Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE

PurposeTo reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images.MethodsWe jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed.ResultsUsing identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type.ConclusionAPIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images.     

高分辨率傅里叶变换成像光谱仪光谱重建并行算法

高分辨率傅里叶变换成像光谱仪具有高空间分辨率和高光谱分辨率的特点,但光谱重建时间冗长。通过对傅里叶变换光谱重建流程分析,为研制的1024pixel(光谱维)×1024piexl(像宽)×1024piexl(像高)高分辨率紫外傅里叶变换成像光谱仪的数据立方体反演,设计了一种并行优化算法。实验表明,在6核处理器上对512M和2G的数据立方体进行变换,时间分别只需88.33s和489.75s,加速比分别为3.70和3.04,大幅度提高了运算效率。如将该算法应用到更多内核处理器上,可得到更高的加速比和更少的运算时间。     

Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters and suitable for characterizing and optimizing k-space-based reconstruction in parallel imaging.     

A fast screening protocol for carotid plaques imaging using 3D multi-contrast MRI without contrast agent

PurposeTo implement a fast (~ 15 min) MRI protocol for carotid plaque screening using 3D multi-contrast MRI sequences without contrast agent on a 3 Tesla MRI scanner.Materials and methods7 healthy volunteers and 25 patients with clinically confirmed transient ischemic attack or suspected cerebrovascular ischemia were included in this study. The proposed protocol, including 3D T1-weighted and T2-weighted SPACE (variable-flip-angle 3D turbo spin echo), and T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) was performed first and was followed by 2D T1-weighted and T2-weighted turbo spin echo, and post-contrast T1-weighted SPACE sequences. Image quality, number of plaques, and vessel wall thicknesses measured at the intersection of the plaques were evaluated and compared between sequences.ResultsAverage examination time of the proposed protocol was 14.6 min. The average image quality scores of 3D T1-weighted, T2-weighted SPACE, and T1-weighted magnetization prepared rapid acquisition gradient echo were 3.69, 3.75, and 3.48, respectively. There was no significant difference in detecting the number of plaques and vulnerable plaques using pre-contrast 3D images with or without post-contrast T1-weighted SPACE. The 3D SPACE and 2D turbo spin echo sequences had excellent agreement (R = 0.96 for T1-weighted and 0.98 for T2-weighted, p < 0.001) regarding vessel wall thickness measurements.ConclusionThe proposed protocol demonstrated the feasibility of attaining carotid plaque screening within a 15-minute scan, which provided sufficient anatomical coverage and critical diagnostic information. This protocol offers the potential for rapid and reliable screening for carotid plaques without contrast agent.     

On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks

Multi-contrast (MC) Magnetic Resonance Imaging (MRI) of the same patient usually requires long scanning times, despite the images sharing redundant information. In this work, we propose a new iterative network that utilizes the sharable information among MC images for MRI acceleration. The proposed network has reinforced data fidelity control and anatomy guidance through an iterative optimization procedure of Gradient Descent, leading to reduced uncertainties and improved reconstruction results. Through a convolutional network, the new method incorporates a learnable regularization unit that is capable of extracting, fusing, and mapping shareable information among different contrasts. Specifically, a dilated inception block is proposed to promote multi-scale feature extractions and increase the receptive field diversity for contextual information incorporation. Lastly, an optimal MC information feeding protocol is built through the design of a complementary feature extractor block. Comprehensive experiments demonstrated the superiority of the proposed network, both qualitatively and quantitatively.     

Phase-constrained parallel MR image reconstruction

A generalized method for phase-constrained parallel MR image reconstruction is presented that combines and extends the concepts of partial-Fourier reconstruction and parallel imaging. It provides a framework for reconstructing images employing either or both techniques and for comparing image quality achieved by varying k-space sampling schemes. The method can be used as a parallel image reconstruction with a partial-Fourier reconstruction built in. It can also be used with trajectories not readily handled by straightforward combinations of partial-Fourier and SENSE-like parallel reconstructions, including variable-density, and non-Cartesian trajectories. The phase constraint specifies a better-conditioned inverse problem compared to unconstrained parallel MR reconstruction alone. This phase-constrained parallel MRI reconstruction offers a one-step alternative to the standard combination of homodyne and SENSE reconstructions with the added benefit of flexibility of sampling trajectory. The theory of the phase-constrained approach is outlined, and its calibration requirements and limitations are discussed. Simulations, phantom experiments, and in vivo experiments are presented.     

Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics

The statistical properties of background noise such as its standard deviation and mean value are frequently used to estimate the original noise level of the acquired data. This requires the knowledge of the statistical intensity distribution of the background signal, that is, the probability density of the occurrence of a certain signal intensity. The influence of many new MRI techniques and, in particular, of various parallel-imaging methods on the noise statistics has neither been rigorously investigated nor experimentally demonstrated yet. In this study, the statistical distribution of background noise was analyzed for MR acquisitions with a single-channel and a 32-channel coil, with sum-of-squares (SoS) and spatial-matched-filter (SMF) data combination, with and without parallel imaging using k-space and image-domain algorithms, with real-part and conventional magnitude reconstruction and with several reconstruction filters. Depending on the imaging technique, the background noise could be described by a Rayleigh distribution, a noncentral chi-distribution or the positive half of a Gaussian distribution. In particular, the noise characteristics of SoS-reconstructed multichannel acquisitions (with k-space-based parallel imaging or without parallel imaging) differ substantially from those with image-domain parallel imaging or SMF combination. These effects must be taken into account if mean values or standard deviations of background noise are employed for data analysis such as determination of local noise levels. Assuming a Rayleigh distribution as in conventional MR images or a noncentral chi-distribution for all multichannel acquisitions is invalid in general and may lead to erroneous estimates of the signal-to-noise ratio or the contrast-to-noise ratio.     

Cardiac magnetic resonance imaging using radial k-space sampling and self-calibrated partial parallel reconstruction

Radial sampling has been demonstrated to be potentially useful in cardiac magnetic resonance imaging because it is less susceptible to motion than Cartesian sampling. Nevertheless, its capability of imaging acceleration remains limited by undersampling-induced streaking artifacts. In this study, a self-calibrated reconstruction method was developed to suppress streaking artifacts for highly accelerated parallel radial acquisitions in cardiac magnetic resonance imaging. Two- (2D) and three-dimensional (3D) radial k-space data were collected from a phantom and healthy volunteers. Images reconstructed using the proposed method and the conventional regridding method were compared based on statistical analysis on a four-point scale imaging scoring. It was demonstrated that the proposed method can effectively remove undersampling streaking artifacts and significantly improve image quality (P<.05). With the use of the proposed method, image score (1–4, 1=poor, 2=good, 3=very good, 4=excellent) was improved from 2.14 to 3.34 with the use of an undersampling factor of 4 and from 1.09 to 2.5 with the use of an undersampling factor of 8. Our study demonstrates that the proposed reconstruction method is effective for highly accelerated cardiac imaging applications using parallel radial acquisitions without calibration data.     

Iterative reconstruction algorithm for optoacoustic imaging

Optoacoustic imaging is based on the generation of thermoelastic stress waves by heating an object in an optically heterogeneous medium with a short laser pulse. The stress waves contain information about the distribution of structures with preferential optical absorption. Detection of the waves with an array of broadband ultrasound detectors at the surface of the medium and applying a backprojection algorithm is used to create a map of absorbed energy inside the medium. With conventional reconstruction methods a large number of detector elements and filtering of the signals are necessary to reduce backprojection artifacts. As an alternative this study proposes an iterative procedure. The algorithm is designed to minimize the error between measured signals and signals calculated from the reconstructed image. In experiments using broadband optical ultrasound detectors and in simulations the algorithm was used to obtain three-dimensional images of multiple optoacoustic sources. With signals from a planar array of 3x3 detector elements a significant improvement was observed after about 10 iterations compared to the simple radial backprojection. Compared to conventional methods using filtered backprojection, the iterative method is computationally more intensive but requires less time and instrumentation for signal acquisition.     

Fast reconstruction of digital holograms for extended depths of field

Past research has demonstrated that a static, three-dimensional(3D) object scene can be directly recorded as a complex digital hologram. However, numerical reconstruction of the object scene, which may comprise multiple sections located at unknown distances from the hologram, is a complicated and computation-intensive process.To the best of our knowledge, we propose, for the first time, a low complexity method that is capable of reconstructing a complex hologram, such that sections at different depths in the 3D object scene can be automatically reconstructed at the correct focal distances and merged into a single image for an extended depth of field. We demonstrate an order of magnitude increase of the depth of field for binary objects. With the use of a graphical processing unit, the reconstruction of a 512 × 512 complex hologram can be accomplished in about 100 ms,equivalent to around 10 frames per second.     

Uniform distribution of projection data for improved reconstruction quality of 4D EPR imaging

In continuous wave (CW) electron paramagnetic resonance imaging (EPRI), high quality of reconstruction in a limited acquisition time is a high priority. It has been shown for the case of 3D EPRI, that a uniform distribution of the projection data generally enhances reconstruction quality. In this work, we have suggested two data acquisition techniques for which the gradient orientations are more evenly distributed over the 4D acquisition space as compared to the existing methods. The first sampling technique is based on equal solid angle partitioning of 4D space, while the second technique is based on Fekete points estimation in 4D to generate a more uniform distribution of data. After acquisition, filtered backprojection (FBP) is applied to carry out the reconstruction in a single stage. The single-stage reconstruction improves the spatial resolution by eliminating the necessity of data interpolation in multi-stage reconstructions. For the proposed data distributions, the simulations and experimental results indicate a higher fidelity to the true object configuration. Using the uniform distribution, we expect about 50% reduction in the acquisition time over the traditional method of equal linear angle acquisition.     

Coherent imaging of extended objects

When used with coherent light, optical imaging systems are inherently unable to reproduce both the amplitude and the phase of a two-dimensional field distribution. This is because their impulse response function varies slowly from point to point, a property known as non-isoplanatism. For sufficiently small objects, this usually results in a phase distortion and has no impact on the measured intensity. Here, we show that the intensity distribution can be dramatically distorted when extended objects are imaged. We illustrate the problem using two simple examples: the pinhole camera and the thin lens. The effects predicted by our theoretical analysis are confirmed by experimental observations.     

STrategically Acquired Gradient Echo (STAGE) imaging,part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method

One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors' product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinson's disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.     

Radial stereo imaging system for three-dimensional reconstruction

In this paper, we propose a radial stereo imaging system for three-dimensional reconstruction of an object. This system uses a novel optical device which consists of a camera and two reflected mirrors. It can reconstruct three-dimensional model of an object by capturing one image. Depending on the reflection of the mirrors, each world point in the scene has a pair of points which sit in the inner and outer of the captured image, respectively. We firstly analyze the imaging constraint of the proposed system, and then derive the geometrical relationship between world point and image point pair to calculate the three-dimensional coordinate values of an arbitrary point on the object, which means that the three-dimensional model of the object can be recovered. Experiments are carried out to prove our design, and the results show that our radial stereo imaging system is convenient and effective for the three-dimensional reconstruction of an object. This system can be used in variety of applications, such as virtual reality and commodity show.     

MAP-regularized robust reconstruction for underwater imaging detection

In order to enhance the visual quality of underwater images, applications such as enhancement and restoration can be applied, but the resolution is still limited. Super-resolution reconstruction is a widely used technique for improving resolution beyond the limit of imaging system. With knowledge of the point spread function and techniques of regularization, the performance of reconstruction can be further enhanced. The presented effort proposed a robust image super-resolution reconstruction method under maximum a posteriori framework with regularization by the point spread function for underwater imaging detection. Objective image quality metrics are used to quantify the effectiveness of the reconstruction. Experimental results showed that the proposed method can effectively improve the resolution and quality of underwater imaging detection.     

中子半影成像图像反演方法研究

给出了中子半影成像技术的原理;分析了系统分辨率的影响因子;利用MCNP软件对中子半影成像过程进行模拟;用改进的逆滤波和维纳滤波两种方法对成像主轴上一点源和偏轴两点源的编码图像进行重建,并对编码像在降低噪声和去除本底前后的反演结果进行了比较。模拟结果表明维纳滤波法能够更好地抑制统计噪声。     

中子半影成像图像反演方法研究

 给出了中子半影成像技术的原理;分析了系统分辨率的影响因子;利用MCNP软件对中子半影成像过程进行模拟;用改进的逆滤波和维纳滤波两种方法对成像主轴上一点源和偏轴两点源的编码图像进行重建,并对编码像在降低噪声和去除本底前后的反演结果进行了比较。模拟结果表明维纳滤波法能够更好地抑制统计噪声。     

SNR-optimized myocardial perfusion imaging using parallel acquisition for effective density-weighted saturation recovery imaging

The concept of density-weighted imaging and parallel acquisition for effective density-weighted (PLANED) imaging was transferred to saturation recovery (SR) sequences, in order to increase the SNR in first-pass myocardial perfusion imaging. Filtering in combination with density-weighted imaging allows SNR-optimized data weighting and the free choice of the corresponding spatial response function (SRF) simultaneously. This method was evaluated in simulations and applied successfully to phantom and in vivo first-pass myocardial perfusion studies. Unfiltered, Cartesian sampled images were compared to images acquired with SR-PLANED, which has been adjusted to result in an identical SRF as the Cartesian imaging. SNR-optimized SR-PLANED imaging improved the SNR up to 15% without changing acquisition time, the SRF or the field of view (FOV). The presented method provides high image quality and optimized SNR for first-pass myocardial perfusion imaging.     

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process,we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.     

相干场成像全相位目标直接重构法

回波信号处理和目标重构算法是相干场成像中的核心数据处理技术, 直接影响系统的成像质量. 基于全相位谱分析理论, 提出一种新的系统融合处理算法, 对接收端回波信号直接提取全相位谱相位及幅值信息实现目标图像重构, 能够有效抑制各种因素带来的频率误差. 经室外实验系统验证, 成像能力大大优于传统重构算法, 重构目标分辨率接近理论极限值.