Korean J Radiol.  2012 Aug;13(4):391-402. 10.3348/kjr.2012.13.4.391.

Influence of Signal Intensity Non-Uniformity on Brain Volumetry Using an Atlas-Based Method

Affiliations
  • 1Department of Radiological Technology, University of Tokyo Hospital, Tokyo 113-8655, Japan. car6_pa2_rw@yahoo.co.jp
  • 2Graduate School of Medical Science, Kanazawa University, Ishikawa 920-0293, Japan.
  • 3Department of Radiology, Nihon University School of Medicine, Tokyo 113-8602, Japan.
  • 4Japan Applied Science Laboratory, GE Healthcare, Tokyo 191-8503, Japan.
  • 5Department of Radiology, University of Tokyo Hospital, Tokyo 113-8655, Japan.
  • 6Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo 113-8655, Japan.
  • 7Department of Neuropathology, University of Tokyo 113-8655, Japan.
  • 8Department of Radiology, National Center Hospital of Neurology and Psychiatry, Tokyo 187-8551, Japan.
  • 9Department of Nuclear Medicine, Saitama Medical University International Medical Center, Saitama 350-1298, Japan.
  • 10Department of Radiology, Juntendo University, Tokyo 113-8421, Japan.

Abstract


OBJECTIVE
Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry.
MATERIALS AND METHODS
Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 x [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level.
RESULTS
A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction.
CONCLUSION
The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.

Keyword

Atlas-based; Bias correction; Brain volumetry; Intensity non-uniformity; Non-parametric non-uniform intensity normalization

MeSH Terms

Adult
Atlases as Topic
Brain Mapping/*methods
Female
Humans
Image Enhancement/methods
Image Processing, Computer-Assisted/*methods
Magnetic Resonance Imaging/*methods
Male
Middle Aged
Software
Statistics, Nonparametric

Figure

  • Fig. 1 Change ratio of structural volume of white matter, for each system. Bias-correction levels are shown at top of figure. Change ratios showed decrease in increasing bias-correction power (i.e., Regularization 10 < 0.0001 < 0 < 0 with N3); this trend was strongest for GE 3T PA coil protocol. 3T = 3 tesla, PA = phased-array, QD = quadrature

  • Fig. 2 Change ratio of structural volume of temporal lobe for each system. Bias-correction levels are shown at top of figure. Change ratios showed decrease with increasing bias-correction power (i.e., Regularization 10 < 0.0001 < 0 < 0 with N3); this trend was strongest for GE 3T PA coil protocol. 3T = 3 tesla, PA = phased-array, QD = quadrature

  • Fig. 3 Change ratio of structural volume of parietal lobe for each system. Bias-correction levels are shown at top of figure. Change ratios showed decrease with increasing bias-correction power (i.e., Regularization 10 < 0.0001 < 0 < 0 with N3); this trend was strongest for GE 3T PA coil protocol. 3T = 3 tesla, PA = phased-array, QD = quadrature

  • Fig. 4 Change ratio of structural volume of occipital lobe, for each system. Bias-correction levels are shown at top of figure. Change ratios showed decrease with increasing bias-correction power (i.e., Regularization 10 < 0.0001 < 0 < 0 with N3); this trend was strongest for GE 3T PA coil protocol. 3T = 3 tesla, PA = phased-array, QD = quadrature

  • Fig. 5 Change ratio of structural volume on hippocampus, for each system. Bias-correction levels are shown at top of figure. Change ratios showed decrease with increasing bias-correction power (i.e., Regularization 10 < 0.0001 < 0 < 0 with N3); this trend was strongest for GE 3T PA coil protocol. 3T = 3 tesla, PA = phased-array, QD = quadrature

  • Fig. 6 T1 weighted-images of GE 3 tesla phased-array coil protocol with native space for three subjects. Subject C has more 0.1 change ratios, while subject A and B have less 0.1 change ratios.

  • Fig. 7 Normalized gray matter images of GE 3 tesla phased-array coil protocol for three subjects. Subject C has more than 0.1 change ratios, and subject A and B have less than 0.1 change ratios. Bias-correction levels are shown at top of Figure.


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