637 research outputs found
The Effect of B-value on HARDI Reconstruction
No full textThe aim of this study was to investigate the effect of the b-value on the high angular resolution diffusion imaging (HARDI) reconstruction and to seek for the appropriate b-value for orientation distribution function (ODF) reconstruction from clinical HARDI data. The full width at half maximum (FWHM) of the ODF and the angular difference of the peaks extracted from ODF were measured to investigate the effect of b-value on the ODF reconstruction. Visual inspection of the ODF was used to evaluate the reconstructions. More detail is provided in "How Does B-Value Affect HARDI Reconstruction using Clinical Diffusion MRI Data?", PLoS ONE (forthcoming)
Direct and comparative visualization techniques for HARDI Data
No full textDWI is an MRI imaging technique used to gain information concerning the diffusion process in tissue. Using DTI techniques, a diffusion profile can be constructed for fiber tract analysis. Recently developed HARDI techniques increase the detail to visualization on the process of diffusion. While HARDI reconstruction methods are used to model the underlying diffusion process, the HARDI signal attenuation data can be used for a better understanding of noise in DWI data. This project addresses the direct visualization of HARDI data without any intermediate processing steps between acquisition and visualization. We present new glyph shapes for direct and comparative visualization of HARDI data using the signal attenuation or ADC and a multiple linked views layout. We developed new difference metrics to create a complete comparative visualization pipeline to identify and explore areas of interest. Evaluation of our developed methods by means of a case study, indicates the techniques to be a valued addition. The comparative visualization allows for quick identification of areas of interest. The glyph representation allows for rapid exploration of local diffusion data.Computer GraphicsMediamaticsElectrical Engineering, Mathematics and Computer Scienc
Fast classification scheme for HARDI data simplification
No full textHigh angular resolution diffusion imaging (HARDI) is able to capture the water diffusion pattern in areas of complex intravoxel fiber configurations. However, compared to diffusion tensor imaging (DTI), HARDI adds extra complexity (e.g., high post-processing time and memory costs, nonintuitive visualization). Separating the data into Gaussian and non-Gaussian areas can allow to use complex HARDI models just when it is necessary. We study HARDI anisotropy measures as classification criteria applied to different HARDI models. The chosen measures are fast to calculate and provide interactive data classification. We show that increasing b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, denoising enables better quality classifications even with low b-values and low sampling schemes. We study the measures quantitatively on an ex-vivo crossing phantom, and qualitatively on real data under different acquisition schemes
How does B-value affect HARDI reconstruction using clinical diffusion MRI data?
No full textBackground: A number of imaging factors can affect the orientation distribution function (ODF) reconstruction in high angular resolution diffusion imaging (HARDI). The aim of this study was to investigate the effect of the b-value on the HARDI reconstruction and to seek for the appropriate b-value for ODF reconstruction from clinical HARDI data. Methods: Diffusion MRI data with various b-values were collected on a GE 3T MRI scanner. To reconstruct the diffusion ODF and fiber ODF, decomposition-based spherical polar Fourier imaging and deconvolution-based constrained spherical deconvolution approaches were applied separately. The full width at half maximum (FWHM) of the ODF and the angular difference of the peaks extracted from ODF were measured to investigate the effect of b-value on the ODF reconstruction. Visual inspection of the ODF was used to evaluate the reconstructions. Results: The FWHM of the ODFs in the corpus callosum, which was chosen as the region of interest (ROI), decreased with increasing b-values. The differences in the FWHM for the diffusion ODF and the fiber ODF between the b-values of 2000 s/mm and 2500 s/mm were not significant. The angular differences of the ODF between 2000 s/mm and 2500 s/mm were lowest in both single-directional and two-directional situations. The ODFs became sharper and crossing-fiber situations were detected with an increase in b-value. B = 2000 s/mm and above revealed most of the two-way or three-way crossing-fiber structures. Conclusions: Considering both the signal-to-noise ratio and the acquisition time, b = 2000 s/mm is the basic requirement for ODF reconstruction using current HARDI methods on clinical data. This study can provide a useful reference for researchers and clinicians attempting to set appropriate scan protocols for specific HARDI experiments
Classification study of DTI and HARDI 1 anisotropy measures for HARDI data 2 simplification
No full textHigh angular resolution diffusion imaging (HARDI) captures the angular diffusion pattern of water molecules more accurately than diffusion tensor imaging (DTI). This is of importance mainly in areas of complex intra-voxel fiber configurations. However, the extra complexity of HARDI models has many disadvantages that make it unattractive for clinical applications. One of the main drawbacks is the long post-processing time for calculating the diffusion models. Also intuitive and fast visualization is not possible, and the memory requirements are far from modest. Separating the data into anisotropic-Gaussian (i.e., modeled by DTI) and non-Gaussian areas can alleviate some of the above mentioned issues, by using complex HARDI models only when necessary. This work presents a study of DTI and HARDI anisotropy measures applied as classification criteria for detecting non- Gaussian diffusion profiles. We quantify the classification power of these measures using a statistical test of receiver operation characteristic (ROC) curves applied on ex-vivo ground truth crossing phantoms. We show that some of the existing DTI and HARDI measures in the literature can be successfully applied for data classification to the diffusion tensor or different HARDI models respectively. The chosen measures provide fast data classification that can enable data simplification.We also show that increasing the b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, we show that a denoising pre-processing step improves the classification. This denoising enables better quality classifications even with low b-values and low sampling schemes. Finally, the findings of this study are qualitatively illustrated on real diffusion data under different acquisition schemes
Classification study of DTI and HARDI 1 anisotropy measures for HARDI data 2 simplification
No full textHigh angular resolution diffusion imaging (HARDI) captures the angular diffusion pattern of water molecules more accurately than diffusion tensor imaging (DTI). This is of importance mainly in areas of complex intra-voxel fiber configurations. However, the extra complexity of HARDI models has many disadvantages that make it unattractive for clinical applications. One of the main drawbacks is the
long post-processing time for calculating the diffusion models. Also intuitive and fast visualization is not possible, and the memory requirements are far from modest. Separating the data into anisotropic-Gaussian (i.e., modeled by DTI) and non-Gaussian areas can alleviate some of the above mentioned issues, by using complex HARDI models only when necessary. This work presents a study of DTI and HARDI anisotropy measures applied as classification criteria for detecting non-
Gaussian diffusion profiles. We quantify the classification power of these measures using a statistical test of receiver operation characteristic (ROC) curves applied on ex-vivo ground truth crossing phantoms. We show that some of the existing DTI and HARDI measures in the literature can be successfully applied for data classification to the diffusion tensor or different HARDI models respectively. The chosen measures provide fast data classification that can enable data simplification.We also show that increasing the b-value and number of diffusion measurements above clinically
accepted settings does not significantly improve the classification power of the measures. Moreover, we show that a denoising pre-processing step improves the classification. This denoising enables better quality classifications even with low b-values and low sampling schemes. Finally, the findings of this study are qualitatively illustrated on real diffusion data under different acquisition schemes
Riemann-Finsler multi-valued geodesic tractography for HARDI
No full textWe introduce a geodesic based tractography method for High Angular Resolution Diffusion Imaging (HARDI). The concepts used are similar to the ones in geodesic based tractography for Diffusion Tensor Imaging (DTI). In DTI, the inverse of the second-order diffusion tensor is used to define the manifold where the geodesics are traced. HARDI models have been developed to resolve complex fiber populations within a voxel, and higher order tensors (HOT) are possible representations for HARDI data. In our framework, we apply Finsler geometry, which extends Riemannian geometry to a directionally dependent metric. A Finsler metric is defined in terms of HARDI higher order tensors. Furthermore, the Euler-Lagrange geodesic equations are derived based on the Finsler geometry. In contrast to other geodesic based tractography algorithms, the multi-valued numerical solution of the geodesic equations can be obtained. This gives the possibility to capture all geodesics arriving at a single voxel instead of only computing the shortest one. Results are analyzed to show the potential and characteristics of our algorithm
HARDI DATA DENOISING USING VECTORIAL TOTAL VARIATION AND LOGARITHMIC BARRIER
No full textIn this work, we wish to denoise HARDI (High Angular Resolution Diffusion Imaging) data arising in medical brain imaging. Diffusion imaging is a relatively new and powerful method to measure the three-dimensional profile of water diffusion at each point in the brain. These images can be used to reconstruct fiber directions and pathways in the living brain, providing detailed maps of fiber integrity and connectivity. HARDI data is a powerful new extension of diffusion imaging, which goes beyond the diffusion tensor imaging (DTI) model: mathematically, intensity data is given at every voxel and at any direction on the sphere. Unfortunately, HARDI data is usually highly contaminated with noise, depending on the b-value which is a tuning parameter pre-selected to collect the data. Larger b-values help to collect more accurate information in terms of measuring diffusivity, but more noise is generated by many factors as well. So large b-values are preferred, if we can satisfactorily reduce the noise without losing the data structure. Here we propose two variational methods to denoise HARDI data. The first one directly denoises the collected data S, while the second one denoises the so-called sADC (spherical Apparent Diffusion Coefficient), a field of radial functions derived from the data. These two quantities are related by an equation of the form S = S(0) exp (-b.sADC) (in the noise-free case). By applying these two different models, we will be able to determine which quantity will most accurately preserve data structure after denoising. The theoretical analysis of the proposed models is presented, together with experimental results and comparisons for denoising synthetic and real HARDI data.open2
SHARD: Spherical harmonic-based robust outlier detection for HARDI methods
No full textHigh Angular Resolution Diffusion Imaging (HARDI) models are used to capture complex intra-voxel microarchitectures. The magnetic resonance imaging sequences that are sensitized to diffusion are often highly accelerated and prone to motion, physiologic, and imaging artifacts. In diffusion tensor imaging, robust statistical approaches have been shown to greatly reduce these adverse factors without human intervention. Similar approaches would be possible with HARDI methods, but robust versions of each distinct HARDI approach would be necessary. To avoid the computational and pragmatic burdens of creating individual robust HARDI analysis variants, we propose a robust outlier imputation model to mitigate outliers prior to traditional HARDI analysis. This model uses a weighted spherical harmonic fit of diffusion weighted magnetic resonance imaging scans to estimate the values which had been corrupted during acquisition to restore them. Briefly, spherical harmonics of 6th order were used to generate basis function which were weighted by diffusion signal for detection of outliers. For validation, a single healthy volunteer was scanned for a single session comprising of two scans one without head movement and the other with deliberate head movement at a b-value of 3000 s/mm2 with 64 diffusion weighted directions with a single b0 (5 averages) per scan. The deliberate motion from the volunteer created natural artifacts in the acquisition of one of the scans. The imputation model shows reduction in root mean squared error of the raw signal intensities and improvement for the HARDI method Q-ball in terms of the Angular Correlation Coefficient. The results reveal that there is quantitative and qualitative improvement. The proposed model can be used as general pre-processing model before implementing any HARDI model in general to restore the artifacts which are created because of the outlier diffusion signal in certain gradient volumes. ?? COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only
B.-A. Pocquet du Haut-Jussé. Philippe le Hardi, régent de Bretagne (1402-1404). Dijon, s. d. [1934].
No full textWaquet Henri. B.-A. Pocquet du Haut-Jussé. Philippe le Hardi, régent de Bretagne (1402-1404). Dijon, s. d. [1934].. In: Bibliothèque de l'école des chartes. 1935, tome 96. p. 141
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