干旱区生态环境知识资源中心(Arid): Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

Arid

DOI	10.1007/s10334-024-01185-4
	Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis
	Senra Filho, Antonio Carlos da S.; Murta Junior, Luiz Otavio; Paschoal, Andre Monteiro
通讯作者	Senra, ACD
来源期刊	MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
ISSN	1352-8661
出版年	2024
英文摘要	ObjectDiffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of L & oacute;pez-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).Materials and MethodsThe OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.ResultsThe DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.DiscussionIn the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.
英文关键词	Diffusion tensor imaging Statistical complexity MRI Brain
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001278178300001
WOS关键词	CLINICAL-APPLICATIONS ; ALZHEIMERS-DISEASE ; WHITE ; PRINCIPLES ; REGIONS ; DTI
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404878
推荐引用方式 GB/T 7714	Senra Filho, Antonio Carlos da S.,Murta Junior, Luiz Otavio,Paschoal, Andre Monteiro. Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis[J],2024.
APA	Senra Filho, Antonio Carlos da S.,Murta Junior, Luiz Otavio,&Paschoal, Andre Monteiro.(2024).Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis.MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE.
MLA	Senra Filho, Antonio Carlos da S.,et al."Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis".MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE (2024).