Validation of mutual information-based registration of CT and bone SPECT images in dual-isotope studies.
The registration of computed tomography (CT) and nuclear medicine (NM) images can substantially enhance patient diagnosis as it allows for the fusion of anatomical and functional information, as well as the attenuation correction of NM images. However, irrespective of the method used, registration accuracy depends heavily on the characteristics of the images that are registered and the degree of similarity between them. This poses a challenge for registering CT and NM images as they have very different characteristics and content. To address the particular problem of registering single photon emission computed tomography (SPECT) oncology studies with corresponding CT, we have proposed to perform a dual-isotope study with simultaneous injection of a tumor tracer and a bone imaging agent to obtain a tumor SPECT and a bone SPECT image that are inherently registered. As bone structures are generally visible in both CT and bone SPECT, performing registration of these images will be more easily attainable than registration of CT and tumor SPECT. By subsequently applying the spatial transformation determined from this registration to the tumor SPECT acquired from the same dual-isotope study, the optimal alignment between the CT and tumor SPECT images can be obtained. In this paper, we present the proof-of-concept of the proposed approach, the MI-based algorithm employed, and the techniques used to select the algorithm's parameters. Our objectives are to show the feasibility of CT and bone SPECT registration using this algorithm and to validate quantitatively the results generated using clinical data.
Tang
Lisa
L
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada. lisat@sfu.ca
Hamarneh
Ghassan
G
Celler
Anna
A
D000465:Algorithms; D001842:Bone and Bones / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods; D008962:Models, Theoretical; D010388:Pelvis / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013131:Spine / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D013909:Thorax / Q000033:anatomy & histology*; D015899:Tomography, Emission-Computed, Single-Photon / Q000295:instrumentation* / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000295:instrumentation* / Q000379:methods