Limits...
Data quality in diffusion tensor imaging studies of the preterm brain: a systematic review.

Pieterman K, Plaisier A, Govaert P, Leemans A, Lequin MH, Dudink J - Pediatr Radiol (2015)

Bottom Line: There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings.Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively.Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal.

View Article: PubMed Central - PubMed

Affiliation: Division of Neonatology, Department of Pediatrics, Erasmus Medical Center - Sophia, dr. Molewaterplein 60, 3015, GJ, Rotterdam, The Netherlands, kaypieterman@gmail.com.

ABSTRACT

Background: To study early neurodevelopment in preterm infants, evaluation of brain maturation and injury is increasingly performed using diffusion tensor imaging, for which the reliability of underlying data is paramount.

Objective: To review the literature to evaluate acquisition and processing methodology in diffusion tensor imaging studies of preterm infants.

Materials and methods: We searched the Embase, Medline, Web of Science and Cochrane databases for relevant papers published between 2003 and 2013. The following keywords were included in our search: prematurity, neuroimaging, brain, and diffusion tensor imaging.

Results: We found 74 diffusion tensor imaging studies in preterm infants meeting our inclusion criteria. There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings. Nineteen studies (26%) reported the use of neonatal hardware. Data quality assessment was not reported in 13 (18%) studies. Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively. Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal.

Conclusion: Diffusion tensor imaging acquisition and processing settings are incompletely described in current literature, vary considerably, and frequently do not meet the highest standards.

No MeSH data available.


Related in: MedlinePlus

Overview of the processing pipeline for diffusion tensor imaging acquisition and analysis. Because all these steps determine data quality and analysis, reporting of these settings is valuable. Note: Outliers indicate motion-corrupted slices. FA fractional anisotropy, ROI regions of interest, TBSS tracts-based spatial statistics
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4526590&req=5

Fig2: Overview of the processing pipeline for diffusion tensor imaging acquisition and analysis. Because all these steps determine data quality and analysis, reporting of these settings is valuable. Note: Outliers indicate motion-corrupted slices. FA fractional anisotropy, ROI regions of interest, TBSS tracts-based spatial statistics

Mentions: Ideally, MRI workstations should be equipped with state-of-the-art quality-checking software, with direct feedback during image acquisition. Such on-the-flight correction allows immediate re-scanning of slices that contain artefacts. Further refinement of these techniques might lead to significant improvements in data quality. Development of even more sophisticated diffusion tensor imaging acquisition schemes, implementation of higher-order processing algorithms in neonatal neuroimaging and further development of user-friendly software to detect and correct poor-quality datasets can result in significant improvements in data quality [26]. Furthermore, providing samples of actual diffusion data as Electronic supplementary material would be very useful to allow the readers to assess image quality. Furthermore, because alterations in myelination, water content and synaptogenesis result in rapidly changing diffusion characteristics within the first year of life, population-specific, standardized acquisition settings and processing pipelines of neonatal diffusion data are urgently needed (Fig. 2) [100, 101].Fig. 2


Data quality in diffusion tensor imaging studies of the preterm brain: a systematic review.

Pieterman K, Plaisier A, Govaert P, Leemans A, Lequin MH, Dudink J - Pediatr Radiol (2015)

Overview of the processing pipeline for diffusion tensor imaging acquisition and analysis. Because all these steps determine data quality and analysis, reporting of these settings is valuable. Note: Outliers indicate motion-corrupted slices. FA fractional anisotropy, ROI regions of interest, TBSS tracts-based spatial statistics
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4526590&req=5

Fig2: Overview of the processing pipeline for diffusion tensor imaging acquisition and analysis. Because all these steps determine data quality and analysis, reporting of these settings is valuable. Note: Outliers indicate motion-corrupted slices. FA fractional anisotropy, ROI regions of interest, TBSS tracts-based spatial statistics
Mentions: Ideally, MRI workstations should be equipped with state-of-the-art quality-checking software, with direct feedback during image acquisition. Such on-the-flight correction allows immediate re-scanning of slices that contain artefacts. Further refinement of these techniques might lead to significant improvements in data quality. Development of even more sophisticated diffusion tensor imaging acquisition schemes, implementation of higher-order processing algorithms in neonatal neuroimaging and further development of user-friendly software to detect and correct poor-quality datasets can result in significant improvements in data quality [26]. Furthermore, providing samples of actual diffusion data as Electronic supplementary material would be very useful to allow the readers to assess image quality. Furthermore, because alterations in myelination, water content and synaptogenesis result in rapidly changing diffusion characteristics within the first year of life, population-specific, standardized acquisition settings and processing pipelines of neonatal diffusion data are urgently needed (Fig. 2) [100, 101].Fig. 2

Bottom Line: There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings.Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively.Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal.

View Article: PubMed Central - PubMed

Affiliation: Division of Neonatology, Department of Pediatrics, Erasmus Medical Center - Sophia, dr. Molewaterplein 60, 3015, GJ, Rotterdam, The Netherlands, kaypieterman@gmail.com.

ABSTRACT

Background: To study early neurodevelopment in preterm infants, evaluation of brain maturation and injury is increasingly performed using diffusion tensor imaging, for which the reliability of underlying data is paramount.

Objective: To review the literature to evaluate acquisition and processing methodology in diffusion tensor imaging studies of preterm infants.

Materials and methods: We searched the Embase, Medline, Web of Science and Cochrane databases for relevant papers published between 2003 and 2013. The following keywords were included in our search: prematurity, neuroimaging, brain, and diffusion tensor imaging.

Results: We found 74 diffusion tensor imaging studies in preterm infants meeting our inclusion criteria. There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings. Nineteen studies (26%) reported the use of neonatal hardware. Data quality assessment was not reported in 13 (18%) studies. Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively. Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal.

Conclusion: Diffusion tensor imaging acquisition and processing settings are incompletely described in current literature, vary considerably, and frequently do not meet the highest standards.

No MeSH data available.


Related in: MedlinePlus