Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average [Formula: see text] HU and the ME [Formula: see text] HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of [Formula: see text] in the PTV for [Formula: see text], and between [Formula: see text] and 0.05% in the PTV, bladder, rectum and femur heads for D mean and [Formula: see text]. Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP

Joint Segmentation and CT Synthesis for MRI-only Radiotherapy Treatment Planning

Accurate knowledge of organ location and tissue attenuation properties are the two essential components to perform radiotherapy treatment planning (RTP). Computed tomography (CT) has been the modality of choice for RTP as it easily provides electron density information. However, its low soft tissue contrast limits the accuracy of organ delineation. On the contrary, magnetic resonance (MR) provides images with excellent soft tissue contrast but its use for RTP is limited by the fact that it does not readily provide tissue attenuation information. In this work we propose a multi-atlas information propagation scheme that jointly segments the organs at risk and generates pseudo CT data from MR images. We demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, bypassing the need for CT scan for accurate RTP

Image synthesis for the attenuation correction and analysis of PET/MR data

While magnetic resonance imaging (MRI) provides high-resolution anatomical information, positron emission tomography (PET) provides functional information. Combined PET/MR scanners are expected to offer a new range of clinical applications but efforts are still necessary to mitigate some limitations of this promising technology. One of the factors limiting the use of PET/MR scanners, especially in the case of neurology studies, is the imperfect attenuation correction, leading to a strong bias of the PET activity. Exploiting the simultaneous acquisition of both modalities, I explored a new family of methods to synthesise X-ray computed tomography (CT) images from MR images. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MR/CT image pairs, using a local image similarity measure. The proposed algorithm provides a significant improvement in PET reconstruction accuracy when compared with the current correction, allowing an unbiased analysis of the PET images. A similar image synthesis scheme was then used to better identify abnormalities in cerebral glucose metabolism measured by [18]F-fluorodeoxyglucose (FDG) PET. This framework consists of creating a subject-specific healthy PET model based on the propagation of morphologically-matched PET images, and comparing the subject's PET image to the model via a Z-score. By accounting for inter-subject morphological differences, the proposed method reduces the variance of the normal population used for comparison in the Z-score, thus increasing the sensitivity. To demonstrate that the applicability of the proposed CT synthesis method is not limited to PET/MR attenuation correction, I redesigned the synthesis process to derive tissue attenuation properties from MR images in the head & neck and pelvic regions to facilitate MR-based radiotherapy treatment planning