Template-based tractography for clinical neonatal diffusion imaging data

Natasha Leporé; Fernando Yepes; Yi Lao; Ashok Panigrahy; Rafael Ceschin; Subhashree Ravichandran; Marvin D. Nelson; Pierre Fillard

doi:10.1117/12.912601

23 February 2012 Template-based tractography for clinical neonatal diffusion imaging data

Natasha Leporé, Fernando Yepes, Yi Lao, Ashok Panigrahy, Rafael Ceschin, Subhashree Ravichandran, Marvin D. Nelson, Pierre Fillard

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83151H (2012) https://doi.org/10.1117/12.912601
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

In imaging studies of neonates, particularly in the clinical setting, diusion tensor imaging-based tractography is typically unreliable due to the use of fast acquisition protocols that yield low resolution and signal-to-noise ratio (SNR). These image acquisition protocols are implemented with the aim of reducing displacement artifacts that may be produced by the movement of the neonate's head during the scanning session. In addition, axons are not yet fully myelinated in these subjects. As a result, the water molecules' movements are not as constrained as in older brains, making it even more dicult to dene structure by means of diusion proles. Here, we introduce a post-processing method that overcomes some of the diculties described above, allowing the determination of reliable tracts in newborns. We test our method using neonatal data and in particular, we successfully extract some of the limbic, association and commissural bers, all of which are typically dicult to obtain by direct tractography. The method is further validated through visual inspection by expert pediatric neuroradiologists.

Citation Download Citation

Natasha Leporé, Fernando Yepes, Yi Lao, Ashok Panigrahy, Rafael Ceschin, Subhashree Ravichandran, Marvin D. Nelson, and Pierre Fillard "Template-based tractography for clinical neonatal diffusion imaging data", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151H (23 February 2012); https://doi.org/10.1117/12.912601

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KEYWORDS

Brain

Diffusion tensor imaging

Signal to noise ratio

Image quality

Image registration

Axons

Data acquisition

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