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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Angelini, E.D. Atif, J. Delon, J. Mandonnet, E. Duffau, H. Capelle, L. |
| Copyright Year | 2007 |
| Description | Author affiliation: GET, Ecole Nat. Superieure des Telecommun., Paris (Angelini, E.D.; Atif, J.; Delon, J.) |
| Abstract | Detection of millimetric brain tumor growth patterns on longitudinal MRI acquisitions remains challenging in clinical practice. A simple difference map of two longitudinal co-registered MRI volumes fails to detect specific tumor evolution, due to non-linear contrast change between the two data sets. This paper presents a novel method for detection and quantification of tumor evolution in longitudinal single-protocol MRI studies. A computational framework was designed to enable comparison of co-registered MRI volumes based on gray-scale "normalization" via midway histogram equalization and computation of difference maps. Midway-based difference maps provided very selective representations of structural modifications within pathological areas and on the surrounding structures. Quantitative tumor growth parameters between times $t_{2}$ and $t_{2}$ were computed on the difference maps, provided that a manual segmentation of the tumor is available at time $t_{1}.$ The method was evaluated on longitudinal SPGR (T1-weighted) and FLAIR (T2-weighted) MRI volumes for two patients harboring a WHO grade II glioma. Results for quantification of tumor growth from midway difference maps are presented, showing sub-millimetric precision of clinical growth indices, when compared to manual tracing estimations |
| Starting Page | 49 |
| Ending Page | 52 |
| File Size | 287386 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424406714 |
| DOI | 10.1109/ISBI.2007.356785 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Magnetic resonance imaging Neoplasms Evolution (biology) Gray-scale Image segmentation Protocols Histograms Dynamic range Pathology Biomedical imaging |
| Content Type | Text |
| Resource Type | Article |
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