Literature DB >> 18051108

Automated planning of scan geometries in spine MRI scans.

Vladimir Pekar1, Daniel Bystrov, Harald S Heese, Sebastian P M Dries, Stefan Schmidt, Rüdiger Grewer, Chiel J den Harder, René C Bergmans, Arjan W Simonetti, Arianne M van Muiswinkel.   

Abstract

Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.

Mesh:

Year:  2007        PMID: 18051108     DOI: 10.1007/978-3-540-75757-3_73

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  3 in total

Review 1.  On computerized methods for spine analysis in MRI: a systematic review.

Authors:  Marko Rak; Klaus D Tönnies
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-02-09       Impact factor: 2.924

2.  Prospective image registration for automated scan prescription of follow-up knee images in quantitative studies.

Authors:  Janet Goldenstein; Joseph Schooler; Jason C Crane; Eugene Ozhinsky; Jean-Baptiste Pialat; Julio Carballido-Gamio; Sharmila Majumdar
Journal:  Magn Reson Imaging       Date:  2011-05-05       Impact factor: 2.546

3.  CT brush and CancerZap!: two video games for computed tomography dose minimization.

Authors:  Graham Alvare; Richard Gordon
Journal:  Theor Biol Med Model       Date:  2015-05-12       Impact factor: 2.432

  3 in total

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