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Objectives: To evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements.Methods: Eleven patients with early-stage knee osteoarthritis and ten healthy controls underwent dGEMRIC twice at 3 T. Controls underwent unenhanced imaging. ROIs were manually drawn on the femoral and tibial cartilage. T1 calculation was performed with and without registration of the T1-weighted images. Automated three-dimensional rigid registration was performed on the femur and tibia cartilage separately. Registration quality was evaluated using the square root Cramér-Rao lower bound (CRLB(σ)). Additionally, the reproducibility of dGEMRIC was assessed by comparing automated registration with manual slice-matching.Results: Automated registration of the T1-weighted images improved the T1 maps as the 90% percentile of the CRLB(σ) was significantly (P < 0.05) reduced with a median reduction of 55.8 ms (patients) and 112.9 ms (controls). Manual matching and automated registration of the re-imaged T1 map gave comparable intraclass correlation coefficients of respectively 0.89/0.90 (patients) and 0.85/0.85 (controls).Conclusions: Registration in dGEMRIC reduces movement artefacts on T1 maps and provides a good alternative to manual slice-matching in longitudinal studies. [ABSTRACT FROM AUTHOR]

Purpose: Myocardial blood flow (MBF) obtained by dynamic CT perfusion (CTP) has been recently introduced to assess hemodynamic significance of coronary stenosis in coronary artery disease. The diagnostic performance of dynamic CTP MBF is limited due to subjective interpretation of MBF maps and MBF variations caused by physiological, methodological, and technical issues. In this paper, we introduce a novel method to quantify the hypoperfused volume (HPV) in myocardial territories derived from CT angiography (CTA) to overcome the limitations of current dynamic CTP MBF analysis methods., Methods: The diagnostic performance of HPV in classifying significant stenoses was evaluated on 22 patients (57 vessels) that underwent CTA, CTP and invasive fractional flow reserve (FFR). FFR was used as the standard of reference to determine stenosis significance. The diagnostic performance was compared to that of the mean MBF computed in regions manually annotated by an expert (MA-MBF). HPV was derived by thresholding the MBF in myocardial territories constructed from CTA by locating the closest artery. Diagnostic performance was evaluated using leave-one-case out cross-validation. Inter-observer reproducibility was assessed by performing annotations of coronary seeds (HPV) and manual regions (MA-MBF) with two users. In addition, the influence of different parameter settings on the diagnostic performance of HPV was assessed., Results: Leave-one-case out cross-validation showed that HPV has an accuracy of 72% (58-83%) with sensitivity of 72% (47-90%) and specificity of 72% (58-83%). The accuracy of MA-MBF was 70% (57-82%) with a sensitivity of 50% (26-74%) and a specificity of 79% (64-91%). The Spearman correlation and the kappa statistic was (ρ = 0.94, κ = 0.86) for HPV and (ρ = 0.72, κ = 0.82) for MA-MBF. The influence of parameter settings on HPV based diagnostic performance was not significant., Conclusions: The proposed HPV accurately classifies hemodynamically significant stenoses with a level of accuracy comparable to the mean MBF in regions annotated by an expert. HPV improves inter-observer reproducibility as compared to MA-MBF by providing a more objective criterion to associate the stenotic coronary with the supplied myocardial territory., (© 2017 American Association of Physicists in Medicine.)

Purpose: This paper presents and studies a framework for reliable modeling of diffusion MRI using a data-acquisition adaptive prior., Methods: Automated relevance determination estimates the mean of the posterior distribution of a rank-2 dual tensor model exploiting Jeffreys prior (JARD). This data-acquisition prior is based on the Fisher information matrix and enables the assessment whether two tensors are mandatory to describe the data. The method is compared to Maximum Likelihood Estimation (MLE) of the dual tensor model and to FSL's ball-and-stick approach., Results: Monte Carlo experiments demonstrated that JARD's volume fractions correlated well with the ground truth for single and crossing fiber configurations. In single fiber configurations JARD automatically reduced the volume fraction of one compartment to (almost) zero. The variance in fractional anisotropy (FA) of the main tensor component was thereby reduced compared to MLE. JARD and MLE gave a comparable outcome in data simulating crossing fibers. On brain data, JARD yielded a smaller spread in FA along the corpus callosum compared to MLE. Tract-based spatial statistics demonstrated a higher sensitivity in detecting age-related white matter atrophy using JARD compared to both MLE and the ball-and-stick approach., Conclusions: The proposed framework offers accurate and precise estimation of diffusion properties in single and dual fiber regions., Competing Interests: The authors have declared that no competing interests exist.

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Purpose: A novel three-dimensional (3D) T1 and T2 mapping protocol for the carotid artery is presented., Methods: A 3D black-blood imaging sequence was adapted allowing carotid T1 and T2 mapping using multiple flip angles and echo time (TE) preparation times. B1 mapping was performed to correct for spatially varying deviations from the nominal flip angle. The protocol was optimized using simulations and phantom experiments. In vivo scans were performed on six healthy volunteers in two sessions, and in a patient with advanced atherosclerosis. Compensation for patient motion was achieved by 3D registration of the inter/intrasession scans. Subsequently, T1 and T2 maps were obtained by maximum likelihood estimation., Results: Simulations and phantom experiments showed that the bias in T1 and T2 estimation was < 10% within the range of physiological values. In vivo T1 and T2 values for carotid vessel wall were 844 ± 96 and 39 ± 5 ms, with good repeatability across scans. Patient data revealed altered T1 and T2 values in regions of atherosclerotic plaque., Conclusion: The 3D T1 and T2 mapping of the carotid artery is feasible using variable flip angle and variable TE preparation acquisitions. We foresee application of this technique for plaque characterization and monitoring plaque progression in atherosclerotic patients., (© 2015 Wiley Periodicals, Inc.)

Purpose: Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images., Method: An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation., Results: Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error., Conclusion: The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time., (© 2015 Wiley Periodicals, Inc.)

Diffusion-weighted magnetic resonance imaging permits assessment of the structural integrity of the brain's white matter. This requires unbiased and precise quantification of diffusion properties. We aim to estimate such properties in simple and complex fiber geometries up to three-way fiber crossings using rank-2 tensor model selection. A maximum a-posteriori (MAP) estimator is employed to determine the parameters of a constrained triple tensor model. A prior is imposed on the parameters to avoid the degeneracy of the model estimation. This prior maximizes the divergence between the three tensor's principal orientations. A new model selection approach quantifies the extent to which the candidate models are appropriate, i.e. a single-, dual- or triple-tensor model. The model selection precludes overfitting to the data. It is based on the goodness of fit and information complexity measured by the total Kullback-Leibler divergence (ICOMP-TKLD). The proposed framework is compared to maximum likelihood estimation on phantom data of three-way fiber crossings. It is also compared to the ball-and-stick approach from the FMRIB Software Library (FSL) on experimental data. The spread in the estimated parameters reduces significantly due to the prior. The fractional anisotropy (FA) could be precisely estimated with MAP down to an angle of approximately 40° between the three fibers. Furthermore, volume fractions between 0.2 and 0.8 could be reliably estimated. The configurations inferred by our method corresponded to the anticipated neuro-anatomy both in single fibers and in three-way fiber crossings. The main difference with FSL was in single fiber regions. Here, ICOMP-TKLD predominantly inferred a single fiber configuration, as preferred, whereas FSL mostly selected dual or triple order ball-and-stick models. The prior of our MAP estimator enhances the precision of the parameter estimation, without introducing a bias. Additionally, our model selection effectively balances the trade-off between the goodness of fit and information complexity. The proposed framework can enhance the sensitivity of statistical analysis of diffusion tensor MRI.

Background: To evaluate the influence of image registration on apparent diffusion coefficient (ADC) images obtained from abdominal free-breathing diffusion-weighted MR images (DW-MRIs)., Methods: A comprehensive pipeline based on automatic three-dimensional nonrigid image registrations is developed to compensate for misalignments in DW-MRI datasets obtained from five healthy subjects scanned twice. Motion is corrected both within each image and between images in a time series. ADC distributions are compared with and without registration in two abdominal volumes of interest (VOIs). The effects of interpolations and Gaussian blurring as alternative strategies to reduce motion artifacts are also investigated., Results: Among the four considered scenarios (no processing, interpolation, blurring and registration), registration yields the best alignment scores. Median ADCs vary according to the chosen scenario: for the considered datasets, ADCs obtained without processing are 30% higher than with registration. Registration improves voxelwise reproducibility at least by a factor of 2 and decreases uncertainty (Fréchet-Cramér-Rao lower bound). Registration provides similar improvements in reproducibility and uncertainty as acquiring four times more data., Conclusion: Patient motion during image acquisition leads to misaligned DW-MRIs and inaccurate ADCs, which can be addressed using automatic registration., (© 2014 Wiley Periodicals, Inc.)

Purpose: To develop and validate a method for improving image resolution of late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) for accurate assessment of myocardial scar., Materials and Methods: In a cohort of 37 postinfarction patients, LGE was performed prior to ventricular tachycardia catheter ablation therapy at 1.5T. A super-resolution reconstruction (SRR) technique was applied to the three anisotropic views: short-axis (SA), two-chamber, and four-chamber, to reconstruct a single isotropic volume. For compensation of the interscan heart motion, a joint localized gradient-correlation-based scheme was developed. Scar was identified as either core or gray zone in both the SRR and original SA volumes, and evaluated based on the clinically established bipolar voltage range of the in vivo electroanatomical voltage mapping (EAVM)., Results: Compared to the SA volume, the SRR method resulted in significantly (P < 0.05) reduced myocardial scar gray zone sizes (10.5 ± 8.8 g vs. 9.2 ± 8.1 g) and improved agreement of the bipolar voltage range of scar gray zone (0.99 ± 0.65 mV vs. 1.46 ± 1.15 mV)., Conclusion: We propose an SRR method to automatically reconstruct a high-quality isotropic LGE volume from three orthogonal views. Analysis of the in vivo EAVM demonstrated improved myocardial scar assessment from the SRR volume compared with the SA LGE alone., (© 2014 Wiley Periodicals, Inc.)

In this work we present a framework for reliably detecting significant differences in quantitative magnetic resonance imaging and evaluate it with diffusion tensor imaging (DTI) experiments. As part of this framework we propose a new spatially regularized maximum likelihood estimator that simultaneously estimates the quantitative parameters and the spatially-smoothly-varying noise level from the acquisitions. The noise level estimation method does not require repeated acquisitions. We show that the amount of regularization in this method can be set a priori to achieve a desired coefficient of variation of the estimated noise level. The noise level estimate allows the construction of a Cramér-Rao-lower-bound based test statistic that reliably assesses the significance of differences between voxels within a scan or across different scans. We show that the regularized noise level estimate improves upon existing methods and results in a substantially increased precision of the uncertainty estimates of the DTI parameters. It enables correct specification of the null distribution of the test statistic and with it the test statistic obtains the highest sensitivity and specificity. The source code of the estimation framework, test statistic and experiment scripts are made available to the community.

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

The visualization of activity in mouse brain using inversion recovery spin echo (IR-SE) manganese-enhanced MRI (MEMRI) provides unique contrast, but suffers from poor resolution in the slice-encoding direction. Super-resolution reconstruction (SRR) is a resolution-enhancing post-processing technique in which multiple low-resolution slice stacks are combined into a single volume of high isotropic resolution using computational methods. In this study, we investigated, first, whether SRR can improve the three-dimensional resolution of IR-SE MEMRI in the slice selection direction, whilst maintaining or improving the contrast-to-noise ratio of the two-dimensional slice stacks. Second, the contrast-to-noise ratio of SRR IR-SE MEMRI was compared with a conventional three-dimensional gradient echo (GE) acquisition. Quantitative experiments were performed on a phantom containing compartments of various manganese concentrations. The results showed that, with comparable scan times, the signal-to-noise ratio of three-dimensional GE acquisition is higher than that of SRR IR-SE MEMRI. However, the contrast-to-noise ratio between different compartments can be superior with SRR IR-SE MEMRI, depending on the chosen inversion time. In vivo experiments were performed in mice receiving manganese using an implanted osmotic pump. The results showed that SRR works well as a resolution-enhancing technique in IR-SE MEMRI experiments. In addition, the SRR image also shows a number of brain structures that are more clearly discernible from the surrounding tissues than in three-dimensional GE acquisition, including a number of nuclei with specific higher brain functions, such as memory, stress, anxiety and reward behavior., (Copyright © 2014 John Wiley & Sons, Ltd.)

Purpose: To evaluate the reproducibility and sensitivity of the modified CINE inversion recovery (mCINE-IR) acquisition on rats for measuring the myocardial T1 at 7 Tesla., Materials and Methods: The recently published mCINE-IR acquisition on humans was applied on rats for the first time, enabling the possibility of translational studies with an identical sequence. Simulations were used to study signal evolution and heart rate dependency. Gadolinium phantoms, a heart specimen and a healthy rat were used to study reproducibility. Two cryo-infarcted rats were scanned to measure late gadolinium enhancement (LGE)., Results: In the phantom reproducibility studies the T1 measurements had a maximum coefficient of variation (COV) of 1.3%. For the in vivo reproducibility the COV was below 5% in the anterior cardiac segments. In simulations with phantoms and specimens, a heart rate dependency of approximately 0.5 ms/bpm was present. The T1 maps of the cryo-infarcted rats showed a clear lowering of T1 in de LGE region., Conclusion: The results show that mCINE-IR is highly reproducible and that the sensitivity allows detecting T1 changes in the rat myocardium., (Copyright © 2013 Wiley Periodicals, Inc.)

In MRI, the relatively thick slices of multi-slice acquisitions often hamper visualization and analysis of the underlying anatomy. A group of post-processing techniques referred to as super-resolution reconstruction (SRR) have been developed to address this issue. In this study, we present a novel approach to SRR in MRI, which exploits the high-resolution content usually available in the 2D slices of MRI slice stacks to reconstruct isotropic high-resolution 3D images. Relying on the assumption of local self-similarity of anatomical structures, the method can be applied both to a single slice stack and to the combination of multiple slice stacks that differ in the orientation of their field of view. We evaluate the method quantitatively on synthetic brain MRI and qualitatively on MRI of the lungs. The results show that the method outperforms state-of-the-art MRI super-resolution methods.

Image resolution is an important factor for accurate myocardial scar assessment from late gadolinium enhanced (LGE) MR. It has been shown that the conventionally used short-axis (SA) LGE acquisition with anisotropic resolution may overestimate the scar size due to partial volume effect, undermining the prognostic and diagnostic accuracy of LGE MRI in critical clinical applications. In this work, we present a method for combining three complementary anisotropic orthogonal LGE sequences of the heart region into a single isotropic volume. Our algorithm is based on the super-resolution reconstruction technique and employs joint localized gradient-correlation-based technique for compensation of breathing motion. The proposed method was validated on the gold standard electroanatomical voltage mapping (EAVM) data of 15 post-infarction patients. The reconstructed myocardial scar image demonstrated improved agreement with the EAVM compared to the conventional SA image, especially at the clinically significant gray zone region.

Diffusion weighted magnetic resonance images are often acquired with single shot multislice imaging sequences, because of their short scanning times and robustness to motion. To minimize noise and acquisition time, images are generally acquired with either anisotropic or isotropic low resolution voxels, which impedes subsequent posterior image processing and visualization. In this article, we propose a super-resolution method for diffusion weighted imaging that combines anisotropic multislice images to enhance the spatial resolution of diffusion tensor data. Each diffusion weighted image is reconstructed from a set of arbitrarily oriented images with a low through-plane resolution. The quality of the reconstructed diffusion weighted images was evaluated by diffusion tensor metrics and tractography. Experiments with simulated data, a hardware DTI phantom, as well as in vivo human brain data were conducted. Our results show a significant increase in spatial resolution of the diffusion tensor data while preserving high signal to noise ratio., (Copyright © 2012 Wiley Periodicals, Inc.)

Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal-to-noise ratio, longer acquisition time or both. This study investigates whether so-called super-resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high-resolution acquisition in terms of the signal-to-noise ratio and acquisition time trade-offs. The performance of six super-resolution reconstruction methods and direct high-resolution acquisitions was compared with respect to these trade-offs. The methods are based on iterative back-projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low-resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super-resolution reconstruction can indeed improve the resolution, signal-to-noise ratio and acquisition time trade-offs compared with direct high-resolution acquisition., (Copyright © 2012 Wiley Periodicals, Inc.)

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion-weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b-value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non-Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b-value-independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusion-weighted rat data, which was acquired with eight different b-values, uniformly distributed in a range of [0,2800 sec/mm(2)]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusion-weighted data will result in an overestimated degree of non-Gaussian diffusion and a b-value-dependent underestimation of diffusivity measures, a Rician noise model was used in this study., (© 2010 Wiley-Liss, Inc.)

There is an ongoing debate on how to model diffusivity in fiber crossings. We propose an optimization framework for the selection of a dual tensor model and the set of diffusion weighting parameters b, such that both the diffusion shape and orientation parameters can be precisely as well as accurately estimated. For that, we have adopted the Cramér-Rao lower bound (CRLB) on the variance of the model parameters, and performed Monte Carlo simulations. We have found that the axial diffusion lambda(parallel) needs to be constrained, while an isotropic fraction can be modeled by a single parameter f(iso). Under these circumstances, the Fractional Anisotropy (FA) of both tensors can theoretically be independently estimated with a precision of 9% (at SNR = 25). Levenberg-Marquardt optimization of the Maximum Likelihood function with a Rician noise model approached this precision while the bias was insignificant. A two-element b-vector b = [1.0 3.5] x 10(3) mm(-2)s was found to be sufficient for estimating parameters of heterogeneous tissue with low error. This has allowed us to estimate consistent FA-profiles along crossing tracts. This work defines fundamental limits for comparative studies to correctly analyze crossing white matter structures.

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 B(0) 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., (Copyright 2010 Elsevier Inc. All rights reserved.)

Diffusion kurtosis imaging (DKI) is a new magnetic resonance imaging (MRI) model that describes the non-Gaussian diffusion behavior in tissues. It has recently been shown that DKI parameters, such as the radial or axial kurtosis, are more sensitive to brain physiology changes than the well-known diffusion tensor imaging (DTI) parameters in several white and gray matter structures. In order to estimate either DTI or DKI parameters with maximum precision, the diffusion weighting gradient settings that are applied during the acquisition need to be optimized. Indeed, it has been shown previously that optimizing the set of diffusion weighting gradient settings can have a significant effect on the precision with which DTI parameters can be estimated. In this paper, we focus on the optimization of DKI gradients settings. Commonly, DKI data are acquired using a standard set of diffusion weighting gradients with fixed directions and with regularly spaced gradient strengths. In this paper, we show that such gradient settings are suboptimal with respect to the precision with which DKI parameters can be estimated. Furthermore, the gradient directions and the strengths of the diffusion-weighted MR images are optimized by minimizing the Cramér-Rao lower bound of DKI parameters. The impact of the optimized gradient settings is evaluated, both on simulated as well as experimentally recorded datasets. It is shown that the precision with which the kurtosis parameters can be estimated, increases substantially by optimizing the gradient settings.

In this paper, a method is developed that reconstructs a high resolution image from an arbitrary set of multi-slice 3D MR images with a high in-plane resolution and a low through-plane resolution. Such images are often recorded to increase the efficiency of the acquisition. With a model of the acquisition of MR images, which is improved compared to previous super-resolution methods for MR images, a large system with linear equations is obtained. With the conjugated gradient method and this linear system, a high resolution image is reconstructed from MR images of an object. Also, a new and efficient method to apply an affine transformation to multi-dimensional images is presented. This method is used to efficiently reconstruction the high resolution image from multislice MR images with arbitrary orientations of the slices.

Functional magnetic resonance imaging (fMRI) data that are corrupted by temporally colored noise are generally preprocessed (i.e., prewhitened or precolored) prior to functional activation detection. In this paper, we propose likelihood-based hypothesis tests that account for colored noise directly within the framework of functional activation detection. Three likelihood-based tests are proposed: the generalized likelihood ratio (GLR) test, the Wald test, and the Rao test. The fMRI time series is modeled as a linear regression model, where one regressor describes the task-related hemodynamic response, one regressor accounts for a constant baseline and one regressor describes potential drift. The temporal correlation structure of the noise is modeled as an autoregressive (AR) model. The order of the AR model is determined from practical null data sets using Akaike's information criterion (with penalty factor 3) as order selection criterion. The tests proposed are based on exact expressions for the likelihood function of the data. Using Monte Carlo simulation experiments, the performance of the proposed tests is evaluated in terms of detection rate and false alarm rate properties and compared to the current general linear model (GLM) test, which estimates the coloring of the noise in a separate step. Results show that theoretical asymptotic distributions of the GLM, GLR, and Wald test statistics cannot be reliably used for computing thresholds for activation detection from finite length time series. Furthermore, it is shown that, for a fixed false alarm rate, the detection rate of the proposed GLR test statistic is slightly, but (statistically) significantly improved compared to that of the common GLM-based tests. Finally, simulations results reveal that all tests considered show seriously inferior performance if the order of the AR model is not chosen sufficiently high to give an adequate description of the correlation structure of the noise, whereas the effects of (slightly) overmodeling are observed to be less harmful.