scholarly journals Diffusion tensor imaging of the human kidney: Does image registration permit scanning without respiratory triggering?

2016 ◽  
Vol 44 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Maryam Seif ◽  
Laila Yasmin Mani ◽  
Huanxiang Lu ◽  
Chris Boesch ◽  
Mauricio Reyes ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Human Kidney ◽  
Respiratory Triggering

scholarly journals DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Zhe Guo ◽  
Yi Wang ◽  
Tao Lei ◽  
Yangyu Fan ◽  
Xiuwei Zhang
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Sample Selection ◽  
Nerve Fibers ◽  
Residual Error ◽  
Fiber Bundles ◽  
Finite Sample ◽  
Registration Method ◽  
Sample Set

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

The Role of Diffusion Tensor Imaging According to Tensor Registration Algorithm in the Treatment of Neonatal Hypoxic-Ischemic Encephalopathy

2021 ◽  
Vol 11 (1) ◽  
pp. 247-253
Author(s):  
Piqiang Zhang ◽  
Hong Liu ◽  
Xuemei Sun ◽  
Hongjuan Li
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Internal Capsule ◽  
Centrum Semiovale ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Sensitivity Specificity ◽  
Ischemic Encephalopathy

This research aimed to analyze the diagnostic value of diffusion tensor imaging (DTI) based on tensor image registration algorithm for hypoxia-ischemic encephalopathy (HIE). In this study, 90 newborns diagnosed with HIE who were admitted to our hospital from October 30, 2016 to January 30, 2019 were selected as experimental group (EG) and were divided into mild group (EG 1), moderate group (EG 2), and severe group (EG 3). In addition, 45 normal newborns of the same period were selected as the control group (CG). All subjects underwent DTI, which was processed by image registration algorithm, and the regions of interest (ROIs) were manually selected, including anterior limb of internal capsule (ALIC), posterior limb of internal capsule (PLIC), frontal white matter (FWM), centrum semiovale, corpus callosum (CC), and lenticular nucleus. Fractional anisotropy (FA) values for each ROI were recorded. Receiver operating characteristic (ROC) curve was used to analyze the accuracy, sensitivity, specificity, and area under curve (AUC) of each ROI in diagnosing HIE. The results showed that the images processed by the registration algorithm were clearer than the original images, the signal-to-noise ratio (SNR) was increased, and the artifact was decreased, so that the lesions of newborns with HIE could be clearly observed. FA values in some ROIs in EG were significantly lower than those in CG (P < 0.05). FA values of all ROIs in the EG 3 were significantly lower than those in the EG 1 and the EG 2, with significant differences (P < 0.05). The FA values of the ALIC, the PLIC, and the centrum semiovale in the EG 2 were significantly lower than those in the EG 1, with significant differences (P < 0.05). The accuracy, sensitivity, specificity, and AUC area of the ALIC were higher, that is, 87.6%, 87.7%, 89.6%, and 0.879, respectively, indicating that with the aggravation of the condition of HIE, the morphological damage of the white matter fiber bundle became more serious and the FA value decreased. Therefore, DTI imaging is safe and feasible in the diagnosis of HIT lesions, especially with high accuracy, sensitivity, and specificity in the ALIC and FWM.

Impact of blood flow on diffusion coefficients of the human kidney: A time-resolved ECG-triggered diffusion-tensor imaging (DTI) study at 3T

2012 ◽  
Vol 37 (1) ◽  
pp. 233-236 ◽  
Author(s):  
Philipp Heusch ◽  
Hans-Jörg Wittsack ◽  
Patric Kröpil ◽  
Dirk Blondin ◽  
Michael Quentin ◽  
...  
Keyword(s):  
Blood Flow ◽  
Diffusion Tensor Imaging ◽  
Diffusion Coefficients ◽  
Diffusion Tensor ◽  
Human Kidney ◽  
Time Resolved ◽  
Diffusion Tensor Imaging Dti
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