Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies.
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) = 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 SUVmean and SUVmax for every region was high (R(2) = 0.9989, P < 0.0001, and R(2) = 0.9904, P < 0.0001, respectively).


CONCLUSIONS
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-time-based attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications.
Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain Madrid-MIT M+Visión Consortium, Madrid, Spain angel.torrado@urjc.es.
Angel
A
Torrado-Carvajal
Madrid-MIT M+Visión Consortium, Madrid, Spain Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Joaquin L
JL
Herraiz
Department of Computer Science, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain.
Eduardo
E
Alcain
Department of Computer Science, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain.
Antonio S
AS
Montemayor
Hospital Universitario HM Puerta del Sur, HM Hospitales, Móstoles, Madrid, Spain; and.
Lina
L
Garcia-Cañamaque
Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain Madrid-MIT M+Visión Consortium, Madrid, Spain.
Juan A
JA
Hernandez-Tamames
MAP5, CNRS UMR 8145, University Paris Descartes, Paris, France.
Yves
Y
Rozenholc
Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain Madrid-MIT M+Visión Consortium, Madrid, Spain.
Norberto
N
Malpica
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography; D012189:Retrospective Studies; D013997:Time Factors; D014057:Tomography, X-Ray Computed
GPU; PET/MR; attenuation correction; image synthesis; pseudo-CT