Functional MRI using regularized parallel imaging acquisition.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities

Sensitivity encoding (SENSE) with a receiver coil array is typically used as a means of reducing the scan time in MRI. The speed benefit usually comes at some expense in terms of the signal-to-noise ratio (SNR) efficiency, which has been notorious as the main downside of SENSE and parallel MRI in general. In this work it is shown that in steady-state gradient-echo imaging the parallel approach may as well be used to increase the SNR efficiency. The basic idea is to balance reduced phase encoding by increasing the repetition time. In this fashion both the acquisition duty cycle and the steady-state magnetization can be enhanced, resulting in considerable net gains in SNR yield. It is argued that the reduction factor in parallel imaging is essentially an additional degree of freedom in optimizing the SNR. The optimal SENSE factor depends on scan, tissue, and hardware parameters, assuming values up to 3.0 and higher. The achievable SNR benefit also depends on the spoiling regime and is most pronounced for RF-spoiled techniques. The proposed mechanism is demonstrated by simulations and phantom experiments, as well as by contrast-enhanced angiography in vivo, achieving an approximate doubling of the SNR efficiency.

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS-SENSE, sequentially reconstructs a set of aliased reduced-field-of-view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS-SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS.

In parallel magnetic resonance imaging (MRI), the quality of image reconstruction is very important. The signal to noise ratio (SNR) of reconstruction image would be obviously reduced with the increase of the acceleration factors because of the ill-posed problem in the sensitivity encoding (SENSE) reconstruction. Through in-depth analysis of the total variation (TV) regularized SENSE reconstruction model, an efficient Bregman iteration algorithm was introduced to obtain the optimal solution and improve the image reconstruction quality. In this paper, the simulation experiments were performed on the real cardiac data and brain data of MRI, respectively. The experimental results demonstrated that the proposed algorithm can alleviate the aliasing artifacts and has a better image reconstruction effect. Compared with the conventional TV regularized SENSE reconstruction algorithm, under the condition of the higher acceleration factors, the SNR has also been improved and the normalized mean squared error (NMSE) of reconstruction image is also decreased. This shows that the proposed iteration TV SENSE image reconstruction algorithm is effective and feasible.

Background: Time Repetition (TR) is one parameter that can affect the value of Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). The purpose of this research is to know the effect of variation of TR value on SNR and CNR on cervical MRI examination with Sagital T2 Weighted Fast Spin Echo sequence and to know the most optimal TR value from the variation of TR value to SNR and CNR on cervical MRI examination with Sagital T2 Weighted Fast Spin Echo.Methods: The type of this study was experimental study. The study was conducted using MRI 1.5 Tesla at Kasih Ibu Denpasar Hospital. Data were 40 MRI cervical images of sagital Fast Spin Echo from 10 volunteers with four variations of TR (2500 ms, 3000 ms, 3500 ms, and 4000 ms). The SNR and CNR values are measured by identifying the Region of Interest (ROI) in the corpus, discus, cerebro spinal fluid (CSF), and medula spinalis regions to obtain the average signals and compared with the mean deviation of the background. Data was analyzed by regression test to know the influence and by Anova test.Results: The result of the research showed that there was the influence of TR value to SNR and CNR of MRI Cervical Sagital T2 FSE. There was a strong correlation between the variation of TR values with SNR and CNR Cervical with p-value 0.05, the optimal TR value obtained in Cervical Sagital T2 FSE anatomical image on MRI 1,5 Tesla modality was 3500 ms.Conclusion: Time Repetition affected the signal to noise ratio and contrast to noise ratio. TR 3500 ms produced the most optimal cervical MRI image quality.

Functional magnetic resonance imaging (fMRI) is a unique window to the brain, enabling scientists to follow changes in brain activity in response to hormones, ageing, environment, drugs of abuse and other stimuli. There are two features that make fMRI unique when compared with other imaging modalities used in behavioural neuroscience. First, it can be entirely noninvasive: each animal can serve as its own control over the natural course of its life, vital for following neuroadaptation and other developmental processes critical to understanding behaviour. Second, fMRI has the spatial and temporal resolution to observe patterns of neuronal activity across the entire brain in less than a minute. Although fMRI does not have the cellular spatial resolution of immunostaining, nor the millisecond temporal resolution of electrophysiology, synchronised changes in neuronal activity across multiple brain areas seen with functional MRI can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviours. Thus, fMRI affords a systems approach to the study of the brain, complementing and building from other neurobiological techniques to understand how behaviour is organised across multiple brain regions. In this review, we present a general background to fMRI and the different imaging modalities that can be used in fMRI studies. Included are examples of the application of fMRI in behavioural neuroscience research, along with discussion of the advantages and disadvantages of this technology.

To evaluate the impact of parallel acquisition techniques (PATs) on image quality and detection of liver metastases using three-dimensional volumetric interpolated breath-hold examination (VIBE) for clinical liver imaging. Forty-nine patients with various primary malignancies underwent abdominal dynamic contrast-enhanced three-dimensional VIBE magnetic resonance imaging (MRI) (1.5 T) using a standard phased array coil. Recently introduced Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) and SENSitivity Encoding (mSENSE) PAT reconstruction algorithms were added to reduce scan time twofold. Overall image quality, motion, and aliasing artifacts were classified on a 5-point scale. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements were performed for quantitative comparison. All sequences were evaluated concerning the number of detected lesions. PAT resulted in a reduction of data acquisition time from 23 to 13 seconds. Both GRAPPA and mSENSE data sets yielded 30% less SNR (34.8 +/- 14.1 and 33.1 +/- 13.3, P < 0.001) and 35% less CNR (21.2 +/- 15.0 and 20.9 +/- 12.7, P < 0.05) in comparison to unaccelerated VIBE (SNR = 50.8 +/- 20.3/CNR = 32.5 +/- 19.1). Similarly, PAT revealed lower-image-quality scores than unaccelerated VIBE. GRAPPA resulted in more fold-over artifacts than mSENSE. mSENSE revealed slightly fewer motion artifacts than no PAT. The unaccelerated late-venous-phase VIBE sequence revealed 146 lesions in the same patients. Accelerated images with mSENSE reconstruction detected 138 lesions. GRAPPA revealed 127 lesions, and thus performed inferior to mSENSE. At least for arrays with small numbers of elements, such as arrays used in this study, the PAT-induced reduction in scanning times must be weighed against compromises in image quality, which translate into poorer diagnostic performance regarding detection of small hepatic lesions. Thus, the PAT implementations tested in this study should probably be reserved for patients unable to hold their breaths for regular three-dimensional VIBE data sets.

The application of partially parallel imaging techniques to regular clinical MRI studies has brought about the benefit of significantly faster acquisitions but at the cost of amplified and non-uniformly distributed noise, especially, for high acceleration factors. In this work, denoising of the images reconstructed by the sensitivity- encoding (SENSE) algorithm is presented. To efficiently remove noise and simultaneously preserve the image details, weighted total variation smoothing with weighted edge restoration has been developed. Denoising is guided by the g-factor of the SENSE reconstruction and image gradient that is representative of edge information. An automatic iteration termination scheme is proposed to balance denoising and edge preservation, and reduce the difficulty of parameter decision in conventional approaches. The proposed g-factor and gradient weighted denoising with edge restoration (g-DENOISER) method retains the image details better than the denoising techniques guided by only the g-factor and simultaneously suppresses the noise stronger than the techniques where g-factor was not used to adaptively adjust the denoiser parameters according to local noise characteristics. It can be effectively used to reduce noise in images acquired using high acceleration factors and reconstructed using SENSE.

Characterization of high-resolution Gradient Echo and Spin Echo EPI for fMRI in the human visual cortex at 7 T

Functional magnetic resonance imaging (fMRI) and particularly resting state fMRI (rs-fMRI) is widely used to investigate resting state brain networks (RSNs) on the systems level. Echo planar imaging (EPI) is the state-of-the-art imaging technique for most fMRI studies. Therefore, improvements of EPI might lead to increased sensitivity for a large amount of studies performed every day. A number of developments to shorten acquisition time have been recently proposed and the multiband technique, allowing the simultaneous acquisition of multiple slices yielding an equivalent reduction of measurement time, is the most promising among them. While the prospect to significantly reduce acquisition time by means of high multiband acceleration factors (M) appears tempting, signal quality parameters and the sensitivity to detect common RSNs with increasing M-factor have only been partially investigated up to now. In this study, we therefore acquired rs-fMRI data from 20 healthy volunteers to systematically investigate signal characteristics and sensitivity for brain network activity in datasets with increasing M-factor, M = 2 − 4. Combined with an inplane, sensitivity encoding (SENSE), acceleration factor, S = 2, we applied a maximal acceleration factor of 8 (S2×M4). Our results suggest that an M-factor of 2 (total acceleration of 4) only causes negligible SNR decrease but reveals common RSN with increased sensitivity and stability. Further M-factor increase produced random artifacts as revealed by signal quality measures that may affect interpretation of RSNs under common scanning conditions. Given appropriate hardware, a mb-EPI sequence with a total acceleration of 4 significantly reduces overall scanning time and clearly increases sensitivity to detect common RSNs. Together, our results suggest mb-EPI at moderate acceleration factors as a novel standard for fMRI that might increase our understanding of network dynamics in healthy and diseased brains.

Previous studies have shown that skeletal muscle diffusion tensor imaging (DTI) can noninvasively probe changes in the muscle fiber architecture and microstructure in diseased and damaged muscles. However, DTI fiber reconstruction in small muscles and in muscle regions close to aponeuroses and tendons remains challenging because of partial volume effects. Increasing the spatial resolution of skeletal muscle single-shot diffusion-weighted echo planar imaging (DW-EPI) can be hindered by the inherently low signal-to-noise ratio (SNR) of muscle DW-EPI because of the short muscle T(2) and the high sensitivity of single-shot EPI to off-resonance effects and T(2)* blurring. In this article, eddy current-compensated diffusion-weighted stimulated-echo preparation is combined with sensitivity encoding (SENSE) to maintain good SNR properties and to reduce the sensitivity to distortions and T(2)* blurring in high-resolution skeletal muscle single-shot DW-EPI. An analytical framework is developed to optimize the reduction factor and diffusion weighting time to achieve maximum SNR. Arguments for the selection of the experimental parameters are then presented considering the compromise between SNR, B(0)-induced distortions, T(2)* blurring effects and tissue incoherent motion effects. On the basis of the selected parameters in a high-resolution skeletal muscle single-shot DW-EPI protocol, imaging protocols at lower acquisition matrix sizes are defined with matched bandwidth in the phase-encoding direction and SNR. In vivo results show that high-resolution skeletal muscle DTI with minimized sensitivity to geometric distortions and T(2)* blurring is feasible using the proposed methodology. In particular, a significant benefit is demonstrated from a reduction in partial volume effects for resolving multi-pennate muscles and muscles with small cross-sections in calf muscle DTI.

The aim of this study was to implement and evaluate an accelerated three-dimensional (3D) cine phase contrast MRI sequence by combining a randomly sampled 3D k-space acquisition sequence with an echo planar imaging (EPI) readout. An accelerated 3D cine phase contrast MRI sequence was implemented by combining EPI readout with randomly undersampled 3D k-space data suitable for compressed sensing (CS) reconstruction. The undersampled data were then reconstructed using low-dimensional structural self-learning and thresholding (LOST). 3D phase contrast MRI was acquired in 11 healthy adults using an overall acceleration of 7 (EPI factor of 3 and CS rate of 3). For comparison, a single two-dimensional (2D) cine phase contrast scan was also performed with sensitivity encoding (SENSE) rate 2 and approximately at the level of the pulmonary artery bifurcation. The stroke volume and mean velocity in both the ascending and descending aorta were measured and compared between two sequences using Bland-Altman plots. An average scan time of 3 min and 30 s, corresponding to an acceleration rate of 7, was achieved for 3D cine phase contrast scan with one direction flow encoding, voxel size of 2 × 2 × 3 mm(3) , foot-head coverage of 6 cm and temporal resolution of 30 ms. The mean velocity and stroke volume in both the ascending and descending aorta were statistically equivalent between the proposed 3D sequence and the standard 2D cine phase contrast sequence. The combination of EPI with a randomly undersampled 3D k-space sampling sequence using LOST reconstruction allows a seven-fold reduction in scan time of 3D cine phase contrast MRI without compromising blood flow quantification.

High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: comparison with the conventional SENSE parallel acquisition technique

GPU based parallel framework for receiver coil sensitivity estimation in SENSE reconstruction