Publications

Multi-Tensor MAPMRI: how to estimate microstructural information from crossing fibers  (2015)

Authors:
Zucchelli, Mauro; MENDEZ GUERRERO, Carlos Andres; Menegaz, Gloria
Title:
Multi-Tensor MAPMRI: how to estimate microstructural information from crossing fibers
Year:
2015
Type of item:
Contributo in atti di convegno
Tipologia ANVUR:
Contributo in Atti di convegno
Language:
Inglese
Format:
Elettronico
Congresso:
Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI (CDMRI)
Place:
Munich, Germany
Period:
October 4-9, 2015
Publisher:
Springer
Page numbers:
58-67
Keyword:
Diffusion MRI, SHORE
Short description of contents:
Diusion Magnetic Resonance Imaging (MRI) is able to de- tect the properties of tissue microstructure underneath the voxel through the imaging of water molecules diusion. Many reconstruction methods have been proposed to calculate the Orientation Distribution Function (ODF) from the diusion signal in order to distinguish between a coher- ent ber bundle and crossing of bers. The diusion signal was also used to infer other microstructural information such as the axon diameter, but most often in areas with coherent ber direction such as the corpus callosum. In this work we developed a reconstruction model called Multi- Tensor MAPMRI (MT-MAPMRI) that is an extension of the MAPMRI model which improves the performance of MAPMRI for crossing bers. In particular, it provides i) enhanced signal tting; ii) improved ODFs, iii) a more accurate diameter estimation. The model was tested and val- idated on both simulated and in vivo data.
Product ID:
87816
Handle IRIS:
11562/925785
Last Modified:
November 1, 2022
Bibliographic citation:
Zucchelli, Mauro; MENDEZ GUERRERO, Carlos Andres; Menegaz, Gloria, Multi-Tensor MAPMRI: how to estimate microstructural information from crossing fibers  in Proceedings of the Workshop on Computational Diffusion (CRMRI 2015)SpringerProceedings of "Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI (CDMRI)" , Munich, Germany , October 4-9, 2015 , 2015pp. 58-67

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