Sci‐AM1 Sat ‐ 08: Towards MR‐based treatment planning: Characterisation of geometric distortion in 3T MR images

Abstract

Because of the excellent soft‐tissue detail provided by MR images, it is the optimum imaging modality for treatment planning target delineation. While the structure of a tumor can be seen in great detail on MR images, the geometric accuracy of the images is limited by the homogeneity of the background field, the linearity of the applied gradients, and the magnetic susceptibility of the imaged tissues. As such, MR images cannot be used alone for novel treatment planning purposes (i.e. MR simulation), or in conjunction with CT because of geometric distortion. Our research seeks to quantify the amount of distortion in 3T MR images due to both background inhomogeneities and gradient nonlinearities on a sequence by sequence basis by using a specialized grid phantom and an in‐house developed software program. The matlab‐based program accurately determines the 3D coordinates of over 9000 control points distributed throughout the phantom's volume. Three dimensional distortion maps can be generated by comparing the control point coordinates determined from an MR scan to the control point coordinates determined from a CT scan. Control point locations can be determined to an accuracy of 0.2 mm and distortions as large as 13 mm have been measured. With appropriate post‐processing correction factors derived from the 3D distortion maps, MR images can be undistorted and either combined or used individually for new treatment planning methods that benefit from the superior soft‐tissue information that magnetic resonance techniques provide.

Assessment of dose reconstruction errors in image-guided radiation therapy

“…With the introduction of frequent imaging during the treatment process, such as cone-beam computed tomography (CT) systems (Jaffray et al 2002, Oldham et al 2005, Sykes et al 2005, CT-on-rails systems (Ma andPaskalev 2006, Zhang et al 2007) and proposed on-board magnetic resonance systems (Baldwin et al 2005, Fox et al 2006, Tomas et al 2006 coupled with the use of deformable image registration (DIR), determination of the accumulated dose for arbitrary tissue voxels over a course or fraction of therapy when tissues move with respect to each other is becoming clinically practical (Christensen et al 2001, Foskey et al 2005, Flampouri et al 2005, Schaly et al 2004, Yan et al 1997, 1999. For current DIR algorithms, the displacement vector fields (DVFs) of image registrations, which are used to map the patient dose from one instance of a patient's anatomy (e.g., the time of treatment) to a reference anatomic instance (e.g., the planning image set), are not without error (Wang et al 2005, Li et al 2005, Foskey et al 2005, Webb 2006).…”