Publications

Authors:

Gabusi, Ilaria; Battocchio, Matteo; Bosticardo, Sara; Schiavi, Simona; Daducci, Alessandro

Title:

Blurred streamlines: A novel representation to reduce redundancy in tractography

Year:

2024

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

MEDICAL IMAGE ANALYSIS

ISSN of journal:

1361-8415

:

Elsevier Science Limited:Oxford Fulfillment Center, PO Box 800, Kidlington Oxford OX5 1DX United Kingdom:011 44 1865 843000, 011 44 1865 843699, EMAIL: asianfo@elsevier.com, tcb@elsevier.co.UK, INTERNET: http://www.elsevier.com, http://www.elsevier.com/locate/shpsa/, Fax: 011 44 1865 843010

Page numbers:

1-11

Keyword:

Microstructure-informed tractographyRedundancyClusteringBlurred streamlines

Short description of contents:

Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical – but usually underrated – parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called “blurred streamlines”, which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.

Product ID:

137520

Handle IRIS:

11562/1118653

Last Modified:

October 9, 2024

Bibliographic citation:

Gabusi, Ilaria; Battocchio, Matteo; Bosticardo, Sara; Schiavi, Simona; Daducci, Alessandro, Blurred streamlines: A novel representation to reduce redundancy in tractography «MEDICAL IMAGE ANALYSIS» , 2024 , pp. 1-11

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