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An improved fiber tracking algorithm based on fiber assignment using the continuous tracking algorithm and two-tensor model.

Zhu L, Guo G - Neural Regen Res (2012)

Bottom Line: Different models and tracking decisions were used by judging the type of estimation of each voxel.This method should solve the cross-track problem.Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Xiamen Second Hospital, Teaching Hospital of Fujian Medical University, Xiamen 361021, Fujian Province, China.

ABSTRACT
This study tested an improved fiber tracking algorithm, which was based on fiber assignment using a continuous tracking algorithm and a two-tensor model. Different models and tracking decisions were used by judging the type of estimation of each voxel. This method should solve the cross-track problem. This study included eight healthy subjects, two axonal injury patients and seven demyelinating disease patients. This new algorithm clearly exhibited a difference in nerve fiber direction between axonal injury and demyelinating disease patients and healthy control subjects. Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

No MeSH data available.


Related in: MedlinePlus

Raw data preprocessing. The directions of A and B are inconsistent, because of automatic rotation of X axis and Y axis during DICOM data processing using MATLAB. Thus, we should rotate the X axis and Y axis in advance during preprocessing to avoid direction error.(A) Before preprocessing.(B) After preprocessing (fractional intensity = 0.3).
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Figure 6: Raw data preprocessing. The directions of A and B are inconsistent, because of automatic rotation of X axis and Y axis during DICOM data processing using MATLAB. Thus, we should rotate the X axis and Y axis in advance during preprocessing to avoid direction error.(A) Before preprocessing.(B) After preprocessing (fractional intensity = 0.3).

Mentions: Preprocessing raw data were necessary to remove effects caused by movement or noise after acquisition. There are several traditional preprocessing steps using statistical parametric mapping software, as follows: realign-slice timing-normalizing-smoothing. In performing data preprocessing, it is important to maintain the originality of the data. However, it is unclear to what degree our preprocessing method is able to achieve this. Statistical parametric mapping preprocessing might be an appropriate method, but is time-consuming. As such, we chose a simpler “fast preprocessing” method to keep maximum data originality. Our fast preprocessing method is described as follows. First, transfer the DICOM data into an analyzed data format (“.img, .hdr” in MRIcro software; www.mricro.com). Second, smoothing is performed to reduce the data noise. Third, skull removal is used to remove background noise, to avoid analyzing data outside the brain when performing subsequent fiber tracking. Figure 6 shows data before and after preprocessing. Compared with Figure 6A, Figure 6B exhibits less image noise.


An improved fiber tracking algorithm based on fiber assignment using the continuous tracking algorithm and two-tensor model.

Zhu L, Guo G - Neural Regen Res (2012)

Raw data preprocessing. The directions of A and B are inconsistent, because of automatic rotation of X axis and Y axis during DICOM data processing using MATLAB. Thus, we should rotate the X axis and Y axis in advance during preprocessing to avoid direction error.(A) Before preprocessing.(B) After preprocessing (fractional intensity = 0.3).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308771&req=5

Figure 6: Raw data preprocessing. The directions of A and B are inconsistent, because of automatic rotation of X axis and Y axis during DICOM data processing using MATLAB. Thus, we should rotate the X axis and Y axis in advance during preprocessing to avoid direction error.(A) Before preprocessing.(B) After preprocessing (fractional intensity = 0.3).
Mentions: Preprocessing raw data were necessary to remove effects caused by movement or noise after acquisition. There are several traditional preprocessing steps using statistical parametric mapping software, as follows: realign-slice timing-normalizing-smoothing. In performing data preprocessing, it is important to maintain the originality of the data. However, it is unclear to what degree our preprocessing method is able to achieve this. Statistical parametric mapping preprocessing might be an appropriate method, but is time-consuming. As such, we chose a simpler “fast preprocessing” method to keep maximum data originality. Our fast preprocessing method is described as follows. First, transfer the DICOM data into an analyzed data format (“.img, .hdr” in MRIcro software; www.mricro.com). Second, smoothing is performed to reduce the data noise. Third, skull removal is used to remove background noise, to avoid analyzing data outside the brain when performing subsequent fiber tracking. Figure 6 shows data before and after preprocessing. Compared with Figure 6A, Figure 6B exhibits less image noise.

Bottom Line: Different models and tracking decisions were used by judging the type of estimation of each voxel.This method should solve the cross-track problem.Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Xiamen Second Hospital, Teaching Hospital of Fujian Medical University, Xiamen 361021, Fujian Province, China.

ABSTRACT
This study tested an improved fiber tracking algorithm, which was based on fiber assignment using a continuous tracking algorithm and a two-tensor model. Different models and tracking decisions were used by judging the type of estimation of each voxel. This method should solve the cross-track problem. This study included eight healthy subjects, two axonal injury patients and seven demyelinating disease patients. This new algorithm clearly exhibited a difference in nerve fiber direction between axonal injury and demyelinating disease patients and healthy control subjects. Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.

No MeSH data available.


Related in: MedlinePlus