title: Evaluation of whole-body MR to CT deformable image registration.
abstract: Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole-body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based on B-spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty-eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters--such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance--were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (> 75%) were in the 0.8-1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (> 0.6). For all patients, the distance error was in 0-10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole-body MR-CT atlas suitable for the development and evaluation of novel MR-guided attenuation correction procedures on hybrid PET-MR systems.
authors.affiliation: Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: A
authors.initials: A
authors.lastname: Akbarzadeh
authors.forename: D
authors.initials: D
authors.lastname: Gutierrez
authors.forename: A
authors.initials: A
authors.lastname: Baskin
authors.forename: M R
authors.initials: MR
authors.lastname: Ay
authors.forename: A
authors.initials: A
authors.lastname: Ahmadian
authors.forename: N
authors.initials: N
authors.lastname: Riahi Alam
authors.forename: K O
authors.initials: KO
authors.lastname: Lövblad
authors.forename: H
authors.initials: H
authors.lastname: Zaidi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D008168:Lung / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*; D055815:Young Adult