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Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. The objective of this study was to validate the reliability and robustness of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), with a typical diffusion MRI acquisition from a clinical context in comparison with DTI with application to clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Four DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics to establish reference results. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. To assess the discrimination power of the diffusion measures, we repeated the statistical comparisons with a region-based analysis employing reduced sample sizes with diverse subsets, decreasing 10 subjects per group for consecutive reductions, and using 5,001 different random subsamples. For each sample size, the stability of the diffusion descriptors was evaluated with the coefficient of quartile variation. AMURA measures showed a greater number of statistically significant differences in the reference comparisons between episodic migraine patients and controls compared to DTI. In contrast, a higher number of differences was found with DTI parameters compared to AMURA in the comparisons between both migraine groups. Regarding the assessments reducing the sample size, the AMURA parameters showed a more stable behavior than DTI, showing a lower decrease for each reduced sample size or a higher number of regions with significant differences. However, most AMURA parameters showed lower stability in relation to higher coefficient of quartile variation values than the DTI descriptors, although two AMURA measures showed similar values to DTI. For the synthetic signals, there were AMURA measures with similar quantification to DTI, while other showed similar behavior. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.

SynopsisBrain-age is an emerging neuroimaging biomarker that represents the aging status of the brain using machine learning techniques from MRI data. It has been successfully applied to the study of different neurological and psychiatric conditions. We hypothesize that patients with migraine may show an increased brain age gap (difference between the age estimated from the MRI data and the chronological age). After building a brain age model from 2,781 healthy subjects, we tested this hypothesis on a dataset with 210 healthy controls and migraine patients. Results showed an increased brain age in chronic migraine patients with respect to healthy controls.Main Findings-Patients with chronic migraine had a statistically significant increased brain age gap with respect to healthy controls (2.27 vs -0.27 years).-No significant differences were found between episodic migraine patients and healthy controls.PurposeMigraine is one of the most common neurological disorders, with a higher prevalence among women, and causes a huge societal and economic burden1. Chronic migraine (CM) patients suffer headaches for no less than 15 days per month for more than three months, with at least eight days of having migrainous symptoms. Patients with episodic migraine (EM), on the other hand, suffer headaches for less than 15 days per month2. Different studies have shown differences in the brain of migraine patients with respect to those of healthy controls (HC) in terms of morphological, connectivity, and metabolic features3,4,5. However, the pathophysiology of migraine is still unclear. To contribute to its understanding, the Brain Age framework has emerged as a neuroimaging technique that predicts the individual age (“brain age”) employing MRI data. To determine the Brain Age Gap Estimation (BrainAGE)6, the predicted and real chronological ages are compared, representing a positive value accelerated brain aging, as previously reported in several disorders such as Alzheimer’s disease, multiple sclerosis, or diabetes type II7,8,9. The aim of this study is to develop a BrainAGE model and estimate the aging status of the brain in patients with migraine.

The term free-water volume fraction (FWVF) refers to the cerebrospinal and interstitial fluids in the extracellular space of the white matter (WM) of the brain, which has been demonstrated as a sensitive biomarker that correlates with the cognitive performance and the neuropathological processes modifying the interstitial extracellular spaces. It can be quantified by properly fitting the isotropic compartment of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N = 287 healthy subjects aged 25-94, this study examines in detail the evolution of the FWVF in the human brain WM across the adult lifespan, which has been previously reported to exhibit a positive trend. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the FWVF parameter with age, at the same time a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW-compensated MR signal leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to standard studies conducted over the raw MR signal. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the source of the variations observed in FA and MD parameters across the lifespan with previous studies with the standard diffusion tensor imaging.

Producción Científica, To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b-values. Data sets considered comprise~32- 64 gradients near b =1000 s/mm2 plus~6 gradients near b =500 s/mm2.Theory and Methods: The spherical means of each diffusion MRI set with the same b- value are computed. These means are related to the inherent diffusion parameters within the voxel (free- and cellular- water fractions; cellular- water diffusivity), which are solved by constrained nonlinear least squares regression.Results: The proposed method outperforms those based on mixtures of two Gaussians for the kind of data sets considered. W.r.t. the accuracy, the former does not introduce significant biases in the scenarios of interest, while the latter can reach a bias of 5%– 7% if fiber crossings are present. W.r.t. the precision, a variance near 10%, compared to 15%, can be attained for usual configurations.Conclusion: It is possible to compute reliable estimates of the free- water fraction inside the white matter by complementing typical DTI acquisitions with few gradients at a lowb- value. It can be done voxel- by- voxel, without imposing spatial regularity constraints., Ministerio de Ciencia e Innovación, (grant RTI2018- 094569- B- I00)

Producción Científica, Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach., Ministerio de Ciencia e Innovación (research grants RTI 2018-094569-B-I00, PRE2019-089176 y PID2020-115339RB-I0), Asociación Española Contra el Cáncer (subvención PRDVL19001MOYA)

Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restricts the evaluation of complex structures. The objective of this study was the assessment of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), in comparison with DTI in clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Three DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. Compared to DTI, statistically significant differences in additional regions and group comparisons were found with AMURA-based measures. Additionally, some AMURA-based measures showed a remarkable discriminative power for quite reduced sample size and, simultaneously, lower stability (higher coefficient of quartile variation) than DTI-based measures. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.

No specific migraine biomarkers have been found in single‐modality MRI studies. We aimed at establishing biomarkers for episodic and chronic migraine using diverse MRI modalities. We employed canonical correlation analysis and joint independent component analysis to find structural connectivity abnormalities that are related to gray matter morphometric alterations. The number of streamlines (trajectories of estimated fiber‐tracts from tractography) was employed as structural connectivity measure, while cortical curvature, thickness, surface area, and volume were used as gray matter parameters. These parameters were compared between 56 chronic and 54 episodic migraine patients, and 50 healthy controls. Cortical curvature alterations were associated with abnormalities in the streamline count in episodic migraine patients compared to controls, with higher curvature values in the frontal and temporal poles being related to a higher streamline count. Lower streamline count was found in migraine compared to controls in connections between cortical regions within each of the four lobes. Higher streamline count was found in migraine in connections between subcortical regions, the insula, and the cingulate and orbitofrontal cortex, and between the insula and the temporal region. The connections between the caudate nucleus and the orbitofrontal cortex presented worse connectivity in chronic compared to episodic migraine. The hippocampus was involved in connections with higher and lower number of streamlines in chronic migraine. Strengthening of structural networks involving pain processing and subcortical regions coexists in migraine with weakening of cortical networks within each lobe. The multimodal analysis offers a new insight about the association between brain structure and connectivity., Planchuelo‐Gómez et al. associate structural connectivity with gray matter morphometry. In chronic and episodic migraine, strengthened connectivity with pain processing regions, and higher cortical curvature related to weakened connectivity within each lobe were found. These results give a new insight about migraine neuroimaging biomarkers.

Purpose It has been shown, theoretically and in vivo, that using the Stejskal‐Tanner pulsed‐gradient, or linear tensor encoding (LTE), and in tissue exhibiting a “stick‐like” diffusion geometry, the direction‐averaged diffusion‐weighted MRI signal at high b‐values ( urn:x-wiley:07403194:media:mrm28191:mrm28191-math-0001) follows a power‐law, decaying as urn:x-wiley:07403194:media:mrm28191:mrm28191-math-0002. It has also been shown, theoretically, that for planar tensor encoding (PTE), the direction‐averaged diffusion‐weighted MRI signal decays as 1/b. We aimed to confirm this theoretical prediction in vivo. We then considered the direction‐averaged signal for arbitrary b‐tensor shapes and different tissue substrates to look for other conditions under which a power‐law exists. Methods We considered the signal decay for high b‐values for encoding geometries ranging from 2‐dimensional PTE, through isotropic or spherical tensor encoding to LTE. When a power‐law behavior was suggested, this was tested using in silico simulations and, when appropriate, in vivo using ultra‐strong (300 mT/m) gradients. Results Our in vivo results confirmed the predicted 1/b power law for PTE. Moreover, our analysis showed that using an axisymmetric b‐tensor a power‐law only exists under very specific conditions: (a) “stick‐like” tissue geometry and purely LTE or purely PTE waveforms; and (b) "pancake‐like" tissue geometry and a purely LTE waveform. Conclusions A complete analysis of the power‐law dependencies of the diffusion‐weighted signal at high b‐values has been performed. Only three specific forms of encoding result in a power‐law dependency, pure linear and pure PTE when the tissue geometry is “stick‐like” and pure LTE when the tissue geometry is "pancake‐like". The different exponents of these encodings could be used to provide independent validation of the presence of different tissue geometries in vivo.

Background White matter alterations have been observed in patients with migraine. However, no microstructural white matter alterations have been found particularly in episodic or chronic migraine patients, and there is limited research focused on the comparison between these two groups of migraine patients. Methods Fifty-one healthy controls, 55 episodic migraine patients and 57 chronic migraine patients were recruited and underwent brain T1-weighted and diffusion-weighted MRI acquisition. Using Tract-Based Spatial Statistics (TBSS), fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity were compared between the different groups. On the one hand, all migraine patients were compared against healthy controls. On the other hand, patients from each migraine group were compared between them and also against healthy controls. Correlation analysis between clinical features (duration of migraine in years, time from onset of chronic migraine in months, where applicable, and headache and migraine frequency, where applicable) and Diffusion Tensor Imaging measures was performed. Results Fifty healthy controls, 54 episodic migraine and 56 chronic migraine patients were finally included in the analysis. Significant decreased axial diffusivity (p Conclusions These findings suggest global white matter structural differences between episodic migraine and chronic migraine. Patients with chronic migraine could present axonal integrity impairment in the first months of chronic migraine with respect to episodic migraine patients. White matter changes after the onset of chronic migraine might reflect a set of maladaptive plastic changes.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings. Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]

Objective To identify possible structural connectivity alterations in patients with episodic and chronic migraine using magnetic resonance imaging data. Methods Fifty-four episodic migraine, 56 chronic migraine patients and 50 controls underwent T1-weighted and diffusion-weighted magnetic resonance imaging acquisitions. Number of streamlines (trajectories of estimated fiber-tracts), mean fractional anisotropy, axial diffusivity and radial diffusivity were the connectome measures. Correlation analysis between connectome measures and duration and frequency of migraine was performed. Results Higher and lower number of streamlines were found in connections involving regions like the superior frontal gyrus when comparing episodic and chronic migraineurs with controls ( p Conclusions Structural strengthening of connections involving subcortical regions associated with pain processing and weakening in connections involving cortical regions associated with hyperexcitability may coexist in migraine.

Purpose\ud \ud The apparent propagator anisotropy (APA) is a new diffusion MRI metric that, while drawing on the benefits of the ensemble averaged propagator anisotropy (PA) compared to the fractional anisotropy (FA), can be estimated from single‐shell data.\ud Theory and Methods\ud \ud Computation of the full PA requires acquisition of large datasets with many diffusion directions and different b‐values, and results in extremely long processing times. This has hindered adoption of the PA by the community, despite evidence that it provides meaningful information beyond the FA. Calculation of the complete propagator can be avoided under the hypothesis that a similar sensitivity/specificity may be achieved from apparent measurements at a given shell. Assuming that diffusion anisotropy (DiA) is nondependent on the b‐value, a closed‐form expression using information from one single shell (ie, b‐value) is reported.\ud Results\ud \ud Publicly available databases with healthy and diseased subjects are used to compare the APA against other anisotropy measures. The structural information provided by the APA correlates with that provided by the PA for healthy subjects, while it also reveals statistically relevant differences in white matter regions for two pathologies, with a higher reliability than the FA. Additionally, APA has a computational complexity similar to the FA, with processing‐times several orders of magnitude below the PA.\ud Conclusions\ud \ud The APA can extract more relevant white matter information than the FA, without any additional demands on data acquisition. This makes APA an attractive option for adoption into existing diffusion MRI analysis pipelines.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Objective This study evaluates different parameters describing the gray matter structure to analyze differences between healthy controls, patients with episodic migraine, and patients with chronic migraine. Design Cohort study. Setting Spanish community. Subjects Fifty-two healthy controls, 57 episodic migraine patients, and 57 chronic migraine patients were included in the study and underwent T1-weighted magnetic resonance imaging acquisition. Methods Eighty-four cortical and subcortical gray matter regions were extracted, and gray matter volume, cortical curvature, thickness, and surface area values were computed (where applicable). Correlation analysis between clinical features and structural parameters was performed. Results Statistically significant differences were found between all three groups, generally consisting of increases in cortical curvature and decreases in gray matter volume, cortical thickness, and surface area in migraineurs with respect to healthy controls. Furthermore, differences were also found between chronic and episodic migraine. Significant correlations were found between duration of migraine history and several structural parameters. Conclusions Migraine is associated with structural alterations in widespread gray matter regions of the brain. Moreover, the results suggest that the pattern of differences between healthy controls and episodic migraine patients is qualitatively different from that occurring between episodic and chronic migraine patients.

The white matter state in migraine has been investigated using diffusion tensor imaging (DTI) measures, but results using this technique are conflicting. To overcome DTI measures, we employed ensemble average diffusion propagator measures obtained with apparent measures using reduced acquisitions (AMURA). The AMURA measures were return-to-axis (RTAP), return-to-origin (RTOP) and return-to-plane probabilities (RTPP). Tract-based spatial statistics was used to compare fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity from DTI, and RTAP, RTOP and RTPP, between healthy controls, episodic migraine and chronic migraine patients. Fifty healthy controls, 54 patients with episodic migraine and 56 with chronic migraine were assessed. Significant differences were found between both types of migraine, with lower axial diffusivity values in 38 white matter regions and higher RTOP values in the middle cerebellar peduncle in patients with a chronic migraine (p < 0.05 family-wise error corrected). Significantly lower RTPP values were found in episodic migraine patients compared to healthy controls in 24 white matter regions (p < 0.05 family-wise error corrected), finding no significant differences using DTI measures. The white matter microstructure is altered in a migraine, and in chronic compared to episodic migraine. AMURA can provide additional results with respect to DTI to uncover white matter alterations in migraine.

Magnetic Resonance Imaging (MRI) of hard biological tissues is challenging due to the fleeting lifetime and low strength of their response to resonant stimuli, especially at low magnetic fields. Consequently, the impact of MRI on some medical applications, such as dentistry, continues to be limited. Here, we present three-dimensional reconstructions of ex-vivo human teeth, as well as a rabbit head and part of a cow femur, all obtained at a field strength of only 260 mT. These images are the first featuring soft and hard tissues simultaneously at sub-Tesla fields, and they have been acquired in a home-made, special-purpose, pre-medical MRI scanner designed with the goal of demonstrating dental imaging at low field settings. We encode spatial information with two variations of zero-echo time (ZTE) pulse sequences: Pointwise-Encoding Time reduction with Radial Acquisition (PETRA) and a new sequence we have called Double Radial Non-Stop Spin Echo (DRaNSSE), which we find to perform better than the former. For image reconstruction we employ Algebraic Reconstruction Techniques (ART) as well as standard Fourier methods. A noise analysis of the resulting images shows that ART reconstructions exhibit a higher signal to noise ratio with a more homogeneous noise distribution., 12 pages, comments welcome

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.

A novel diffusion anisotropy metric is presented. It is based on dissimilarity between the acquired diffusion signal and its isotropic equivalent. Using the inner product of signals, a closed form expression is obtained, which allows its computation using spherical harmonics from a reduced set of acquired data, compatible with most popular diffusion MRI acquisition protocols. Results show that the proposed metric (1) is able to discriminate among different microstructure scenarios; (2) shows a robust behaviour in clinical studies.

In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion summarized by the b-value - constitutes yet another explorable “dimension” to modify the image contrast which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it.

Purpose To characterize the noise distributions in 3D-MRI accelerated acquisitions reconstructed with GRAPPA using an exact noise propagation analysis that operates directly in k-space. Theory and methods We exploit the extensive symmetries and separability in the reconstruction steps to account for the correlation between all the acquired k-space samples. Monte Carlo simulations and multi-repetition phantom experiments were conducted to test both the accuracy and feasibility of the proposed method; a high-resolution in-vivo experiment was performed to assess the applicability of our method to clinical scenarios. Results Our theoretical derivation shows that the direct k-space analysis renders an exact noise characterization under the assumptions of stationarity and uncorrelation in the original k-space. Simulations and phantom experiments provide empirical support to the theoretical proof. Finally, the high-resolution in-vivo experiment demonstrates the ability of the proposed method to assess the impact of the sub-sampling pattern on the overall noise behavior. Conclusions By operating directly in the k-space, the proposed method is able to provide an exact characterization of noise for any Cartesian pattern sub-sampled along the two phase-encoding directions. Exploitation of the symmetries and separability into independent blocks through the image reconstruction procedure allows us to overcome the computational challenges related to the very large size of the covariance matrices involved.

The Propagator Anisotropy (PA) is a measurement of the orientational variability inside a tissue estimated from diffusion MRI using the Ensemble Average diffusion Propagator (EAP). It is based on the quantification of the angular difference between the propagator in a specific voxel and its isotropic counterpart. The PA has shown the ability to reveal microstructural information of interest and meaningful descriptive maps inside the white matter. However, the use of PA is not generalized among the clinical community, due to the great amount of data needed for its calculation, together with the associated long processing times. In order to calculate the PA, the EAP must also be properly estimated. This task would require a dense sampling of the Cartesian q-space. Alternatively, more efficient techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values and long processing times.In this work, we propose an alternative implementation to drastically reduce the number of samples needed, as well as boosting the estimation procedure. We avoid the calculation of the whole EAP by assuming that the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, we achieve a closed-form expression for a measure similar to the PA but using information from one single shell: the Apparent Propagator Anisotropy (APA). The new measure remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). The intention of the APA is not to exactly replicate the PA but inferring microstructural information with comparable discrimination power as the PA but using a reduced amount of data.We report extensive results showing that the proposed measures present a robust behavior in clinical studies and they are computationally efficient and robust when compared with PA and other anisotropy measures.

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP).In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust.

PurposeIt has been shown previously that for the conventional Stejskal-Tanner pulsed gradient, or linear tensor encoding (LTE), as well as planar tensor encoding (PTE) and in tissue in which diffusion exhibits a ‘stick-like’ geometry, the diffusion-weighted MRI signal at extremely high b-values follows a power-law. Specifically, the signal decays as a in LTE and 1/b in PTE. Here, the direction-averaged signal for arbitrary diffusion encoding waveforms is considered to establish whether power-law behaviors occur with other encoding wave-forms and for other (non-stick-like) diffusion geometries.MethodsWe consider the signal decay for high b-values for encoding geometries ranging from 2-dimensional planar tensor encoding (PTE), through isotropic or spherical tensor encoding (STE) to linear tensor encoding. When a power-law behavior was suggested, this was tested using in-silico simulations and in-vivo using an ultra-strong gradient (300 mT/m) Connectom scanner.ResultsThe results show that using an axisymmetric b-tensor a power-law only exists for two scenarios: For stick-like geometries, (i) the already-discovered LTE case; and (ii) for pure planar encoding. In this latter case, to first order, the signal decays as 1/b. Our in-silico and in-vivo experiments confirm this 1/b relationship.ConclusionA complete analysis of the power-law dependencies of the diffusion-weighted signal at high b-values has been performed. Only two forms of encoding result in a power-law dependency, pure linear and pure planar tensor encoding and when the diffusion geometry is ‘stick-like’. The different exponents of these encodings could be used to provide independent validation of the presence of stick-like geometries in-vivo.

In this manuscript we derive the Cramer-Rao Lower Bound (CRLB) of the monoexponential diffusion-weighted signal model under a realistic noise assumption, and propose a formulation to obtain optimized sets of b-values that maximize the noise performance of the Apparent Diffusion Coefficient (ADC) maps given a target ADC and a signal-to-noise ratio. Therefore, for various sets of parameters (S0 and ADC), signal-to-noise ratios (SNR) and noise distribution, we computed optimized sets of b-values using CRLB-based analysis in two different ways: (i) through a greedy algorithm where b-values from a pool of candidates were added iteratively to the set, and (ii) through a two b-value search algorithm were all two b-value combinations of the pool of candidates were tested. Further, optimized sets of b-values were computed from synthetic data, phantoms, and in-vivo liver diffusion-weighted imaging (DWI) experiments to validate the CRLB-based analysis. The optimized sets of b-values obtained through the proposed CRLB-based analysis showed good agreement with the optimized sets obtained experimentally from synthetic, phantoms, and in-vivo liver data. The variance of the ADC maps decreased when employing the optimized set of b-values compared to various sets of b-values proposed in the literature for in-vivo liver DWI, although differences of notable magnitude between noise models and optimization strategies were not found. In addition, the higher b-values decreased for lower SNR under the Rician noise distribution. Optimization of the set b-values is critical to maximize the noise performance (i.e., maximize the precision and minimize the variance) of the estimated ADC maps in diffusion-weighted MRI. Hence, the proposed approach may help to optimize and standardize liver diffusion-weighted MRI acquisitions by computing optimized set of b-values for a given set of parameters.

The Ensemble Average Propagator (EAP) provides a compact theoretical framework to explore the underlying microstructural properties of the tissues with diffusion magnetic resonance imaging. To model tissue characteristics, it is usually required to fit a functional basis to a densely sampled q-space data and then retrieve the EAP-related maps. In this work, we analytically derive a new closed-form formula to calculate one of the EAP features the Return-To-the-Origin Probability (RTOP) map directly from the data leaving aside the EAP estimation step. Our RTOP estimation approach exploits only single-shell data and additionally handles noise-induced bias using a non-stationary log-Rician statistics. We validated our proposal using an in vivo Human Connectome Project database achieving an increased accuracy of the method when subsampling of the q-space was considered and strong correlations to multiple-shell state-of-the-art methods.

The Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP requires a dense sampling of the \({\mathbf {q}}\)-space data. Although alternative techniques have been proposed, all of them require a high number of gradients and several b-values to be calculated. Once the EAP is calculated scalar measures must be directly derived. In this work, we propose a method to drastically reduce the number of points needed for the estimation of one of the measures, the return-to-axis probability (RTAP), efficiently estimating the \({\mathbf {q}}\)-space diffusion measure from a single shell acquisition. The proposal avoids the calculation of the EAP assuming that the diffusion does not depend on the radial direction. By applying this assumption locally, we achieve closed-form expressions of the measure using information from only one b-value, compatible with acquisitions protocols used for HARDI. Results have shown that the measures are highly correlated with the same measures calculated with state-of-the-art EAP estimators and highly accelerated execution times.

A number of computational techniques have been lately devised to image the Ensemble Average Propagator (EAP) within the white matter of the brain, propelled by the deployment of multi-shell acquisition protocols and databases: approaches like Mean Apparent Propagator Imaging (MAP-MRI) and its Laplacian-regularized version (MAPL) aim at describing the low frequency spectrum of the EAP (limited by the maximum b-value acquired) and afterwards computing scalar indices that embed useful descriptions of the white matter, e. g. the Return-to-Origin, Plane, or Axis Probabilities (RTOP, RTPP, RTAP). These methods resort to a non-parametric, bandwidth limited representation of the EAP that implies fitting a set of 3-D basis functions in a large-scale optimization problem. We propose a semi-parametric approach inspired by signal theory: the EAP is approximated as the spherical convolution of a Micro-Structure adaptive Gaussian kernel with a non-parametric orientation histogram, which aims at representing the low-frequency response of an ensemble of coherent sets of fiber bundles at the white matter. This way, the optimization involves just the 2 to 3 parameters that describe the kernel, making our approach far more efficient than the related state of the art. We devise dual Fourier domains Integral Transforms to analytically compute RTxP-like scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The so-called MiSFIT is both time efficient (a typical multi-shell data set can be processed in roughly one minute) and accurate: it provides estimates of widely validated indices like RTOP, RTPP, and RTAP comparable to MAPL for a wide variety of white matter configurations.

Introduccion. El topiramato es el unico tratamiento preventivo oral con nivel de evidencia I para la migrana cronica. Objetivo. Evaluar los parametros de la sustancia gris, obtenidos mediante resonancia magnetica, como marcadores de respuesta al tratamiento con topiramato en pacientes con migrana cronica. Pacientes y metodos. La muestra se compuso de 57 pacientes con migrana cronica atendidos por primera vez en una unidad de cefaleas como consecuencia de migrana cronica, a los que se realizo una resonancia magnetica de 3 T. Posteriormente, se inicio el tratamiento preventivo con topiramato. Se evaluaron la respuesta y la tolerancia a los tres meses y se definio respuesta como disminucion de al menos un 50% en el numero de dias de cefalea al mes. Mediante procesamiento de imagenes de resonancia magnetica ponderadas en T1 y difusion, se obtuvieron los parametros de la sustancia gris (68 estructuras corticales y 16 subcorticales). Se obtuvo un modelo de regresion logistica para la valoracion predictiva. Resultados. Se analizo a 42 pacientes que toleraron el tratamiento, con respuesta terapeutica en 23 de ellos (54,7%). El modelo final de prediccion se construyo con parametros de la sustancia gris con resultados significativos. En dicho modelo, a mayor curvatura del cuneo izquierdo y area de la insula derecha, mayor probabilidad de respuesta, y menor probabilidad a mayor volumen de la corteza inferior parietal derecha y area del giro temporal superior izquierdo. La precision del modelo predictivo fue del 95%. Conclusion. Los parametros de la sustancia gris pueden ser marcadores utiles de respuesta al tratamiento preventivo con topiramato en la migrana cronica.

Purpose In this work, we have proposed a methodology for the estimation of the apparent diffusion coefficient in the body from multiple breath hold diffusion weighted images, which is robust to two preeminent confounding factors: noise and motion during acquisition. Methods We have extended a method for the joint groupwise multimodal registration and apparent diffusion coefficient estimation, previously proposed by the authors, in order to correct the bias that arises from the non-Gaussianity of the data and the registration procedure. Results Results show that the proposed methodology provides a statistically significant improvement both in robustness for displacement fields calculation and in terms of accuracy for the apparent diffusion coefficient estimation as compared with traditional sequential approaches. Reproducibility has also been measured on real data in terms of the distribution of apparent diffusion coefficient differences obtained from different b-values subsets. Conclusions Our proposal has shown to be able to effectively correct the estimation bias by introducing additional computationally light procedures to the original method, thus providing robust apparent diffusion coefficient maps in the liver and allowing an accurate and reproducible analysis of the tissue.

In this chapter, we will review some of the methods proposed in literature to remove the speckle pattern from ultrasound data based on nonlinear processing. Note that some filters that can also be considered as nonlinear are left aside since they will be deeply treated in other chapters. That is the case of the wavelet-based methods and Bayesian methods. Other methods also treated in other chapters, like diffusion-based schemes are only briefly reviewed in order to place them inside the global partial differential equation (PDE) classification. On the other hand, note that some of the filters here reviewed are not initially proposed for ultrasound imaging but derived for Synthetic Aperture Radar (SAR) images, where noise can be modeled similarly. In those images, the multiplicative model for speckle holds and therefore, many of the methods defined in literature for SAR can be easily extrapolated to ultrasound denoising. This is the case of some of the most popular speckle filters. In addition, we would like to remark that the effectiveness of many of these schemes lays on a proper modeling of the speckle statistics. For some purposes, a simple multiplicative model will suffice, while for some specific applications, more accurate models must be used. Finally, as we stated in the previous chapter, the filtering method must be selected following the specific needs of the problem. There is no all-purpose filter that, with the same configuration parameters, could perform excellent in all situations.

This chapter is focused on the image registration techniques that estimate the motion and strain by tracking the speckle pattern directly from the intensity of the B-mode US images assuming a temporal relationship between images. The fixed and moving images are noted as It and It-1 with t meaning a specific time.

Producción Científica, Purpose: To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms for diffusion MRI. Methods: ODGD motion-compensated waveforms were designed for various moment-nullings Mn (n=0,1,2), for a range of b-values, and spatial resolutions, both without (ODGD-Mn) and with CG-nulling (ODGD-Mn-CG). Phantom and in-vivo (brain and liver) experiments were conducted with various ODGD waveforms to compare motion robustness, signal-to-noise ratio (SNR), and apparent diffusion coefficient (ADC) maps with state-of-the-art waveforms. Results:ODGD-Mn and ODGD-Mn-CG waveforms reduced the TE of state-of-the-art waveforms. This TE reduction resulted in significantly higher SNR (P < 0.05) in both phantom and in-vivo experiments. ODGD-M1 improved the SNR of BIPOLAR (42.8+-5.3 versus 32.9+-3.3) in the brain, and ODGD-M2 the SNR of motion-compensated (MOCO) and Convex Optimized Diffusion Encoding-M2 (CODE-M2) (12.3+-3.6 versus 9.7+-2.9 and 10.2+-3.4, respectively) in the liver. Further, ODGD-M2 also showed excellent motion robustness in the liver. ODGD-M2-CG waveforms reduced the CG-related dephasing effects of non CG-nulling waveforms in phantom and in-vivo experiments, resulting in accurate ADC maps. Conclusions: ODGD waveforms enable motion-robust diffusion MRI with reduced TEs, increased SNR, and reduced ADC bias compared to state-of-the-art waveforms in theoretical results, simulations, phantoms and in-vivo experiments., TEC2013-44194-P, VA069U16

Thresholding is a direct and simple approach to extract different regions from an image. In its basic formulation, thresholding searches for a global value that maximizes the separation between output classes. The use of a single hard threshold value is precisely the source of important segmentation errors in many scenarios like noisy images or uneven illumination. If no connectivity or closed objects are considered, the method is prone to produce isolated pixels. In this paper a new multiregion thresholding methodology is presented to overcome the common drawbacks of thresholding methods when images are corrupted with artifacts and noise. It is based on relating each pixel in the image to different output centroids via a fuzzy membership function, avoiding any initial hard decision. The starting point of the technique is the definition of the output centroids using a clustering method compatible with most thresholding techniques in the literature. The method makes use of the spatial information through a local aggregation step where the membership degree of each pixel is modified by local information that takes into account the memberships of the surrounding pixels. This makes the method robust to noise and artifacts. The general formulation of the proposed methodology allows the design of spatial aggregations for multiple applications, including the possibility of including heuristic information via a fuzzy inference rule base.

An imaging biomarker is a biologic feature in an image that is relevant to a patient's diagnosis or prognosis. In order to qualify as a biomarker, a measure must be robust and reproducible. However, the usual scalar measures derived from diffusion tensor imaging are known to be highly dependent on the variation of the acquisition parameters, which prevents their possible use as biomarkers. In this work, we propose a new set of quantitative measures based on diffusion magnetic resonance imaging from single-shell acquisitions that are designed to be robust to the variations of several acquisition parameters (number of gradient directions, b-value and SNR) while keeping a high discrimination power on differences in the diffusion characteristics of the tissue. These new scalar measures are analytically obtained from a generic diffusion function that does not require the calculation of a diffusion tensor. This way, on one hand, we avoid the use of a specific diffusion model and, on the other hand, we make easier the statistical characterization of the measures. Accordingly, the analysis of the measures bias is carried out and it is used to minimize their dependency with respect to the acquisition noise for different SNRs. The robustness and discrimination power of the measures are tested for different number of gradients, b-values and SNRs using a realistic phantom and three real datasets: (1) 13 control subjects and different acquisition parameters; (2) a public data set from a single subject acquired using multiple shells and (3) 32 schizophrenia patients and 32 age and sex-matched healthy controls with a varying number of gradient directions. The proposed quantitative measures exhibit low variability to the changes of the acquisition parameters, while at the same time they preserve a discrimination power that is able to detect significant changes in the anisotropy of the diffusion.

Noise characterization in MRI has multiple applications, including quality assurance and protocol optimization. It is particularly important in the presence of parallel imaging acceleration, where the noise distribution can contain severe spatial heterogeneities. If the parallel imaging reconstruction is a linear process, an exact noise analysis is possible by taking into account the correlations between all the samples involved. However, for k-space based techniques like GRAPPA, the exact analysis has been considered computationally prohibitive due to the very large size of the noise covariance matrices required to characterize the noise propagation from k-space to image-space. Previous methods avoid this computational burden by approximating the GRAPPA reconstruction as a pixel-wise linear operation performed in the image-space. However, these methods are not exact in the presence of non-uniform k-space undersampling (e.g.: containing a calibration region). For this reason, in this work we develop an exact characterization of the noise distribution for self-calibrated parallel imaging in the presence of arbitrary Cartesian undersampling patterns. By exploiting the symmetries and separability in the noise propagation process, the proposed method is computationally efficient and does not require large matrices. In this manuscript, we present the proposed noise characterization method and compare it to previous techniques using Monte-Carlo simulations as well as phantom acquisitions., 24 pages, 6 figures

Purpose The purpose of this work is to develop a groupwise elastic multimodal registration algorithm for robust ADC estimation in the liver on multiple breath hold diffusion weighted images. Methods We introduce a joint formulation to simultaneously solve both the registration and the estimation problems. In order to avoid non-reliable transformations and undesirable noise amplification, we have included appropriate smoothness constraints for both problems. Our metric incorporates the ADC estimation residuals, which are inversely weighted according to the signal content in each diffusion weighted image. Results Results show that the joint formulation provides a statistically significant improvement in the accuracy of the ADC estimates. Reproducibility has also been measured on real data in terms of the distribution of ADC differences obtained from different b-values subsets. Conclusions The proposed algorithm is able to effectively deal with both the presence of motion and the geometric distortions, increasing accuracy and reproducibility in diffusion parameters estimation.

The Apparent Diffusion Coefficient (ADC) is a quantitative measure derived from MRI that is able to asses the amount of diffusion within living tissues. It is employed to characterize different diseases and to evaluate response to therapy. For ADC estimation, the diffusion signal needs to be sampled using a small number of values, presenting distortions due to the aliasing and windowing effect. In this work we theoretically study these effects and propose some new robust estimators for ADC based on the Fourier Transform of the signal.

Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respi- ratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdomi- nal diffusion weighted imaging by using different well-known metrics under the groupwise paradigm. A three-stage validation of the methodology is carried out on a computational diffusion phantom, a watery solution phantom and a set of voluntary patients. Diffusion estimation accuracy has been directly calculated on the computational phantom and indirectly by means of a residual analysis on the real data. On the other hand, effectiveness in distortion correction has been measured on the phan- tom. Results have shown statistical significant improvements compared to pairwise registration being able to cope with elastic deformations.

Most automated segmentation approaches to the mitral valve and left ventricle in 3-D echocardiography require a manual initialization. In this article, we propose a fully automatic scheme to initialize a multicavity segmentation approach in 3-D transesophageal echocardiography by detecting the left ventricle long axis, the mitral valve and the aortic valve location. Our approach uses a probabilistic and structural tissue classification to find structures such as the mitral and aortic valves; the Hough transform for circles to find the center of the left ventricle; and multidimensional dynamic programming to find the best position for the left ventricle long axis. For accuracy and agreement assessment, the proposed method was evaluated in 19 patients with respect to manual landmarks and as initialization of a multicavity segmentation approach for the left ventricle, the right ventricle, the left atrium, the right atrium and the aorta. The segmentation results revealed no statistically significant differences between manual and automated initialization in a paired t-test (p > 0.05). Additionally, small biases between manual and automated initialization were detected in the Bland-Altman analysis (bias, variance) for the left ventricle (-0.04, 0.10); right ventricle (-0.07, 0.18); left atrium (-0.01, 0.03); right atrium (-0.04, 0.13); and aorta (-0.05, 0.14). These results indicate that the proposed approach provides robust and accurate detection to initialize a multicavity segmentation approach without any user interaction. (C) 2014 World Federation for Ultrasound in Medicine & Biology.

This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.

This paper proposes a methodology to design a physiologically realistic computer simulator of images of the left ventricle myocardium based on a patient-specific biomechanical model. The simulator takes a magnetic resonance image of a given patient at end diastole, uses a manual segmentation of that image to model the geometry of the myocardium and sets the parameters of the constitutive model used for biomechanical simulation according to a regional labeling of the contractility of the myocardium for that patient. The simulated deformations are used to warp the magnetic resonance dataset throughout the cardiac cycle to generate different image modalities. The simulator is validated by quantifying its ability to model actual deformations in a set of patients affected by an acute myocardial infarction. Specifically a high correlation has been encountered between the ejection fraction derived from the simulated end systolic deformation of the myocardium and the myocardium segmented from actual data. Additionally, most of the parameters that describe the simulated deformation compare well with reported values. Overall, the simulator is intended as a testbed for extensive comparisons of myocardial motion tracking methods due to its ability to relate the impaired myocardial function with the associated ventricular remodeling, a novel contribution in the literature of cardiac image simulators.

In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a sub-sampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach.

Strain and related tensors play a major role in cardiac function characterization, so correct estimation of the local phase in tagged images is crucial for quantitative myocardial motion studies. We propose an Harmonic Phase related procedure that is adaptive in the spatial and the spectral domains: as for the former, we use an angled-steered analysis window prior to the Fourier Transform; as for the latter, the bandpass filter is also angle-adaptive. Both of them are narrow in the modulation direction and wide in the orthogonal direction. Moreover, no parameters are manually set since their values are partially based on the information available at the DICOM headers and additional information is estimated from data. The procedure is tested in terms of accuracy (on synthetic data) and reproducibility (on real data) of the deformation gradient tensor, measured by means of the distribution of the Frobenius norm differences between two tensor datasets.

This chapter is devoted to the problem of noise filtering in MRI from a signal estimation perspective. Noise filtering can be found in the literature under very different denominations: noise filtering, noise removal, denoising, noise reduction and signal estimation. Here, we analyze and categorize the different approaches and evaluate their goodness for specific purposes: the selection of a method must be based on the precise needs of the problem in hand. There is no all-purpose filter that could perform excellent in all situations with the same configuration of parameters. To that end, first, we establish the basic requirements to use a filtering scheme in medical imaging in general and in MRI in particular. Then, we review the different uses that filtering can have and we show some examples of the advantage of carry out a noise reduction procedure on MRI. Later, we analyze the different approaches in the literature and evaluate their performance for specific purposes. As a case study, we examine the different modifications provided in literature over a well-known filter, the LMMSE estimator for Rician noise, in order to better cope with different modalities of MRI.

The different approaches to estimate \(\sigma ^2\) out of Rician magnitude MR images are reviewed and classified, assuming the noise to be stationary, i.e., \(\sigma \) does not depend on the position. Most of the approaches to noise estimation in MRI in the literature are precisely focused on this kind of noise. The different methods are systematized and classified attending their nature. As a case study, a special kind of estimators, those based on the calculation of local moments, is deeply studied. This case serves as an illustration of the different considerations that must be taken into account when implementing an estimation procedure. In the last part of the chapter, the advantages and drawbacks of the different methods are analyzed through synthetic and real data controlled experiments. The estimators for Rician noise of here reviewed are the basis for many of the estimators proposed in the following chapters, which can be seen as extensions of these.

This is the first of several chapters that deal with the problem of noise estimation in MRI. Here, we focus on the case of stationary additive Gaussian noise. In MRI acquisitions, noise is assumed to be a Gaussian process that equally affects to all the frequencies in the k-space, independent and identically distributed for both the real and imaginary components. Thus, the stationary Gaussian assumption is also valid for each coil in the x-space, before the magnitude is considered. In addition, The Gaussian model can also be used as a simplification of more complex models of noise in the magnitude signal for high SNR. In this chapter we review some of the methods proposed in literature to estimate the variance of noise \(\sigma ^{2}\) in univariate and multivariate Gaussian models, and the covariance between coils \(\sigma _\mathrm{xy}\) when multiple correlated coils are considered. The analysis done over the complex Gaussian data shows many advantages when compared to other models, since it is supported by highly validated techniques and well-grounded assumptions. All the methods reviewed are defined in the x-space, but results can be easily extrapolated to k-space. In the second part of the chapter the proposed estimators are compared and a performance analysis is carried out over synthetic and real data.

In this preliminary chapter, the origin of the problem of noise in MR acquisitions is introduced and set in context. The three main issues dealt along the book are presented: noise modeling, noise estimation and noise filtering. The different parts and chapters of the book are reviewed.

Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system. Fil: Curiale, Ariel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Fundación Escuela Medicina Nuclear; Argentina. Universidad de Valladolid; España Fil: Vegas Sánchez Ferrero, Gonzalo. Harvard Medical School; Estados Unidos. Universidad Politécnica de Madrid; España Fil: Aja Fernández, Santiago. Universidad de Valladolid; España

Parallel imaging (pMRI) methods allow increasing the acquisition rate in MRI via subsampling of the k-space. As a consequence of the subsampling image in the x-space presents some artifacts that must be corrected with the so-called pMRI methods. Among them, SENSE and GRAPPA are two of the most popular. In most pMRI methods, the reconstruction process yields to a value of the variance of noise in the final image that depends on the position. Hence, traditional noise estimation methods, based on a single noise level for the whole image, fail. In this chapter, we review a methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. The estimation follows a parametric methodology: the models for noise are known and some parameters of those models are assumed to be known beforehand: the sensitivity maps of each receiver coil, the reconstruction weights, and the correlation among coils. Although we only focus on two pMRI methods, the results here presented are easily extrapolated to other parallel algorithms.