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Background: Intravoxel incoherent motion (IVIM) tensor imaging is a promising technique for diagnosis and monitoring of cardiovascular diseases. Knowledge about measurement repeatability, however, remains limited. Purpose: To evaluate short-term repeatability of IVIM tensor imaging in normal in vivo human hearts. Study Type: Prospective. Population: Ten healthy subjects without history of heart diseases. Field Strength/Sequence: Balanced steady-state free-precession cine sequence and single-shot spin-echo echo planar IVIM tensor imaging sequence (9 b-values, 0-400 seconds/mm2 and six diffusion-encoding directions) at 3.0 T. Assessment: Subjects were scanned twice with an interval of 15 minutes, leaving the scanner between studies. The signal-to-noise ratio (SNR) was evaluated in anterior, lateral, septal, and inferior segments of the left ventricle wall. Fractional anisotropy (FA), mean diffusivity (MD), mean fraction (MF), and helix angle (HA) in the four segments were independently measured by five radiologists. Statistical Tests: IVIM tensor indexes were compared between observers using a one-way analysis of variance or between scans using a paired t-test (normal data) or a Wilcoxon rank-sum test (non-normal data). Interobserver agreement and test-retest repeatability were assessed using the intraclass correlation coefficient (ICC), within-subject coefficient of variation (WCV), and Bland-Altman limits of agreements. Results: SNR of inferior segment was significantly lower than the other three segments, and inferior segment was therefore excluded from repeatability analysis. Interobserver repeatability was excellent for all IVIM tensor indexes (ICC: 0.886-0.972; WCV: 0.62%-4.22%). Test-retest repeatability was excellent for MD of the self-diffusion tensor (D) and MF of the perfusion fraction tensor (fp) (ICC: 0.803-0.888; WCV: 1.42%-9.51%) and moderate for FA and MD of the pseudo-diffusion tensor (D*) (ICC: 0.487-0.532; WCV: 6.98%-10.89%). FA of D and fp and HA of D presented good test-retest repeatability (ICC: 0.732-0.788; WCV: 3.28%-8.71%). Data Conclusion: The D and fp indexes exhibited satisfactory repeatability, but further efforts were needed to improve repeatability of D* indexes. [ABSTRACT FROM AUTHOR]

Computation of B1 fields is a key issue for determination and correction of intensity nonuniformity in magnetic resonance images. This paper presents a new method for computing transmitted and received B1 fields. Our method combines a modified MRI acquisition protocol and an estimation technique based on the Levenberg-Marquardt algorithm and spatial filtering. It enables accurate estimation of transmitted and received B1 fields for both homogeneous and heterogeneous Objects. The method is validated using numerical simulations and experimental data from phantom and human scans. The experimental results are in agreement with theoretical expectations. [ABSTRACT FROM AUTHOR]

A data fusion approach for improving weld inspection by multimodal nondestructive testing (NDT) is investigated. The method relies on the combined use of evidence theory and fuzzy logic. In this framework, X-ray and ultrasonic inspection is modeled using fuzzy sets and mass function in evidence theory, and objects corresponding to X-ray and ultrasonic testing are fused at the level of features. The results show that the fusion improves significantly the reliability of defect detection and the defect sizing. [ABSTRACT FROM AUTHOR]

A new method for predicting the gene acceptor site based on multi-objective optimization is introduced in this paper. The models for the acceptor, branch and distance between acceptor site and branch site were constructed according to the characteristics of the sequences from the exon-intron database and using common biological knowledge. The acceptor function, branch function and distance function were defined respectively, and the multi-objective optimization model was constructed to recognize the splice site. The test results show that the algorithm used in this study performs better than the Splice Predictor, which is one of the leading acceptor site detectors. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a non-invasive technique used to obtain the 3D fiber structure of whole human hearts, for both in vivo and ex vivo cases. However, by essence, DTI does not measure directly the orientations of myocardial fibers. In contrast, polarized light imaging (PLI) allows for physical measurements of fiber orientations, but only for ex vivo case. This work aims at quantitatively comparing the myocardial fiber orientations of whole human hearts obtained from cardiac DTI with those measured by PLI. Whole human neonatal and infant hearts were first imaged using DTI. The same whole hearts were then imaged using PLI. After DTI and PLI data are registered, the orientations of fibers from the two imaging modalities were finally quantitatively compared. The results show that DTI and PLI have similar variation patterns of elevation and azimuth angles, with some differences in transmural elevation angle range. DTI itself induces an underestimation of the range of transmural elevation angles by a factor of about 25° at the basal and equatorial slices and the reduction of spatial resolution further decreases this range. PLI data exhibit a 15°  ±  5° (P  <  0.01) narrower transmural elevation angle range at apical slices than in basal or equatorial slices. This phenomenon is not observed in DTI data. In both modalities, the azimuth angle maps exhibit curved or twisting boundaries at the basal and apical slices. The experimental results globally enforce DTI as a valid imaging technique to reasonably characterize fiber orientations of the human heart noninvasively. [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate the effect of multiple perfusion components on the pseudo-diffusion coefficient D* in the bi-exponential intravoxel incoherent motion (IVIM) model. Simulations were first performed to examine how the presence of multiple perfusion components influences D*. The real data of livers (n  =  31), spleens (n  =  31) and kidneys (n  =  31) of 31 volunteers was then acquired using DWI for in vivo study and the number of perfusion components in these tissues was determined together with their perfusion fraction and D*, using an adaptive multi-exponential IVIM model. Finally, the bi-exponential model was applied to the real data and the mean, standard variance and coefficient of variation of D* as well as the fitting residual were calculated over the 31 volunteers for each of the three tissues and compared between them. The results of both the simulations and the in vivo study showed that, for the bi-exponential IVIM model, both the variance of D* and the fitting residual tended to increase when the number of perfusion components was increased or when the difference between perfusion components became large. In addition, it was found that the kidney presented the fewest perfusion components among the three tissues. The present study demonstrated that multi-component perfusion is a main factor that causes high variance of D* and the bi-exponential model should be used only when the tissues under investigation have few perfusion components, for example the kidney. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging and high angular resolution diffusion imaging are often used to analyze the fiber complexity of tissues. In these imaging techniques, the most commonly calculated metric is anisotropy, such as fractional anisotropy (FA), generalized anisotropy (GA), and generalized fractional anisotropy (GFA). The basic idea underlying these metrics is to compute the deviation from free or spherical diffusion. However, in many cases, the question is not really to know whether it concerns spherical diffusion. Instead, the main concern is to describe and quantify fiber complexity such as fiber crossing in a voxel. In this context, it would be more direct and effective to compute the deviation from a single fiber bundle instead of a sphere. We propose a new metric, called PEAM (PEAnut Metric), which is based on computing the deviation of orientation diffusion functions (ODFs) from a single fiber bundle ODF represented by a peanut. As an example, the proposed PEAM metric is used to classify intravoxel fiber configurations. The results on simulated data, physical phantom data and real brain data consistently showed that the proposed PEAM provides greater accuracy than FA, GA and GFA and enables parallel and complex fibers to be better distinguished. [ABSTRACT FROM AUTHOR]