A comparison study of multishot vs. single-shot DWI-EPI in the neonatal brain: reduced effects of ghosting compared to adults

In the neonatal brain, it is important to use a fast imaging technique to acquire all diffusion weighted images (DWI) for apparent diffusion coefficient (ADC) calculation. Taking into account the occurrence of typical echo planar imaging (EPI) artifacts, we have investigated whether single-shot (SSh) or multishot (MSh) DWI-EPI should be preferred. In 14 neonates, 17 adult patients and 5 adult volunteers, DWIs are obtained both with SSh and MSh EPI. The occurrence of artifacts and their influence on the ADC are explored and further quantified using simulations and phantom studies. Two radiologists scored overall image quality and diagnosability of all images. Single-shot and MSh DWI-EPI scored equally well in neonates with respect to overall image quality and diagnosability. In newborns, more motion artifacts in MSh can be noticed while N/2-ghost artifacts in SSh occur less frequently than in adults. Both N/2-ghost and motion artifacts result in significant ADC abnormalities. There is a serious risk that these artifacts will be mistaken for genuine diffusion abnormalities. N/2-ghost artifacts are hardly noticed in the neonatal brain, which might be due to smaller cerebrospinal fluid (CSF) velocity than in adults. Apparent diffusion coefficient values in MSh are unreliable if motion occurs. We conclude that for ADC calculations in neonates SSh DWI-EPI is more reliable than MSh.     

High resolution diffusion weighted magnetic resonance imaging of the pancreas using reduced field of view single-shot echo-planar imaging at 3 T

Diffusion weighted magnetic resonance imaging (DWI) has been mostly acquired using single-shot echo-planar imaging (ss EPI) to minimize motion induced artifacts. The spatial resolution, however, is inherently limited in ss EPI especially for abdominal imaging, even with the advances in parallel imaging. A novel method of reduced Field of View ss EPI (rFOV ss EPI) has achieved high resolution DWI in human carotid artery, spinal cord with reduced blurring and higher spatial resolution than conventional ss EPI, but it has not been used to pancreas imaging. In the work, comparisons between the full FOV ss-DW EPI and rFOV ss-DW EPI in image qualities and ADC values of pancreatic tumors and normal pancreatic tissues were performed to demonstrate the feasibility of pancreatic high resolution rFOV DWI. There were no significant differences in the mean ADC values between full FOV DWI and rFOV DWI for the 17 subjects using b = 600 s/mm² (P = 0.962). However, subjective scores of image quality was significantly higher at rFOV ss DWI (P = 0.008 and 0.000 for b-value = 0 s/mm² and 600 s/mm² respectively). The spatial resolution of DWI for pancreas was increased by a factor of over 2.0 (from almost 3.0 mm/pixel to 1.25 mm/pixel) using rFOV ss EPI technique. Reduced FOV ss EPI can provide good DW images and is promising to benefit applications for pancreatic diseases.     

Semiautomated segmentation of the human spine based on echoplanar images

The number of functional magnetic resonance imaging (fMRI) studies performed on the human spinal cord (SC) has considerably increased in recent years. The lack of a validated processing pipeline is, however, a significant obstacle to the spread of SC fMRI. One component likely to be involved in any such pipeline is the process of SC masking, analogous to brain extraction in cerebral fMRI. In general, SC masking has been performed manually, with the incumbent costs of being very time consuming and operator dependent. To overcome these drawbacks, we have developed a tailored semiautomatic method for segmenting echoplanar images (EPI) of human spine that is able to identify the spinal canal and the SC. The method exploits both temporal and spatial features of the EPI series and was tested and optimized on EPI images of cervical spine acquired at 3 T. The dependence of algorithm performance on the degree of EPI image distortion was assessed by computing the displacement warping field that best matched the EPI to the corresponding high-resolution T(2) images. Segmentation accuracy was above 80%, a significant improvement over values obtained with similar approaches, but not exploiting temporal information. Geometric distortion was found to explain about 50% of the variance of algorithm classification efficiency.     

High-resolution diffusion imaging using phase-corrected segmented echo-planar imaging

Diffusion magnetic resonance imaging (MRI) was performed with a high-resolution segmented echo-planar imaging technique, which provided images with substantially less susceptibility artifacts than images obtained with single-shot echo-planar imaging (EPI). Diffusion imaging performed with any multishot pulse sequence is inherently sensitive to motion artifacts and in order to reduce motion artifacts, the presented method utilizes navigator echo phase corrections, performed after a one-dimensional Fourier transform along the frequency-encoding direction. Navigator echo phases were fitted to a straight line prior to phase correction to avoid errors from internal motion. In vivo imaging was performed using electro cardiographic (ECG) triggering. Apparent diffusion coefficient (ADC) maps were calculated on a pixel-by-pixel basis using up to seven diffusion sensitivities, ranging from b = 0 to 1129 x 10(6) s/m(2).     

Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging

The accuracy of measuring voxel intensity changes between stimulus and rest images in fMRI echo-planar imaging (EPI) data is severely degraded in the presence of head motion. In addition, EPI is sensitive to susceptibility-induced geometric distortions. Head motion causes image shifts and associated field map changes that induce different geometric distortion at different time points. Conventionally, geometric distortion is "corrected" with a static field map independently of image registration. That approach ignores all field map changes induced by head motion. This work evaluates the improved motion correction capability of mapping slice to volume with concurrent iterative field corrected reconstruction using updated field maps derived from an initial static field map that has been spatially transformed and resampled. It accounts for motion-induced field map changes for translational and in-plane rotation motion. The results from simulated EPI time series data, in which motion, image intensity and activation ground truths are available, show improved accuracy in image registration, field corrected image reconstruction and activation detection.     

Parallel EPI artifact correction (PEAC) for N/2 ghost suppression in neuroimaging applications

Echo Planar Imaging (EPI) is a neuroimaging tool for clinical practice and research investigation. Due to odd-even echo phase inconsistencies, however, EPI suffers from Nyquist N/2 ghost artifacts. In standard neuroimaging protocols, EPI artifacts are suppressed using phase correction techniques that require reference data collected from a reference scan. Because reference-scan based techniques are sensitive to subject motion, EPI performance is sub-optimal in neuroimaging applications. In this technical note, we present a novel EPI data processing technique which we call Parallel EPI Artifact Correction (PEAC). By introducing an implicit data constraint associated with multi-coil sensitivity in parallel imaging, PEAC converts phase correction into a constrained problem that can be resolved using an iterative algorithm. This enables “reference-less” EPI that can improve neuroimaging performance. In the presented work, PEAC is investigated using a standard functional magnetic resonance imaging (fMRI) protocol with multi-slice 2D EPI. It is demonstrated that PEAC can suppress ghost artifacts as effectively as the standard reference-scan based phase correction technique used on a clinical MRI system. We also found that PEAC can achieve dynamic phase correction when motion occurs.     

Abdominal apparent diffusion coefficient measurements: effect of diffusion-weighted image quality and usefulness of anisotropic images

This study aimed to assess the effect of diffusion-weighted image (DWI) quality on abdominal apparent diffusion coefficient (ADC) measurements and the usefulness of anisotropic images. Twenty-six patients (10 men and 16 women; mean, 58.1 years) who underwent DW imaging and were diagnosed not to have any abdominal diseases were analyzed. Single-shot spin-echo echo-planar DW imaging was performed, and one isotropic and three orthogonal anisotropic images were created. ADCs were calculated for liver (four segments), spleen, pancreas (head, body, tail) and renal parenchyma. Image quality for each organ part was scored visually. We estimated the correlation between ADC and image quality and evaluated the feasibility of using anisotropic images. ADCs and image quality were affected by motion probing gradient directions in the liver and pancreas. A significant inverse correlation was found between ADC and image quality. The r values for isotropic images were −.46, −.48, −.70 and −.28 for the liver, spleen, pancreas and renal parenchyma, respectively. Anisotropic images had the best quality and lowest ADC in at least one organ part in 17 patients. DWIs with the best quality among isotropic and anisotropic images should be used in the liver and pancreas.     

Improved B0 field map estimation for high field EPI

Echo planar imaging (EPI) is an ultrafast magnetic resonance imaging (MRI) technique that allows one to acquire a 2D image in about 100 ms. Unfortunately, the standard EPI images suffer from substantial geometric distortions, mainly originating from susceptibility differences in adjacent tissues. To reduce EPI distortions, correction methods based on a field map, which is a map of the off-resonance frequencies, have been developed. In this work, a nonlinear least squares estimator is used to optimize the estimation of the field map of the B₀ field. The model of the EPI and reference data includes parameters for the phase evolution, the complex magnitude, the relaxation of the MRI signal and the EPI-specific phase difference between odd and even echoes, and from these parameters, additional corrections might be computed. The reference data required to estimate the field map can be acquired with a modified EPI-sequence. The proposed method is tested on simulated as well as experimental data and proves to be significantly more robust against noise, compared to the previously suggested method.     

Coil-combined split slice-GRAPPA for simultaneous multi-slice diffusion MRI

Objective: To develop a kernel optimization method called coil-combined split slice-GRAPPA (CC-SSG) to improve the accuracy of the reconstructed coil-combined images for simultaneous multi-slice (SMS) diffusion weighted imaging (DWI) data.Methods: The CC-SSG method optimizes the tuning parameters in the k-space SSG kernels to achieve an optimal trade-off between the intra-slice artifact and inter-slice leakage after the root-sum-of-squares (rSOS) coil combining of the de-aliased SMS DWI data. A detailed analysis is conducted to evaluate the contributions of the intra-slice artifact and inter-slice leakage to the total reconstruction error after coil combining.Results: Comparisons of the proposed CC-SSG method with the slice-GRAPPA (SG) and split slice-GRAPPA (SSG) methods are provided using two in-vivo readout-segmented (RS) EPI datasets collected from stroke patients. The CC-SSG method demonstrates improved accuracy of the reconstructed coil-combined images and the estimated diffusion tensor imaging (DTI) maps.Conclusion: CC-SSG strikes a good balance between the intra-slice artifact and inter-slice leakage for rSOS coil combining, and so can yield better reconstruction performance compared to SG and SSG for rSOS reconstruction. The optimal trade-off between the two artifacts is robust to the contrast of SMS data and the choice of the coil combining method.     

Theoretical and experimental study of the nonlinear resonance vibration of cementitious materials with an application to damage characterization

This paper presents a theoretical and experimental study of the nonlinear flexural vibration of a cement-based material with distributed microcracks caused by an important deterioration mechanism, alkali-silica reaction (ASR). The general equation of motion is derived for the flexural vibration of a slender beam with the nonlinear hysteretic constitutive relationship for consolidated materials, and then an approximate formula for excitation-dependent resonance frequency is obtained. A downward shift of the resonance frequency is related to the nonlinearity parameters defined in the constitutive relationship. Vibration experiments are conducted on standard mortar bar samples undergoing progressive ASR damage. The absolute nonlinearity parameters are determined from these experimental results using the theoretical solution in order to investigate their dependence on the damage state of the material. With the progress of the ASR damage, the absolute value of the hysteresis nonlinearity parameter increases by as much as six times from the intact (undamaged) state in the sample with highly reactive aggregate; this is in contrast to a change of about 16% in the linear resonance frequency. It is demonstrated that the combined theoretical and experimental approach developed in this research can be used to quantitatively characterize ASR damage in mortar samples and other cement-based materials.     

Improved optimization strategies for autofocusing motion compensation in MRI via the analysis of image metric maps

Autofocusing is a postprocessing technique for motion correction, which optimizes an image quality metric against various trial motions. In this work, image metric maps, which are measures of image quality plotted as a function of in-plane 2-D trial translations, are systematically studied to develop improved autofocusing motion correction algorithms. It is shown that determining object motion with autofocusing is equivalent to an image metric map optimization problem. These maps provide insights into the motion compensation process and help improve several aspects of the correction algorithm, including the selection of the image metric and motion search strategy. A highly efficient and robust 2-D global optimization method is devised, exploiting the properties of the metric map pattern. The improved algorithm is used to correct phantom and clinical MR images with in-plane 2-D translational motion and is shown to be more effective than existing methods.     

Activation of area V5 by visual perception of motion demonstrated with echoplanar MR imaging

Cortical activation in visual association areas known to be responsible for the perception of motion was investigated in two volunteers who viewed a projected animated cartoon periodically “run” and “frozen” during collection of echoplanar MR images. Ten axial, contiguous, 5 mm thick, T₂-weighted, gradient-echo images (TE 40 ms, TR 3000 ms) depicting BOLD contrast were acquired through the occipital lobe using a GE Signa 1.5 T system with an advanced NMR operating console. Images were analysed by time series regression modelling estimating power in the MR signal at the ON-OFF frequency of motion. Highly significant activation in response to motion perception was identified in both subjects bilaterally in area V5.     

改进GSO与二维OTSU融合的红外图像多阈值分割方法

针对二维最大累间方差（Nobuyuki OTSU,OTSU）图像分割算法在电气设备故障诊断与定位中,其红外图像的多阈值分割中存在的耗时多、分割精度低、误分割等不足,造成故障区域欠分割或者过分割的问题,提出一种改进的萤火虫算法（glowworm swarm optimization,GSO）与二维OTSU的融合算法来提高电气设备红外图像多阈值分割的实时性与准确度。寻优过程中,将局部寻优扩展到全局寻优,并引入非线性递减步长及新的移动策略对GSO进行优化改进。实验结果表明:该融合算法在分割结果上较二维OTSU及未改进GSO与二维OTSU融合算法更能准确分割运行电气设备图像异常区域,分割速度分别提高19倍、1.28倍,为红外图像早期故障的有效识别与定位奠定基础。     

Optimization of 3D MP-RAGE for neonatal brain imaging at 3.0 T

Three-dimensional (3D) magnetic resonance imaging (MRI) has shown great potential for studying the impact of prematurity and pathology on brain development. We have investigated the potential of optimized T1-weighted 3D magnetization-prepared rapid gradient-echo imaging (MP-RAGE) for obtaining contrast between white matter (WM) and gray matter (GM) in neonates at 3 T. Using numerical simulations, we predicted that the inversion time (TI) for obtaining strongest contrast at 3 T is approximately 2 s for neonates, whereas for adults, this value is approximately 1.3 s. The optimal neonatal TI value was found to be insensitive to reasonable variations of the assumed T1 relaxation times. The maximum theoretical contrast for neonates was found to be approximately one third of that for adults. Using the optimized TI values, MP-RAGE images were obtained from seven neonates and seven adults at 3 T, and the contrast-to-noise ratio (CNR) was measured for WM versus five GM regions. Compared to adults, neonates exhibited lower CNR between cortical GM and WM and showed a different pattern of regional variation in CNR. These results emphasize the importance of sequence optimization specifically for neonates and demonstrate the challenge in obtaining strong contrast in neonatal brain with T1-weighted 3D imaging.     

Apparent diffusion coefficient (ADC) measurements may be more reliable and reproducible than lesion volume on diffusion-weighted images from patients with acute ischaemic stroke-implications for study design

Early ischemic change after stroke can be demonstrated with diffusion-weighted imaging (DWI) and quantified by measuring the apparent diffusion coefficient (ADC) and/or lesion volume. We examined the reliability and reproducibility of lesion volume and ADC measurement on DWI images, and discuss the implications for clinical studies. Using 38 DWI scans from 15 stroke patients, two observers (a physicist and a neuroscience graduate) blind to each other, recorded the lesion volume on DWI sequences, measured the ADC values in this volume and calculated the ratio of ischemic: control ADC (ADCr). One observer repeated his measurements blind to his first, and also examined the effect on lesion volume and ADC of deliberately varying by only one pixel, the outline of the visible boundary of the lesion. The inter and intra-rater reliability were worse for lesion volume than ADC or ADCr measurements: lesion volume, inter-rater coefficient of variation (CoV) 85 +/- 130%, intra-rater CoV 20+/-SD80% (p < 0.05); ADC inter-rater CoV 7.7 +/- SD 19%, intra-rater CoV 0.2 +/- SD 12% (p = NS); and ADCr inter-rater CoV 8 +/- SD27%, intra-rater CoV 0.8 +/- SD73% (p = NS). Altering the position of the outline tracing of the lesion boundary by one pixel altered the measured volumes by 22 +/- SD25% (p < 0.05), but ADC values were altered by only 2.9 +/- SD4.9% and ADCr by 2.7 +/- SD4.8% (p = NS). ADC and ADCr values are more reliable and reproducible than DWI lesion size in acute ischemic stroke because altering where the lesion boundary is measured has a much greater impact on lesion volume than on the ADC or ADCr. This effect is greatest in large lesions.     

Preoperative fast MRI of brain tumors using three-dimensional segmented echo planar imaging compared to three-dimensional gradient echo technique

The purpose of this study was to compare the diagnostic efficacy of a newly developed T(1)-weighted three-dimensional segmented echo planar imaging (3D EPI) sequence versus a conventional T(1)-weighted three dimensional spoiled gradient echo (3D GRE) sequence in the evaluation of brain tumors. Forty-four patients with cerebral tumors and infections were examined on a 1.0 T MR unit with 23 mT/m gradient strength. The total scan time for the T(1) 3D EPI sequence was 2 min 12 s, and for a conventional 3D GRE sequence it was 4 min 59 s. Both sequences were performed after administration of a contrast agent. The images were analyzed by three radiologists. Image assessment criteria included lesion conspicuity, contrast between different types of normal tissue, and image artifacts. In addition, signal-to-noise and contrast-to-noise-ratio (C/N) were calculated. The gray-white differentiation and C/N ratio of 3D EPI were found to be inferior to conventional 3D GRE images, but the difference was not statistically significant. In the qualitative comparison, lesion detection and conspicuity of 3D EPI images and conventional 3D GRE images were similar, but a tow-fold reduction of the scanning time was obtained. With the 3D EPI technique, a 50% scan time reduction could be achieved with acceptable image quality compared to conventional 3D GRE. Thus, the 3D EPI technique could replace conventional 3D GRE in the preoperative imaging of brain.     

掺镱双包层光纤放大器分布泵浦方式下的优化算法

 利用平均反转率迭代算法计算掺镱双包层光纤放大器分布泵浦方式下的稳态速率方程组,并采用遗传算法对分布泵浦功率的大小和每段光纤长度同时进行优化。评估函数中引入了每段光纤中最高温度的标准方差,以确保每段光纤中的最高工作温度是相同的。通过优化,7段泵浦的最高温度和标准方差分别为126.34 ℃和1.95 ℃ ,8段泵浦条件下的最高温度和标准方差分别为119.76 ℃和2.12 ℃。而未经优化的7段泵浦的最高温度和标准方差分别为147.12 ℃和21.37 ℃,8段泵浦条件下的最高温度和标准方差分别为139.95 ℃和20.83 ℃。计算结果表明：泵浦方式的优化降低了最高温度和标准方差,实现了光纤中温度分布的均匀性,并且通过增加泵浦段数可以进一步降低最高温度和平坦温度分布。     

An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images

Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying structural image, are unavoidable distortions in EPI. Traditional susceptibility artifact correction (SAC) methods estimate the displacement field by optimizing an objective function that involves one or more pairs of reversed phase-encoding (PE) images. The estimated displacement field is then used to unwarp the distorted images and produce the corrected images. Since this conventional approach is time-consuming, we propose an end-to-end deep learning technique, named S-Net, to correct the susceptibility artifacts the reversed-PE image pair. The proposed S-Net consists of two components: (i) a convolutional neural network to map a reversed-PE image pair to the displacement field; and (ii) a spatial transform unit to unwarp the input images and produce the corrected images. The S-Net is trained using a set of reversed-PE image pairs and an unsupervised loss function, without ground-truth data. For a new image pair of reversed-PE images, the displacement field and corrected images are obtained simultaneously by evaluating the trained S-Net directly. Evaluations on three different datasets demonstrate that S-Net can correct the susceptibility artifacts in the reversed-PE images. Compared with two state-of-the-art SAC methods (TOPUP and TISAC), the proposed S-Net runs significantly faster: 20 times faster than TISAC and 369 times faster than TOPUP, while achieving a similar correction accuracy. Consequently, S-Net accelerates the medical image processing pipelines and makes the real-time correction for MRI scanners feasible. Our proposed technique also opens up a new direction in learning-based SAC.     

Quantitative assessment of motion correction for high angular resolution diffusion imaging

Several methods have been proposed for motion correction of high angular resolution diffusion imaging (HARDI) data. There have been few comparisons of these methods, partly due to a lack of quantitative metrics of performance. We compare two motion correction strategies using two figures of merit: displacement introduced by the motion correction and the 95% confidence interval of the cone of uncertainty of voxels with prolate tensors. What follows is a general approach for assessing motion correction of HARDI data that may have broad application for quality assurance and optimization of postprocessing protocols. Our analysis demonstrates two important issues related to motion correction of HARDI data: (1) although neither method we tested was dramatically superior in performance, both were dramatically better than performing no motion correction, and (2) iteration of motion correction can improve the final results. Based on the results demonstrated here, iterative motion correction is strongly recommended for HARDI acquisitions.     

Detection of bone metastases using diffusion weighted magnetic resonance imaging: comparison with C-methionine PET and bone scintigraphy

Purpose

We evaluated the ability of diffusion-weighted imaging (DWI) to detect bone metastasis by comparing the results obtained using this modality with those obtained using ¹¹C-methionine (MET) positron emission tomography (PET) and bone scintigraphy.

Materials and methods

This retrospective study involved 29 patients with bone metastasis. DWI was obtained using a single-shot echo planar imaging (EPI) sequence with fat suppression using a short inversion time inversion recovery sequence. The detection capabilities of DWI for bone metastases were compared with those of whole body MET PET (in 19 patients) and 99mTc-methylene diphosphonate bone scintigraphy (in 15 patients).

Results

Among the 19 patients who were diagnosed using DWI and PET, the PET identified 39 bone metastases, while the DWI identified 60 metastases out of 69 metastases revealed with conventional magnetic resonance imaging (MRI). Among the 15 patients who were diagnosed using DWI and bone scintigraphy, the bone scintigraphy identified 18 bone metastases, while the DWI identified 72 metastases out of 78 metastases revealed with conventional MRI. The overall bone metastasis detection rates were 56.5% for PET, 23.1% for bone scintigraphy and 92.3% for DWI.

Conclusion

DWI is a very sensitive method for detecting bone metastasis and is superior to MET PET and bone scintigraphy in terms of its detection capabilities.