在获取被试的张量数据后通常对其进行多通道线性平均以得到张量模板.但线性平均不仅会忽略张量中的向量信息，还会使灰质和白质的交界处过于平滑，降低模板的分辨率.为了解决以上问题，本文引入了四元数及高斯加权平均来构建高斯扩散张量成像（Diffusion Tensor Imaging，DTI）脑模板.本文首先对55个健康被试的DTI数据进行预处理，使得数据伪影最小化；再通过扩散张量成像工具包（Diffusion Tensor Imaging ToolKit，DTI-TK）将预处理后的数据进行初步空间标准化；然后将张量通过特征分解得到特征向量和特征值；最后，将由特征向量转化的四元数标量和特征值分别进行高斯加权平均得到平均后的特征向量和特征值，并对其进行重建得到张量模板.实验结果表明相比于线性DTI模板，高斯DTI模板在DTED、COH、DVED、OVL、corr_FA评估指标上表现更优，而IA指标较差，说明本文提出的高斯DTI模板在整体信息保留方面有所优化，但方向信息有所丢失.

基于扩散磁共振成像的纤维追踪技术为非侵入性观测脑白质结构提供了有力的手段，约束球面反卷积作为一种多纤维追踪模型，能够对体素内纤维的方向信息进行建模，进而实现脑纤维的重构.针对约束球面反卷积模型的不适定性以及细节信息丢失问题，本文在约束球面反卷积的基础上，结合邻域信息和分数阶正则化，提出了一种基于非局部约束球面反卷积模型的确定型纤维追踪算法，分数阶的非局部特性使得纤维方向分布模型估计的误差更小，而邻域信息的引入保证了空间一致性，可以减少噪声的影响.分别利用模拟数据、人脑实际数据对本文算法及基于约束球面反卷积的确定型纤维追踪算法作对比实验，结果表明，利用本文算法追踪的纤维不仅整体视觉效果上较整洁，而且对交叉纤维的重建结果更完整准确.

Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.

This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.Copyright © 2011 Elsevier B.V. All rights reserved.

The aim of the present study was to present a novel non-local mean (NLM) method to denoise diffusion tensor imaging (DTI) data in the tensor space. Compared with the original NLM method, which uses intensity similarity to weigh the voxel, the proposed method weighs the voxel using tensor similarity measures in the diffusion tensor space. Euclidean distance with rotational invariance, and Riemannian distance and Log-Euclidean distance with affine invariance were implemented to compare the geometric and orientation features of the diffusion tensor comprehensively. The accuracy and efficacy of the proposed novel NLM method using these three similarity measures in DTI space, along with unbiased novel NLM in diffusion-weighted image space, were compared quantitatively and qualitatively in the present study.

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.Copyright © 2019 Elsevier B.V. All rights reserved.

To estimate the spatially varying noise map using a redundant series of magnitude MR images.We exploit redundancy in non-Gaussian distributed multidirectional diffusion MRI data by identifying its noise-only principal components, based on the theory of noisy covariance matrices. The bulk of principal component analysis eigenvalues, arising due to noise, is described by the universal Marchenko-Pastur distribution, parameterized by the noise level. This allows us to estimate noise level in a local neighborhood based on the singular value decomposition of a matrix combining neighborhood voxels and diffusion directions.We present a model-independent local noise mapping method capable of estimating the noise level down to about 1% error. In contrast to current state-of-the-art techniques, the resultant noise maps do not show artifactual anatomical features that often reflect physiological noise, the presence of sharp edges, or a lack of adequate a priori knowledge of the expected form of MR signal.Simulations and experiments show that typical diffusion MRI data exhibit sufficient redundancy that enables accurate, precise, and robust estimation of the local noise level by interpreting the principal component analysis eigenspectrum in terms of the Marchenko-Pastur distribution. Magn Reson Med 76:1582-1593, 2016. © 2015 International Society for Magnetic Resonance in Medicine.© 2015 International Society for Magnetic Resonance in Medicine.

We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution.Copyright © 2016 Elsevier Inc. All rights reserved.

Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.Copyright © 2017. Published by Elsevier Inc.

目的 研究一种新颖的基于高阶奇异值分解（HOSVD）的扩散加权图像去噪算法，用以提高扩散加权（DW）图像的信噪比以及后续量化参数的准确性。方法 我们提出一种基于HOSVD稀疏约束和Rician噪声校正模型的去噪方法，将Rician噪声信号期望融合到传统的HOSVD去噪框架中，从而能够直接对带有Rician噪声的DW图像进行去噪。此外，考虑到对相似块组成的高维数组进行HOSVD去噪处理，容易引入条形伪影，因此本文直接对每个局部DW图像块进行HOSVD去噪，从而解决了条形伪影问题。为了验证所提方法的有效性，我们将本方法与低秩+边缘约束（LR+Edge）、基于全局指导下的局部高阶奇异值分解（GL-HOSVD）、基于块匹配的三维滤波（BM3D）和非局部均值（NLM）4种去噪算法进行了实验对比。结果 实验结果表明，所提方法能够有效降低DW图像噪声，同时较好的保留图像细节以及边缘结构信息。无论是从DW图像的峰值信噪比（PSNR）和结构相似性（SSIM）以及各向异性分数均方根误差定量指标，还是从去噪图像和各向异性分数图的视觉效果来看，本算法都要明显优于LR+Edge，BM3D和NLM。此外，GL-HOSVD虽然可以得到较好的去噪结果，但是在高噪声水平下，会引入条形伪影，而本文方法不但可以得到较好的去噪结果，并且不存在伪影问题。结论 本文提出了一种新颖的HOSVD去噪方法，可以直接处理带有Rician噪声的DW图像，并且解决了同类算法中伪影问题，去噪效果明显，能够为临床提供更准确的量化参数结果，更好服务于临床影像诊断。

To develop a joint denoising method that effectively exploits natural information redundancy in MR DWIs via low-rank patch matrix approximation.A denoising method is introduced to jointly reduce noise in DWI dataset by exploiting nonlocal self-similarity as well as local anatomical/structural similarity within multiple 2D DWIs acquired with the same anatomical geometry but different diffusion directions. Specifically, for each small 3D reference patch sliding within 2D DWI, nonlocal but similar patches are searched by matching image contents within entire DWI dataset and then structured into a patch matrix. The resulting patch matrices are denoised by enforcing low-rankness via weighted nuclear norm minimization and finally are back-distributed to DWI space. The proposed procedure was evaluated with simulated and in vivo brain diffusion tensor imaging (DTI) datasets and then compared to existing Marchenko-Pastur principal component analysis denoising method.The proposed method achieved significant noise reduction while preserving structural details in all DWIs for both simulated and in vivo datasets. Quantitative evaluation of error maps demonstrated it consistently outperformed Marchenko-Pastur principal component analysis method. Further, the denoised DWIs led to substantially improved DTI parametric maps, exhibiting significantly less noise and revealing more microstructural details.The proposed method denoises DWI dataset by utilizing both nonlocal self-similarity and local structural similarity within DWI dataset. This weighted nuclear norm minimization-based low-rank patch matrix denoising approach is effective and highly applicable to various diffusion MRI applications, including DTI as a postprocessing procedure.© 2022 International Society for Magnetic Resonance in Medicine.

Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR.Copyright © 2015 John Wiley & Sons, Ltd.

Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably address the issue of Gaussian noise. Optimization models based on deep learning are effective in estimating the real noise. However, there has thus far been little related research to summarize the different deep learning techniques for image denoising. In this paper, we offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images; the deep CNNs for real noisy images; the deep CNNs for blind denoising and the deep CNNs for hybrid noisy images, which represents the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of the different types of deep learning methods. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analyses. Finally, we point out some potential challenges and directions of future research.Copyright © 2020 Elsevier Ltd. All rights reserved.

With the increased interest in computer-aided image analysis methods, there is a greater need for objective methods of algorithm evaluation. Validation of in vivo MRI studies is complicated by a lack of reference data and the difficulty of constructing anatomically realistic physical phantoms. We present here an extensible MRI simulator that efficiently generates realistic three-dimensional (3-D) brain images using a hybrid Bloch equation and tissue template simulation that accounts for image contrast, partial volume, and noise. This allows image analysis methods to be evaluated with controlled degradations of image data.

Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction and hand-crafted parameter tuning. Although multiple studies have shown the promising applications of image denoising using convolutional neural networks (CNNs), none of them have considered denoising multiple b-value DWIs using a multichannel CNN model.To present a joint denoising CNN (JD-CNN) model to improve the SNR of multiple b-value DWI.Retrospective technical development.Twenty healthy rats and two rats with clinically confirmed focal cortical dysplasia were included to evaluate the performance of the proposed method.11.7T MRI, a multiple b-values DWI sequence.The total variation (TV) and BM3D denoising methods were also performed on the same dataset for comparison. Peak SNR (PSNR) and normalized mean square error (NMSE) were calculated for the assessment of image qualities.A paired Student's t-test was conducted to compare the diffusion parameter measurements between different approaches. P < 0.01 was considered statistically significant.Simulation results showed substantial improvement of image quality after JD-CNN denoising (PSNR of original image: 23.15 ± 1.77; PSNR of denoised image: 42.94 ± 2.12). The proposed method outperforms the state-of-the-art methods on high b-value DWIs in terms of PSNR (TV: 33.51 ± 0.83, BM3D: 35.12 ± 0.94, JD-CNN: 46.52 ± 0.98). In addition, the NMSE of the estimated apparent diffusion coefficient (ADC) reduces from 0.72 ± 0.13 to 0.45 ± 0.06 (P < 0.01) with the application of the JD-CNN model.The proposed method is able to remove noise with a wide range of noise levels in multiple b-value DWI and improve the diffusion parameter estimation. This shows potential clinical promise.2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1937-1947.© 2019 International Society for Magnetic Resonance in Medicine.

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography.SuperDTI was developed to learn the nonlinear relationship between DWIs and the corresponding diffusion tensor parameter maps. It bypasses the tensor fitting procedure, which is highly susceptible to noises and motions in DWIs. The network was trained and tested using data sets from the Human Connectome Project and patients with ischemic stroke. Results from SuperDTI were compared against widely used methods for tensor parameter estimation and fiber tracking.Using training and testing data acquired using the same protocol and scanner, SuperDTI was shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs, with a quantification error of less than 5% in all white-matter and gray-matter regions of interest. It was robust to noises and motions in the testing data. Furthermore, the network trained using healthy volunteer data showed no apparent reduction in lesion detectability when directly applied to stroke patient data.Our results demonstrate the feasibility of superfast DTI and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications.© 2021 International Society for Magnetic Resonance in Medicine.

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

Diffusion tensor imaging (DTI) is a promising technique for non-invasively investigating the myocardial fiber structures of human heart. However, low signal-to-noise ratio has been a major limit of cardiac DTI to prevent us from detecting myocardium structure accurately. Therefore, it is important to remove the effect of noise on DW images.Although the conventional and deep learning-based denoising methods have shown the potential to deal with effectively the noise in diffusion weighted (DW) images, most of them are redundant information dependent or require the noise-free images as golden standard. In addition, the existed DW image denoising methods often suffer from problems of over-smoothing. To address these issues, we propose a self-supervised learning model, structural similarity based convolutional neural network with edge-weighted loss (SSECNN), to remove the noise effectively in cardiac DTI.Considering that the DW images acquired along different diffusion directions have structural similarity, and the noise in these DW images is independent and identically distributed, the structural similarity-based matching algorithm is proposed to search for the most similar DW images. Such similar noisy DW image pairs are then used as the input and target of the denoising network SSECNN, which consists of several convolutional and residual blocks. Through the self-supervised training with these image pairs, the network can restore the clean DW images and retain the correlations between the denoised DW images along different directions. To avoid the over-smoothing problem, we design a novel edge-weighted loss which enables the network to adaptively adjust the loss weights with iterations and therefore to improve the detail preserve ability of the model. To verify the superiority of the proposed method, comparisons with state-of-the-art (SOTA) denoising methods are performed on both synthetic and real acquired DTI datasets.Experimental results show that SSECNN can effectively reduce the noise in the DW images while preserving detailed texture and edge information and therefore achieve better performance in DTI reconstruction. For synthetic dataset, compared to the SOTA method, the root mean square error (RMSE), peak signal to noise ratio (PSNR) and structure similarity (SSIM) between the denoised DW images obtained with SSECNN and noise-free DW images are improved by 6.94%, 1.98% and 0.76% respectively when the noise level is 10%. As for the acquired cardiac DTI dataset, the SSECNN method could significantly improve signal to noise ratio (SNR) and contrast to noise ratio (CNR) of cardiac DW images and achieve more regular helix angle (HA) and transverse angle (TA) maps. The ablation experimental results validate that using the structure similarity-based method to search the similar DW image pairs yield the smallest loss, and with the help of the edge-weighted loss, the denoised DW images and diffusion metric maps can preserve more details.The proposed SSECNN method can fully explore the similarity between the DW images along different diffusion directions. Using such similarity and an edge-weighted loss enable us to denoise cardiac DTI effectively in a self-supervised manner. Our method can overcome the redundancy information dependence and over-smoothing problem of the SOTA methods. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.

Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis. Over the past few years, many algorithms with impressive performances have been proposed. In this paper, inspired by the idea of deep learning, we introduce an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN). Specifically, to explore the structure similarity between neighboring slices, a 3D configuration is utilized as the basic processing unit. Residual autoencoders combined with deconvolution operations are introduced into the generator network. Furthermore, to alleviate the oversmoothing shortcoming of the traditional mean squared error (MSE) loss function, the perceptual similarity, which is implemented by calculating the distances in the feature space extracted by a pretrained VGG-19 network, is incorporated with the MSE and adversarial losses to form the new loss function. Extensive experiments are implemented to assess the performance of the proposed method. The experimental results show that the proposed RED-WGAN achieves performance superior to several state-of-the-art methods in both simulated and real clinical data. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.Copyright © 2019 Elsevier B.V. All rights reserved.

Single-shot (SS) EPI is widely used for clinical DWI. This study aims to develop an end-to-end deep learning-based method with a novel loss function in an improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI DWI.Point-spread-function (PSF)-encoded EPI can provide high-resolution, distortion-free DWI images. A distorted image from SS-EPI can be described as the convolution between a PSF function with a distortion-free image. The deconvolution process to recover the distortion-free image can be achieved with a convolution neural network, which also learns the mapping function between low-resolution SS-EPI and high-resolution reference PSF-EPI to achieve superresolution. To suppress the oversmoothing effect, we proposed a modified generative adversarial network structure, in which a dense net with gradient map guidance and a multilevel fusion block was used as the generator. A fractional anisotropy loss was proposed to utilize the diffusion anisotropy information among diffusion directions. In vivo brain DWI data were used to test the proposed method.The results show that distortion-corrected high-resolution DWI images with restored structural details can be obtained from low-resolution SS-EPI images by taking advantage of the high-resolution anatomical images. Additionally, the proposed network can improve the quantitative accuracy of diffusion metrics compared with previously reported networks.Using high-resolution, distortion-free EPI-DWI images as references, a deep learning-based method to simultaneously increase the perceived resolution and correct distortions for low-resolution SS-EPI was proposed. The results show that DWI image quality and diffusion metrics can be improved.© 2023 International Society for Magnetic Resonance in Medicine.

Phantom-based validation of diffusion-weighted image processing techniques is an important key to innovation in the field and is widely used. Openly available and user friendly tools for the flexible generation of tailor-made datasets for the specific tasks at hand can greatly facilitate the work of researchers around the world.We present an open-source framework, Fiberfox, that enables (1) the intuitive definition of arbitrary artificial white matter fiber tracts, (2) signal generation from those fibers by means of the most recent multi-compartment modeling techniques, and (3) simulation of the actual MR acquisition that allows for the introduction of realistic MRI-related effects into the final image.We show that real acquisitions can be closely approximated by simulating the acquisition of the well-known FiberCup phantom. We further demonstrate the advantages of our framework by evaluating the effects of imaging artifacts and acquisition settings on the outcome of 12 tractography algorithms.Our findings suggest that experiments on a realistic software phantom might change the conclusions drawn from earlier hardware phantom experiments. Fiberfox may find application in validating and further developing methods such as tractography, super-resolution, diffusion modeling or artifact correction.Copyright © 2013 Wiley Periodicals, Inc.