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Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique.

Chai JW, Chen CC, Wu YY, Chen HC, Tsai YH, Chen HM, Lan TH, Ouyang YC, Lee SK - PLoS ONE (2015)

Bottom Line: The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity.The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups.In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan; College of Medicine, China Medical University, Taichung, Taiwan.

ABSTRACT
A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

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The results of brain classification images from 3D multispectral-multislice MRI.Left side reveals 3D multispectral MRI of FLAIR, T1WI and T2WI and right side is the classification images. Upper, middle and lower rows show GM, WM and CSF images. (A) A 20 year old young female with 587.2 ml, 433.6 ml and 154.8 ml of GM, WM and CSF, and 49.9%, 36.9% and 13.2% of GM, WM and CSF volume fractions. (B) A 60 year old healthy male with 636.0 ml, 587.3 ml and 326.8 ml of GM, WM and CSF, and 41.0%, 37.9% and 21.1% of GM, WM and CSF volume fractions. (C) A 76 year old dementia patient with 562.3 ml, 454.3 ml and 333.1 ml of GM, WM and CSF, and 41.7%, 33.7% and 24.7% of GM, WM and CSF volume fractions.
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pone.0115527.g002: The results of brain classification images from 3D multispectral-multislice MRI.Left side reveals 3D multispectral MRI of FLAIR, T1WI and T2WI and right side is the classification images. Upper, middle and lower rows show GM, WM and CSF images. (A) A 20 year old young female with 587.2 ml, 433.6 ml and 154.8 ml of GM, WM and CSF, and 49.9%, 36.9% and 13.2% of GM, WM and CSF volume fractions. (B) A 60 year old healthy male with 636.0 ml, 587.3 ml and 326.8 ml of GM, WM and CSF, and 41.0%, 37.9% and 21.1% of GM, WM and CSF volume fractions. (C) A 76 year old dementia patient with 562.3 ml, 454.3 ml and 333.1 ml of GM, WM and CSF, and 41.7%, 33.7% and 24.7% of GM, WM and CSF volume fractions.

Mentions: Effective classification and volume measurement was performed for three study groups consisting of 30 subjects. After the pre-processing step, the TRIO algorithm took approximately 30 seconds to complete classification processing of a multislice-multispectral 3DFT MRI data in MATLAB 7.12 (MathWorks, Inc. Natick, Massachusetts) running on an Intel 3.40 Giga-Hz CPU system with 8.00 Giga-Bytes of RAM memory. The time for generating the training data usually took less than another 30 seconds. Fig. 2 illustrates examples of GM, WM, and CSF segmented images from three study groups. Although no gold standard was available for in vivo studies, the quantitative data of the brain tissue and CSF volumes aligned with the brain morphometrics in these three study groups. As for the analysis of GM and WM volume measurements, the results revealed a larger reduction in the mean absolute volume of GM than that of WM in elderly volunteers compared to young adults, while an equal reduction in the mean absolute volume of GM and WM in dementia patients compared to healthy elderlies. As for analysis of brain volume fractions, the results showed a decrease in mean GM volume fraction and an increase in mean WM volume fraction in elderly volunteers compared to young adults. For dementia patients, reductions of the mean volume fractions were demonstrated in both GM and WM as compared to the elderly volunteers.


Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique.

Chai JW, Chen CC, Wu YY, Chen HC, Tsai YH, Chen HM, Lan TH, Ouyang YC, Lee SK - PLoS ONE (2015)

The results of brain classification images from 3D multispectral-multislice MRI.Left side reveals 3D multispectral MRI of FLAIR, T1WI and T2WI and right side is the classification images. Upper, middle and lower rows show GM, WM and CSF images. (A) A 20 year old young female with 587.2 ml, 433.6 ml and 154.8 ml of GM, WM and CSF, and 49.9%, 36.9% and 13.2% of GM, WM and CSF volume fractions. (B) A 60 year old healthy male with 636.0 ml, 587.3 ml and 326.8 ml of GM, WM and CSF, and 41.0%, 37.9% and 21.1% of GM, WM and CSF volume fractions. (C) A 76 year old dementia patient with 562.3 ml, 454.3 ml and 333.1 ml of GM, WM and CSF, and 41.7%, 33.7% and 24.7% of GM, WM and CSF volume fractions.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4339724&req=5

pone.0115527.g002: The results of brain classification images from 3D multispectral-multislice MRI.Left side reveals 3D multispectral MRI of FLAIR, T1WI and T2WI and right side is the classification images. Upper, middle and lower rows show GM, WM and CSF images. (A) A 20 year old young female with 587.2 ml, 433.6 ml and 154.8 ml of GM, WM and CSF, and 49.9%, 36.9% and 13.2% of GM, WM and CSF volume fractions. (B) A 60 year old healthy male with 636.0 ml, 587.3 ml and 326.8 ml of GM, WM and CSF, and 41.0%, 37.9% and 21.1% of GM, WM and CSF volume fractions. (C) A 76 year old dementia patient with 562.3 ml, 454.3 ml and 333.1 ml of GM, WM and CSF, and 41.7%, 33.7% and 24.7% of GM, WM and CSF volume fractions.
Mentions: Effective classification and volume measurement was performed for three study groups consisting of 30 subjects. After the pre-processing step, the TRIO algorithm took approximately 30 seconds to complete classification processing of a multislice-multispectral 3DFT MRI data in MATLAB 7.12 (MathWorks, Inc. Natick, Massachusetts) running on an Intel 3.40 Giga-Hz CPU system with 8.00 Giga-Bytes of RAM memory. The time for generating the training data usually took less than another 30 seconds. Fig. 2 illustrates examples of GM, WM, and CSF segmented images from three study groups. Although no gold standard was available for in vivo studies, the quantitative data of the brain tissue and CSF volumes aligned with the brain morphometrics in these three study groups. As for the analysis of GM and WM volume measurements, the results revealed a larger reduction in the mean absolute volume of GM than that of WM in elderly volunteers compared to young adults, while an equal reduction in the mean absolute volume of GM and WM in dementia patients compared to healthy elderlies. As for analysis of brain volume fractions, the results showed a decrease in mean GM volume fraction and an increase in mean WM volume fraction in elderly volunteers compared to young adults. For dementia patients, reductions of the mean volume fractions were demonstrated in both GM and WM as compared to the elderly volunteers.

Bottom Line: The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity.The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups.In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan; College of Medicine, China Medical University, Taichung, Taiwan.

ABSTRACT
A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

Show MeSH
Related in: MedlinePlus