Automatic registration of PET and CT studies for clinical use in thoracic and abdominal conformal radiotherapy.
OBJECTIVE
Implementation and validation of an automatic registration method based on mutual information (MI) for the integration of thoracic and abdominal positron emission tomography (PET)/computed tomography (CT) studies, with the purpose to facilitate in a clinical context the inclusion of PET metabolic information in conformal radiotherapy (RT).


METHODS
Registration was obtained by modeling a rigid spatial transformation between CT and PET transmission studies. The registration method was based on Normalized Mutual Information (NMI), by iteratively transforming the PET volume, until its optimal alignment to the CT study is achieved, in correspondence of the maximum of NMI. To avoid entrapment in local maxima and to improve convergence speed we introduced a multiresolution scheme. Accuracy of the proposed approach was investigated in experimental data, relative to phantom and patient studies, acquired in conditions similar to clinical situations.


RESULTS
In phantom studies the mean error in the 3D space is 3.6 mm (range 3-4 mm) in thoracic region and 3.2 mm (range 2.9-3.7 mm) in abdominal region, considerably less than PET spatial resolution. In patient studies the spatial mean error increases with respect to phantom studies (5.4 mm and 5.2 mm for thorax and abdomen, respectively) but remains comparable to the PET spatial resolution. The accuracy of spatial realignment was thus found adequate for the registration of PET/CT registration, if good patient repositioning was adopted.


CONCLUSIONS
The proposed registration method, based on MI, was validated for the integration of PET/CT studies of patients candidate for thoracic and abdominal conformal RT. The method is automatic and provided with a user interface, thus suitable for clinical use.
Rizzo
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Institute of Molecular Bioimaging and Physiology (IBFM)-CNR, Milan, Italy. giovanna.rizzo@ibfm.cnr.it
Castiglioni
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Arienti
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Cattaneo
G M
GM
Landoni
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Artioli
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Gilardi
M C
MC
Messa
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Reni
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Ceresoli
G L
GL
Fazio
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D000008:Abdominal Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*; D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013899:Thoracic Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*