Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
A novel method for analyzing DSCE-images with an application to tumor grading
Investigative Radiology, Volume 43, No. 12, Year 2008
Notification
URL copied to clipboard!
Description
Objectives:: (a) The development of a novel analysis method, named Dynamic pixel intensity Histogram Analysis (DHA) allowing for pixel intensity-histogram- model-parameter fitting of arbitrary-shaped regions defined in dynamic-susceptibility-contrast-enhanced (DSCE) difference MR-image time-series, and (b) its prospective application and evaluation for glioma grading. Materials and methods:: For each difference-image, pixel intensity histograms of arbitrary-shaped ROIs were computed and fitted using the Levenberg-Marquardt algorithm. Time-dependent histogram center-position- and width-parameters are computed during bolus-passage. The method was applied to 25 patients with low and high grade gliomas. Results:: During bolus outflow-time, histogram-center-position-parameter and histogram-width-parameter reach highest significance levels and discriminate gliomas of different grades. The histogram center-position-parameter discriminated grade-II from grade-III, grade-II from grade-IV but not grade-III from grade-IV. The observed histogram width-parameters discriminated grade-II from grade-III (P < 0.00022), grade-II from grade-IV (P <8.3 10), and grade-III from grade-IV (P < 0.00063). Conclusions:: DHA is a easy-to-use method for glioma grading; the histogram width parameter is best indicator for histologic grade. Copyright © 2008 by Lippincott Williams & Wilkins.
Authors & Co-Authors
Slotboom, Johannes
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Switzerland, Bern
University of Bern
Schaer, Ralph
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Ozdoba, Christoph
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Reinert, Michael
Switzerland, Freiburg Im Breisgau
Inselspital
Vajtai, Istvan
Switzerland, Bern
University of Bern, Institute of Pathology
El-Koussy, Marwan Mohamed
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Egypt, Cairo
Faculty of Medicine
Kiefer, Claus
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Zbinden, Martin
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Schroth, Gerhard
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Wiest, Roland G.
Switzerland, Lausanne
Institute of Diagnostic and Interventional Neuroradiology
Statistics
Citations: 20
Authors: 10
Affiliations: 5
Identifiers
Doi:
10.1097/RLI.0b013e3181893605
ISSN:
00209996
Research Areas
Cancer
Study Design
Cohort Study