Influence Of Partial Volume Effect Research Articles

Fiber direction estimation using constrained spherical deconvolution based on multi-model response function

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

The Potential for Quantifying Regional Distributions of Radial and Shear Strain in the Thoracic and Abdominal Aortic Wall Using Spiral Cine DENSE Magnetic Resonance Imaging.

Aortic displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) was recently developed to assess heterogeneities in aortic wall circumferential strain (CS). However, previous studies neglected potential radial and shear strain (RSS) distributions. Herein, we present an improved aortic DENSE MRI postprocessing method to assess the feasibility of quantifying all components of the two-dimensional (2D) strain tensor. 32 previously acquired 2D DENSE scans from three distinct aortic locations were re-analyzed. Contrasting previous studies, displacements of the inner and outer aortic wall layers were processed separately to preserve RSS. Differences in regional strain between the new and old postprocessing methods were evaluated, along with interobserver, intraobserver, and interscan repeatability for all strain components. The new postprocessing method revealed an overall mean absolute difference in regional CS of 0.01 ± 0.01 compared to the prior method, with minimal impact on CS repeatability. Mean absolute magnitudes of regional RSS increased significantly compared to changes in CS (radial 0.04 ± 0.05, p < 0.001; shear 0.04 ± 0.04, p = 0.02). Most repeatability metrics for RSS were significantly worse than for CS. The unique distributions of RSS for each axial location associated well with local periaortic structures and mean aortic displacement. The new postprocessing method captures heterogeneous distributions of nonzero RSS which may provide new information for improving clinical diagnostics and computational modeling of heterogeneous aortic wall mechanics. However, future studies are required to improve the repeatability of RSS and assess the influence of partial volume effects.

Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation

BackgroundAlthough supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory. New methodsTo address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume. ResultsThe performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods. ConclusionsThe proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.

The correlation between 1H MRS choline concentrations and MR diffusion trace values in human brain tumors

The aim of this study was to develop a method for evaluating the spatial distribution of human brain gliomas in individual subjects by evaluating the correlation between the Choline (Cho) signal intensity and the diffusion trace (Tr(ADC)) values. Eleven patients with different histopathologic diagnoses and five healthy subjects were examined with diffusion-weighted EPI-trace sequence and (1)H MR spectroscopic imaging. The calculation of the correlation between choline and Tr(ADC) values on a pixel-by-pixel basis and simulations estimating the influence of partial volume effects on the result were performed. Statistical evaluation of the data in the patients with a glioblastoma showed that pixels corresponding to different tissue states are situated in different areas in the Cho-Tr(ADC) correlation graph. Namely, points forming an inverse linear dependence interpreted as an area of an active tumor were observed. Different types of correlations were found in grade II and III gliomas. No statistically significant correlation was found in healthy subjects. Simulations proved that the observed linear dependence cannot be attributed solely to partial volume effects. The analysis of the correlation between Cho concentrations and Tr(ADC) values on a pixel-by-pixel basis should help the regional identification of the pathological state of a tissue in patients with a glioblastoma.

Coronary stent assessability by 64 slice multi‐detector computed tomography

We evaluated the assessability of contemporary stent platforms by 64-slice multi-detector computed tomography (MDCT). Patients undergoing coronary stenting were included in a prospective protocol of MDCT imaging within 48 hr of stent implantation. MDCT data were acquired using a "Sensation 64" MDCT scanner (Siemens Medical Solutions, Forchheim, Germany). Stent assessability was assessed by two independent blinded observers and disagreement was resolved by a third observer. Assessability was defined at visualization of the in-stent lumen without influence of partial volume effects, beam hardening, motion, calcification, or contrast to noise limitations. Fifty four stents (Cypher n = 25, Vision/Minivision n = 19, Taxus Express n = 8, Liberte n = 1, Driver n = 1) in 44 patients were included in the study. The two independent observers classified 30 of 54 stents (56%) as assessable. Interobserver reproducibility was good with kappa = 0.66. Stent size was the most important determinant of assessability. Consistently assessable stents were 3.0 mm or larger (85%), whereas those under 3 mm were mostly nonassessable (26%). Contemporary stent designs evaluated on a 64-slice MDCT scanner showed artifact free assessability only in larger stents. Increase in spatial resolution of MDCT scanners or modifications in stent design will be necessary to noninvasive evaluate stents <3 mm in diameter, where in-stent restenosis is more frequent.

Predicting mortality among patients hospitalized for heart failure. Derivation and validation of a clinical model

Although supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory.To address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume.The performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods.The proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.

Individual Modeling Method Based on the X-Ray CT Images. Influence of Partial Volume Effect on the Modeling.

Reliable stress analysis of a bone requires not only precision in shape of the finite element model but also proper setting of material constants for the model. This paper deals with estimation of Young's modulus to the finite elements based on the X-ray CT data. At first, partial volume effect on the modeling was discussed by analytical and experimental ways using acrylic cylindrical specimen. Second, a simple correction method which transforms the CT value around border of the object was proposed. In this correction method, CT value larger than the threshold value was pulled up to the target CT value. To confirm validity of the proposed correction method, a load test and stress analyses using a dry mandible were performed. Analytical displacement between condyles and maximum principal strains were well coincided with these of experiments.

Brain SPET abnormalities in Alzheimer's disease before and after atrophy correction

The aim of this study was to determine which brain structures show the greatest influence of partial volume effects (PVE) in single-photon emission tomography (SPET) studies on Alzheimer's disease (AD). Brain perfusion SPET was performed in 30 patients with probable AD and 62 age-matched healthy volunteers. SPET images were corrected for PVE using grey matter volume segmented from magnetic resonance images. The most prominent changes after PVE correction were observed in the medial temporal structures. The PVE correction revealed a selective decrease in regional cerebral blood flow (rCBF) in the parahippocampal gyrus of AD without rCBF decreases in the hippocampus, which had been observed before correction. This correction seems to be essential in order to achieve accurate measurements of rCBF in SPET, which has limited spatial resolution.

265 X 線 CT 画像に基づく個体別有限要素モデリング : CT 値の補正と画像補間に関する考察

Reliable stress analysis of a bone requires not only precision in shape of the finite element model but also proper setting of material constants for the model. This study proposes two methods to recognize proper shape and to evaluate mechanical properties from X-ray CT images. The former method corrects CT values considering influence of partial volume effect. The latter interpolates outlines of an object in muti-sliced CT images. This paper shows that these methods perform a proper correction and a clear interpolation using CT images of an acrylic cylindrical specimen.

Automatic detection of arterial input functions for quantification of perfusion

Regional cerebral blood volume (rCBV) provides valuable information about the nature and progress of diseases of the central nervous system. While relative rCBV maps can be derived directly from dynamic susceptibility contrast data, the arterial input function (AIF) has to be measured for absolute rCBV quantification. For determination of the AIF pixels located completely within a feeding artery must be selected. However, by using a region-of-interest (ROI) based selection some confounding effects can occur, especially if single shot echo planar imaging (EPI) with low spatial resolution is used. In this study we analyzed the influence of partial volume effects and spatial misregistration due to frequency shifts induced by paramagnetic contrast agents. We analyzed AIFs from the internal carotid artery (ICA), the vertebral artery (VA) and the middle cerebral artery (MCA) using gamma variate function based parameterization. The concentration time curves (CTC) of several pixels which were selected on the basis of strong signal drop appeared distorted during the bolus passage. Moreover, the amplitudes of input functions derived from the MCA were smaller by a factor of three as compared to those of the ICA and VA. Simulations revealed that these effects can be attributed to a spatial shift of the vessel along phase-encoding direction during the passage of the bolus. We therefore developed a procedure for a pixel selection based on cluster analysis which classifies pixels according to the parameters of the fitted gamma variate functions. This approach accounted for misregistration of the vessel and yielded very consistent results for a group of normal subjects.

Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements

Regional cerebral blood volume (rCBV) provides valuable information about the nature and progress of diseases of the central nervous system. While relative rCBV maps can be derived directly from dynamic susceptibility contrast data, the arterial input function (AIF) has to be measured for absolute rCBV quantification. For determination of the AIF pixels located completely within a feeding artery must be selected. However, by using a region-of-interest (ROI) based selection some confounding effects can occur, especially if single shot echo planar imaging (EPI) with low spatial resolution is used. In this study we analyzed the influence of partial volume effects and spatial misregistration due to frequency shifts induced by paramagnetic contrast agents. We analyzed AIFs from the internal carotid artery (ICA), the vertebral artery (VA) and the middle cerebral artery (MCA) using gamma variate function based parameterization. The concentration time curves (CTC) of several pixels which were selected on the basis of strong signal drop appeared distorted during the bolus passage. Moreover, the amplitudes of input functions derived from the MCA were smaller by a factor of three as compared to those of the ICA and VA. Simulations revealed that these effects can be attributed to a spatial shift of the vessel along phase-encoding direction during the passage of the bolus. We therefore developed a procedure for a pixel selection based on cluster analysis which classifies pixels according to the parameters of the fitted gamma variate functions. This approach accounted for misregistration of the vessel and yielded very consistent results for a group of normal subjects.