Main Paths of Brain Fibers in Diffusion Images Mixed with a Noise to Improve Performance of Tractography Algorithm-Evaluation in Phantom.

Background: Some voxels may alter the tractography results due to unintentional alteration of noises and other unwanted factors. Objective: This study aimed to investigate the effect of local phase features on tractography results providing data are mixed by a Gaussian or random distribution noise. Material and Methods: In this simulation study, a mask was firstly designed based on the local phase features to decrease false-negative and -positive tractography results. The local phase features are calculated according to the local structures of images, which can be zero-dimensional, meaning just one point (equivalent to noise in tractography algorithm), a line (equivalent to a simple fiber), or an edge (equivalent to structures more complex than a simple fiber). A digital phantom evaluated the feasibility current model with the maximum complexities of configurations in fibers, including crossing fibers. In this paper, the diffusion images were mixed separately by a Gaussian or random distribution noise in 2 forms a zero-mean noise and a noise with a mean of data. Results: The local mask eliminates the pixels of unfitted values with the main structures of images, due to noise or other interferer factors. Conclusion: The local phase features of diffusion images are an innovative solution to determine principal diffusion directions.

Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study.

Background: The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise. Objective: This study aims to improve the accuracy of IVIM output parameters. Material and Methods: In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method purifies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters. Results: Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively. Conclusion: The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.

Intravoxel Incoherent Motion Quantification Dependent on Measurement SNR and Tissue Perfusion: A Simulation Study.

Background: The intravoxel incoherent motion (IVIM) model extracts both functional and structural information of a tissue using motion-sensitizing gradients. Objective: The Objective of the present work is to investigate the impact of signal to noise ratio (SNR) and physiologic conditions on the validity of IVIM parameters. Material and Methods: This study is a simulation study, modeling IVIM at a voxel, and also done 10,000 times for every single simulation. Complex noises with various standard deviations were added to signal in-silico to investigate SNR effects on output validity. Besides, some blood perfusion situations for different tissues were considered based on their physiological range to explore the impacts of blood fraction at each voxel on the validity of the IVIM outputs. Coefficient variation (CV) and bias of the estimations were computed to assess the validity of the IVIM parameters. Results: This study has shown that the validity of IVIM output parameters highly depends on measurement SNR and physiologic characteristics of the studied organ. Conclusion: IVIM imaging could be useful if imaging parameters are correctly selected for each specific organ, considering hardware limitations.