scholarly journals Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors

2004 ◽  
Vol 5 (3) ◽  
pp. 66-79 ◽  
Author(s):  
Theo Veninga ◽  
Henkjan Huisman ◽  
Richard W.M. van der Maazen ◽  
Henk Huizenga
Keyword(s):  
Brain Tumors ◽  
Mutual Information ◽  
Clinical Validation ◽  
Mr Images ◽  
Normalized Mutual Information ◽  
Information Method

scholarly journals Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images

2010 ◽  
Author(s):  
Marc Modat ◽  
Tom Vercauteren ◽  
Gerard R. Ridgway ◽  
David J. Hawkes ◽  
Nick C. Fox ◽  
...  
Keyword(s):  
Mutual Information ◽  
Mr Images ◽  
Information Evaluation ◽  
Normalized Mutual Information ◽  
Brain Mr Images

scholarly journals To Align Multimodal Lumbar Spine Images via Bending Energy Constrained Normalized Mutual Information

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shibin Wu ◽  
Pin He ◽  
Shaode Yu ◽  
Shoujun Zhou ◽  
Jun Xia ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Mutual Information ◽  
Blood Vessel ◽  
Soft Tissues ◽  
Disease Diagnosis ◽  
Local Deformation ◽  
Interventional Treatment ◽  
Mr Images ◽  
Bending Energy ◽  
Normalized Mutual Information

To align multimodal images is important for information fusion, clinical diagnosis, treatment planning, and delivery, while few methods have been dedicated to matching computerized tomography (CT) and magnetic resonance (MR) images of lumbar spine. This study proposes a coarse-to-fine registration framework to address this issue. Firstly, a pair of CT-MR images are rigidly aligned for global positioning. Then, a bending energy term is penalized into the normalized mutual information for the local deformation of soft tissues. In the end, the framework is validated on 40 pairs of CT-MR images from our in-house collection and 15 image pairs from the SpineWeb database. Experimental results show high overlapping ratio (in-house collection, vertebrae 0.97±0.02, blood vessel 0.88±0.07; SpineWeb, vertebrae 0.95±0.03, blood vessel 0.93±0.10) and low target registration error (in-house collection, ≤2.00±0.62 mm; SpineWeb, ≤2.37±0.76 mm) are achieved. The proposed framework concerns both the incompressibility of bone structures and the nonrigid deformation of soft tissues. It enables accurate CT-MR registration of lumbar spine images and facilitates image fusion, spine disease diagnosis, and interventional treatment delivery.

Integrated positron emission tomography and magnetic resonance imaging–guided resection of brain tumors: a report of 103 consecutive procedures

2006 ◽  
Vol 104 (2) ◽  
pp. 238-253 ◽  
Author(s):  
Benoît Pirotte ◽  
Serge Goldman ◽  
Olivier Dewitte ◽  
Nicolas Massager ◽  
David Wikler ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Brain Tumors ◽  
Mr Imaging ◽  
Tumor Resection ◽  
Tracer Uptake ◽  
Emission Tomography ◽  
Mr Images ◽  
Pet Tracer ◽  
Positron Emission ◽  
Pet Scanning

Object The aim of this study was to evaluate the integration of positron emission tomography (PET) scanning data into the image-guided resection of brain tumors. Methods Positron emission tomography scans obtained using fluorine-18 fluorodeoxyglucose (FDG) and l-[methyl-11C]methionine (MET) were combined with magnetic resonance (MR) images in the navigational planning of 103 resections of brain tumors (63 low-grade gliomas [LGGs] and 40 high-grade gliomas [HGGs]). These procedures were performed in 91 patients (57 males and 34 females) in whom tumor boundaries could not be accurately identified on MR images for navigation-based resection. The level and distribution of PET tracer uptake in the tumor were analyzed to define the lesion contours, which in turn yielded a PET volume. The PET scanning–demonstrated lesion volume was subsequently projected onto MR images and compared with MR imaging data (MR volume) to define a final target volume for navigation-based resection—the tumor contours were displayed in the microscope’s eyepiece. Maximal tumor resection was accomplished in each case, with the intention of removing the entire area of abnormal metabolic activity visualized during surgical planning. Early postoperative MR imaging and PET scanning studies were performed to assess the quality of tumor resection. Both pre- and postoperative analyses of MR and PET images revealed whether integrating PET data into the navigational planning contributed to improved tumor volume definition and tumor resection. Metabolic information on tumor heterogeneity or extent was useful in planning the surgery. In 83 (80%) of 103 procedures, PET studies contributed to defining a final target volume different from that obtained with MR imaging alone. Furthermore, FDG-PET scanning, which was performed in a majority of HGG cases, showed that PET volume was less extended than the MR volume in 16 of 21 cases and contributed to targeting the resection to the hypermetabolic (anaplastic) area in 11 (69%) of 16 cases. Performed in 59 LGG cases and 23 HGG cases, MET-PET demonstrated that the PET volume did not match the MR volume and improved the tumor volume definition in 52 (88%) of 59 and 18 (78%) of 23, respectively. Total resection of the area of increased PET tracer uptake was achieved in 54 (52%) of 103 procedures. Conclusions Imaging guidance with PET scanning provided independent and complementary information that helped to assess tumor extent and plan tumor resection better than with MR imaging guidance alone. The PET scanning guidance could help increase the amount of tumor removed and target image-guided resection to tumor portions that represent the highest evolving potential.

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