Searching across hundreds of databases
The present study aimed to explore the application of diffusion tensor tractography (DTT) in the preoperative planning and prognostic evaluation of tumors located in the functional areas of the brain. A total of 42 patients diagnosed with intracranial tumors were randomly assigned to either the trial or the control group. DT imaging (DTI) was performed on the basis of preoperative conventional magnetic resonance imaging (MRI) and analyzed for patients in the trial group. Patients in the control group underwent only routine MRI scans. The effect of DTT on the prognosis of patients was evaluated by tumor resection rate and quality of life evaluation using Karnofsky performance score (KPS) comparison between the trial and control groups. There were no significant differences for total tumor removal rate in the trial group (85.71%) compared with that in the control group (71.43%) (P>0.05). The rate of postoperative symptom improvement in the trial group (85.71%) was significantly higher compared with that in the control group (47.62%) (P<0.05). The KPS value of the trial group was significantly higher postoperatively (78.57±17.40) compared with that preoperatively (66.67±16.23) (P<0.05). The KPS value of the control group postoperatively (72.38±19.21) was significantly higher compared with that preoperatively (66.67±16.00) (P<0.05). The postoperative KPS improvement rate [postoperative value-preoperative value)/preoperative value] of the trial group was significantly higher compared with that in the control group. In conclusion, the use of DTT is an effective supplement to traditional MRI, with particular relevance in preoperative planning, particularly for tumors in the functional area of the brain, and can significantly improve the prognostic function of patients.
Pubmed ID: 31897177 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
TrackVis is software tool that can visualize and analyze fiber track data from diffusion MR imaging (DTI/DSI/HARDI/Q-Ball) tractography. It does NOT perform actual fiber tracking. Diffusion Toolkit is a set of tools that reconstruct diffusion imaging data and generate fiber track data for TrackVis to visualize. Because these two sets of tools were developed and maintained separately and each has distinguished funtionalities, they decided to distribute them as two separate programs for the ease of maintenance and upgrade. You do need both of them to perform complete diffusion data processing and analysis. Features of TrackVis include: * Cross-platform. Works on Windows, Mac OS X and Linux with native look and feel. * A variety of track filters (track selecting methods) allowing users to explore and locate specific bundles with ease. * Multiple rendering modes with customizable scalar-driven color codes. * Real-time parameter adjustment and 3D render. * Open format of the track data file allowing users to integrate customized scalar data into the track file and visualize and analyze it. Save and restore scenes in XML style scene file. * Statistical scalar analysis of tracks and ROIs. * Synchronized real-time multiple dataset analysis and display allowing time-point and/or subject comparison. Synchronized analysis and display on same dataset can also be performed in real-time remotely over the network. * Upfront in-line parameter adjustment in real-time. No tedious pop-up dialogs. TrackVis works with Track File created by Diffusion Toolkit. Diffusion Toolkit processes raw DICOM, Nifti format and ANALYZE images. TrackVis and Diffusion Toolkit are cross-platform software. They can run on Windows XP, Mac OS X as well as Linux.
View all literature mentionsA software for diffusion MR images analysis. The provided functions include reconstruction (DTI, QBI, DSI, and GQI), deterministic fiber tracking, and 3D visualization. It has a window-based interface and operates on Microsoft Windows system.
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
