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Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model.

Raj A, Pandya S, Shen X, LoCastro E, Nguyen TD, Gauthier SA - PLoS ONE (2014)

Bottom Line: The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions.Due to efficient design, the additional spatial aspect does not cause an increase in processing time.While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.

ABSTRACT
The brain's myelin content can be mapped by T2-relaxometry, which resolves multiple differentially relaxing T2 pools from multi-echo MRI. Unfortunately, the conventional fitting procedure is a hard and numerically ill-posed problem. Consequently, the T2 distributions and myelin maps become very sensitive to noise and are frequently difficult to interpret diagnostically. Although regularization can improve stability, it is generally not adequate, particularly at relatively low signal to noise ratio (SNR) of around 100-200. The purpose of this study was to obtain a fitting algorithm which is able to overcome these difficulties and generate usable myelin maps from noisy acquisitions in a realistic scan time. To this end, we restrict the T2 distribution to only 3 distinct resolvable tissue compartments, modeled as Gaussians: myelin water, intra/extra-cellular water and a slow relaxing cerebrospinal fluid compartment. We also impose spatial smoothness expectation that volume fractions and T2 relaxation times of tissue compartments change smoothly within coherent brain regions. The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions. Due to efficient design, the additional spatial aspect does not cause an increase in processing time. The proposed method was applied to fast spiral acquisitions on which conventional fitting gives uninterpretable results. While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry.

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Related in: MedlinePlus

Examination of elevated MWF, relaxation curves and T2 distribution.Detailed investigation of elevated MWF in deep gray matter. Relaxation curves for three brain areas are shown on the middle panel: WM, GM and deep brain GM – these areas are denoted by circles on the MWF map generated from spatial constrained method (left panel). Dark red circle indicates the WM area, blue indicates the GM area, and green indicates the GM with fast relaxation rate area. The fitted T2 distribution from each region is shown in the right panel - curves correspond to the ROI average
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pone-0098391-g007: Examination of elevated MWF, relaxation curves and T2 distribution.Detailed investigation of elevated MWF in deep gray matter. Relaxation curves for three brain areas are shown on the middle panel: WM, GM and deep brain GM – these areas are denoted by circles on the MWF map generated from spatial constrained method (left panel). Dark red circle indicates the WM area, blue indicates the GM area, and green indicates the GM with fast relaxation rate area. The fitted T2 distribution from each region is shown in the right panel - curves correspond to the ROI average

Mentions: Figure 5 shows processed MWF maps from T2prep spiral scans of a healthy volunteer, which has a different appearance compared to MESE, due to spatial inhomogeneity and other artifacts introduced by the fast sequence. Although this has made the NNLS method practically unusable, our approach is able to retrieve anatomically faithful myelin maps from this data. However, there is upward bias in GM, which should be lower than depicted (see Discussion). The values in WM, however, are in line with reported numbers, as indicated in Table 2. Figure 6 shows another example with similar characteristics, but it additionally illustrates an oversized effect of T2 shortening induced by iron deposition in the basal ganglia (see Discussion). The difference in MWF between whole brain WM and GM was significant in all subjects (p<0.001) for both methods, indicating good tissue differentiation. Corresponding COV data (Table 3) shows greatly reduced variability from our algorithm, reinforcing the earlier visual impression of smooth, noise-free maps. Figure 7 shows typical T2 decay curves from WM, GM and iron-rich deep GM and their estimated T2 distributions – they show expected behavior, with fast initial decay in WM (and iron rich nuclei).


Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model.

Raj A, Pandya S, Shen X, LoCastro E, Nguyen TD, Gauthier SA - PLoS ONE (2014)

Examination of elevated MWF, relaxation curves and T2 distribution.Detailed investigation of elevated MWF in deep gray matter. Relaxation curves for three brain areas are shown on the middle panel: WM, GM and deep brain GM – these areas are denoted by circles on the MWF map generated from spatial constrained method (left panel). Dark red circle indicates the WM area, blue indicates the GM area, and green indicates the GM with fast relaxation rate area. The fitted T2 distribution from each region is shown in the right panel - curves correspond to the ROI average
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4045663&req=5

pone-0098391-g007: Examination of elevated MWF, relaxation curves and T2 distribution.Detailed investigation of elevated MWF in deep gray matter. Relaxation curves for three brain areas are shown on the middle panel: WM, GM and deep brain GM – these areas are denoted by circles on the MWF map generated from spatial constrained method (left panel). Dark red circle indicates the WM area, blue indicates the GM area, and green indicates the GM with fast relaxation rate area. The fitted T2 distribution from each region is shown in the right panel - curves correspond to the ROI average
Mentions: Figure 5 shows processed MWF maps from T2prep spiral scans of a healthy volunteer, which has a different appearance compared to MESE, due to spatial inhomogeneity and other artifacts introduced by the fast sequence. Although this has made the NNLS method practically unusable, our approach is able to retrieve anatomically faithful myelin maps from this data. However, there is upward bias in GM, which should be lower than depicted (see Discussion). The values in WM, however, are in line with reported numbers, as indicated in Table 2. Figure 6 shows another example with similar characteristics, but it additionally illustrates an oversized effect of T2 shortening induced by iron deposition in the basal ganglia (see Discussion). The difference in MWF between whole brain WM and GM was significant in all subjects (p<0.001) for both methods, indicating good tissue differentiation. Corresponding COV data (Table 3) shows greatly reduced variability from our algorithm, reinforcing the earlier visual impression of smooth, noise-free maps. Figure 7 shows typical T2 decay curves from WM, GM and iron-rich deep GM and their estimated T2 distributions – they show expected behavior, with fast initial decay in WM (and iron rich nuclei).

Bottom Line: The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions.Due to efficient design, the additional spatial aspect does not cause an increase in processing time.While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America.

ABSTRACT
The brain's myelin content can be mapped by T2-relaxometry, which resolves multiple differentially relaxing T2 pools from multi-echo MRI. Unfortunately, the conventional fitting procedure is a hard and numerically ill-posed problem. Consequently, the T2 distributions and myelin maps become very sensitive to noise and are frequently difficult to interpret diagnostically. Although regularization can improve stability, it is generally not adequate, particularly at relatively low signal to noise ratio (SNR) of around 100-200. The purpose of this study was to obtain a fitting algorithm which is able to overcome these difficulties and generate usable myelin maps from noisy acquisitions in a realistic scan time. To this end, we restrict the T2 distribution to only 3 distinct resolvable tissue compartments, modeled as Gaussians: myelin water, intra/extra-cellular water and a slow relaxing cerebrospinal fluid compartment. We also impose spatial smoothness expectation that volume fractions and T2 relaxation times of tissue compartments change smoothly within coherent brain regions. The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions. Due to efficient design, the additional spatial aspect does not cause an increase in processing time. The proposed method was applied to fast spiral acquisitions on which conventional fitting gives uninterpretable results. While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry.

Show MeSH
Related in: MedlinePlus