Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies

By Ninon Burgos, M. Jorge Cardoso, K. Thielemans, Marc Modat, Stefano Pedemonte, J. Dickson, A. Barnes, R. Ahmed, Colin J. Mahoney, Jonathan M. Schott and 5 more. IEEE Transactions on Medical Imaging, 2014

390 citations recorded by the source corpus.

Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6bd4169a457f498520613ae4e2382c1a1271f94a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Multimodal MR Synthesis via Modality-Invariant Latent Representation (2017, background): “, [4], [5]), to improve algorithm performance on other medical imaging tasks, such as image segmentation and registration [1], [6], and others.”
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion (2015, methodology): “The central idea of this paper can also be used in the context of image synthesis [33], atrophy simulation [34] and to strati-ﬁed voxel based morphometry [35], showing its general applicability.”
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks (2018, methodology): “Multi-atlas registration in conjunction with intensity fusion can alleviate this limitation, and has been successfully used in synthesizing CT from MR images [6], [7].”
Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis (2020, methodology): “These methods [31], [32] adopted the paired image atlases from the source- and target-modalities to calculate the atlasto-image transformation in the source-modality, which is then applied for synthesizing target-modality-like images from their corresponding target-modality atlases.”
Medical Image Synthesis with Deep Convolutional Adversarial Networks (2018, background): “Similarly, in [14], the authors proposed an information propagation scheme, here for a given source patch, the system looks for similar patches in the source dataset, and constructs the target image based on their corresponding target (which is known in the training set).”