Journal Article DKFZ-2022-00183

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png
A novel gradient echo data based vein segmentation algorithm and its application for the detection of regional cerebral differences in venous susceptibility.

 ;  ;  ;  ;  ;

2022
Academic Press Orlando, Fla.

NeuroImage 250, 118931 () [10.1016/j.neuroimage.2022.118931]
 GO

Abstract: Accurate segmentation of cerebral venous vasculature from gradient echo data is of central importance in several areas of neuroimaging such as for the susceptibility-based assessment of brain oxygenation or planning of electrode placement in deep brain stimulation. In this study, a vein segmentation algorithm for single- and multi-echo gradient echo data is proposed. First, susceptibility maps, true susceptibility-weighted images, and, in the multi-echo case, R2* maps were generated from the gradient echo data. These maps were filtered with an inverted Hamming filter to suppress background contrast as well as artifacts from field inhomogeneities at the brain boundaries. A shearlet-based scale-wise representation was generated to calculate a vesselness function and to generate segmentations based on local thresholding. The accuracy of the proposed algorithm was evaluated for different echo times and image resolutions using a manually generated reference segmentation and two vein segmentation algorithms (Frangi vesselness-based, recursive vesselness filter) as a reference with the Dice and Cohen's coefficients as well as the modified Hausdorff distance. The Frangi-based and recursive vesselness filter methods were significantly outperformed with regard to all error metrics. Applying the algorithm, susceptibility differences likely related to differences in blood oxygenation between superficial and deep venous territories could be demonstrated.

Keyword(s): arteries ; brain vessels ; magnetic resonance imaging ; quantitative susceptibility mapping ; segmentation ; veins

Classification:

Note: #EA:E020# / Volume 250, 15 April 2022, 118931

Contributing Institute(s):
  1. E020 Med. Physik in der Radiologie (E020)
  2. E010 Radiologie (E010)
Research Program(s):
  1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2022
Database coverage:
Medline ; Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND (No Version) ; DOAJ ; Article Processing Charges ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
Click to display QR Code for this record

The record appears in these collections:
Document types > Articles > Journal Article
Institute Collections > E010
Institute Collections > E020
Public records
Publications database

 Record created 2022-01-28, last modified 2024-02-29



Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)