SENSE phase-constrained magnitude reconstruction with iterative phase refinement.

Conventional sensitivity encoding (SENSE) reconstruction is based on equations in the complex domain. However, for many MRI applications only the magnitude is relevant. If there exists an estimate of the underlying phase information, a magnitude-only phase-constrained reconstruction can help to improve the conditioning of the SENSE reconstruction problem. Consequently, this reduces g-factor-related noise enhancement. In previous attempts at phase-constrained SENSE reconstruction, image quality was hampered by strong aliasing artifacts resulting from inadequate phase estimates and high sensitivity to phase errors. If a full-resolution phase image is used, a significant reduction in aliasing errors and better noise properties compared to SENSE can be obtained. An iterative scheme that improves the phase estimate to better approximate the phase is presented. The mathematical framework of the new approach is provided together with comparisons of conventional SENSE, phase-constrained SENSE, and the new phase-refinement method. Both theory and experimental verification demonstrate significantly better noise performance at high reduction factors, i.e., close to the theoretical limit. For applications that need only magnitude data, an iterative phase-constrained SENSE reconstruction can provide substantial SNR improvement over SENSE reconstruction and less artifacts than phase-constrained SENSE.     

Computationally rapid method of estimating signal‐to‐noise ratio for phased array image reconstructions

Measuring signal‐to‐noise ratio (SNR) for parallel MRI reconstructions is difficult due to spatially dependent noise amplification. Existing approaches for measuring parallel MRI SNR are limited because they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A new SNR estimation approach is proposed, a hybrid of the repeated image acquisitions method detailed in the National Electrical Manufacturers Association (NEMA) standard and the Monte Carlo based pseudo‐multiple replica method, in which the difference between images reconstructed from the unaltered acquired data and that same data reconstructed after the addition of calibrated pseudo‐noise is used to estimate the noise in the parallel MRI image reconstruction. This new noise estimation method can be used to rapidly compute the pixel‐wise SNR of the image generated from any parallel MRI reconstruction of a single acquisition. SNR maps calculated with the new method are validated against existing SNR calculation techniques. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Improved diffusion imaging through SNR‐enhancing joint reconstruction

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal‐to‐noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstruction method that takes advantage of the high level of structural correlation observed in typical datasets. Compared to existing denoising methods, the proposed method performs reconstruction directly from the measured complex k‐space data, allowing for Gaussian noise modeling and theoretical characterizations of the resolution and signal‐to‐noise ratio of the reconstructed images. In addition, the proposed method is compatible with many different models of the diffusion signal (e.g., diffusion tensor modeling and q‐space modeling). The joint reconstruction method can provide significant improvements in signal‐to‐noise ratio relative to conventional reconstruction techniques, with a relatively minor corresponding loss in image resolution. Results are shown in the context of diffusion spectrum imaging tractography and diffusion tensor imaging, illustrating the potential of this signal‐to‐noise ratio‐enhancing joint reconstruction approach for a range of different diffusion imaging experiments. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Conditional entropy maximization for PET image reconstruction using adaptive mesh model.

Iterative image reconstruction algorithms have been widely used in the field of positron emission tomography (PET). However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations is high. In this paper, we propose a new algorithm to reconstruct an image from the PET emission projection data by using the conditional entropy maximization and the adaptive mesh model. In a traditional tomography reconstruction method, the reconstructed image is directly computed in the pixel domain. Unlike this kind of methods, the proposed approach is performed by estimating the nodal values from the observed projection data in a mesh domain. In our method, the initial Delaunay triangulation mesh is generated from a set of randomly selected pixel points, and it is then modified according to the pixel intensity value of the estimated image at each iteration step in which the conditional entropy maximization is used. The advantage of using the adaptive mesh model for image reconstruction is that it provides a natural spatially adaptive smoothness mechanism. In experiments using the synthetic and clinical data, it is found that the proposed algorithm is more robust to noise compared to the common pixel-based MLEM algorithm and mesh-based MLEM with a fixed mesh structure.     

Comprehensive quantification of signal‐to‐noise ratio and g‐factor for image‐based and k‐space‐based parallel imaging reconstructions

Parallel imaging reconstructions result in spatially varying noise amplification characterized by the g‐factor, precluding conventional measurements of noise from the final image. A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal‐to‐noise ratio and g‐factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment. Only a simple “prescan” measurement of noise amplitude and correlation in the phased‐array receiver, and a single accelerated image acquisition are required, allowing robust assessment of signal‐to‐noise ratio and g‐factor. The “pseudo multiple replica” method has been rigorously validated in phantoms and in vivo, showing excellent agreement with true multiple replica and analytical methods. This method is universally applicable to the parallel imaging reconstruction techniques used in clinical applications and will allow pixel‐by‐pixel image noise measurements for all parallel imaging strategies, allowing quantitative comparison between arbitrary k‐space trajectories, image reconstruction, or noise conditioning techniques. Magn Reson Med 60:895–907, 2008. © 2008 Wiley‐Liss, Inc.     

On optimality of parallel MRI reconstruction in k-space.

Parallel MRI reconstruction in k-space has several advantages, such as tolerance to calibration data errors and efficient non-Cartesian data processing. These benefits largely accrue from the approximation that a given unsampled k-space datum can be synthesized from only a few local samples. In this study, several aspects of parallel MRI reconstruction in k-space are studied: the design of optimized reconstruction kernels, the effect of regularization on image error, and the accuracy of different k-space-based parallel MRI methods. Reconstruction of parallel MRI data in k-space is posed as the problem of approximating the pseudoinverse with a sparse matrix. The error of the approximation is used as an optimization criterion to find reconstruction kernels optimized for the given coil setup. An efficient algorithm for automatic selection of reconstruction kernels is described. Additionally, a total error metric is introduced for validation of the reconstruction kernel and choice of regularization parameters. The new methods yield reduced reconstruction and noise errors in both simulated and real data studies when compared with existing methods. The new methods may be useful for reduction of image errors, faster data processing, and validation of parallel MRI reconstruction design for a given coil system and k-space trajectory.     

An elliptical SPECT system with slit-slat collimation for cardiac imaging

Cardiac studies are a good candidate for SPECT (single photon emission computed tomography) because of the large clinical demand and the need for improved image quality. But SPECT imaging suffers from poor spatial resolution and high statistical noise. A new SPECT system with slit-slat collimation arranged on an elliptical arc for cardiac imaging is proposed in this paper. Simulated emission computed tomography data are generated along an elliptical moving orbit with system configuration parameters. The iterative reconstruction techniques are used to implement the cardiac imaging of the proposed SPECT system. Image reconstruction can be done using the OS-EM algorithm from the data collected. This system is developed to improve the reconstructed image if attenuation correction and depth-dependent correction are included in the reconstruction, and the body contour is used in the reconstruction for severely truncated data. The simulation results show that the present methods can provide significant improvement in the spatial resolution and the image quality of SPECT sets.     

基于多模型的迭代重建技术对低剂量CT结肠成像图像质量的影响

目的 评价Revolution CT应用不同水平基于多模型的迭代重建算法（ASIR-V）对提高低剂量CT结肠成像图像质量的能力。方法 选用离体猪结肠获得模拟息肉30个,使用Revolution CT在不同扫描条件（管电压120 kVp,管电流10、30、50、70、90、100、120、140、160、180、200、220、240、260 mA）下扫描,分别应用6种不同水平ASIR-V（0、10%、30%、50%、70%、90%）算法进行图像重建。两名观察者盲法对84组CT结肠成像重组图像[CT仿真内镜（CTVE）、多平面重建（MPR）、容积再现（VR）、虚拟分割（VD）]分别进行主观质量评分（4分制）,同时独立测量图像噪声（SD）、信噪比（SNR）及对比噪声比（CNR）。比较图像质量主观评分一致性及不同管电流、不同水平ASIR-V重建图像的SD、SNR、CNR差异。结果 两观察者图像质量主观评分一致性好（Kappa值=0.683）,管电流（r=0.734,P=0.000）及ASIR-V水平（r=0.220,P=0.044）的变化与图像质量主观评分相关,相同管电流条件下,50% ASIR-V重建图像质量主观评分最高。两观察者客观数据一致性良好。不同管电流、不同水平ASIR-V重建图像的SD、SNR及CNR差异均具有统计学意义（F=423.58、124.26、1 030.17,P<0.05）。同一管电流水平下,随着ASIR-V水平增高,图像SD降低,CNR增大;图像SNR在管电流为10、120、140、160、220、240、260 mA水平下差异具有统计学意义（F=8.75~31.36,P<0.05）。相同水平的ASIR-V重建,随管电流增大,图像SD下降,SNR及CNR逐渐升高。结论 CT结肠成像中,应用ASIR-V算法可以显著降低噪声,提高图像对比噪声比,提高图像质量,且50% ASIR-V水平在降噪能力方面更为显著。     

Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction

A novel coefficient penalized regularization method for generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is developed for improving MR image quality. In this method, the fitting coefficients of the source data are weighted with different penalty factors, which are highly dependent upon the relative displacements from the source data to the target data in k‐space. The imaging data from both phantom testing and in vivo MRI experiments demonstrate that the coefficient penalized regularization method in GRAPPA reconstruction is able to reduce noise amplification to a greater degree. Therefore, the method enhances the quality of images significantly when compared to the previous least squares and Tikhonov regularization methods. Magn Reson Med 69:1109–1114, 2013. © 2012 Wiley Periodicals, Inc.     

Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations

Despite continuous hardware advances, MRI is frequently subject to field perturbations that are of higher than first order in space and thus violate the traditional k‐space picture of spatial encoding. Sources of higher order perturbations include eddy currents, concomitant fields, thermal drifts, and imperfections of higher order shim systems. In conventional MRI with Fourier reconstruction, they give rise to geometric distortions, blurring, artifacts, and error in quantitative data. This work describes an alternative approach in which the entire field evolution, including higher order effects, is accounted for by viewing image reconstruction as a generic inverse problem. The relevant field evolutions are measured with a third‐order NMR field camera. Algebraic reconstruction is then formulated such as to jointly minimize artifacts and noise in the resulting image. It is solved by an iterative conjugate‐gradient algorithm that uses explicit matrix‐vector multiplication to accommodate arbitrary net encoding. The feasibility and benefits of this approach are demonstrated by examples of diffusion imaging. In a phantom study, it is shown that higher order reconstruction largely overcomes variable image distortions that diffusion gradients induce in EPI data. In vivo experiments then demonstrate that the resulting geometric consistency permits straightforward tensor analysis without coregistration. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

低辐射剂量肝脏CT增强扫描：80kVp结合迭代重建技术的初步研究

目的：探讨80 kVp管电压结合迭代重建技术（SAFIRE）在正常体重人群肝脏CT增强扫描中应用的可行性。方法：前瞻性地对身体质量指数（BMI）18～24 kg／m2、临床怀疑肝脏疾病的46例患者采用低辐射剂量（80 kVp、420 mAs）进行CT增强扫描,对比剂注射采用个性化方案。以滤波反投影法（FBP）和5种强度SAFIRE（1～5）方法分别进行图像重建。图像质量的客观评估指标：图像噪声、对比噪声比（CNR）、品质指数（FOM）。图像质量的主观评估：对图像噪声、血管显示、伪影及图像整体质量进行主观评分,并对动脉晚期的期相进行评估。结果：图像质量客观评估结果：SAFIRE-5图像的噪声最小,CNR及 FOM 值最高,与其余5组图像间差异均有统计学意义（P＜0．05）。图像质量主观评估结果：SAFIRE-5图像的噪声、血管显示情况及硬化伪影的评估结果均优于其它5组,但“蜡样”伪影的评分明显低于其它各组,使其图像整体评分受到影响;SAFIRE-3图像的整体评分最高,与其它各组间差异有统计学意义;95．6％（44／46）的动脉晚期图像质量合格。结论：正常体型人群行肝脏CT增强扫描时,使用80kVp结合迭代重建技术及个性化对比剂注射方案,能得到较好的图像质量;此种条件下,建议使用SAFIRE-3进行图像重建。     

Bootstrap methods for estimating PET image noise: experimental validation and an application to evaluation of image reconstruction algorithms

Objective

Accurate and validated methods for estimating regional PET image noise are helpful for optimizing image processing. The bootstrap is a data-based simulation method for statistical inference, which can be used to estimate the PET image noise without repeated measurements. The aim of this study was to experimentally validate bootstrap-based methods as a tool for estimating PET image noise and demonstrate its usefulness for evaluating image reconstruction algorithms.

Methods

Two bootstrap-based method, the list-mode data bootstrap (LMBS) and the sinogram bootstrap (SNBS), were implemented on a clinical PET scanner. A uniform cylindrical phantom filled with ¹⁸F solution was scanned using list-mode acquisition. A reference standard deviation (SD) map was calculated from 60 statistically independent measured list-mode data. Using one of the 60 list-mode data, 60 bootstrap replicates were generated and used to calculate bootstrap SD maps. Brain ¹⁸F-FDG data from a healthy volunteer were also processed as an example of the bootstrap application. Three reconstruction algorithms, FBP 2D and both 2D and 3D versions of dynamic row-action maximum likelihood algorithm (DRAMA), were assessed.

Results

For all the reconstruction algorithms used, the bootstrap SD maps agreed well with the reference SD map, confirming the validity of the bootstrap methods for assessing image noise. The two bootstrap methods were equivalent with respect to the performance of image noise estimation. The bootstrap analysis of the FDG data showed the better contrast–noise relation curve for DRAMA 3D compared to DRAMA 2D and FBP 2D.

Conclusions

The bootstrap methods provide the estimates of image noise for various reconstruction algorithms with reasonable accuracy, require only a single measurement, not repeated measures, and are, therefore, applicable for a human PET study.     

Image reconstruction: An overview for clinicians

Image reconstruction plays a critical role in the clinical use of magnetic resonance imaging (MRI). The MRI raw data is not acquired in image space and the role of the image reconstruction process is to transform the acquired raw data into images that can be interpreted clinically. This process involves multiple signal processing steps that each have an impact on the image quality. This review explains the basic terminology used for describing and quantifying image quality in terms of signal‐to‐noise ratio and point spread function. In this context, several commonly used image reconstruction components are discussed. The image reconstruction components covered include noise prewhitening for phased array data acquisition, interpolation needed to reconstruct square pixels, raw data filtering for reducing Gibbs ringing artifacts, Fourier transforms connecting the raw data with image space, and phased array coil combination. The treatment of phased array coils includes a general explanation of parallel imaging as a coil combination technique. The review is aimed at readers with no signal processing experience and should enable them to understand what role basic image reconstruction steps play in the formation of clinical images and how the resulting image quality is described . J. Magn. Reson. Imaging 2014 . © 2014 Wiley Periodicals, Inc. J. Magn. Reson. Imaging 2015;41:573–585. © 2014 Wiley Periodicals, Inc.     

A clinical perspective of accelerated statistical reconstruction

Although the potential benefits of maximum likelihood reconstruction have been recognised for many years, the technique has only recently found widespread popularity in clinical practice. Factors which have contributed to the wider acceptance include improved models for the emission process, better understanding of the properties of the algorithm and, not least, the practicality of application with the development of acceleration schemes and the improved speed of computers. The objective in this article is to present a framework for applying maximum likelihood reconstruction for a wide range of clinically based problems. The article draws particularly on the experience of the three authors in applying an acceleration scheme involving use of ordered subsets to a range of applications. The potential advantages of statistical reconstruction techniques include: (a) the ability to better model the emission and detection process, in order to make the reconstruction converge to a quantitative image, (b) the inclusion of a statistical noise model which results in better noise characteristics, and (c) the possibility to incorporate prior knowledge about the distribution being imaged. The great flexibility in adapting the reconstruction for a specific model results in these techniques having wide applicability to problems in clinical nuclear medicine.     

Image reconstruction in SNR units: a general method for SNR measurement.

The method for phased array image reconstruction of uniform noise images may be used in conjunction with proper image scaling as a means of reconstructing images directly in SNR units. This facilitates accurate and precise SNR measurement on a per pixel basis. This method is applicable to root-sum-of-squares magnitude combining, B(1)-weighted combining, and parallel imaging such as SENSE. A procedure for image reconstruction and scaling is presented, and the method for SNR measurement is validated with phantom data. Alternative methods that rely on noise only regions are not appropriate for parallel imaging where the noise level is highly variable across the field-of-view. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units. The procedure includes scaling for noise equivalent bandwidth of digital receivers, FFTs and associated window functions (raw data filters), and array combining.     

Characteristic of evaluation of new image filter devised for improvement of workflow in CT examination

In this study, we processed reconstructed images with a new image filter (Siemens Medical Systems, Adaptive Image Filter: AIF). As one of its characteristics, the filter uses low-pass filtering. When an image that emphasizes a high-frequency element is changed to one with a reduced high-frequency element, an image suitable for clinical use can be obtained. For the resolving characteristic and the noise characteristic, we evaluated the degree of transition, using the modulation transfer factor (MTF) and Wiener spectrum (WS). Moreover, we used the signal-to-noise ratio (SNR) to examine the total loss of signal detection capability after use of the AIF. The results showed that, when we changed to images using the AIF and made it the same level as B30 and U40, we had to hold down the kernel level to at least B60 and U80. The use of an image filter did not recognize less of an SNR in comparison with the reconstruction image. In this study, changes in detailed characteristics of the image and SNR could be evaluated objectively using the AIF. As for the effective method by AIF that raw data isn't used for is available for the control of an image (times) using reconstruction and the change of an image on database. Therefore, we consider the AIF useful to improve workflow in CT examinations.     

A new silent magnetic resonance imaging using a rotating DC gradient

A new approach to silent MR imaging using a rotating DC gradient has been explored and experimentally studied. Acoustic or sound noise has been one of the problems in examining patients, mainly due to the fast gradient pulsings in interaction with the main magnetic field. The sound noise has been noted to be proportionately louder as the magnetic field strength becomes larger. In this report, we describe a new imaging technique using a mechanically rotating DC gradient coil. The rotating DC gradient coil can effectively replace both phase encoding as well as readout gradient pulsings, and data obtained in this manner can provide a set of projection data that later can be used for projection reconstruction. With some interpolation techniques one can also perform conventional two-dimensional fast Fourier transform image reconstruction. The sound noise intensity compared with the conventional imaging technique, such as the spin-echo sequence, has been reduced down to about ?20.7 dB or 117.5 times with this new technique. The experimental pulse sequence and its principle are described and images obtained by the new silent MR imaging technique are reported.     

U-SPECT-I: a novel system for submillimeter-resolution tomography with radiolabeled molecules in mice.

A major advance in biomedical science and diagnosis was accomplished with the development of in vivo techniques to image radiolabeled molecules, but limited spatial resolution has slowed down applications to small experimental animals. Here, we present a SPECT system (U-SPECT-I) dedicated to radionuclide imaging of murine organs at a submillimeter resolution. METHODS: The high performance of U-SPECT-I is based on a static triangular detector setup, with a cylindric imaging cavity in the center and 75 gold micropinhole apertures in the cavity wall. The pinholes are focused on a small volume of interest such as the mouse heart or spine to maximize the detection yield of gamma-photons. Three-dimensional molecular distributions are iteratively estimated using the detector data and a statistical reconstruction algorithm that takes into account system blurring and data noise to increase resolution and reduce image noise. RESULTS: With 0.6-mm-diameter pinholes, the maximum fraction of detected photons emitted by a point source (peak sensitivity) is 0.22% for a 15%-wide energy window and remains higher than 0.12% in the central 12 mm of the central plane. In a resolution phantom, radioactively filled capillaries as small as 0.5 mm and separated by 0.5 mm can be distinguished clearly in reconstructions. Projection data needed for the reconstruction of cross sections of molecular distributions in mouse organs can readily be obtained without the need for any mechanical movements. Images of a mouse spine show 99mTc-hydroxymethylene diphosphonate uptake down to the level of tiny parts of vertebral processes. These are separated clearly from the vertebral and intervertebral foramina. Using another tracer, one can monitor myocardial perfusion in the left and right ventricular walls, even in structures as small as the papillary muscles. CONCLUSION: U-SPECT-I allows discrimination between molecular concentrations in adjacent volumes of as small as about 0.1 muL, which is significantly smaller than can be imaged by any existing SPECT or PET system. Our initial in vivo images of the mouse heart and spine show that U-SPECT-I can be used for novel applications in the study of dynamic biologic systems with a clear projection to clinical applications. The combination of high resolution and detection efficiency of U-SPECT-I opens up new possibilities for the suborgan-level study of radiotracers in mouse models.     

New iterative reconstruction techniques for cardiovascular computed tomography: how do they work, and what are the advantages and disadvantages?

The radiation doses associated with diagnostic CT scans has recently come under scrutiny. In the process of developing protocols with lower doses, it has become apparent that images reconstructed with a filtered back projection (FBP) technique are often inadequate. Although very fast and robust, FBP images are prone to high noise, streak artifacts and poor low contrast detectability in low dose situations. Manufacturers of CT equipment have responded to this limitation by developing new image reconstruction techniques that derive more information from the data set. These techniques are based on the use of maximum likelihood algorithms and are referred to at iterative reconstructions. This iterative process can be used on the slice data alone, a combination of raw and slice data or on the raw data alone. The latter approach, which is referred to as model based iterative reconstruction, is the most computationally demanding as it models the entire process, from the shape of the focal spot on the anode, the shape of the emerging x-ray beam, the three-dimensional interaction of the beam with the voxel in the patient and the two-dimensional interation of the beam with the detector. This article discusses the fundamentals of iterative reconstruction techniques, the pros and cons of the various manufacturer approaches and specific applications, especially to cardiovascular CT.     

Evaluation of non-linear adaptive smoothing filter by digital phantom

As a result of the development of multi-slice CT, diagnoses based on three-dimensional reconstruction images and multi-planar reconstruction have spread. For these applications, which require high z-resolution, thin slice imaging is essential. However, because z-resolution is always based on a trade-off with image noise, thin slice imaging is necessarily accompanied by an increase in noise level. To improve the quality of thin slice images, a non-linear adaptive smoothing filter has been developed, and is being widely applied to clinical use. We developed a digital bar pattern phantom for the purpose of evaluating the effect of this filter and attempted evaluation from an addition image of the bar pattern phantom and the image of the water phantom. The effect of this filter was changed in a complex manner by the contrast and spatial frequency of the original image. We have confirmed the reduced effect of image noise in the low frequency component of the image, but decreased contrast or increased quantity of noise in the image of the high frequency component. This result represents the effect of change in the adaptation of this filter. The digital phantom was useful for this evaluation, but to understand the total effect of filtering, much improvement of the shape of the digital phantom is required.