsoidal MR spectroscopic imaging of gliomas at 3T

Purpose

The goal of this study was to implement time efficient data acquisition and reconstruction methods for 3D magnetic resonance spectroscopic imaging (MRSI) of gliomas at a field strength of 3T using parallel imaging techniques.

Methods

The point spread functions, signal to noise ratio (SNR), spatial resolution, metabolite intensity distributions and Cho:NAA ratio of 3D ellipsoidal, 3D sensitivity encoding (SENSE) and 3D combined ellipsoidal and SENSE (e-SENSE) k-space sampling schemes were compared with conventional k-space data acquisition methods.

Results

The 3D SENSE and e-SENSE methods resulted in similar spectral patterns as the conventional MRSI methods. The Cho:NAA ratios were highly correlated (P<.05 for SENSE and P<.001 for e-SENSE) with the ellipsoidal method and all methods exhibited significantly different spectral patterns in tumor regions compared to normal appearing white matter. The geometry factors ranged between 1.2 and 1.3 for both the SENSE and e-SENSE spectra. When corrected for these factors and for differences in data acquisition times, the empirical SNRs were similar to values expected based upon theoretical grounds. The effective spatial resolution of the SENSE spectra was estimated to be same as the corresponding fully sampled k-space data, while the spectra acquired with ellipsoidal and e-SENSE k-space samplings were estimated to have a 2.36–2.47-fold loss in spatial resolution due to the differences in their point spread functions.

Conclusion

The 3D SENSE method retained the same spatial resolution as full k-space sampling but with a 4-fold reduction in scan time and an acquisition time of 9.28 min. The 3D e-SENSE method had a similar spatial resolution as the corresponding ellipsoidal sampling with a scan time of 4:36 min. Both parallel imaging methods provided clinically interpretable spectra with volumetric coverage and adequate SNR for evaluating Cho, Cr and NAA.     

Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA

We introduce a novel noniterative algorithm for the fast and accurate reconstruction of nonuniformly sampled MRI data. The proposed scheme derives the reconstructed image as the nonuniform inverse Fourier transform of a compensated dataset. We derive each sample in the compensated dataset as a weighted linear combination of a few measured k-space samples. The specific k-space samples and the weights involved in the linear combination are derived such that the reconstruction error is minimized. The computational complexity of the proposed scheme is comparable to that of gridding. At the same time, it provides significantly improved accuracy and is considerably more robust to noise and undersampling. The advantages of the proposed scheme makes it ideally suited for the fast reconstruction of large multidimensional datasets, which routinely arise in applications such as f-MRI and MR spectroscopy. The comparisons with state-of-the-art algorithms on numerical phantoms and MRI data clearly demonstrate the performance improvement.     

Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization

Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation.     

A statistical method for characterizing the noise in nonlinearly reconstructed images from undersampled MR data: The POCS example

The projection-onto-convex-sets (POCS) algorithm is a powerful tool for reconstructing high-resolution images from undersampled k-space data. It is a nonlinear iterative method that attempts to estimate values for missing data. The convergence of the algorithm and its other deterministic properties are well established, but relatively little is known about how noise in the source data influences noise in the final reconstructed image. In this paper, we present an experimental treatment of the statistical properties in POCS and investigate 12 stochastic models for its noise distribution beside its nonlinear point spread functions. Statistical results show that as the ratio of the missing k-space data increases, the noise distribution in POCS images is no longer Rayleigh as with conventional linear Fourier reconstruction. Instead, the probability density function for the noise is well approximated by a lognormal distribution. For small missing data ratios, however, the noise remains Rayleigh distributed. Preliminary results show that in the presence of noise, POCS images are often dominated by POCS-enhanced noise rather than POCS-induced artifacts. Implicit in this work is the presentation of a general statistical method that can be used to assess the noise properties in other nonlinear reconstruction algorithms.     

An algorithm for sparse MRI reconstruction by Schatten p-norm minimization

In recent years, there has been a concerted effort to reduce the MR scan time. Signal processing research aims at reducing the scan time by acquiring less K-space data. The image is reconstructed from the subsampled K-space data by employing compressed sensing (CS)-based reconstruction techniques. In this article, we propose an alternative approach to CS-based reconstruction. The proposed approach exploits the rank deficiency of the MR images to reconstruct the image. This requires minimizing the rank of the image matrix subject to data constraints, which is unfortunately a nondeterministic polynomial time (NP) hard problem. Therefore we propose to replace the NP hard rank minimization problem by its nonconvex surrogate — Schatten p-norm minimization. The same approach can be used for denoising MR images as well.Since there is no algorithm to solve the Schatten p-norm minimization problem, we derive an efficient first-order algorithm. Experiments on MR brain scans show that the reconstruction and denoising accuracy from our method is at par with that of CS-based methods. Our proposed method is considerably faster than CS-based methods.     

Three-dimensional fluid-suppressed T2-prep flow-independent peripheral angiography using balanced SSFP

Accurate depiction of the vessels of the lower leg, foot or hand benefits from suppression of bright MR signal from lipid (such as bone marrow) and long-T1 fluid (such as synovial fluid and edema). Signal independence of blood flow velocities, good arterial/muscle contrast and arterial/venous separation are also desirable. The high SNR, short scan times and flow properties of balanced steady-state free precession (SSFP) make it an excellent candidate for flow-independent angiography. In this work, a new magnetization-prepared 3D SSFP sequence for flow-independent peripheral angiography is presented. The technique combines a number of component techniques (phase-sensitive fat detection, inversion recovery, T2-preparation and square-spiral phase-encode ordering) to achieve high-contrast peripheral angiograms at only a modest scan time penalty over simple 3D SSFP. The technique is described in detail, a parameter optimization performed and preliminary results presented achieving high contrast and 1-mm isotropic resolution in a normal foot.     

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.     

Accelerated passive MR catheter tracking into the carotid artery of canines

Background

Using magnetic resonance (MR) imaging for navigating catheters has several advantages when compared with the current “gold standard” modality of X-ray imaging. A significant drawback to interventional MR is inferior temporal and spatial resolutions, as high spatial resolution images cannot be collected and displayed at rates equal to X-ray imaging. In particular, passive MR catheter tracking experiments that use positive contrast mechanisms have poor temporal imaging rates and signal-to-noise ratio. As a result, with passive methods, it is often difficult to reconstruct motion artifact-free tracking images from areas with motion, such as the thoracic cavity.

Methods

In this study, several accelerated MR acquisition strategies, including parallel imaging and compressed sensing (CS), were evaluated to determine which method is most effective at improving the frame rate and passive detection of catheters in regions of physiological motion. Device navigation was performed both in vitro, through the aortic arch of an anthropomorphic chest phantom, and in vivo from the femoral artery, up the descending aorta into the supra-aortic branching vessels in canines.

Results and Discussion

The different parallel imaging methods produced images of low quality. CS with a two-fold acceleration was found to be the most effective method for generating tracking images, improving the image frame rate to 5.2 Hz, while maintaining a relatively high in-plane resolution. Using CS, motion artifact was decreased and the catheters were visualized with good conspicuity near the heart.

Conclusions

The improvement in the imaging frame rate by image acceleration was sufficient to overcome motion artifacts and to better visualize catheters in the thoracic cavity with passive tracking. CS preformed best at tracking. Navigation with passive MR catheter tracking was demonstrated from the femoral artery to the carotid artery in canines.     

Remarks on q-space MR propagator in partially restricted,axially-symmetric,and isotropic environments

The problem of reconstruction of an apparent propagator from a series of diffusion-attenuated magnetic resonance (MR) signals is revisited. In nonimaging acquisitions, the inverse Fourier transform of the MR signal attenuation is consistent with the notion of an ensemble average propagator. However, in image acquisitions where one is interested in quantifying a displacement distribution in every voxel of the image, the propagator derived in the traditional way may lead to a counter-intuitive profile when it is nonsymmetric, which could be a problem in partially restricted environments. By exploiting the reciprocity of the diffusion propagator, an alternative is introduced, which implies a forward Fourier transform of the MR signal attenuations yielding a propagator reflected around the origin. Two simple problems were considered as examples. In the case of diffusion in the proximity of a restricting barrier, the reflected propagator yields a more meaningful result, whereas in the case of curving fibers, the original propagator is more intuitive. In the final section of the article, two more one-dimensional transformations are introduced, which enable the reconstruction of two- and three-dimensional propagators in, respectively, axially symmetric and isotropic environments — in both cases, from one-dimensional q-space MR data.     

Referenceless PRFS MR Thermometry Using Partial Separability Model

Many areas of magnetic resonance (MR)-guided thermal therapy research would benefit from temperature maps with high spatial and temporal resolution. Conventional thermometry relies on the subtraction of baseline images, which makes it sensitive to tissue motion and frequency drift during the course of treatment. For another case is the limit of magnetic resonance imaging sampling speed, it is hard to accurately achieve MR thermometry with high spatiotemporal resolution especially for dynamic organs. To address these issues, a novel method for MR thermometry is presented by exploiting the data redundancy based on partial separability (PS) model and the referenceless thermometry. The PS model highly sparse sample two datasets in the (k–t) space for image reconstruction, which respectively determine the spatial and temporal resolutions. After the phase information is extracted from the images reconstructed by the PS model, the background phase outside the heated region from each acquired phase image through a polynomial fitting is estimated. Extrapolation of the polynomial to the heated region serves as the background phase estimate, which is then subtracted from the actual phase. The thermometry results showed that this method could accurately capture the dynamic change of MR thermometric images with 1.5 mm × 1.5 mm spatial resolution and 250 ms temporal resolution, respectively. The in vivo experiment of MR-guided high intensity focused ultrasound research and the cardiac dynamic MR thermometry are shown to demonstrate the benefits of the proposed method in high spatiotemporal resolution MR thermometry.     

Image domain propeller fast spin echo

A new pulse sequence for high-resolution T2-weighted (T2-w) imaging is proposed — image domain propeller fast spin echo (iProp-FSE). Similar to the T2-w PROPELLER sequence, iProp-FSE acquires data in a segmented fashion, as blades that are acquired in multiple TRs. However, the iProp-FSE blades are formed in the image domain instead of in the k-space domain. Each iProp-FSE blade resembles a single-shot fast spin echo (SSFSE) sequence with a very narrow phase-encoding field of view (FOV), after which N rotated blade replicas yield the final full circular FOV. Our method of combining the image domain blade data to a full FOV image is detailed, and optimal choices of phase-encoding FOVs and receiver bandwidths were evaluated on phantom and volunteers. The results suggest that a phase FOV of 15–20%, a receiver bandwidth of ± 32–63 kHz and a subsequent readout time of about 300 ms provide a good tradeoff between signal-to-noise ratio (SNR) efficiency and T2 blurring. Comparisons between iProp-FSE, Cartesian FSE and PROPELLER were made on single-slice axial brain data, showing similar T2-w tissue contrast and SNR with great anatomical conspicuity at similar scan times — without colored noise or streaks from motion. A new slice interleaving order is also proposed to improve the multislice capabilities of iProp-FSE.     

Z intensity-weighted position self-respiratory gating method for free-breathing 3D cardiac CINE imaging

A free-breathing 3D cine steady-state free precession (SSFP) technique was developed using the z intensity-weighted position (ZIP) which is the center of mass of a projection along the slice direction as a respiratory gating signal. The ZIP signal was continuously acquired using a slice encoded k-space center sampling in every TR. The performance of this gating method was compared with a method using the k-space center signal (KC) and with conventional 2D breath-hold cine SSFP in healthy subjects by measuring image quality and left ventricular function. The preliminary data obtained here demonstrated that the ZIP gating method provided superior respiratory motion artifact suppression when compared to the KC gating and provided left ventricular ejection fractions, and end-diastolic and end-systolic volumes similar to those obtained with the breath-hold 2D cine SSFP acquisition.