Automatic age estimation of children based on brain matter composition using quantitative MRI
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The development of a child can be monitored by studying the changes in physical appearance or the development of capabilities e.g. walking and talking. But is it possible to find a quantitative measure for brain development? The aim of this thesis work is to investigate that possibility using quantitative magnetic resonance imaging (qMRI) images by answering the following questions:
- Can brain development be determined using qMRI? If so, what properties of the brain can be used?
- Can the age of a child be automatically detected with an algorithm? If so, how can this algorithm function? With what accuracy?
Previous studies have shown that it is possible to detect properties in the brain changing with age, based on MRI images. These properties have e.g. been changes in T1 and T2 relaxation time, i.e. properties in water signal behavior that can be measured using multiple MR acquisitions. In the literature this was linked to a rapid myelination process that occurs after birth. Furthermore the organization and growth of the brain is a property that can be measured and monitored.
This thesis have investigated several different properties in the brain based on qMRI images in order to identify those who have a strong correlation with age in the range 0-20 years. The properties that were found to have a high correlation were:
- Position of the first histogram peak in T1 weighted qMRI images,
- Fraction of white matter in the brain,
- Mean pixel value of PD weighted qMRI images,
- Volume of white matter in the brain,
Curves on the form f(x) = ae^(-bx) +c are fitted to the data sets and confidence intervals are calculated to frame the statistical insecurity of the curve. The mean error in percent for the different properties can be seen in the list below:
Property, Mean error [%] 0-20 years, Mean error [%] 0-3 years
Peak position: 53.84, 98.17
Fraction of WM: 118.97, 71.67
Mean pixel value: 200.89, 126.28
Volume of WM: 241.72, 72.58
The conclusions drawn based on the presented results are that there are properties in the brain that correlates well to aging, but the error is too large for making a valid prediction of age over the entire range of 0-20 years. When decreasing the age range to 0-3 years the mean error becomes smaller, but it is still too large. More data is needed to evaluate and improve this result.
Place, publisher, year, edition, pages
2015. , 53 p.
qMRI, quantitative MRI, Brain development, Age estimation
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-119431ISRN: LiTH-IMT/MI30-A-EX--15/528--SEOAI: oai:DiVA.org:liu-119431DiVA: diva2:822673
Subject / course
2015-06-12, Linköping, 13:15 (English)
Andersson, MatsWarntjes, Marcel