SP-0659 MRI techniques for MR-only simulation

van den Berg, CAT, Maspero, M, Dinkla, AM, Savenije, MHF, Meijer, GJ, Seevinck, PR, Lagendijk, JJW, Raaymakers, BW


Radiotherapy & Oncology 127 p. S348-S349


The increasing usage of MRI in the simulation process stems from its superior soft tissue contrast. This allows better delineation of tumor volume and OARs and thus contributes to overall improvement of treatment quality. As MRI does not provide direct information of the tissue electron density and bony anatomy for position verification, patients have to undergo two imaging exams in clinical practice: a CT and an MRI scan. Besides extra patient burden and medical costs, this introduces inevitable geometrical errors related to interscan differences and image fusion. This has been the rationale behind the development of MR-only simulation where all information needed for delineation, position verification and electron density is derived from MR images.

A first prerequisite is of course the geometric fidelity of MR images. In the last two decades improvements in magnet design, gradient coils and image corrections have led to the possibility to perform geometrically accurate MR imaging for radiotherapy. Of course, this requires a quality assurance program to monitor geometrical fidelity and needs to be supplemented by proper sequence design (e.g. a high readout bandwidth). A second adaptation crucial for MR-only simulation is the possibility to scan the patient in treatment position. Both aspects have been acknowledged in the last years by MR vendors and they introduced wide bore MR systems and launched special MR-RT solutions such as flat table tops, QA phantoms+procedures, positioning lasers and special coil options.

Much of the research towards MR-only has focused on different methodologies to generate socalled synthetic CT from MR images. A crucial aspect is the visualization of the bony anatomy with MRI. Due to the low proton spin density and very short T2*, cortical bone appears as a signal void on MRI. Simple segmentation of these signal voids to identify cortical bone on MR images will not be successful as inner air also appears a signal void. Dedicated MR sequences such as ultra short echo time (UTE) sequences to obtain signal from cortical bone have been applied with mixed success. Other methods have approached the problem as a quantitative MRI problem to convert quantitative MR images into synthetic CTs using signal-to-houndfields unit conversion models. Recently, various commercial solutions have become available that use rather standard MR sequences combined with clever image processing solutions to generate synthetic CT images. A very new technique, deep learning based synthetic CT generation is even more flexible in terms of requirements for image contrast. Deep learning methods like convolutional neural networks are able to classify bony or air voxels by a learning approach including local, contextual image information. With these new deep learning methods standard MR sequences that are primarily intended for delineation purposes can be utilized for synthetic CT generation. More general, deep learning methods offer great potential for other steps in the MR-only simulation process, e.g. for automatic contouring of organs-at-risk on MR images.

A somewhat overlooked aspect of MR-only simulation is the need to generate reference images for position verification from MR images. The synthetic CT will suffice as a reference image for tumor sites where position verification is based on registration of kV or MV in-room images to a reference image. However, the situation is more challenging in the case of position verification for VMAT or IMRT prostate irradiation where implanted gold fiducials are used. Fiducials are easily localized on CT images due to their distinct local streaking artifacts. However, on MR images the appearance is less distinct and they manifest themselves as local signal voids in magnitude MR images. Correct manual classification of these signal voids as fiducials is feasible but sometimes complicated by the presence of calcifications that can appear in very similar fashion. Recently, our group has introduced an automatic method to localize fiducials based on the fiducial's distinct distortion of the local magnetic field, which can be detected on phase images. The accuracy of this method is comparable to CT based localization.

Conclusions The superior soft tissue of MRI over CT greatly facilitates the critical step in the simulation process: the tumor and OAR delineation. Currently, CT is still the master image modality as it provides the information on electron density and bony anatomy. Nowadays, thanks to innovations in MR technology and image processing, this is no longer the case. Accurate electron density maps and reference images can be obtained with MRI in a reliable manner. Thus, from an MRI perspective, the traditionally largest technical obstacles to allow MRI to become the sole imaging modality for treatment simulation has been overcome. It is up to radiotherapy clinics to start using MR-only simulation to improve treatment quality, patient comfort and logistics.