Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies

Abstract

Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps. Methods: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI-CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU). Results: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R 2 5 0.9980, P , 0.0001), and the Bland-Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUV mean and SUV max for every region was high (R 2 5 0.9989, P , 0.0001, and R 2 5 0.9904, P , 0.0001, respectively). Conclusion:The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-timebased attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.

On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners

“…Additionally, the co-registration task is more challenging because of the higher intersubject variability in organ and body shape and size. To address this issue, either more accurate co-registration methods or patch-based methods [31] could be used.…”

MR-Based Attenuation Correction in Brain PET/MR Studies: A Short Review

“…Therefore, when used in patients, they can induce errors that are expected to a larger amount as much normal anatomy is disrupted by the disease [3]. Atlas-based methods use Dixon, T1-, and/or T2-weighted sequences [27][28].…”

Transmission imaging for integrated PET-MR systems

“…Approaches that use MR images alone without utilizing CT training sets include methods that produce segmented μ -maps (Catana et al 2010) based on ultra-short echo time (UTE) sequences or, recently, continuous valued pseudo-CT μ -maps via a robust classifier and a combination of pulse sequences (Su et al 2015). Algorithms that utilize CT training data; however, may have improved performance for modeling bone attenuation and include methods to continuously map UTE intensities to bone LACs (Ladefoged et al 2015) or that register CT atlases to create patient specific μ -maps (Paulus et al 2015, Izquierdo-Garcia et al 2014, Torrado-Carvajal et al 2016). …”